research article utaut2 based predictions of factors...
TRANSCRIPT
Research ArticleUTAUT2 Based Predictions of Factors Influencingthe Technology Acceptance of Phablets by DNP
Chi-Yo Huang and Yu-Sheng Kao
Program of Technology Management Department of Industrial Education National Taiwan Normal UniversityTaipei 10610 Taiwan
Correspondence should be addressed to Chi-Yo Huang georgeh168gmailcom
Received 5 September 2014 Accepted 21 November 2014
Academic Editor Stephen D Prior
Copyright copy 2015 C-Y Huang and Y-S Kao This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited
The smart mobile devices have emerged during the past decade and have become one of the most dominant consumer electronicproducts Therefore exploring and understanding the factors which can influence the acceptance of novel mobile technology havebecome the essential task for the vendors and distributors of mobile devices The Phablets integrated smart devices combiningthe functionality and characteristics of both tablet PCs and smart phones have gradually become possible alternatives for smartphones Therefore predicting factors which can influence the acceptance of Phablets have become indispensable for designingmanufacturing andmarketing of suchmobile devices However such predictions are not easyMeanwhile very few researches triedto study related issues Consequently the authors aim to explore and predict the intentions to use and use behaviors of PhabletsThesecond generation of the Unified Theory of Acceptance and Use of Technology (UTAUT2) is introduced as a theoretic basis TheDecisionMaking Trial and Evaluation Laboratory (DEMATEL) basedNetwork Process (DNP)will be used to construct the analyticframework In light of the analytic results the causal relationships being derived by theDEMATEL demonstrate the direct influenceof the habit on other dimensions Also based on the influence weights being derived the use intention hedonic motivation andperformance expectancy are the most important dimensions The analytic results can serve as a basis for concept developmentsmarketing strategy definitions and new product designs of the future Phablets The proposed analytic framework can also be usedfor predicting and analyzing consumersrsquo preferences toward future mobile devices
1 Introduction
Advances in the smart mobile devices during the past yearshave significantly influenced consumer behaviors life stylesand the development of the electronic industry Thereforediscovering and exploring the crucial factors which caninfluence the acceptance and continuous usage of the smartmobile devices have become indispensable for marketers anddesigners to enable such devices to reach better and to satisfycustomersrsquo anticipation [1 2]
Exploring and studying the issues of consumer technol-ogy adoption have been discussed in a variety of domainsDuring the past decades social psychologists have con-stantly developed and proposed many theories as predictingframeworks for precisely analyzing consumer behaviors of
adopting new technology for example the Theory of Rea-soned Action (TRA) theTheory of Planned Behavior (TPB)the Technology Acceptance Model (TAM) the Diffusion ofInnovation (DOI) and the Unified Theory of Acceptanceand Use of Technology (UTAUT) These theories have beenwidely accepted and applied in many of the different fieldssuch as behavior science system engineering managementcomputer science and education
Past researches on technology adoption can be dividedinto two categories the firm-level issues as well as theindividual-level ones On one hand the researches on organi-zation level issues regarding to how employees in the organi-zation assess the usage satisfaction and usefulness regardingthe adoption of a new technology in the work processes Onthe other hand the researches on the individual level referred
Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015 Article ID 603747 23 pageshttpdxdoiorg1011552015603747
2 Mathematical Problems in Engineering
to how users or customers evaluate the satisfaction withrespect to the adoptions and usages of a novel technology intheir lives from the dimensions of perceived ease of use andusefulness and so forth In general the model of technologyacceptance has always been regarded as a useful analyticframework for verifying or exploring user acceptances andadoptions of new high technology Due to the fast progress ofemerging smart mobile technologies consumer-electronicsfirms such as Apple Samsung LG and SONY aggressivelyexpand their scope to smart mobile devices and furthermaximize the profitability and the market share
During the past years the Phablet an integrated smartdevice combining the functionality and characteristics ofboth tablet PCs and smart phone has gradually becomepossible alternatives for smart phones Therefore predictingfactors which can influence the acceptance of Phablets havebecome indispensable for designing manufacturing andmarketing of suchmobile devices However such predictionsare not easy Meanwhile very few researches tried to studyrelated issues Consequently the authors aim to explore andpredict the intentions to use and use behaviors of Phablets
To predict precisely consumersrsquo preferences toward thePhablet products the lead user method will be adoptedfor investigating innovators or early adoptersrsquo perspectivesRogers [3] has indicated that the early adoptersrsquo or innovatorsrsquousage experience and use behavior would influence laterbulk of users to accept new technology or product [3]Further most consumer electronics providers often investi-gated consumersrsquo preferences toward disruptive innovativeelectronics products (eg the migration from the traditionalfunction phones to the configurable smart phones) based oninnovatorsrsquo or early adoptersrsquo use experiences as the basis todefine or improve their productsThus the lead usermethodwhich is a useful theory will be adopted by this researchMoreover based on the authorrsquos very limited knowledgeexisting researches on smart mobile devices ignored the roleof Phablets In order to cross this knowledge gap this studywill investigate the probable existence of Phablets technologyacceptance
The UTAUT2 a theoretic framework being derivedfrom the TAM and the UTAUT2 is a powerful predictingframework being proposed by Venkatesh et al [4] TheUTAUT2 can effectively explain and analyze peoplersquos technol-ogy acceptance behaviors for novel information technology(IT) products Consequently this research introduced theUTAUT2 as an analyticmodel Further since the functions ofnovel smart mobile devices are usually very hard for normalconsumers to understand the traditional market researchapproaches based on surveys of consumersrsquo opinions are notfeasible for such novel products Therefore this research willsurvey lead usersrsquo opinions for the preferences toward thenext generation smart mobile devices
The criteria for evaluating the lead usersrsquo preferences willfirst be derived from the UTAUT2Then the criteria will fur-ther be confirmed by the modified Delphi methodTheDeci-sion Making Trial and Evaluation Laboratory (DEMATEL)will be introduced to construct the influence relationshipsbetween criteria Further the DEMATEL based networkprocess (DNP) will be used to derive the influence weights
versus each dimension and criterion Finally the influencesof the criteria on consumersrsquo preferences toward novel smartdevices can be derived
Based on the empirical study results being derived byTaiwanese Phablet experts the causal relationships beingconstructed by the DEMATEL demonstrate that the habitdimension has direct influence on other dimensions Theuse intention and performance expectancy dimensions havethe least influences on other dimensions In practice thesedimensions should be prioritized in improvement than therest of the dimensions Furthermore the weights beingassociated with the criterion and construct reveal that the useintention hedonic motivation and performance expectancyare the most important dimensions The research resultscan serve as a basis for related Phablet devicesrsquo marketingstrategy definition and product improvement The proposedmethodology can also be used for predicting usersrsquo adoptingbehavioral preferences and be employed for improving thegaps among the Phablet use factors
The remainder of the paper is organized as followsSection 2 is a review of the related literature and the-ories which include the predictions of high-technologyconsumer behaviors and the lead user method as wellas the UTAUT and UTAUT2 In Section 3 the researchmethod will be introduced to construct the decision frame-work An empirical study for selecting the most influ-ential factors on the acceptances of novel smart mobiledevices will be detailed in Section 4 Discussions will bepresented in Section 5 Section 6 will conclude the paper withobservations summaries and recommendations for futurestudies
2 Literature Review
In this section the past researches regarding the predictionsof high-technology consumer behaviors will be reviewedat first Then the lead user method will be introducedThe UTAUT and UTAUT2 theoretic models will then bereviewed Finally the possible criteria and dimensions whichmay influence usersrsquo technology adoptions based on thetheoretic model will be reviewed and summarized
21 Prediction of High-Technology Consumer Behaviors Overthe years marketers and researchers have constantly exploredthe many motivating influences on purchase behavior [5ndash7]Booth and Shepherd [8] argued that the factors of cultureeconomy emotions value and attitudes will influence thedecision process of consumer purchase behavior Loudon andDella Bitta [9] also pointed out that the consumer purchasebehavior is a decision process in which customers can chooseand use the products and services In other words throughthe decision making process consumer can examine theiractions and the reason for why they would like to purchasethis product Subsequently Kotler [10] further identified thatconsumer purchase behavior is affected by cultural socialpersonal and psychological factors On the other handconsumer behavior is often goal-oriented not haphazard oraccidental For instance consumers have a goal or a set of
Mathematical Problems in Engineering 3
goals seeking to satisfy presently unfulfilled needs From theabove obviously consumer behavior is themodel of behaviorthat people follow in looking for buying using or evaluatinggoods services and ideas that they expect to fulfill their needsand wants [11]
Prediction of consumer behavior is often an importanttask for marketing managers They have to understand thepossible purchasing behavior of consumer and the factorsaffecting consumer acceptance of products so that they canpropose ideas for RampD staffs or related employees to improveand enhance the products In high-technology consumerbehavior the concept of purchase behavior is important forits implication that consumers are different thus the mar-keting managers should develop differentiation marketingstrategies to cope with variety of situations de Bellis et al[12] argued that the appropriate marketing strategies willfoster rich interactions with their customers and enhancemarketing efficiency and effectiveness Thus understandingthe consumer behavior of high technology and definingthe appropriate policy are a critical task for the Phabletmanufacturers and marketers
High-technologymarketing often includes twoparts tan-gible and intangible technology marketing In this researchwe will focus on the tangible dimensions of electronic prod-ucts Marketing about technology products is an importantexternal factor that influences the acceptance rate of a techno-logical innovation [13] Technology marketing often consistsof advertising word-of-mouth communication marketingactivities Internet forums and television product placements[13] For technology usage of consumer behavior Rogers [3]suggested that there are differences in consumersrsquo dispositiontoward using technology He further defined consumer intofive groups illustrating their character ranging from inno-vators to laggards [3] Due to the differences of consumersrsquotraits technology readiness index being proposed to describeconsumersrsquo beliefs regarding various dimensions of technol-ogy differs The definition of technology readiness is dividedinto four dimensions of consumers [14 15] (1) Optimismoptimism is a positive view of technology and belief offeringconsumers increased control flexibility and efficiency in lifedue to technology (2) Innovativeness innovativeness is thetendency to be a technology pioneer and thought leader(3) Discomfort discomfort means having a need for thecontrol and sense of being overwhelmed (4) Insecurityinsecurity means disturbing technology for security andprivacy reasons
Through the studies ofmarketing and consumer purchas-ing behavior marketers and researchers would aggressivelylike to develop appropriate marketing strategies to helpelectronic firms Above all these firms should understandthose issues as follows before they definemarketing strategies[16] (1) how consumers think feel choose from differentproducts and make decisions (2) how the consumers areaffected by the environment and their background (iemedia and family) (3) the behavior of consumers whilemaking buying decisions (4) the decisions influenced bythe limitation of consumer information abilities (5) howconsumersrsquo decision strategies andmotivation differ betweenproducts that are different in level of interest or importance
(6) how companies can adapt and improve their marketingstrategies to more effectively achieve the consumersrsquo needs
In comparison to the traditional market Moriarty andKosnik [17] summarized the characteristics of a high-technologymarket from three dimensions themarket uncer-tainty the technological uncertainty and the competitivevolatility (1) Market uncertainty refers to the ambiguityabout the type and extent of consumer needs that can be satis-fied by a particular technology [17 18] There are five sourceswhich can result in the high-technologymarket certaintyThesources include the needs which might be met by the newtechnology the possible changes of the needs in the futurethe adoption of industry standards or not the diffusion rateof innovations and the potential size of the high-technologymarket (2) Technological uncertainty means that whetherthe technology canmeet specific needs is unclear Five factorsgive rise to technological uncertainty The factors includethe new product function the delivery timetable the servicequality and the sustainability problem being raised by thenew technology (3) Competitive volatility refers to both theintensity in extent of change in the competitive landscapeand the uncertainty about competitors and their strategies[19] Competitive volatility is composed of three sourcesthe new competitors in the future new competitive tacticsand new products to compete with Figure 1 summarizes theabovementioned characteristics of high-technology market
22 The Lead User Method Increasingly firms are recogniz-ing the power in innovation idea development [20] Eitherservice design concepts or RampD such idea often inspires achallenge of companies [21] In novel product developmentmany literatures suggest techniques for idea creation suchas benchmarking [22] user observation [23] or lead usermethod [24] Among these the lead user method has beenshown to provide the highest potential to create commerciallyattractive and highly novel innovations [20] (eg [25 26])In other words lead user theory can effectively understandconsumer purchase behavior and serve as a developmentbasis for next generation product Moreover most of thetarget users for your product will have difficulties expressingtheir needs for your products [27] And a vast majority ofyour target users will certainly not be able to come up withthe innovations themselves [27] There will always be someusers who are exceptions These are the lead users of yourproducts or services For example if you are designing thesoftware of smart phones you will be looking at the peoplewho are already designing programming for your software
In high-technology industries the world moves sorapidly The related real-world experiences of ordinary usersare often rendered obsolete by the time a product is devel-oped or during the time of its projected commercial lifetime[28]Thus in the research of innovative products VonHippeldefined ldquolead userrdquo as a person displaying two characteristicsregarding a given new product [24 28] (1) The lead usersface needs that will diffuse in the marketplace but face thembefore the bulk of that marketplace encounters them (2)The lead users are positioned to benefit significantly fromobtaining a solution to those needs Since the studies oncustomer involvement in successful innovation prove the
4 Mathematical Problems in Engineering
Source [17]
Market uncertainty
Technological uncertainty
High technology
market
Competitive volatility
Figure 1 Characterizing the high-technology market
importance of lead users [29] who can effectively foster theproduct improvement [30] and develop products that areready for the market [31] the lead user analysis is used toidentify the characteristics such as traits knowledge andstatus Subsequently based on lead user theory Belz andBaumbach [32] further defined six characteristics of onlineconsumer purchasing behavior including ahead of trenddissatisfaction product-related knowledge use experienceinvolvement and opinion leadership Research on lead usersshows that many products are initially thought of and evenprototyped by users rather than manufacturers [19 33]For instance Table 1 shows that across various industriesthe number of innovations conceived of by users is quitehigh
The traditional analytic methods for defining incremen-tal innovation and radical innovation products or servicesusually initiate by deriving consumersrsquo needs at the verybeginning Then such needs will be summarized by the newproduct development team to create innovations Howeverthese methods do not take users into consideration in theinnovation process Consumers with usage experience ofrelated products will be able to explain where they haveproblems with the innovative product what their specificneeds are andwhat the functions they use are [27]Thereforean introduction of the opinions being provided by lead usersis especially important and useful for high-technology firmsThe steps for innovations based on lead usersrsquo opinionsinclude [27] the following (1) find the lead users (2) preparefor a lead user workshop (3) run the workshop and (4)
document the results and proceed to the output
23 UTAUT The UTAUT was proposed by Venkatesh et al[34] as an integrated framework of eight related technologyacceptance theories or models Those theories or modelsinclude the diffusion of innovation theory the TRA the TPBthe motivation theory the hybrid model of TPB and TAMthe original TAM the PC utilization model and the socialcognitive theory The perceived ease of use and the perceivedusefulness were incorporated in thismodel by using the effort
Table 1 The number of innovations across various industries
Industry Source of innovationUser Manufacturer Other
Computer industry 33 67Chemical industry 70 30Pultrusion-process machinery 85 15Scientific instrument with majorfunctional improvement 82 18
Semiconductor-electronic processequipment with major functionalimprovements
63 21 16lowast
Electronic assembly 11 33 56+
Surface chemistry instruments withnew functional capability 82 18
lowastJoint usermdashmanufacturer innovation+Supplier innovationSource [28 106]
expectancy and the performance expectancy dimensionsTheUTAUTwas conducted in two rounds of studies in which thedata was collected from six organizations in three rounds ofsurveys The variance of explanations in two rounds reachedabout 70 and 50 respectively
In addition to the two most important constructs ofperformance expectancy and effort expectancy the otherconstructs which include the social influence the facilitatingconditions the intentions to use and the usage behaviorswere also included in this model Venkatesh et al [34] exam-ined the three constructs consisting of self-efficacy anxietyand attitude toward using technology in UTAUT modelHowever these three constructs have no strong impact onothers Thus three constructs are removed from UTAUTmodel
24 UTAUT2 The UTAUT was developed as a comprehen-sive integrated model for better understanding consumeracceptance toward new technology or system Accordingto Venkatesh there are three types which can enhance theprediction ratio of technology acceptance For the first typeVenkatesh considers the consumer acceptance of new tech-nology in variety of contexts such as culture and populationFor the second type Venkatesh considered to add differentconcepts to the model so as to widen the theoretic relation-ships of UTAUT For the third type Venkatesh consideredto synthesize new predictor of variables into the UTAUTDespite the integrated model in which some variables areusually added Venkatesh et al [4] emphasizes the needs toinclude salient predictor variables that can be used withina user technology use context They also examined morerelated consumer behavior of studies and alter the priorperspective (from organizations to individuals) by adjustingUTAUT model to establish a new prediction frameworknamely UTAUT2 Currently this newestmodel has graduallybeen adopted for exploring various issues such as self-technology service smart mobile device adoption learningmanagement software acceptance and healthcare industry
Mathematical Problems in Engineering 5
With regard to this prediction model the hedonic moti-vation construct was regarded as an important predictor andwas integrated into the UTAUT2 for more stressing utilityTheprice value construct was also introduced in theUTAUT2model because product quality cost and price will influenceadoption decisions [35] Venkatesh et al [4] also consideredthat the recent studies have stressed the roles of behaviorintention they thus incorporated a new construct of habitinto the UTAUT2 The introduction of the habit constructwas due to the following two reasons First the habit isregarded as prior behavior [36] Second the habit can bedefined as the degree to which people believe the behavior tobe automatic [37] These new added constructs were verifiedconstantly in previous researches as the critical determinantsfor usersrsquo technology adoptions Therefore such constructscan be used in the investigations of usersrsquo adoption ofPhablets
The prior model of the UTAUT has been used to describeusersrsquo technology adoption behavior in organizational con-text [34] Instead the UTAUT2 model was extended fromthe UTAUT and was focused on individual perspectives intechnology adoptions The new model was significantly anenhanced one for explaining variances in usersrsquo technologyintention Since the purpose of this research is to explorethe possible factors influencing individual usersrsquo adoptions ofPhablets the UTAUT2 framework can provide more insightsand thus will be adopted as the research model of this work
25 ResearchModel Researchers have summarized purchaseand usage behaviors of consumers by using the TPB modelTAM model and UTAUT model [38ndash40] The UTAUT2model will be introduced as the theoretic foundation of theresearch model to explore the purchase behavior towardPhablets (Figure 2) After reviewing the related TAM theoryand UTAUT this section will propose an analytic frame-work based on the UTAUT2 being discussed above Theprediction model will be used to explore the influencerelationships between the constructs which include theperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitand behavioral intention Following the constructs will beintroduced
251 Performance Expectancy Theperformance expectancyan important construct for the behavior intention in theUTAUT or UTAUT2 models means the extent to which theusage of a new technology or a new technology productcan provide consumers the benefits in performing specificactivities [4]The performance expectancy construct consistsof four criteria the perceived usefulness the extrinsic moti-vation the job fit and the relative advantage (1) Perceivedusefulness The perceived usefulness is defined as the extentto which people believe that using a new technology canimprove their job performance [41] (2) Extrinsic motivationthe extrinsic motivation is the perceptions whether peoplewould like to perform an activity when such an activity isperceived to be instrumental in achieving valued outcomesthat are different from the activity itself (Chong [42] and
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 2 Research model
Teo et al [43] defined) (3) Job fit Thompson et al [44] andJeng and Tzeng [45] articulated that the job fit is how thecapabilities of a new technology will increase peoplersquos jobperformance (4) Relative advantage Rogers [3] stated thatrelative advantage refers to the benefit of adopting a newtechnology or a technology product compared to the costs
The Phablet users expect the product performance toenhance their job performance Such smart mobile deviceshave been developed to satisfy customersrsquo needs to improvethe job performance Moreover most of the consumersused smart phones as a communication tool in generaland an entertainment tool specially Based on this reasonperformance expectancy impacts the behavioral intention touse Phablet
252 Effort Expectancy Effort expectancy refers to thedegree of the ease of use which is associated with theusage of a new technology or a technology product [4] Theconstruct is similar to the perceived ease of use variable of theTAM or the ease of use variable and the complexity variablewhich belongs to the diffusion of innovation theory In thetechnology adoption context the effort and the performanceexpectancies are themost important determinants for analyz-ing the technology usage behavior and the behavioral inten-tion [41 44 46 47] According to literature review resultsthe effort expectancy construct consists of three criteria theperceived ease of use the complexity and the ease of use(1) Perceived ease of use the perceived ease of use refers tothe degree to which people believe that using a technologywould be free of effort [34] (2) Ease of use in comparisonto the perceived ease of use the ease of use is definedas the degree to which using an innovative technology orproduct is identified as being difficult or easy to use [45 48]Rogers [3] indicated that complexity is the degree to whichan innovative technology is identified as relatively difficultto use and understand The complexity of new technologywould have negative impacts on its acceptance rate [3] Inaccordance with previous empirical studies which has beendemonstrated that effort expectancy would influence theconsumersrsquo attitude of use in both mandatory and voluntaryusage [4 34 48]
6 Mathematical Problems in Engineering
253 Social Influence Researches have broadly explored theconcepts of the social influence and proved the influences ofthe social influence on shaping usersrsquo behaviors For instanceRogers [3] indicated that the usersrsquo decision making processof adopting an innovative technology is influenced by thesocial notion beyond an individualrsquos decision thinking Ingeneral the social influence can be classified into two partsthe social norms and the critical mass The social normsinclude two different influences the informational influenceand the normative influence The informational influencerefers to peoplersquos obtaining of information from other peopleThe normative influence refers to a userrsquos conformation to theexpectation of other people to gain a reward or avoid a pun-ishment [49] Venkatesh et al [34] defined social influence asthe degree of importance being recognized by others to usea novel technology The social influence construct consistsof three variables the subjective norm the social factor andthe image (1) Subjective norm the subjective norm is theperceived social pressure to perform or not to perform thebehavior [50] In this research the subjective norm is theperceived social pressure to use Phablets (2) Social factorthe social factor is an individualrsquos internalization from thesocial systemrsquos subjective culture and particular interpersonalagreements that the individual in particular social situationhas made with others [51] (3) Image the image is defined asthe degree to which an individual identifies that the using ofan innovative technology can enhance an individualrsquos statusin his or her social organization [48] Drawing the aboveliterature review the usage of an innovative product can bedetermined by the behavioral intention
254 FacilitatingConditions Thefacilitating conditions con-struct is defined as the degree to which a person believes thatan organization and a technical infrastructure exist to supportthe usage of a system [34] Previous researches on factorsinfluencing acceptance of some specific technology haveexhibited that facilitating conditions have a significant impacton innovative technology adoptions and usage behaviors [3444 46 48 52ndash54] These researches summarized that thefacilitating conditions are strong predictors which can beused for forecasting technology acceptances and usages
255 HedonicMotivation Thehedonicmotivation is definedas the motivation to do something due to the internalsatisfaction [55] From the hedonic perspective of indi-vidual behaviors the hedonic motivation is related to theessence of individualrsquos psychological and emotive experienceswhich can be triggered by both the individual traits andthe cognitive states [56] Based on prior studies Magniet al [56] explored the relationships between consumersand technology products by analyzing consumersrsquo intentionTo explore consumersrsquo purchase motivations Magni et aldeveloped and tested amodel to examine the effect of hedonicmotivations Besides similar to the flow theory many offormer empirical studies have demonstrated that hedonicexperiences and traits will influence consumer technologyacceptances from both individual and organizational con-texts [57ndash60] In otherwords individualrsquos hedonic experience
of using a technology product such as a Phablet is more likelyto perform experimental behavior
256 Price Value The price value construct originated fromthe perceived value which is often regarded as an importantindicator in predicting the purchase behavior which caninfluence a companyrsquos competitive advantage [61 62] Tradi-tionally the definition of the price value is a trade-off betweenbenefits and sacrifices [63] Recently the price value has beenemphasized by the researchers in the information technologyfields and themarketers of consumer-electronics devicesTheconcept was adopted to analyze usersrsquo adoption of emergingtechnologies or smart mobile devices The findings indicatedthat the price value concept is crucial in attracting consumers[64ndash66]The price value is positive when the benefits of usinga technology are identified to be greater than the monetarycosts Such price value has a positive impact on intentions [4]
Based on these ideas Venkatesh et al [4] described theprice value as consumersrsquo cognitive tradeoffs between theperceived benefits of the applications and monetary costsfor using them [67] In the marketing context the pricevalue encompasses two perspectives monetary costs andnonmonetary costs The monetary costs refer to the valuebeing identified in contrast to the price paid [68] Thenonmonetary costs refer to the value being identified inreturn for costs such as time and efforts being expended [69]In this research the price value combines both the monetaryand nonmonetary values for exploring factors influencingconsumersrsquo acceptances of Phablets
257 Habit The habit construct has been broadly discussedin a variety of domains such as psychology consumersrsquo pur-chase behaviors education health science andmanagementTriandis [51] derived the relationship between attitude andbehavior where behavioral intention is postulated to forecastuser behavior to the extent that the habit component isweak when habit is strong Aarts et al [70] found that thehabit strength attenuates the amount of information beingacquired before the decision is made Limayem et al [37]and Venkatesh et al [4] defined the habit as the degree towhich consumers tend to perform the usage of technologiesor the usage of technology products behaviors automaticallybecause of learning The habit construct consists of three cri-teria the past behavior the reflex behavior and the individualexperience The past behavior is described as usersrsquo priorbehaviors [37] The reflex behavior refers to usersrsquo behaviorsequence or customs which are regular parts of the daily life[37] The individual experience refers to the accumulation ofexperiences from usersrsquo established stable routines normsand habits for using technology products Such experiencesdecreased the needs for discussions coordination or effortfuldecision making [37] Researches on habitual intentions andhabitual usage behaviors have demonstrated that the habitis a strong predictor of technology usages in promotingbehavioral changes [4 62 71 72]
258 Behavioral Intention Social psychologists have broadlyexplored behavioral intentions and the relations to future
Mathematical Problems in Engineering 7
behavior [70] Behavioral intention refers to the degree towhich a person has formulated conscious plans to performor not perform some specified future behavior(s) [70]Behavioral intention was frequentlymeasured as the conativeloyalty which is an important goal in marketing [73] In themarketing context the loyalty is defined as what customersare willing to repurchase a product from the company andsupport the company with positive word-of-mouth commu-nications [71] For marketers and manufacturers of Phabletssuch outcomes are very important asmembers become agentsfor the firms encouraging friends and acquaintances to pur-chase their products However predictions of actual purchasebehaviors are always difficult Despite this many of the priorstudies have still proven that the behavioral intention playsa significant role in actual behaviors [74] In this researchto investigate the factors influencing consumersrsquo acceptancesof Phablets the repurchase intention the positive word-of-mouth communications and the service quality are selectedas the criteria for exploring the consumersrsquo behavioral inten-tions
259 Performance Expectancy A great number of researchesrevealed that past behaviors will influence future behaviorsSome researchers have further proven that past usage behav-iors are the antecedents of future behaviors [75] In orderto derive the factors influencing consumersrsquo acceptances ofPhablets three factors will be introduced as the criteria of theusage behaviorsThe factors include the usage time the usagefrequency and the usage variety Venkatesh et al [4] indicatedthat the usage behavior construct should bemeasured by boththe variety and the frequencyMathieson [76] andAl-Gahtaniet al [77] also indicated that the usage behavior constructconsists of four dimensions for measuring the technologyusage (1) the amount of time being spent in using technologyproducts per day (2) the usage frequency of technology prod-ucts (3) the number of various software applications beingused and (4) the number of various job tasks being supportedthrough technology product usages In this research theusage time usage frequency and the number of various jobtasks being supported will be introduced
The constructs being summarized based on literaturereview results being demonstrated above are demonstratedin Table 2The research model comprises of night constructsperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitbehavioral intention and use behavior In the later empiricalstudy Section 4 the influence relationships among the con-structs will be established
3 Research Methods
To construct the analytic framework for deriving factors forpredictions of usersrsquo acceptances of Phablets this researchreviewed the related research works of social psychologyand literature being related to factors for predicting usersrsquoadoption of technology such as theUTAUT and theUTAUT2for collecting the possible dimensions and criteria Nextthe DEMATEL method is employed to establish the causal
Table 2 Dimensions and criteria for analyzing usersrsquo Phabletacceptance
Dimensions Criteria
Performance expectancy (1198631)
Perceived usefulness (11988811)
Extrinsic motivation (11988812)
Job fit (11988813)
Relative advantage (11988814)
Effort expectancy (1198632)
Perceived ease of use (11988821)
Complexity (11988822)
Ease of use (11988823)
Social influence (1198633)
Subjective norm (11988831)
Social factor (11988832)
Image (11988833)
Facilitating conditions (1198634)
Perceived behavioral control (11988841)
Facilitating conditions (11988842)
Compatibility (11988843)
Hedonic motivations (1198635)
Enjoyment (11988851)
Interest (11988852)
Curiosity (11988853)
Price value (1198636)
Quality (11988861)
Value (11988862)
Price (11988863)
Habit (1198637)
Past behavior (11988871)
Reflex behavior (11988872)
Individual experience (11988873)
Use intention (1198638)
Repurchase intentions (11988881)
Positive word-of-mouthcommunication (119888
82)
Service quality (11988883)
Use behavior (1198639)
Usage time (11988891)
Usage frequency (11988892)
Use variety (11988893)
relationships Then the DNP will be applied to derive theinfluence weights based on the lead usersrsquo perspectives Insummary the assessment model consists of three main steps(1) deriving the requirement by literature review (2) struc-turing the causal relationship based on lead usersrsquo opinion byapplying DEMATEL and (3) evaluating the weights versuseach criterion by using the DNP
31 Modified Delphi Method The Delphi method wasdesigned by Dalkey and Helmer [78] After the Delphimethod Murry and Hammons [79] tried to identify issuesand problems that were collected from a group of technologyeducation professionals using themodifiedDelphi techniqueThe modified Delphi simplified the step of conducting thefirst round of a survey and replaced the conventionallyadopted open style survey [80] The purpose of the modifiedDelphi method is to save time (the experts can focus onresearch themes eliminating the need for speculation onthe open questionnaire) and to improve the response of the
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
2 Mathematical Problems in Engineering
to how users or customers evaluate the satisfaction withrespect to the adoptions and usages of a novel technology intheir lives from the dimensions of perceived ease of use andusefulness and so forth In general the model of technologyacceptance has always been regarded as a useful analyticframework for verifying or exploring user acceptances andadoptions of new high technology Due to the fast progress ofemerging smart mobile technologies consumer-electronicsfirms such as Apple Samsung LG and SONY aggressivelyexpand their scope to smart mobile devices and furthermaximize the profitability and the market share
During the past years the Phablet an integrated smartdevice combining the functionality and characteristics ofboth tablet PCs and smart phone has gradually becomepossible alternatives for smart phones Therefore predictingfactors which can influence the acceptance of Phablets havebecome indispensable for designing manufacturing andmarketing of suchmobile devices However such predictionsare not easy Meanwhile very few researches tried to studyrelated issues Consequently the authors aim to explore andpredict the intentions to use and use behaviors of Phablets
To predict precisely consumersrsquo preferences toward thePhablet products the lead user method will be adoptedfor investigating innovators or early adoptersrsquo perspectivesRogers [3] has indicated that the early adoptersrsquo or innovatorsrsquousage experience and use behavior would influence laterbulk of users to accept new technology or product [3]Further most consumer electronics providers often investi-gated consumersrsquo preferences toward disruptive innovativeelectronics products (eg the migration from the traditionalfunction phones to the configurable smart phones) based oninnovatorsrsquo or early adoptersrsquo use experiences as the basis todefine or improve their productsThus the lead usermethodwhich is a useful theory will be adopted by this researchMoreover based on the authorrsquos very limited knowledgeexisting researches on smart mobile devices ignored the roleof Phablets In order to cross this knowledge gap this studywill investigate the probable existence of Phablets technologyacceptance
The UTAUT2 a theoretic framework being derivedfrom the TAM and the UTAUT2 is a powerful predictingframework being proposed by Venkatesh et al [4] TheUTAUT2 can effectively explain and analyze peoplersquos technol-ogy acceptance behaviors for novel information technology(IT) products Consequently this research introduced theUTAUT2 as an analyticmodel Further since the functions ofnovel smart mobile devices are usually very hard for normalconsumers to understand the traditional market researchapproaches based on surveys of consumersrsquo opinions are notfeasible for such novel products Therefore this research willsurvey lead usersrsquo opinions for the preferences toward thenext generation smart mobile devices
The criteria for evaluating the lead usersrsquo preferences willfirst be derived from the UTAUT2Then the criteria will fur-ther be confirmed by the modified Delphi methodTheDeci-sion Making Trial and Evaluation Laboratory (DEMATEL)will be introduced to construct the influence relationshipsbetween criteria Further the DEMATEL based networkprocess (DNP) will be used to derive the influence weights
versus each dimension and criterion Finally the influencesof the criteria on consumersrsquo preferences toward novel smartdevices can be derived
Based on the empirical study results being derived byTaiwanese Phablet experts the causal relationships beingconstructed by the DEMATEL demonstrate that the habitdimension has direct influence on other dimensions Theuse intention and performance expectancy dimensions havethe least influences on other dimensions In practice thesedimensions should be prioritized in improvement than therest of the dimensions Furthermore the weights beingassociated with the criterion and construct reveal that the useintention hedonic motivation and performance expectancyare the most important dimensions The research resultscan serve as a basis for related Phablet devicesrsquo marketingstrategy definition and product improvement The proposedmethodology can also be used for predicting usersrsquo adoptingbehavioral preferences and be employed for improving thegaps among the Phablet use factors
The remainder of the paper is organized as followsSection 2 is a review of the related literature and the-ories which include the predictions of high-technologyconsumer behaviors and the lead user method as wellas the UTAUT and UTAUT2 In Section 3 the researchmethod will be introduced to construct the decision frame-work An empirical study for selecting the most influ-ential factors on the acceptances of novel smart mobiledevices will be detailed in Section 4 Discussions will bepresented in Section 5 Section 6 will conclude the paper withobservations summaries and recommendations for futurestudies
2 Literature Review
In this section the past researches regarding the predictionsof high-technology consumer behaviors will be reviewedat first Then the lead user method will be introducedThe UTAUT and UTAUT2 theoretic models will then bereviewed Finally the possible criteria and dimensions whichmay influence usersrsquo technology adoptions based on thetheoretic model will be reviewed and summarized
21 Prediction of High-Technology Consumer Behaviors Overthe years marketers and researchers have constantly exploredthe many motivating influences on purchase behavior [5ndash7]Booth and Shepherd [8] argued that the factors of cultureeconomy emotions value and attitudes will influence thedecision process of consumer purchase behavior Loudon andDella Bitta [9] also pointed out that the consumer purchasebehavior is a decision process in which customers can chooseand use the products and services In other words throughthe decision making process consumer can examine theiractions and the reason for why they would like to purchasethis product Subsequently Kotler [10] further identified thatconsumer purchase behavior is affected by cultural socialpersonal and psychological factors On the other handconsumer behavior is often goal-oriented not haphazard oraccidental For instance consumers have a goal or a set of
Mathematical Problems in Engineering 3
goals seeking to satisfy presently unfulfilled needs From theabove obviously consumer behavior is themodel of behaviorthat people follow in looking for buying using or evaluatinggoods services and ideas that they expect to fulfill their needsand wants [11]
Prediction of consumer behavior is often an importanttask for marketing managers They have to understand thepossible purchasing behavior of consumer and the factorsaffecting consumer acceptance of products so that they canpropose ideas for RampD staffs or related employees to improveand enhance the products In high-technology consumerbehavior the concept of purchase behavior is important forits implication that consumers are different thus the mar-keting managers should develop differentiation marketingstrategies to cope with variety of situations de Bellis et al[12] argued that the appropriate marketing strategies willfoster rich interactions with their customers and enhancemarketing efficiency and effectiveness Thus understandingthe consumer behavior of high technology and definingthe appropriate policy are a critical task for the Phabletmanufacturers and marketers
High-technologymarketing often includes twoparts tan-gible and intangible technology marketing In this researchwe will focus on the tangible dimensions of electronic prod-ucts Marketing about technology products is an importantexternal factor that influences the acceptance rate of a techno-logical innovation [13] Technology marketing often consistsof advertising word-of-mouth communication marketingactivities Internet forums and television product placements[13] For technology usage of consumer behavior Rogers [3]suggested that there are differences in consumersrsquo dispositiontoward using technology He further defined consumer intofive groups illustrating their character ranging from inno-vators to laggards [3] Due to the differences of consumersrsquotraits technology readiness index being proposed to describeconsumersrsquo beliefs regarding various dimensions of technol-ogy differs The definition of technology readiness is dividedinto four dimensions of consumers [14 15] (1) Optimismoptimism is a positive view of technology and belief offeringconsumers increased control flexibility and efficiency in lifedue to technology (2) Innovativeness innovativeness is thetendency to be a technology pioneer and thought leader(3) Discomfort discomfort means having a need for thecontrol and sense of being overwhelmed (4) Insecurityinsecurity means disturbing technology for security andprivacy reasons
Through the studies ofmarketing and consumer purchas-ing behavior marketers and researchers would aggressivelylike to develop appropriate marketing strategies to helpelectronic firms Above all these firms should understandthose issues as follows before they definemarketing strategies[16] (1) how consumers think feel choose from differentproducts and make decisions (2) how the consumers areaffected by the environment and their background (iemedia and family) (3) the behavior of consumers whilemaking buying decisions (4) the decisions influenced bythe limitation of consumer information abilities (5) howconsumersrsquo decision strategies andmotivation differ betweenproducts that are different in level of interest or importance
(6) how companies can adapt and improve their marketingstrategies to more effectively achieve the consumersrsquo needs
In comparison to the traditional market Moriarty andKosnik [17] summarized the characteristics of a high-technologymarket from three dimensions themarket uncer-tainty the technological uncertainty and the competitivevolatility (1) Market uncertainty refers to the ambiguityabout the type and extent of consumer needs that can be satis-fied by a particular technology [17 18] There are five sourceswhich can result in the high-technologymarket certaintyThesources include the needs which might be met by the newtechnology the possible changes of the needs in the futurethe adoption of industry standards or not the diffusion rateof innovations and the potential size of the high-technologymarket (2) Technological uncertainty means that whetherthe technology canmeet specific needs is unclear Five factorsgive rise to technological uncertainty The factors includethe new product function the delivery timetable the servicequality and the sustainability problem being raised by thenew technology (3) Competitive volatility refers to both theintensity in extent of change in the competitive landscapeand the uncertainty about competitors and their strategies[19] Competitive volatility is composed of three sourcesthe new competitors in the future new competitive tacticsand new products to compete with Figure 1 summarizes theabovementioned characteristics of high-technology market
22 The Lead User Method Increasingly firms are recogniz-ing the power in innovation idea development [20] Eitherservice design concepts or RampD such idea often inspires achallenge of companies [21] In novel product developmentmany literatures suggest techniques for idea creation suchas benchmarking [22] user observation [23] or lead usermethod [24] Among these the lead user method has beenshown to provide the highest potential to create commerciallyattractive and highly novel innovations [20] (eg [25 26])In other words lead user theory can effectively understandconsumer purchase behavior and serve as a developmentbasis for next generation product Moreover most of thetarget users for your product will have difficulties expressingtheir needs for your products [27] And a vast majority ofyour target users will certainly not be able to come up withthe innovations themselves [27] There will always be someusers who are exceptions These are the lead users of yourproducts or services For example if you are designing thesoftware of smart phones you will be looking at the peoplewho are already designing programming for your software
In high-technology industries the world moves sorapidly The related real-world experiences of ordinary usersare often rendered obsolete by the time a product is devel-oped or during the time of its projected commercial lifetime[28]Thus in the research of innovative products VonHippeldefined ldquolead userrdquo as a person displaying two characteristicsregarding a given new product [24 28] (1) The lead usersface needs that will diffuse in the marketplace but face thembefore the bulk of that marketplace encounters them (2)The lead users are positioned to benefit significantly fromobtaining a solution to those needs Since the studies oncustomer involvement in successful innovation prove the
4 Mathematical Problems in Engineering
Source [17]
Market uncertainty
Technological uncertainty
High technology
market
Competitive volatility
Figure 1 Characterizing the high-technology market
importance of lead users [29] who can effectively foster theproduct improvement [30] and develop products that areready for the market [31] the lead user analysis is used toidentify the characteristics such as traits knowledge andstatus Subsequently based on lead user theory Belz andBaumbach [32] further defined six characteristics of onlineconsumer purchasing behavior including ahead of trenddissatisfaction product-related knowledge use experienceinvolvement and opinion leadership Research on lead usersshows that many products are initially thought of and evenprototyped by users rather than manufacturers [19 33]For instance Table 1 shows that across various industriesthe number of innovations conceived of by users is quitehigh
The traditional analytic methods for defining incremen-tal innovation and radical innovation products or servicesusually initiate by deriving consumersrsquo needs at the verybeginning Then such needs will be summarized by the newproduct development team to create innovations Howeverthese methods do not take users into consideration in theinnovation process Consumers with usage experience ofrelated products will be able to explain where they haveproblems with the innovative product what their specificneeds are andwhat the functions they use are [27]Thereforean introduction of the opinions being provided by lead usersis especially important and useful for high-technology firmsThe steps for innovations based on lead usersrsquo opinionsinclude [27] the following (1) find the lead users (2) preparefor a lead user workshop (3) run the workshop and (4)
document the results and proceed to the output
23 UTAUT The UTAUT was proposed by Venkatesh et al[34] as an integrated framework of eight related technologyacceptance theories or models Those theories or modelsinclude the diffusion of innovation theory the TRA the TPBthe motivation theory the hybrid model of TPB and TAMthe original TAM the PC utilization model and the socialcognitive theory The perceived ease of use and the perceivedusefulness were incorporated in thismodel by using the effort
Table 1 The number of innovations across various industries
Industry Source of innovationUser Manufacturer Other
Computer industry 33 67Chemical industry 70 30Pultrusion-process machinery 85 15Scientific instrument with majorfunctional improvement 82 18
Semiconductor-electronic processequipment with major functionalimprovements
63 21 16lowast
Electronic assembly 11 33 56+
Surface chemistry instruments withnew functional capability 82 18
lowastJoint usermdashmanufacturer innovation+Supplier innovationSource [28 106]
expectancy and the performance expectancy dimensionsTheUTAUTwas conducted in two rounds of studies in which thedata was collected from six organizations in three rounds ofsurveys The variance of explanations in two rounds reachedabout 70 and 50 respectively
In addition to the two most important constructs ofperformance expectancy and effort expectancy the otherconstructs which include the social influence the facilitatingconditions the intentions to use and the usage behaviorswere also included in this model Venkatesh et al [34] exam-ined the three constructs consisting of self-efficacy anxietyand attitude toward using technology in UTAUT modelHowever these three constructs have no strong impact onothers Thus three constructs are removed from UTAUTmodel
24 UTAUT2 The UTAUT was developed as a comprehen-sive integrated model for better understanding consumeracceptance toward new technology or system Accordingto Venkatesh there are three types which can enhance theprediction ratio of technology acceptance For the first typeVenkatesh considers the consumer acceptance of new tech-nology in variety of contexts such as culture and populationFor the second type Venkatesh considered to add differentconcepts to the model so as to widen the theoretic relation-ships of UTAUT For the third type Venkatesh consideredto synthesize new predictor of variables into the UTAUTDespite the integrated model in which some variables areusually added Venkatesh et al [4] emphasizes the needs toinclude salient predictor variables that can be used withina user technology use context They also examined morerelated consumer behavior of studies and alter the priorperspective (from organizations to individuals) by adjustingUTAUT model to establish a new prediction frameworknamely UTAUT2 Currently this newestmodel has graduallybeen adopted for exploring various issues such as self-technology service smart mobile device adoption learningmanagement software acceptance and healthcare industry
Mathematical Problems in Engineering 5
With regard to this prediction model the hedonic moti-vation construct was regarded as an important predictor andwas integrated into the UTAUT2 for more stressing utilityTheprice value construct was also introduced in theUTAUT2model because product quality cost and price will influenceadoption decisions [35] Venkatesh et al [4] also consideredthat the recent studies have stressed the roles of behaviorintention they thus incorporated a new construct of habitinto the UTAUT2 The introduction of the habit constructwas due to the following two reasons First the habit isregarded as prior behavior [36] Second the habit can bedefined as the degree to which people believe the behavior tobe automatic [37] These new added constructs were verifiedconstantly in previous researches as the critical determinantsfor usersrsquo technology adoptions Therefore such constructscan be used in the investigations of usersrsquo adoption ofPhablets
The prior model of the UTAUT has been used to describeusersrsquo technology adoption behavior in organizational con-text [34] Instead the UTAUT2 model was extended fromthe UTAUT and was focused on individual perspectives intechnology adoptions The new model was significantly anenhanced one for explaining variances in usersrsquo technologyintention Since the purpose of this research is to explorethe possible factors influencing individual usersrsquo adoptions ofPhablets the UTAUT2 framework can provide more insightsand thus will be adopted as the research model of this work
25 ResearchModel Researchers have summarized purchaseand usage behaviors of consumers by using the TPB modelTAM model and UTAUT model [38ndash40] The UTAUT2model will be introduced as the theoretic foundation of theresearch model to explore the purchase behavior towardPhablets (Figure 2) After reviewing the related TAM theoryand UTAUT this section will propose an analytic frame-work based on the UTAUT2 being discussed above Theprediction model will be used to explore the influencerelationships between the constructs which include theperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitand behavioral intention Following the constructs will beintroduced
251 Performance Expectancy Theperformance expectancyan important construct for the behavior intention in theUTAUT or UTAUT2 models means the extent to which theusage of a new technology or a new technology productcan provide consumers the benefits in performing specificactivities [4]The performance expectancy construct consistsof four criteria the perceived usefulness the extrinsic moti-vation the job fit and the relative advantage (1) Perceivedusefulness The perceived usefulness is defined as the extentto which people believe that using a new technology canimprove their job performance [41] (2) Extrinsic motivationthe extrinsic motivation is the perceptions whether peoplewould like to perform an activity when such an activity isperceived to be instrumental in achieving valued outcomesthat are different from the activity itself (Chong [42] and
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 2 Research model
Teo et al [43] defined) (3) Job fit Thompson et al [44] andJeng and Tzeng [45] articulated that the job fit is how thecapabilities of a new technology will increase peoplersquos jobperformance (4) Relative advantage Rogers [3] stated thatrelative advantage refers to the benefit of adopting a newtechnology or a technology product compared to the costs
The Phablet users expect the product performance toenhance their job performance Such smart mobile deviceshave been developed to satisfy customersrsquo needs to improvethe job performance Moreover most of the consumersused smart phones as a communication tool in generaland an entertainment tool specially Based on this reasonperformance expectancy impacts the behavioral intention touse Phablet
252 Effort Expectancy Effort expectancy refers to thedegree of the ease of use which is associated with theusage of a new technology or a technology product [4] Theconstruct is similar to the perceived ease of use variable of theTAM or the ease of use variable and the complexity variablewhich belongs to the diffusion of innovation theory In thetechnology adoption context the effort and the performanceexpectancies are themost important determinants for analyz-ing the technology usage behavior and the behavioral inten-tion [41 44 46 47] According to literature review resultsthe effort expectancy construct consists of three criteria theperceived ease of use the complexity and the ease of use(1) Perceived ease of use the perceived ease of use refers tothe degree to which people believe that using a technologywould be free of effort [34] (2) Ease of use in comparisonto the perceived ease of use the ease of use is definedas the degree to which using an innovative technology orproduct is identified as being difficult or easy to use [45 48]Rogers [3] indicated that complexity is the degree to whichan innovative technology is identified as relatively difficultto use and understand The complexity of new technologywould have negative impacts on its acceptance rate [3] Inaccordance with previous empirical studies which has beendemonstrated that effort expectancy would influence theconsumersrsquo attitude of use in both mandatory and voluntaryusage [4 34 48]
6 Mathematical Problems in Engineering
253 Social Influence Researches have broadly explored theconcepts of the social influence and proved the influences ofthe social influence on shaping usersrsquo behaviors For instanceRogers [3] indicated that the usersrsquo decision making processof adopting an innovative technology is influenced by thesocial notion beyond an individualrsquos decision thinking Ingeneral the social influence can be classified into two partsthe social norms and the critical mass The social normsinclude two different influences the informational influenceand the normative influence The informational influencerefers to peoplersquos obtaining of information from other peopleThe normative influence refers to a userrsquos conformation to theexpectation of other people to gain a reward or avoid a pun-ishment [49] Venkatesh et al [34] defined social influence asthe degree of importance being recognized by others to usea novel technology The social influence construct consistsof three variables the subjective norm the social factor andthe image (1) Subjective norm the subjective norm is theperceived social pressure to perform or not to perform thebehavior [50] In this research the subjective norm is theperceived social pressure to use Phablets (2) Social factorthe social factor is an individualrsquos internalization from thesocial systemrsquos subjective culture and particular interpersonalagreements that the individual in particular social situationhas made with others [51] (3) Image the image is defined asthe degree to which an individual identifies that the using ofan innovative technology can enhance an individualrsquos statusin his or her social organization [48] Drawing the aboveliterature review the usage of an innovative product can bedetermined by the behavioral intention
254 FacilitatingConditions Thefacilitating conditions con-struct is defined as the degree to which a person believes thatan organization and a technical infrastructure exist to supportthe usage of a system [34] Previous researches on factorsinfluencing acceptance of some specific technology haveexhibited that facilitating conditions have a significant impacton innovative technology adoptions and usage behaviors [3444 46 48 52ndash54] These researches summarized that thefacilitating conditions are strong predictors which can beused for forecasting technology acceptances and usages
255 HedonicMotivation Thehedonicmotivation is definedas the motivation to do something due to the internalsatisfaction [55] From the hedonic perspective of indi-vidual behaviors the hedonic motivation is related to theessence of individualrsquos psychological and emotive experienceswhich can be triggered by both the individual traits andthe cognitive states [56] Based on prior studies Magniet al [56] explored the relationships between consumersand technology products by analyzing consumersrsquo intentionTo explore consumersrsquo purchase motivations Magni et aldeveloped and tested amodel to examine the effect of hedonicmotivations Besides similar to the flow theory many offormer empirical studies have demonstrated that hedonicexperiences and traits will influence consumer technologyacceptances from both individual and organizational con-texts [57ndash60] In otherwords individualrsquos hedonic experience
of using a technology product such as a Phablet is more likelyto perform experimental behavior
256 Price Value The price value construct originated fromthe perceived value which is often regarded as an importantindicator in predicting the purchase behavior which caninfluence a companyrsquos competitive advantage [61 62] Tradi-tionally the definition of the price value is a trade-off betweenbenefits and sacrifices [63] Recently the price value has beenemphasized by the researchers in the information technologyfields and themarketers of consumer-electronics devicesTheconcept was adopted to analyze usersrsquo adoption of emergingtechnologies or smart mobile devices The findings indicatedthat the price value concept is crucial in attracting consumers[64ndash66]The price value is positive when the benefits of usinga technology are identified to be greater than the monetarycosts Such price value has a positive impact on intentions [4]
Based on these ideas Venkatesh et al [4] described theprice value as consumersrsquo cognitive tradeoffs between theperceived benefits of the applications and monetary costsfor using them [67] In the marketing context the pricevalue encompasses two perspectives monetary costs andnonmonetary costs The monetary costs refer to the valuebeing identified in contrast to the price paid [68] Thenonmonetary costs refer to the value being identified inreturn for costs such as time and efforts being expended [69]In this research the price value combines both the monetaryand nonmonetary values for exploring factors influencingconsumersrsquo acceptances of Phablets
257 Habit The habit construct has been broadly discussedin a variety of domains such as psychology consumersrsquo pur-chase behaviors education health science andmanagementTriandis [51] derived the relationship between attitude andbehavior where behavioral intention is postulated to forecastuser behavior to the extent that the habit component isweak when habit is strong Aarts et al [70] found that thehabit strength attenuates the amount of information beingacquired before the decision is made Limayem et al [37]and Venkatesh et al [4] defined the habit as the degree towhich consumers tend to perform the usage of technologiesor the usage of technology products behaviors automaticallybecause of learning The habit construct consists of three cri-teria the past behavior the reflex behavior and the individualexperience The past behavior is described as usersrsquo priorbehaviors [37] The reflex behavior refers to usersrsquo behaviorsequence or customs which are regular parts of the daily life[37] The individual experience refers to the accumulation ofexperiences from usersrsquo established stable routines normsand habits for using technology products Such experiencesdecreased the needs for discussions coordination or effortfuldecision making [37] Researches on habitual intentions andhabitual usage behaviors have demonstrated that the habitis a strong predictor of technology usages in promotingbehavioral changes [4 62 71 72]
258 Behavioral Intention Social psychologists have broadlyexplored behavioral intentions and the relations to future
Mathematical Problems in Engineering 7
behavior [70] Behavioral intention refers to the degree towhich a person has formulated conscious plans to performor not perform some specified future behavior(s) [70]Behavioral intention was frequentlymeasured as the conativeloyalty which is an important goal in marketing [73] In themarketing context the loyalty is defined as what customersare willing to repurchase a product from the company andsupport the company with positive word-of-mouth commu-nications [71] For marketers and manufacturers of Phabletssuch outcomes are very important asmembers become agentsfor the firms encouraging friends and acquaintances to pur-chase their products However predictions of actual purchasebehaviors are always difficult Despite this many of the priorstudies have still proven that the behavioral intention playsa significant role in actual behaviors [74] In this researchto investigate the factors influencing consumersrsquo acceptancesof Phablets the repurchase intention the positive word-of-mouth communications and the service quality are selectedas the criteria for exploring the consumersrsquo behavioral inten-tions
259 Performance Expectancy A great number of researchesrevealed that past behaviors will influence future behaviorsSome researchers have further proven that past usage behav-iors are the antecedents of future behaviors [75] In orderto derive the factors influencing consumersrsquo acceptances ofPhablets three factors will be introduced as the criteria of theusage behaviorsThe factors include the usage time the usagefrequency and the usage variety Venkatesh et al [4] indicatedthat the usage behavior construct should bemeasured by boththe variety and the frequencyMathieson [76] andAl-Gahtaniet al [77] also indicated that the usage behavior constructconsists of four dimensions for measuring the technologyusage (1) the amount of time being spent in using technologyproducts per day (2) the usage frequency of technology prod-ucts (3) the number of various software applications beingused and (4) the number of various job tasks being supportedthrough technology product usages In this research theusage time usage frequency and the number of various jobtasks being supported will be introduced
The constructs being summarized based on literaturereview results being demonstrated above are demonstratedin Table 2The research model comprises of night constructsperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitbehavioral intention and use behavior In the later empiricalstudy Section 4 the influence relationships among the con-structs will be established
3 Research Methods
To construct the analytic framework for deriving factors forpredictions of usersrsquo acceptances of Phablets this researchreviewed the related research works of social psychologyand literature being related to factors for predicting usersrsquoadoption of technology such as theUTAUT and theUTAUT2for collecting the possible dimensions and criteria Nextthe DEMATEL method is employed to establish the causal
Table 2 Dimensions and criteria for analyzing usersrsquo Phabletacceptance
Dimensions Criteria
Performance expectancy (1198631)
Perceived usefulness (11988811)
Extrinsic motivation (11988812)
Job fit (11988813)
Relative advantage (11988814)
Effort expectancy (1198632)
Perceived ease of use (11988821)
Complexity (11988822)
Ease of use (11988823)
Social influence (1198633)
Subjective norm (11988831)
Social factor (11988832)
Image (11988833)
Facilitating conditions (1198634)
Perceived behavioral control (11988841)
Facilitating conditions (11988842)
Compatibility (11988843)
Hedonic motivations (1198635)
Enjoyment (11988851)
Interest (11988852)
Curiosity (11988853)
Price value (1198636)
Quality (11988861)
Value (11988862)
Price (11988863)
Habit (1198637)
Past behavior (11988871)
Reflex behavior (11988872)
Individual experience (11988873)
Use intention (1198638)
Repurchase intentions (11988881)
Positive word-of-mouthcommunication (119888
82)
Service quality (11988883)
Use behavior (1198639)
Usage time (11988891)
Usage frequency (11988892)
Use variety (11988893)
relationships Then the DNP will be applied to derive theinfluence weights based on the lead usersrsquo perspectives Insummary the assessment model consists of three main steps(1) deriving the requirement by literature review (2) struc-turing the causal relationship based on lead usersrsquo opinion byapplying DEMATEL and (3) evaluating the weights versuseach criterion by using the DNP
31 Modified Delphi Method The Delphi method wasdesigned by Dalkey and Helmer [78] After the Delphimethod Murry and Hammons [79] tried to identify issuesand problems that were collected from a group of technologyeducation professionals using themodifiedDelphi techniqueThe modified Delphi simplified the step of conducting thefirst round of a survey and replaced the conventionallyadopted open style survey [80] The purpose of the modifiedDelphi method is to save time (the experts can focus onresearch themes eliminating the need for speculation onthe open questionnaire) and to improve the response of the
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 3
goals seeking to satisfy presently unfulfilled needs From theabove obviously consumer behavior is themodel of behaviorthat people follow in looking for buying using or evaluatinggoods services and ideas that they expect to fulfill their needsand wants [11]
Prediction of consumer behavior is often an importanttask for marketing managers They have to understand thepossible purchasing behavior of consumer and the factorsaffecting consumer acceptance of products so that they canpropose ideas for RampD staffs or related employees to improveand enhance the products In high-technology consumerbehavior the concept of purchase behavior is important forits implication that consumers are different thus the mar-keting managers should develop differentiation marketingstrategies to cope with variety of situations de Bellis et al[12] argued that the appropriate marketing strategies willfoster rich interactions with their customers and enhancemarketing efficiency and effectiveness Thus understandingthe consumer behavior of high technology and definingthe appropriate policy are a critical task for the Phabletmanufacturers and marketers
High-technologymarketing often includes twoparts tan-gible and intangible technology marketing In this researchwe will focus on the tangible dimensions of electronic prod-ucts Marketing about technology products is an importantexternal factor that influences the acceptance rate of a techno-logical innovation [13] Technology marketing often consistsof advertising word-of-mouth communication marketingactivities Internet forums and television product placements[13] For technology usage of consumer behavior Rogers [3]suggested that there are differences in consumersrsquo dispositiontoward using technology He further defined consumer intofive groups illustrating their character ranging from inno-vators to laggards [3] Due to the differences of consumersrsquotraits technology readiness index being proposed to describeconsumersrsquo beliefs regarding various dimensions of technol-ogy differs The definition of technology readiness is dividedinto four dimensions of consumers [14 15] (1) Optimismoptimism is a positive view of technology and belief offeringconsumers increased control flexibility and efficiency in lifedue to technology (2) Innovativeness innovativeness is thetendency to be a technology pioneer and thought leader(3) Discomfort discomfort means having a need for thecontrol and sense of being overwhelmed (4) Insecurityinsecurity means disturbing technology for security andprivacy reasons
Through the studies ofmarketing and consumer purchas-ing behavior marketers and researchers would aggressivelylike to develop appropriate marketing strategies to helpelectronic firms Above all these firms should understandthose issues as follows before they definemarketing strategies[16] (1) how consumers think feel choose from differentproducts and make decisions (2) how the consumers areaffected by the environment and their background (iemedia and family) (3) the behavior of consumers whilemaking buying decisions (4) the decisions influenced bythe limitation of consumer information abilities (5) howconsumersrsquo decision strategies andmotivation differ betweenproducts that are different in level of interest or importance
(6) how companies can adapt and improve their marketingstrategies to more effectively achieve the consumersrsquo needs
In comparison to the traditional market Moriarty andKosnik [17] summarized the characteristics of a high-technologymarket from three dimensions themarket uncer-tainty the technological uncertainty and the competitivevolatility (1) Market uncertainty refers to the ambiguityabout the type and extent of consumer needs that can be satis-fied by a particular technology [17 18] There are five sourceswhich can result in the high-technologymarket certaintyThesources include the needs which might be met by the newtechnology the possible changes of the needs in the futurethe adoption of industry standards or not the diffusion rateof innovations and the potential size of the high-technologymarket (2) Technological uncertainty means that whetherthe technology canmeet specific needs is unclear Five factorsgive rise to technological uncertainty The factors includethe new product function the delivery timetable the servicequality and the sustainability problem being raised by thenew technology (3) Competitive volatility refers to both theintensity in extent of change in the competitive landscapeand the uncertainty about competitors and their strategies[19] Competitive volatility is composed of three sourcesthe new competitors in the future new competitive tacticsand new products to compete with Figure 1 summarizes theabovementioned characteristics of high-technology market
22 The Lead User Method Increasingly firms are recogniz-ing the power in innovation idea development [20] Eitherservice design concepts or RampD such idea often inspires achallenge of companies [21] In novel product developmentmany literatures suggest techniques for idea creation suchas benchmarking [22] user observation [23] or lead usermethod [24] Among these the lead user method has beenshown to provide the highest potential to create commerciallyattractive and highly novel innovations [20] (eg [25 26])In other words lead user theory can effectively understandconsumer purchase behavior and serve as a developmentbasis for next generation product Moreover most of thetarget users for your product will have difficulties expressingtheir needs for your products [27] And a vast majority ofyour target users will certainly not be able to come up withthe innovations themselves [27] There will always be someusers who are exceptions These are the lead users of yourproducts or services For example if you are designing thesoftware of smart phones you will be looking at the peoplewho are already designing programming for your software
In high-technology industries the world moves sorapidly The related real-world experiences of ordinary usersare often rendered obsolete by the time a product is devel-oped or during the time of its projected commercial lifetime[28]Thus in the research of innovative products VonHippeldefined ldquolead userrdquo as a person displaying two characteristicsregarding a given new product [24 28] (1) The lead usersface needs that will diffuse in the marketplace but face thembefore the bulk of that marketplace encounters them (2)The lead users are positioned to benefit significantly fromobtaining a solution to those needs Since the studies oncustomer involvement in successful innovation prove the
4 Mathematical Problems in Engineering
Source [17]
Market uncertainty
Technological uncertainty
High technology
market
Competitive volatility
Figure 1 Characterizing the high-technology market
importance of lead users [29] who can effectively foster theproduct improvement [30] and develop products that areready for the market [31] the lead user analysis is used toidentify the characteristics such as traits knowledge andstatus Subsequently based on lead user theory Belz andBaumbach [32] further defined six characteristics of onlineconsumer purchasing behavior including ahead of trenddissatisfaction product-related knowledge use experienceinvolvement and opinion leadership Research on lead usersshows that many products are initially thought of and evenprototyped by users rather than manufacturers [19 33]For instance Table 1 shows that across various industriesthe number of innovations conceived of by users is quitehigh
The traditional analytic methods for defining incremen-tal innovation and radical innovation products or servicesusually initiate by deriving consumersrsquo needs at the verybeginning Then such needs will be summarized by the newproduct development team to create innovations Howeverthese methods do not take users into consideration in theinnovation process Consumers with usage experience ofrelated products will be able to explain where they haveproblems with the innovative product what their specificneeds are andwhat the functions they use are [27]Thereforean introduction of the opinions being provided by lead usersis especially important and useful for high-technology firmsThe steps for innovations based on lead usersrsquo opinionsinclude [27] the following (1) find the lead users (2) preparefor a lead user workshop (3) run the workshop and (4)
document the results and proceed to the output
23 UTAUT The UTAUT was proposed by Venkatesh et al[34] as an integrated framework of eight related technologyacceptance theories or models Those theories or modelsinclude the diffusion of innovation theory the TRA the TPBthe motivation theory the hybrid model of TPB and TAMthe original TAM the PC utilization model and the socialcognitive theory The perceived ease of use and the perceivedusefulness were incorporated in thismodel by using the effort
Table 1 The number of innovations across various industries
Industry Source of innovationUser Manufacturer Other
Computer industry 33 67Chemical industry 70 30Pultrusion-process machinery 85 15Scientific instrument with majorfunctional improvement 82 18
Semiconductor-electronic processequipment with major functionalimprovements
63 21 16lowast
Electronic assembly 11 33 56+
Surface chemistry instruments withnew functional capability 82 18
lowastJoint usermdashmanufacturer innovation+Supplier innovationSource [28 106]
expectancy and the performance expectancy dimensionsTheUTAUTwas conducted in two rounds of studies in which thedata was collected from six organizations in three rounds ofsurveys The variance of explanations in two rounds reachedabout 70 and 50 respectively
In addition to the two most important constructs ofperformance expectancy and effort expectancy the otherconstructs which include the social influence the facilitatingconditions the intentions to use and the usage behaviorswere also included in this model Venkatesh et al [34] exam-ined the three constructs consisting of self-efficacy anxietyand attitude toward using technology in UTAUT modelHowever these three constructs have no strong impact onothers Thus three constructs are removed from UTAUTmodel
24 UTAUT2 The UTAUT was developed as a comprehen-sive integrated model for better understanding consumeracceptance toward new technology or system Accordingto Venkatesh there are three types which can enhance theprediction ratio of technology acceptance For the first typeVenkatesh considers the consumer acceptance of new tech-nology in variety of contexts such as culture and populationFor the second type Venkatesh considered to add differentconcepts to the model so as to widen the theoretic relation-ships of UTAUT For the third type Venkatesh consideredto synthesize new predictor of variables into the UTAUTDespite the integrated model in which some variables areusually added Venkatesh et al [4] emphasizes the needs toinclude salient predictor variables that can be used withina user technology use context They also examined morerelated consumer behavior of studies and alter the priorperspective (from organizations to individuals) by adjustingUTAUT model to establish a new prediction frameworknamely UTAUT2 Currently this newestmodel has graduallybeen adopted for exploring various issues such as self-technology service smart mobile device adoption learningmanagement software acceptance and healthcare industry
Mathematical Problems in Engineering 5
With regard to this prediction model the hedonic moti-vation construct was regarded as an important predictor andwas integrated into the UTAUT2 for more stressing utilityTheprice value construct was also introduced in theUTAUT2model because product quality cost and price will influenceadoption decisions [35] Venkatesh et al [4] also consideredthat the recent studies have stressed the roles of behaviorintention they thus incorporated a new construct of habitinto the UTAUT2 The introduction of the habit constructwas due to the following two reasons First the habit isregarded as prior behavior [36] Second the habit can bedefined as the degree to which people believe the behavior tobe automatic [37] These new added constructs were verifiedconstantly in previous researches as the critical determinantsfor usersrsquo technology adoptions Therefore such constructscan be used in the investigations of usersrsquo adoption ofPhablets
The prior model of the UTAUT has been used to describeusersrsquo technology adoption behavior in organizational con-text [34] Instead the UTAUT2 model was extended fromthe UTAUT and was focused on individual perspectives intechnology adoptions The new model was significantly anenhanced one for explaining variances in usersrsquo technologyintention Since the purpose of this research is to explorethe possible factors influencing individual usersrsquo adoptions ofPhablets the UTAUT2 framework can provide more insightsand thus will be adopted as the research model of this work
25 ResearchModel Researchers have summarized purchaseand usage behaviors of consumers by using the TPB modelTAM model and UTAUT model [38ndash40] The UTAUT2model will be introduced as the theoretic foundation of theresearch model to explore the purchase behavior towardPhablets (Figure 2) After reviewing the related TAM theoryand UTAUT this section will propose an analytic frame-work based on the UTAUT2 being discussed above Theprediction model will be used to explore the influencerelationships between the constructs which include theperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitand behavioral intention Following the constructs will beintroduced
251 Performance Expectancy Theperformance expectancyan important construct for the behavior intention in theUTAUT or UTAUT2 models means the extent to which theusage of a new technology or a new technology productcan provide consumers the benefits in performing specificactivities [4]The performance expectancy construct consistsof four criteria the perceived usefulness the extrinsic moti-vation the job fit and the relative advantage (1) Perceivedusefulness The perceived usefulness is defined as the extentto which people believe that using a new technology canimprove their job performance [41] (2) Extrinsic motivationthe extrinsic motivation is the perceptions whether peoplewould like to perform an activity when such an activity isperceived to be instrumental in achieving valued outcomesthat are different from the activity itself (Chong [42] and
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 2 Research model
Teo et al [43] defined) (3) Job fit Thompson et al [44] andJeng and Tzeng [45] articulated that the job fit is how thecapabilities of a new technology will increase peoplersquos jobperformance (4) Relative advantage Rogers [3] stated thatrelative advantage refers to the benefit of adopting a newtechnology or a technology product compared to the costs
The Phablet users expect the product performance toenhance their job performance Such smart mobile deviceshave been developed to satisfy customersrsquo needs to improvethe job performance Moreover most of the consumersused smart phones as a communication tool in generaland an entertainment tool specially Based on this reasonperformance expectancy impacts the behavioral intention touse Phablet
252 Effort Expectancy Effort expectancy refers to thedegree of the ease of use which is associated with theusage of a new technology or a technology product [4] Theconstruct is similar to the perceived ease of use variable of theTAM or the ease of use variable and the complexity variablewhich belongs to the diffusion of innovation theory In thetechnology adoption context the effort and the performanceexpectancies are themost important determinants for analyz-ing the technology usage behavior and the behavioral inten-tion [41 44 46 47] According to literature review resultsthe effort expectancy construct consists of three criteria theperceived ease of use the complexity and the ease of use(1) Perceived ease of use the perceived ease of use refers tothe degree to which people believe that using a technologywould be free of effort [34] (2) Ease of use in comparisonto the perceived ease of use the ease of use is definedas the degree to which using an innovative technology orproduct is identified as being difficult or easy to use [45 48]Rogers [3] indicated that complexity is the degree to whichan innovative technology is identified as relatively difficultto use and understand The complexity of new technologywould have negative impacts on its acceptance rate [3] Inaccordance with previous empirical studies which has beendemonstrated that effort expectancy would influence theconsumersrsquo attitude of use in both mandatory and voluntaryusage [4 34 48]
6 Mathematical Problems in Engineering
253 Social Influence Researches have broadly explored theconcepts of the social influence and proved the influences ofthe social influence on shaping usersrsquo behaviors For instanceRogers [3] indicated that the usersrsquo decision making processof adopting an innovative technology is influenced by thesocial notion beyond an individualrsquos decision thinking Ingeneral the social influence can be classified into two partsthe social norms and the critical mass The social normsinclude two different influences the informational influenceand the normative influence The informational influencerefers to peoplersquos obtaining of information from other peopleThe normative influence refers to a userrsquos conformation to theexpectation of other people to gain a reward or avoid a pun-ishment [49] Venkatesh et al [34] defined social influence asthe degree of importance being recognized by others to usea novel technology The social influence construct consistsof three variables the subjective norm the social factor andthe image (1) Subjective norm the subjective norm is theperceived social pressure to perform or not to perform thebehavior [50] In this research the subjective norm is theperceived social pressure to use Phablets (2) Social factorthe social factor is an individualrsquos internalization from thesocial systemrsquos subjective culture and particular interpersonalagreements that the individual in particular social situationhas made with others [51] (3) Image the image is defined asthe degree to which an individual identifies that the using ofan innovative technology can enhance an individualrsquos statusin his or her social organization [48] Drawing the aboveliterature review the usage of an innovative product can bedetermined by the behavioral intention
254 FacilitatingConditions Thefacilitating conditions con-struct is defined as the degree to which a person believes thatan organization and a technical infrastructure exist to supportthe usage of a system [34] Previous researches on factorsinfluencing acceptance of some specific technology haveexhibited that facilitating conditions have a significant impacton innovative technology adoptions and usage behaviors [3444 46 48 52ndash54] These researches summarized that thefacilitating conditions are strong predictors which can beused for forecasting technology acceptances and usages
255 HedonicMotivation Thehedonicmotivation is definedas the motivation to do something due to the internalsatisfaction [55] From the hedonic perspective of indi-vidual behaviors the hedonic motivation is related to theessence of individualrsquos psychological and emotive experienceswhich can be triggered by both the individual traits andthe cognitive states [56] Based on prior studies Magniet al [56] explored the relationships between consumersand technology products by analyzing consumersrsquo intentionTo explore consumersrsquo purchase motivations Magni et aldeveloped and tested amodel to examine the effect of hedonicmotivations Besides similar to the flow theory many offormer empirical studies have demonstrated that hedonicexperiences and traits will influence consumer technologyacceptances from both individual and organizational con-texts [57ndash60] In otherwords individualrsquos hedonic experience
of using a technology product such as a Phablet is more likelyto perform experimental behavior
256 Price Value The price value construct originated fromthe perceived value which is often regarded as an importantindicator in predicting the purchase behavior which caninfluence a companyrsquos competitive advantage [61 62] Tradi-tionally the definition of the price value is a trade-off betweenbenefits and sacrifices [63] Recently the price value has beenemphasized by the researchers in the information technologyfields and themarketers of consumer-electronics devicesTheconcept was adopted to analyze usersrsquo adoption of emergingtechnologies or smart mobile devices The findings indicatedthat the price value concept is crucial in attracting consumers[64ndash66]The price value is positive when the benefits of usinga technology are identified to be greater than the monetarycosts Such price value has a positive impact on intentions [4]
Based on these ideas Venkatesh et al [4] described theprice value as consumersrsquo cognitive tradeoffs between theperceived benefits of the applications and monetary costsfor using them [67] In the marketing context the pricevalue encompasses two perspectives monetary costs andnonmonetary costs The monetary costs refer to the valuebeing identified in contrast to the price paid [68] Thenonmonetary costs refer to the value being identified inreturn for costs such as time and efforts being expended [69]In this research the price value combines both the monetaryand nonmonetary values for exploring factors influencingconsumersrsquo acceptances of Phablets
257 Habit The habit construct has been broadly discussedin a variety of domains such as psychology consumersrsquo pur-chase behaviors education health science andmanagementTriandis [51] derived the relationship between attitude andbehavior where behavioral intention is postulated to forecastuser behavior to the extent that the habit component isweak when habit is strong Aarts et al [70] found that thehabit strength attenuates the amount of information beingacquired before the decision is made Limayem et al [37]and Venkatesh et al [4] defined the habit as the degree towhich consumers tend to perform the usage of technologiesor the usage of technology products behaviors automaticallybecause of learning The habit construct consists of three cri-teria the past behavior the reflex behavior and the individualexperience The past behavior is described as usersrsquo priorbehaviors [37] The reflex behavior refers to usersrsquo behaviorsequence or customs which are regular parts of the daily life[37] The individual experience refers to the accumulation ofexperiences from usersrsquo established stable routines normsand habits for using technology products Such experiencesdecreased the needs for discussions coordination or effortfuldecision making [37] Researches on habitual intentions andhabitual usage behaviors have demonstrated that the habitis a strong predictor of technology usages in promotingbehavioral changes [4 62 71 72]
258 Behavioral Intention Social psychologists have broadlyexplored behavioral intentions and the relations to future
Mathematical Problems in Engineering 7
behavior [70] Behavioral intention refers to the degree towhich a person has formulated conscious plans to performor not perform some specified future behavior(s) [70]Behavioral intention was frequentlymeasured as the conativeloyalty which is an important goal in marketing [73] In themarketing context the loyalty is defined as what customersare willing to repurchase a product from the company andsupport the company with positive word-of-mouth commu-nications [71] For marketers and manufacturers of Phabletssuch outcomes are very important asmembers become agentsfor the firms encouraging friends and acquaintances to pur-chase their products However predictions of actual purchasebehaviors are always difficult Despite this many of the priorstudies have still proven that the behavioral intention playsa significant role in actual behaviors [74] In this researchto investigate the factors influencing consumersrsquo acceptancesof Phablets the repurchase intention the positive word-of-mouth communications and the service quality are selectedas the criteria for exploring the consumersrsquo behavioral inten-tions
259 Performance Expectancy A great number of researchesrevealed that past behaviors will influence future behaviorsSome researchers have further proven that past usage behav-iors are the antecedents of future behaviors [75] In orderto derive the factors influencing consumersrsquo acceptances ofPhablets three factors will be introduced as the criteria of theusage behaviorsThe factors include the usage time the usagefrequency and the usage variety Venkatesh et al [4] indicatedthat the usage behavior construct should bemeasured by boththe variety and the frequencyMathieson [76] andAl-Gahtaniet al [77] also indicated that the usage behavior constructconsists of four dimensions for measuring the technologyusage (1) the amount of time being spent in using technologyproducts per day (2) the usage frequency of technology prod-ucts (3) the number of various software applications beingused and (4) the number of various job tasks being supportedthrough technology product usages In this research theusage time usage frequency and the number of various jobtasks being supported will be introduced
The constructs being summarized based on literaturereview results being demonstrated above are demonstratedin Table 2The research model comprises of night constructsperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitbehavioral intention and use behavior In the later empiricalstudy Section 4 the influence relationships among the con-structs will be established
3 Research Methods
To construct the analytic framework for deriving factors forpredictions of usersrsquo acceptances of Phablets this researchreviewed the related research works of social psychologyand literature being related to factors for predicting usersrsquoadoption of technology such as theUTAUT and theUTAUT2for collecting the possible dimensions and criteria Nextthe DEMATEL method is employed to establish the causal
Table 2 Dimensions and criteria for analyzing usersrsquo Phabletacceptance
Dimensions Criteria
Performance expectancy (1198631)
Perceived usefulness (11988811)
Extrinsic motivation (11988812)
Job fit (11988813)
Relative advantage (11988814)
Effort expectancy (1198632)
Perceived ease of use (11988821)
Complexity (11988822)
Ease of use (11988823)
Social influence (1198633)
Subjective norm (11988831)
Social factor (11988832)
Image (11988833)
Facilitating conditions (1198634)
Perceived behavioral control (11988841)
Facilitating conditions (11988842)
Compatibility (11988843)
Hedonic motivations (1198635)
Enjoyment (11988851)
Interest (11988852)
Curiosity (11988853)
Price value (1198636)
Quality (11988861)
Value (11988862)
Price (11988863)
Habit (1198637)
Past behavior (11988871)
Reflex behavior (11988872)
Individual experience (11988873)
Use intention (1198638)
Repurchase intentions (11988881)
Positive word-of-mouthcommunication (119888
82)
Service quality (11988883)
Use behavior (1198639)
Usage time (11988891)
Usage frequency (11988892)
Use variety (11988893)
relationships Then the DNP will be applied to derive theinfluence weights based on the lead usersrsquo perspectives Insummary the assessment model consists of three main steps(1) deriving the requirement by literature review (2) struc-turing the causal relationship based on lead usersrsquo opinion byapplying DEMATEL and (3) evaluating the weights versuseach criterion by using the DNP
31 Modified Delphi Method The Delphi method wasdesigned by Dalkey and Helmer [78] After the Delphimethod Murry and Hammons [79] tried to identify issuesand problems that were collected from a group of technologyeducation professionals using themodifiedDelphi techniqueThe modified Delphi simplified the step of conducting thefirst round of a survey and replaced the conventionallyadopted open style survey [80] The purpose of the modifiedDelphi method is to save time (the experts can focus onresearch themes eliminating the need for speculation onthe open questionnaire) and to improve the response of the
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
4 Mathematical Problems in Engineering
Source [17]
Market uncertainty
Technological uncertainty
High technology
market
Competitive volatility
Figure 1 Characterizing the high-technology market
importance of lead users [29] who can effectively foster theproduct improvement [30] and develop products that areready for the market [31] the lead user analysis is used toidentify the characteristics such as traits knowledge andstatus Subsequently based on lead user theory Belz andBaumbach [32] further defined six characteristics of onlineconsumer purchasing behavior including ahead of trenddissatisfaction product-related knowledge use experienceinvolvement and opinion leadership Research on lead usersshows that many products are initially thought of and evenprototyped by users rather than manufacturers [19 33]For instance Table 1 shows that across various industriesthe number of innovations conceived of by users is quitehigh
The traditional analytic methods for defining incremen-tal innovation and radical innovation products or servicesusually initiate by deriving consumersrsquo needs at the verybeginning Then such needs will be summarized by the newproduct development team to create innovations Howeverthese methods do not take users into consideration in theinnovation process Consumers with usage experience ofrelated products will be able to explain where they haveproblems with the innovative product what their specificneeds are andwhat the functions they use are [27]Thereforean introduction of the opinions being provided by lead usersis especially important and useful for high-technology firmsThe steps for innovations based on lead usersrsquo opinionsinclude [27] the following (1) find the lead users (2) preparefor a lead user workshop (3) run the workshop and (4)
document the results and proceed to the output
23 UTAUT The UTAUT was proposed by Venkatesh et al[34] as an integrated framework of eight related technologyacceptance theories or models Those theories or modelsinclude the diffusion of innovation theory the TRA the TPBthe motivation theory the hybrid model of TPB and TAMthe original TAM the PC utilization model and the socialcognitive theory The perceived ease of use and the perceivedusefulness were incorporated in thismodel by using the effort
Table 1 The number of innovations across various industries
Industry Source of innovationUser Manufacturer Other
Computer industry 33 67Chemical industry 70 30Pultrusion-process machinery 85 15Scientific instrument with majorfunctional improvement 82 18
Semiconductor-electronic processequipment with major functionalimprovements
63 21 16lowast
Electronic assembly 11 33 56+
Surface chemistry instruments withnew functional capability 82 18
lowastJoint usermdashmanufacturer innovation+Supplier innovationSource [28 106]
expectancy and the performance expectancy dimensionsTheUTAUTwas conducted in two rounds of studies in which thedata was collected from six organizations in three rounds ofsurveys The variance of explanations in two rounds reachedabout 70 and 50 respectively
In addition to the two most important constructs ofperformance expectancy and effort expectancy the otherconstructs which include the social influence the facilitatingconditions the intentions to use and the usage behaviorswere also included in this model Venkatesh et al [34] exam-ined the three constructs consisting of self-efficacy anxietyand attitude toward using technology in UTAUT modelHowever these three constructs have no strong impact onothers Thus three constructs are removed from UTAUTmodel
24 UTAUT2 The UTAUT was developed as a comprehen-sive integrated model for better understanding consumeracceptance toward new technology or system Accordingto Venkatesh there are three types which can enhance theprediction ratio of technology acceptance For the first typeVenkatesh considers the consumer acceptance of new tech-nology in variety of contexts such as culture and populationFor the second type Venkatesh considered to add differentconcepts to the model so as to widen the theoretic relation-ships of UTAUT For the third type Venkatesh consideredto synthesize new predictor of variables into the UTAUTDespite the integrated model in which some variables areusually added Venkatesh et al [4] emphasizes the needs toinclude salient predictor variables that can be used withina user technology use context They also examined morerelated consumer behavior of studies and alter the priorperspective (from organizations to individuals) by adjustingUTAUT model to establish a new prediction frameworknamely UTAUT2 Currently this newestmodel has graduallybeen adopted for exploring various issues such as self-technology service smart mobile device adoption learningmanagement software acceptance and healthcare industry
Mathematical Problems in Engineering 5
With regard to this prediction model the hedonic moti-vation construct was regarded as an important predictor andwas integrated into the UTAUT2 for more stressing utilityTheprice value construct was also introduced in theUTAUT2model because product quality cost and price will influenceadoption decisions [35] Venkatesh et al [4] also consideredthat the recent studies have stressed the roles of behaviorintention they thus incorporated a new construct of habitinto the UTAUT2 The introduction of the habit constructwas due to the following two reasons First the habit isregarded as prior behavior [36] Second the habit can bedefined as the degree to which people believe the behavior tobe automatic [37] These new added constructs were verifiedconstantly in previous researches as the critical determinantsfor usersrsquo technology adoptions Therefore such constructscan be used in the investigations of usersrsquo adoption ofPhablets
The prior model of the UTAUT has been used to describeusersrsquo technology adoption behavior in organizational con-text [34] Instead the UTAUT2 model was extended fromthe UTAUT and was focused on individual perspectives intechnology adoptions The new model was significantly anenhanced one for explaining variances in usersrsquo technologyintention Since the purpose of this research is to explorethe possible factors influencing individual usersrsquo adoptions ofPhablets the UTAUT2 framework can provide more insightsand thus will be adopted as the research model of this work
25 ResearchModel Researchers have summarized purchaseand usage behaviors of consumers by using the TPB modelTAM model and UTAUT model [38ndash40] The UTAUT2model will be introduced as the theoretic foundation of theresearch model to explore the purchase behavior towardPhablets (Figure 2) After reviewing the related TAM theoryand UTAUT this section will propose an analytic frame-work based on the UTAUT2 being discussed above Theprediction model will be used to explore the influencerelationships between the constructs which include theperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitand behavioral intention Following the constructs will beintroduced
251 Performance Expectancy Theperformance expectancyan important construct for the behavior intention in theUTAUT or UTAUT2 models means the extent to which theusage of a new technology or a new technology productcan provide consumers the benefits in performing specificactivities [4]The performance expectancy construct consistsof four criteria the perceived usefulness the extrinsic moti-vation the job fit and the relative advantage (1) Perceivedusefulness The perceived usefulness is defined as the extentto which people believe that using a new technology canimprove their job performance [41] (2) Extrinsic motivationthe extrinsic motivation is the perceptions whether peoplewould like to perform an activity when such an activity isperceived to be instrumental in achieving valued outcomesthat are different from the activity itself (Chong [42] and
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 2 Research model
Teo et al [43] defined) (3) Job fit Thompson et al [44] andJeng and Tzeng [45] articulated that the job fit is how thecapabilities of a new technology will increase peoplersquos jobperformance (4) Relative advantage Rogers [3] stated thatrelative advantage refers to the benefit of adopting a newtechnology or a technology product compared to the costs
The Phablet users expect the product performance toenhance their job performance Such smart mobile deviceshave been developed to satisfy customersrsquo needs to improvethe job performance Moreover most of the consumersused smart phones as a communication tool in generaland an entertainment tool specially Based on this reasonperformance expectancy impacts the behavioral intention touse Phablet
252 Effort Expectancy Effort expectancy refers to thedegree of the ease of use which is associated with theusage of a new technology or a technology product [4] Theconstruct is similar to the perceived ease of use variable of theTAM or the ease of use variable and the complexity variablewhich belongs to the diffusion of innovation theory In thetechnology adoption context the effort and the performanceexpectancies are themost important determinants for analyz-ing the technology usage behavior and the behavioral inten-tion [41 44 46 47] According to literature review resultsthe effort expectancy construct consists of three criteria theperceived ease of use the complexity and the ease of use(1) Perceived ease of use the perceived ease of use refers tothe degree to which people believe that using a technologywould be free of effort [34] (2) Ease of use in comparisonto the perceived ease of use the ease of use is definedas the degree to which using an innovative technology orproduct is identified as being difficult or easy to use [45 48]Rogers [3] indicated that complexity is the degree to whichan innovative technology is identified as relatively difficultto use and understand The complexity of new technologywould have negative impacts on its acceptance rate [3] Inaccordance with previous empirical studies which has beendemonstrated that effort expectancy would influence theconsumersrsquo attitude of use in both mandatory and voluntaryusage [4 34 48]
6 Mathematical Problems in Engineering
253 Social Influence Researches have broadly explored theconcepts of the social influence and proved the influences ofthe social influence on shaping usersrsquo behaviors For instanceRogers [3] indicated that the usersrsquo decision making processof adopting an innovative technology is influenced by thesocial notion beyond an individualrsquos decision thinking Ingeneral the social influence can be classified into two partsthe social norms and the critical mass The social normsinclude two different influences the informational influenceand the normative influence The informational influencerefers to peoplersquos obtaining of information from other peopleThe normative influence refers to a userrsquos conformation to theexpectation of other people to gain a reward or avoid a pun-ishment [49] Venkatesh et al [34] defined social influence asthe degree of importance being recognized by others to usea novel technology The social influence construct consistsof three variables the subjective norm the social factor andthe image (1) Subjective norm the subjective norm is theperceived social pressure to perform or not to perform thebehavior [50] In this research the subjective norm is theperceived social pressure to use Phablets (2) Social factorthe social factor is an individualrsquos internalization from thesocial systemrsquos subjective culture and particular interpersonalagreements that the individual in particular social situationhas made with others [51] (3) Image the image is defined asthe degree to which an individual identifies that the using ofan innovative technology can enhance an individualrsquos statusin his or her social organization [48] Drawing the aboveliterature review the usage of an innovative product can bedetermined by the behavioral intention
254 FacilitatingConditions Thefacilitating conditions con-struct is defined as the degree to which a person believes thatan organization and a technical infrastructure exist to supportthe usage of a system [34] Previous researches on factorsinfluencing acceptance of some specific technology haveexhibited that facilitating conditions have a significant impacton innovative technology adoptions and usage behaviors [3444 46 48 52ndash54] These researches summarized that thefacilitating conditions are strong predictors which can beused for forecasting technology acceptances and usages
255 HedonicMotivation Thehedonicmotivation is definedas the motivation to do something due to the internalsatisfaction [55] From the hedonic perspective of indi-vidual behaviors the hedonic motivation is related to theessence of individualrsquos psychological and emotive experienceswhich can be triggered by both the individual traits andthe cognitive states [56] Based on prior studies Magniet al [56] explored the relationships between consumersand technology products by analyzing consumersrsquo intentionTo explore consumersrsquo purchase motivations Magni et aldeveloped and tested amodel to examine the effect of hedonicmotivations Besides similar to the flow theory many offormer empirical studies have demonstrated that hedonicexperiences and traits will influence consumer technologyacceptances from both individual and organizational con-texts [57ndash60] In otherwords individualrsquos hedonic experience
of using a technology product such as a Phablet is more likelyto perform experimental behavior
256 Price Value The price value construct originated fromthe perceived value which is often regarded as an importantindicator in predicting the purchase behavior which caninfluence a companyrsquos competitive advantage [61 62] Tradi-tionally the definition of the price value is a trade-off betweenbenefits and sacrifices [63] Recently the price value has beenemphasized by the researchers in the information technologyfields and themarketers of consumer-electronics devicesTheconcept was adopted to analyze usersrsquo adoption of emergingtechnologies or smart mobile devices The findings indicatedthat the price value concept is crucial in attracting consumers[64ndash66]The price value is positive when the benefits of usinga technology are identified to be greater than the monetarycosts Such price value has a positive impact on intentions [4]
Based on these ideas Venkatesh et al [4] described theprice value as consumersrsquo cognitive tradeoffs between theperceived benefits of the applications and monetary costsfor using them [67] In the marketing context the pricevalue encompasses two perspectives monetary costs andnonmonetary costs The monetary costs refer to the valuebeing identified in contrast to the price paid [68] Thenonmonetary costs refer to the value being identified inreturn for costs such as time and efforts being expended [69]In this research the price value combines both the monetaryand nonmonetary values for exploring factors influencingconsumersrsquo acceptances of Phablets
257 Habit The habit construct has been broadly discussedin a variety of domains such as psychology consumersrsquo pur-chase behaviors education health science andmanagementTriandis [51] derived the relationship between attitude andbehavior where behavioral intention is postulated to forecastuser behavior to the extent that the habit component isweak when habit is strong Aarts et al [70] found that thehabit strength attenuates the amount of information beingacquired before the decision is made Limayem et al [37]and Venkatesh et al [4] defined the habit as the degree towhich consumers tend to perform the usage of technologiesor the usage of technology products behaviors automaticallybecause of learning The habit construct consists of three cri-teria the past behavior the reflex behavior and the individualexperience The past behavior is described as usersrsquo priorbehaviors [37] The reflex behavior refers to usersrsquo behaviorsequence or customs which are regular parts of the daily life[37] The individual experience refers to the accumulation ofexperiences from usersrsquo established stable routines normsand habits for using technology products Such experiencesdecreased the needs for discussions coordination or effortfuldecision making [37] Researches on habitual intentions andhabitual usage behaviors have demonstrated that the habitis a strong predictor of technology usages in promotingbehavioral changes [4 62 71 72]
258 Behavioral Intention Social psychologists have broadlyexplored behavioral intentions and the relations to future
Mathematical Problems in Engineering 7
behavior [70] Behavioral intention refers to the degree towhich a person has formulated conscious plans to performor not perform some specified future behavior(s) [70]Behavioral intention was frequentlymeasured as the conativeloyalty which is an important goal in marketing [73] In themarketing context the loyalty is defined as what customersare willing to repurchase a product from the company andsupport the company with positive word-of-mouth commu-nications [71] For marketers and manufacturers of Phabletssuch outcomes are very important asmembers become agentsfor the firms encouraging friends and acquaintances to pur-chase their products However predictions of actual purchasebehaviors are always difficult Despite this many of the priorstudies have still proven that the behavioral intention playsa significant role in actual behaviors [74] In this researchto investigate the factors influencing consumersrsquo acceptancesof Phablets the repurchase intention the positive word-of-mouth communications and the service quality are selectedas the criteria for exploring the consumersrsquo behavioral inten-tions
259 Performance Expectancy A great number of researchesrevealed that past behaviors will influence future behaviorsSome researchers have further proven that past usage behav-iors are the antecedents of future behaviors [75] In orderto derive the factors influencing consumersrsquo acceptances ofPhablets three factors will be introduced as the criteria of theusage behaviorsThe factors include the usage time the usagefrequency and the usage variety Venkatesh et al [4] indicatedthat the usage behavior construct should bemeasured by boththe variety and the frequencyMathieson [76] andAl-Gahtaniet al [77] also indicated that the usage behavior constructconsists of four dimensions for measuring the technologyusage (1) the amount of time being spent in using technologyproducts per day (2) the usage frequency of technology prod-ucts (3) the number of various software applications beingused and (4) the number of various job tasks being supportedthrough technology product usages In this research theusage time usage frequency and the number of various jobtasks being supported will be introduced
The constructs being summarized based on literaturereview results being demonstrated above are demonstratedin Table 2The research model comprises of night constructsperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitbehavioral intention and use behavior In the later empiricalstudy Section 4 the influence relationships among the con-structs will be established
3 Research Methods
To construct the analytic framework for deriving factors forpredictions of usersrsquo acceptances of Phablets this researchreviewed the related research works of social psychologyand literature being related to factors for predicting usersrsquoadoption of technology such as theUTAUT and theUTAUT2for collecting the possible dimensions and criteria Nextthe DEMATEL method is employed to establish the causal
Table 2 Dimensions and criteria for analyzing usersrsquo Phabletacceptance
Dimensions Criteria
Performance expectancy (1198631)
Perceived usefulness (11988811)
Extrinsic motivation (11988812)
Job fit (11988813)
Relative advantage (11988814)
Effort expectancy (1198632)
Perceived ease of use (11988821)
Complexity (11988822)
Ease of use (11988823)
Social influence (1198633)
Subjective norm (11988831)
Social factor (11988832)
Image (11988833)
Facilitating conditions (1198634)
Perceived behavioral control (11988841)
Facilitating conditions (11988842)
Compatibility (11988843)
Hedonic motivations (1198635)
Enjoyment (11988851)
Interest (11988852)
Curiosity (11988853)
Price value (1198636)
Quality (11988861)
Value (11988862)
Price (11988863)
Habit (1198637)
Past behavior (11988871)
Reflex behavior (11988872)
Individual experience (11988873)
Use intention (1198638)
Repurchase intentions (11988881)
Positive word-of-mouthcommunication (119888
82)
Service quality (11988883)
Use behavior (1198639)
Usage time (11988891)
Usage frequency (11988892)
Use variety (11988893)
relationships Then the DNP will be applied to derive theinfluence weights based on the lead usersrsquo perspectives Insummary the assessment model consists of three main steps(1) deriving the requirement by literature review (2) struc-turing the causal relationship based on lead usersrsquo opinion byapplying DEMATEL and (3) evaluating the weights versuseach criterion by using the DNP
31 Modified Delphi Method The Delphi method wasdesigned by Dalkey and Helmer [78] After the Delphimethod Murry and Hammons [79] tried to identify issuesand problems that were collected from a group of technologyeducation professionals using themodifiedDelphi techniqueThe modified Delphi simplified the step of conducting thefirst round of a survey and replaced the conventionallyadopted open style survey [80] The purpose of the modifiedDelphi method is to save time (the experts can focus onresearch themes eliminating the need for speculation onthe open questionnaire) and to improve the response of the
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 5
With regard to this prediction model the hedonic moti-vation construct was regarded as an important predictor andwas integrated into the UTAUT2 for more stressing utilityTheprice value construct was also introduced in theUTAUT2model because product quality cost and price will influenceadoption decisions [35] Venkatesh et al [4] also consideredthat the recent studies have stressed the roles of behaviorintention they thus incorporated a new construct of habitinto the UTAUT2 The introduction of the habit constructwas due to the following two reasons First the habit isregarded as prior behavior [36] Second the habit can bedefined as the degree to which people believe the behavior tobe automatic [37] These new added constructs were verifiedconstantly in previous researches as the critical determinantsfor usersrsquo technology adoptions Therefore such constructscan be used in the investigations of usersrsquo adoption ofPhablets
The prior model of the UTAUT has been used to describeusersrsquo technology adoption behavior in organizational con-text [34] Instead the UTAUT2 model was extended fromthe UTAUT and was focused on individual perspectives intechnology adoptions The new model was significantly anenhanced one for explaining variances in usersrsquo technologyintention Since the purpose of this research is to explorethe possible factors influencing individual usersrsquo adoptions ofPhablets the UTAUT2 framework can provide more insightsand thus will be adopted as the research model of this work
25 ResearchModel Researchers have summarized purchaseand usage behaviors of consumers by using the TPB modelTAM model and UTAUT model [38ndash40] The UTAUT2model will be introduced as the theoretic foundation of theresearch model to explore the purchase behavior towardPhablets (Figure 2) After reviewing the related TAM theoryand UTAUT this section will propose an analytic frame-work based on the UTAUT2 being discussed above Theprediction model will be used to explore the influencerelationships between the constructs which include theperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitand behavioral intention Following the constructs will beintroduced
251 Performance Expectancy Theperformance expectancyan important construct for the behavior intention in theUTAUT or UTAUT2 models means the extent to which theusage of a new technology or a new technology productcan provide consumers the benefits in performing specificactivities [4]The performance expectancy construct consistsof four criteria the perceived usefulness the extrinsic moti-vation the job fit and the relative advantage (1) Perceivedusefulness The perceived usefulness is defined as the extentto which people believe that using a new technology canimprove their job performance [41] (2) Extrinsic motivationthe extrinsic motivation is the perceptions whether peoplewould like to perform an activity when such an activity isperceived to be instrumental in achieving valued outcomesthat are different from the activity itself (Chong [42] and
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 2 Research model
Teo et al [43] defined) (3) Job fit Thompson et al [44] andJeng and Tzeng [45] articulated that the job fit is how thecapabilities of a new technology will increase peoplersquos jobperformance (4) Relative advantage Rogers [3] stated thatrelative advantage refers to the benefit of adopting a newtechnology or a technology product compared to the costs
The Phablet users expect the product performance toenhance their job performance Such smart mobile deviceshave been developed to satisfy customersrsquo needs to improvethe job performance Moreover most of the consumersused smart phones as a communication tool in generaland an entertainment tool specially Based on this reasonperformance expectancy impacts the behavioral intention touse Phablet
252 Effort Expectancy Effort expectancy refers to thedegree of the ease of use which is associated with theusage of a new technology or a technology product [4] Theconstruct is similar to the perceived ease of use variable of theTAM or the ease of use variable and the complexity variablewhich belongs to the diffusion of innovation theory In thetechnology adoption context the effort and the performanceexpectancies are themost important determinants for analyz-ing the technology usage behavior and the behavioral inten-tion [41 44 46 47] According to literature review resultsthe effort expectancy construct consists of three criteria theperceived ease of use the complexity and the ease of use(1) Perceived ease of use the perceived ease of use refers tothe degree to which people believe that using a technologywould be free of effort [34] (2) Ease of use in comparisonto the perceived ease of use the ease of use is definedas the degree to which using an innovative technology orproduct is identified as being difficult or easy to use [45 48]Rogers [3] indicated that complexity is the degree to whichan innovative technology is identified as relatively difficultto use and understand The complexity of new technologywould have negative impacts on its acceptance rate [3] Inaccordance with previous empirical studies which has beendemonstrated that effort expectancy would influence theconsumersrsquo attitude of use in both mandatory and voluntaryusage [4 34 48]
6 Mathematical Problems in Engineering
253 Social Influence Researches have broadly explored theconcepts of the social influence and proved the influences ofthe social influence on shaping usersrsquo behaviors For instanceRogers [3] indicated that the usersrsquo decision making processof adopting an innovative technology is influenced by thesocial notion beyond an individualrsquos decision thinking Ingeneral the social influence can be classified into two partsthe social norms and the critical mass The social normsinclude two different influences the informational influenceand the normative influence The informational influencerefers to peoplersquos obtaining of information from other peopleThe normative influence refers to a userrsquos conformation to theexpectation of other people to gain a reward or avoid a pun-ishment [49] Venkatesh et al [34] defined social influence asthe degree of importance being recognized by others to usea novel technology The social influence construct consistsof three variables the subjective norm the social factor andthe image (1) Subjective norm the subjective norm is theperceived social pressure to perform or not to perform thebehavior [50] In this research the subjective norm is theperceived social pressure to use Phablets (2) Social factorthe social factor is an individualrsquos internalization from thesocial systemrsquos subjective culture and particular interpersonalagreements that the individual in particular social situationhas made with others [51] (3) Image the image is defined asthe degree to which an individual identifies that the using ofan innovative technology can enhance an individualrsquos statusin his or her social organization [48] Drawing the aboveliterature review the usage of an innovative product can bedetermined by the behavioral intention
254 FacilitatingConditions Thefacilitating conditions con-struct is defined as the degree to which a person believes thatan organization and a technical infrastructure exist to supportthe usage of a system [34] Previous researches on factorsinfluencing acceptance of some specific technology haveexhibited that facilitating conditions have a significant impacton innovative technology adoptions and usage behaviors [3444 46 48 52ndash54] These researches summarized that thefacilitating conditions are strong predictors which can beused for forecasting technology acceptances and usages
255 HedonicMotivation Thehedonicmotivation is definedas the motivation to do something due to the internalsatisfaction [55] From the hedonic perspective of indi-vidual behaviors the hedonic motivation is related to theessence of individualrsquos psychological and emotive experienceswhich can be triggered by both the individual traits andthe cognitive states [56] Based on prior studies Magniet al [56] explored the relationships between consumersand technology products by analyzing consumersrsquo intentionTo explore consumersrsquo purchase motivations Magni et aldeveloped and tested amodel to examine the effect of hedonicmotivations Besides similar to the flow theory many offormer empirical studies have demonstrated that hedonicexperiences and traits will influence consumer technologyacceptances from both individual and organizational con-texts [57ndash60] In otherwords individualrsquos hedonic experience
of using a technology product such as a Phablet is more likelyto perform experimental behavior
256 Price Value The price value construct originated fromthe perceived value which is often regarded as an importantindicator in predicting the purchase behavior which caninfluence a companyrsquos competitive advantage [61 62] Tradi-tionally the definition of the price value is a trade-off betweenbenefits and sacrifices [63] Recently the price value has beenemphasized by the researchers in the information technologyfields and themarketers of consumer-electronics devicesTheconcept was adopted to analyze usersrsquo adoption of emergingtechnologies or smart mobile devices The findings indicatedthat the price value concept is crucial in attracting consumers[64ndash66]The price value is positive when the benefits of usinga technology are identified to be greater than the monetarycosts Such price value has a positive impact on intentions [4]
Based on these ideas Venkatesh et al [4] described theprice value as consumersrsquo cognitive tradeoffs between theperceived benefits of the applications and monetary costsfor using them [67] In the marketing context the pricevalue encompasses two perspectives monetary costs andnonmonetary costs The monetary costs refer to the valuebeing identified in contrast to the price paid [68] Thenonmonetary costs refer to the value being identified inreturn for costs such as time and efforts being expended [69]In this research the price value combines both the monetaryand nonmonetary values for exploring factors influencingconsumersrsquo acceptances of Phablets
257 Habit The habit construct has been broadly discussedin a variety of domains such as psychology consumersrsquo pur-chase behaviors education health science andmanagementTriandis [51] derived the relationship between attitude andbehavior where behavioral intention is postulated to forecastuser behavior to the extent that the habit component isweak when habit is strong Aarts et al [70] found that thehabit strength attenuates the amount of information beingacquired before the decision is made Limayem et al [37]and Venkatesh et al [4] defined the habit as the degree towhich consumers tend to perform the usage of technologiesor the usage of technology products behaviors automaticallybecause of learning The habit construct consists of three cri-teria the past behavior the reflex behavior and the individualexperience The past behavior is described as usersrsquo priorbehaviors [37] The reflex behavior refers to usersrsquo behaviorsequence or customs which are regular parts of the daily life[37] The individual experience refers to the accumulation ofexperiences from usersrsquo established stable routines normsand habits for using technology products Such experiencesdecreased the needs for discussions coordination or effortfuldecision making [37] Researches on habitual intentions andhabitual usage behaviors have demonstrated that the habitis a strong predictor of technology usages in promotingbehavioral changes [4 62 71 72]
258 Behavioral Intention Social psychologists have broadlyexplored behavioral intentions and the relations to future
Mathematical Problems in Engineering 7
behavior [70] Behavioral intention refers to the degree towhich a person has formulated conscious plans to performor not perform some specified future behavior(s) [70]Behavioral intention was frequentlymeasured as the conativeloyalty which is an important goal in marketing [73] In themarketing context the loyalty is defined as what customersare willing to repurchase a product from the company andsupport the company with positive word-of-mouth commu-nications [71] For marketers and manufacturers of Phabletssuch outcomes are very important asmembers become agentsfor the firms encouraging friends and acquaintances to pur-chase their products However predictions of actual purchasebehaviors are always difficult Despite this many of the priorstudies have still proven that the behavioral intention playsa significant role in actual behaviors [74] In this researchto investigate the factors influencing consumersrsquo acceptancesof Phablets the repurchase intention the positive word-of-mouth communications and the service quality are selectedas the criteria for exploring the consumersrsquo behavioral inten-tions
259 Performance Expectancy A great number of researchesrevealed that past behaviors will influence future behaviorsSome researchers have further proven that past usage behav-iors are the antecedents of future behaviors [75] In orderto derive the factors influencing consumersrsquo acceptances ofPhablets three factors will be introduced as the criteria of theusage behaviorsThe factors include the usage time the usagefrequency and the usage variety Venkatesh et al [4] indicatedthat the usage behavior construct should bemeasured by boththe variety and the frequencyMathieson [76] andAl-Gahtaniet al [77] also indicated that the usage behavior constructconsists of four dimensions for measuring the technologyusage (1) the amount of time being spent in using technologyproducts per day (2) the usage frequency of technology prod-ucts (3) the number of various software applications beingused and (4) the number of various job tasks being supportedthrough technology product usages In this research theusage time usage frequency and the number of various jobtasks being supported will be introduced
The constructs being summarized based on literaturereview results being demonstrated above are demonstratedin Table 2The research model comprises of night constructsperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitbehavioral intention and use behavior In the later empiricalstudy Section 4 the influence relationships among the con-structs will be established
3 Research Methods
To construct the analytic framework for deriving factors forpredictions of usersrsquo acceptances of Phablets this researchreviewed the related research works of social psychologyand literature being related to factors for predicting usersrsquoadoption of technology such as theUTAUT and theUTAUT2for collecting the possible dimensions and criteria Nextthe DEMATEL method is employed to establish the causal
Table 2 Dimensions and criteria for analyzing usersrsquo Phabletacceptance
Dimensions Criteria
Performance expectancy (1198631)
Perceived usefulness (11988811)
Extrinsic motivation (11988812)
Job fit (11988813)
Relative advantage (11988814)
Effort expectancy (1198632)
Perceived ease of use (11988821)
Complexity (11988822)
Ease of use (11988823)
Social influence (1198633)
Subjective norm (11988831)
Social factor (11988832)
Image (11988833)
Facilitating conditions (1198634)
Perceived behavioral control (11988841)
Facilitating conditions (11988842)
Compatibility (11988843)
Hedonic motivations (1198635)
Enjoyment (11988851)
Interest (11988852)
Curiosity (11988853)
Price value (1198636)
Quality (11988861)
Value (11988862)
Price (11988863)
Habit (1198637)
Past behavior (11988871)
Reflex behavior (11988872)
Individual experience (11988873)
Use intention (1198638)
Repurchase intentions (11988881)
Positive word-of-mouthcommunication (119888
82)
Service quality (11988883)
Use behavior (1198639)
Usage time (11988891)
Usage frequency (11988892)
Use variety (11988893)
relationships Then the DNP will be applied to derive theinfluence weights based on the lead usersrsquo perspectives Insummary the assessment model consists of three main steps(1) deriving the requirement by literature review (2) struc-turing the causal relationship based on lead usersrsquo opinion byapplying DEMATEL and (3) evaluating the weights versuseach criterion by using the DNP
31 Modified Delphi Method The Delphi method wasdesigned by Dalkey and Helmer [78] After the Delphimethod Murry and Hammons [79] tried to identify issuesand problems that were collected from a group of technologyeducation professionals using themodifiedDelphi techniqueThe modified Delphi simplified the step of conducting thefirst round of a survey and replaced the conventionallyadopted open style survey [80] The purpose of the modifiedDelphi method is to save time (the experts can focus onresearch themes eliminating the need for speculation onthe open questionnaire) and to improve the response of the
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
6 Mathematical Problems in Engineering
253 Social Influence Researches have broadly explored theconcepts of the social influence and proved the influences ofthe social influence on shaping usersrsquo behaviors For instanceRogers [3] indicated that the usersrsquo decision making processof adopting an innovative technology is influenced by thesocial notion beyond an individualrsquos decision thinking Ingeneral the social influence can be classified into two partsthe social norms and the critical mass The social normsinclude two different influences the informational influenceand the normative influence The informational influencerefers to peoplersquos obtaining of information from other peopleThe normative influence refers to a userrsquos conformation to theexpectation of other people to gain a reward or avoid a pun-ishment [49] Venkatesh et al [34] defined social influence asthe degree of importance being recognized by others to usea novel technology The social influence construct consistsof three variables the subjective norm the social factor andthe image (1) Subjective norm the subjective norm is theperceived social pressure to perform or not to perform thebehavior [50] In this research the subjective norm is theperceived social pressure to use Phablets (2) Social factorthe social factor is an individualrsquos internalization from thesocial systemrsquos subjective culture and particular interpersonalagreements that the individual in particular social situationhas made with others [51] (3) Image the image is defined asthe degree to which an individual identifies that the using ofan innovative technology can enhance an individualrsquos statusin his or her social organization [48] Drawing the aboveliterature review the usage of an innovative product can bedetermined by the behavioral intention
254 FacilitatingConditions Thefacilitating conditions con-struct is defined as the degree to which a person believes thatan organization and a technical infrastructure exist to supportthe usage of a system [34] Previous researches on factorsinfluencing acceptance of some specific technology haveexhibited that facilitating conditions have a significant impacton innovative technology adoptions and usage behaviors [3444 46 48 52ndash54] These researches summarized that thefacilitating conditions are strong predictors which can beused for forecasting technology acceptances and usages
255 HedonicMotivation Thehedonicmotivation is definedas the motivation to do something due to the internalsatisfaction [55] From the hedonic perspective of indi-vidual behaviors the hedonic motivation is related to theessence of individualrsquos psychological and emotive experienceswhich can be triggered by both the individual traits andthe cognitive states [56] Based on prior studies Magniet al [56] explored the relationships between consumersand technology products by analyzing consumersrsquo intentionTo explore consumersrsquo purchase motivations Magni et aldeveloped and tested amodel to examine the effect of hedonicmotivations Besides similar to the flow theory many offormer empirical studies have demonstrated that hedonicexperiences and traits will influence consumer technologyacceptances from both individual and organizational con-texts [57ndash60] In otherwords individualrsquos hedonic experience
of using a technology product such as a Phablet is more likelyto perform experimental behavior
256 Price Value The price value construct originated fromthe perceived value which is often regarded as an importantindicator in predicting the purchase behavior which caninfluence a companyrsquos competitive advantage [61 62] Tradi-tionally the definition of the price value is a trade-off betweenbenefits and sacrifices [63] Recently the price value has beenemphasized by the researchers in the information technologyfields and themarketers of consumer-electronics devicesTheconcept was adopted to analyze usersrsquo adoption of emergingtechnologies or smart mobile devices The findings indicatedthat the price value concept is crucial in attracting consumers[64ndash66]The price value is positive when the benefits of usinga technology are identified to be greater than the monetarycosts Such price value has a positive impact on intentions [4]
Based on these ideas Venkatesh et al [4] described theprice value as consumersrsquo cognitive tradeoffs between theperceived benefits of the applications and monetary costsfor using them [67] In the marketing context the pricevalue encompasses two perspectives monetary costs andnonmonetary costs The monetary costs refer to the valuebeing identified in contrast to the price paid [68] Thenonmonetary costs refer to the value being identified inreturn for costs such as time and efforts being expended [69]In this research the price value combines both the monetaryand nonmonetary values for exploring factors influencingconsumersrsquo acceptances of Phablets
257 Habit The habit construct has been broadly discussedin a variety of domains such as psychology consumersrsquo pur-chase behaviors education health science andmanagementTriandis [51] derived the relationship between attitude andbehavior where behavioral intention is postulated to forecastuser behavior to the extent that the habit component isweak when habit is strong Aarts et al [70] found that thehabit strength attenuates the amount of information beingacquired before the decision is made Limayem et al [37]and Venkatesh et al [4] defined the habit as the degree towhich consumers tend to perform the usage of technologiesor the usage of technology products behaviors automaticallybecause of learning The habit construct consists of three cri-teria the past behavior the reflex behavior and the individualexperience The past behavior is described as usersrsquo priorbehaviors [37] The reflex behavior refers to usersrsquo behaviorsequence or customs which are regular parts of the daily life[37] The individual experience refers to the accumulation ofexperiences from usersrsquo established stable routines normsand habits for using technology products Such experiencesdecreased the needs for discussions coordination or effortfuldecision making [37] Researches on habitual intentions andhabitual usage behaviors have demonstrated that the habitis a strong predictor of technology usages in promotingbehavioral changes [4 62 71 72]
258 Behavioral Intention Social psychologists have broadlyexplored behavioral intentions and the relations to future
Mathematical Problems in Engineering 7
behavior [70] Behavioral intention refers to the degree towhich a person has formulated conscious plans to performor not perform some specified future behavior(s) [70]Behavioral intention was frequentlymeasured as the conativeloyalty which is an important goal in marketing [73] In themarketing context the loyalty is defined as what customersare willing to repurchase a product from the company andsupport the company with positive word-of-mouth commu-nications [71] For marketers and manufacturers of Phabletssuch outcomes are very important asmembers become agentsfor the firms encouraging friends and acquaintances to pur-chase their products However predictions of actual purchasebehaviors are always difficult Despite this many of the priorstudies have still proven that the behavioral intention playsa significant role in actual behaviors [74] In this researchto investigate the factors influencing consumersrsquo acceptancesof Phablets the repurchase intention the positive word-of-mouth communications and the service quality are selectedas the criteria for exploring the consumersrsquo behavioral inten-tions
259 Performance Expectancy A great number of researchesrevealed that past behaviors will influence future behaviorsSome researchers have further proven that past usage behav-iors are the antecedents of future behaviors [75] In orderto derive the factors influencing consumersrsquo acceptances ofPhablets three factors will be introduced as the criteria of theusage behaviorsThe factors include the usage time the usagefrequency and the usage variety Venkatesh et al [4] indicatedthat the usage behavior construct should bemeasured by boththe variety and the frequencyMathieson [76] andAl-Gahtaniet al [77] also indicated that the usage behavior constructconsists of four dimensions for measuring the technologyusage (1) the amount of time being spent in using technologyproducts per day (2) the usage frequency of technology prod-ucts (3) the number of various software applications beingused and (4) the number of various job tasks being supportedthrough technology product usages In this research theusage time usage frequency and the number of various jobtasks being supported will be introduced
The constructs being summarized based on literaturereview results being demonstrated above are demonstratedin Table 2The research model comprises of night constructsperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitbehavioral intention and use behavior In the later empiricalstudy Section 4 the influence relationships among the con-structs will be established
3 Research Methods
To construct the analytic framework for deriving factors forpredictions of usersrsquo acceptances of Phablets this researchreviewed the related research works of social psychologyand literature being related to factors for predicting usersrsquoadoption of technology such as theUTAUT and theUTAUT2for collecting the possible dimensions and criteria Nextthe DEMATEL method is employed to establish the causal
Table 2 Dimensions and criteria for analyzing usersrsquo Phabletacceptance
Dimensions Criteria
Performance expectancy (1198631)
Perceived usefulness (11988811)
Extrinsic motivation (11988812)
Job fit (11988813)
Relative advantage (11988814)
Effort expectancy (1198632)
Perceived ease of use (11988821)
Complexity (11988822)
Ease of use (11988823)
Social influence (1198633)
Subjective norm (11988831)
Social factor (11988832)
Image (11988833)
Facilitating conditions (1198634)
Perceived behavioral control (11988841)
Facilitating conditions (11988842)
Compatibility (11988843)
Hedonic motivations (1198635)
Enjoyment (11988851)
Interest (11988852)
Curiosity (11988853)
Price value (1198636)
Quality (11988861)
Value (11988862)
Price (11988863)
Habit (1198637)
Past behavior (11988871)
Reflex behavior (11988872)
Individual experience (11988873)
Use intention (1198638)
Repurchase intentions (11988881)
Positive word-of-mouthcommunication (119888
82)
Service quality (11988883)
Use behavior (1198639)
Usage time (11988891)
Usage frequency (11988892)
Use variety (11988893)
relationships Then the DNP will be applied to derive theinfluence weights based on the lead usersrsquo perspectives Insummary the assessment model consists of three main steps(1) deriving the requirement by literature review (2) struc-turing the causal relationship based on lead usersrsquo opinion byapplying DEMATEL and (3) evaluating the weights versuseach criterion by using the DNP
31 Modified Delphi Method The Delphi method wasdesigned by Dalkey and Helmer [78] After the Delphimethod Murry and Hammons [79] tried to identify issuesand problems that were collected from a group of technologyeducation professionals using themodifiedDelphi techniqueThe modified Delphi simplified the step of conducting thefirst round of a survey and replaced the conventionallyadopted open style survey [80] The purpose of the modifiedDelphi method is to save time (the experts can focus onresearch themes eliminating the need for speculation onthe open questionnaire) and to improve the response of the
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 7
behavior [70] Behavioral intention refers to the degree towhich a person has formulated conscious plans to performor not perform some specified future behavior(s) [70]Behavioral intention was frequentlymeasured as the conativeloyalty which is an important goal in marketing [73] In themarketing context the loyalty is defined as what customersare willing to repurchase a product from the company andsupport the company with positive word-of-mouth commu-nications [71] For marketers and manufacturers of Phabletssuch outcomes are very important asmembers become agentsfor the firms encouraging friends and acquaintances to pur-chase their products However predictions of actual purchasebehaviors are always difficult Despite this many of the priorstudies have still proven that the behavioral intention playsa significant role in actual behaviors [74] In this researchto investigate the factors influencing consumersrsquo acceptancesof Phablets the repurchase intention the positive word-of-mouth communications and the service quality are selectedas the criteria for exploring the consumersrsquo behavioral inten-tions
259 Performance Expectancy A great number of researchesrevealed that past behaviors will influence future behaviorsSome researchers have further proven that past usage behav-iors are the antecedents of future behaviors [75] In orderto derive the factors influencing consumersrsquo acceptances ofPhablets three factors will be introduced as the criteria of theusage behaviorsThe factors include the usage time the usagefrequency and the usage variety Venkatesh et al [4] indicatedthat the usage behavior construct should bemeasured by boththe variety and the frequencyMathieson [76] andAl-Gahtaniet al [77] also indicated that the usage behavior constructconsists of four dimensions for measuring the technologyusage (1) the amount of time being spent in using technologyproducts per day (2) the usage frequency of technology prod-ucts (3) the number of various software applications beingused and (4) the number of various job tasks being supportedthrough technology product usages In this research theusage time usage frequency and the number of various jobtasks being supported will be introduced
The constructs being summarized based on literaturereview results being demonstrated above are demonstratedin Table 2The research model comprises of night constructsperformance expectancy effort expectancy social influencefacilitating conditions hedonicmotivation price value habitbehavioral intention and use behavior In the later empiricalstudy Section 4 the influence relationships among the con-structs will be established
3 Research Methods
To construct the analytic framework for deriving factors forpredictions of usersrsquo acceptances of Phablets this researchreviewed the related research works of social psychologyand literature being related to factors for predicting usersrsquoadoption of technology such as theUTAUT and theUTAUT2for collecting the possible dimensions and criteria Nextthe DEMATEL method is employed to establish the causal
Table 2 Dimensions and criteria for analyzing usersrsquo Phabletacceptance
Dimensions Criteria
Performance expectancy (1198631)
Perceived usefulness (11988811)
Extrinsic motivation (11988812)
Job fit (11988813)
Relative advantage (11988814)
Effort expectancy (1198632)
Perceived ease of use (11988821)
Complexity (11988822)
Ease of use (11988823)
Social influence (1198633)
Subjective norm (11988831)
Social factor (11988832)
Image (11988833)
Facilitating conditions (1198634)
Perceived behavioral control (11988841)
Facilitating conditions (11988842)
Compatibility (11988843)
Hedonic motivations (1198635)
Enjoyment (11988851)
Interest (11988852)
Curiosity (11988853)
Price value (1198636)
Quality (11988861)
Value (11988862)
Price (11988863)
Habit (1198637)
Past behavior (11988871)
Reflex behavior (11988872)
Individual experience (11988873)
Use intention (1198638)
Repurchase intentions (11988881)
Positive word-of-mouthcommunication (119888
82)
Service quality (11988883)
Use behavior (1198639)
Usage time (11988891)
Usage frequency (11988892)
Use variety (11988893)
relationships Then the DNP will be applied to derive theinfluence weights based on the lead usersrsquo perspectives Insummary the assessment model consists of three main steps(1) deriving the requirement by literature review (2) struc-turing the causal relationship based on lead usersrsquo opinion byapplying DEMATEL and (3) evaluating the weights versuseach criterion by using the DNP
31 Modified Delphi Method The Delphi method wasdesigned by Dalkey and Helmer [78] After the Delphimethod Murry and Hammons [79] tried to identify issuesand problems that were collected from a group of technologyeducation professionals using themodifiedDelphi techniqueThe modified Delphi simplified the step of conducting thefirst round of a survey and replaced the conventionallyadopted open style survey [80] The purpose of the modifiedDelphi method is to save time (the experts can focus onresearch themes eliminating the need for speculation onthe open questionnaire) and to improve the response of the
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
8 Mathematical Problems in Engineering
main topic [80 81] Following the introduction toDelphi andthe modified Delphi methods is mainly based on the worksby Jones and Hunter [82] Murry and Hammons [79] andHuang et al [83 84]
The primary objective of a Delphi inquiry is to obtain aconsensus as aminimumof 75 percent agreement on any par-ticular item of opinion from a group of respondents Mean-while it is possible to develop consensus on a common coreof management assessment criteria which when combinedwith the institution- unit- and position-specific criteria canform a comprehensive management audit instrument
The Delphi method originated in a series of studiesconducted by the RAND Corporation in the 1950s [82] Theobjective was to develop a technique to obtain the mostreliable consensus from a group of experts [78] Delphimay be characterized as a method for structuring a groupcommunication process so the process is effective in allowinga group of individuals as a whole to deal with a complexproblem while researchers have developed variations ofthe method since its introduction [85] Specific situationshave included a round in which the participants meet todiscuss the process and resolve any uncertainties or ambi-guities in the wording of the questionnaire [82] The Delphimethod proceeds in a series of communication rounds asfollows
Round 1 Either the relevant individuals are invited to provideopinions on a specific matter based upon their knowledgeand experience or the teamundertaking theDelphi expressesopinions on a specific matter and selects suitable expertsto participate in subsequent questionnaire rounds theseopinions are grouped together under a limited number ofheadings and statements are drafted for circulation to allparticipants through a questionnaire [82]
Round 2 Participants rank their agreement with each state-ment in the questionnaire the rankings then are summarizedand included in a repeat version of the questionnaire [82]
Round 3 Participants rerank their agreement with eachstatement in the questionnaire and have the opportunityto change their score in view of the grouprsquos response Thererankings are summarized and assessed for their degree ofconsensus if an acceptable degree of consensus is obtainedthe process may cease with the final results then fed back tothe participants if it is not this third round is repeated [82]
Murry and Hammons [79] modified the traditional Del-phi Technique by eliminating the first-round questionnairecontaining unstructured questions It is simplified to replacethe conventionally adopted open style survey doing so iscommonly referred to as the modified Delphi method [80]The modified Delphi technique is similar to the full Delphiin terms of procedure (ie a series of rounds with selectedexperts) and intent (ie to predict future events and toarrive at consensus) The major modification consists ofbeginning the process with a set of carefully selected itemsThese preselected items may be drawn from various sourcesincluding related competency profiles synthesized reviews ofthe literature and interviews with selected content experts
The primary advantages of this modification to the Delphiare that it (a) typically improves the initial round responserate and (b) provides solid grounding in previously developedwork
Additional advantages related to the use of the modifiedDelphi technique include reducing the effects of bias dueto group interaction assuring anonymity and providingcontrolled feedback to participants [86 87] Brooks [88]noted that three mailings are usually sufficient in order toarrive at consensus
32 The DNP The DNP the DEMATEL technique combin-ing with ANP was proposed by Tzeng [89 90] The DEMA-TEL techniquewas developed by the Battelle Geneva Institute(1) to analyze complex ldquoreal-world problemsrdquo dealing mainlywith interactive map-model techniques [91] and (2) to evalu-ate qualitative and factor-linked aspects of societal problemsTheDNP advanced the tradition decisionmaking frameworkby manipulating the DEMATEL and the ANP individuallywhere a single round of survey of expertsrsquo opinions wouldbe enough for resolving a decision making problem In com-parison to the traditional approach consisting of two roundsof expert opinion surveys the DNP actually eases the surveyprocedureTheDEMATEL technique was developedwith thebelief that the pioneering and proper use of scientific researchmethods could help to illuminate specific and intertwinedphenomena and contribute to the recognition of practicalsolutions through a hierarchical structure DEMATEL hasbeen successfully applied in many situations such as e-business model definitions [92 93] policy definitions [83]and global manufacturing system optimization [94] TheANP is general form of the analytic hierarchy process (AHP)[95] which has been used in multicriteria decision making(MCDM) to be able to release the restriction of hierarchicalstructure
Combining the DEMATEL and ANPmethod which hadbeen reviewed in this section the steps of this method can besummarized as follows
Step 1 Calculate the direct-influence matrix by scores Basedon expertsrsquo opinions evaluations are made of the relation-ships among elements (or variablesattributes) of mutualinfluence using a scale ranging from 1 to 5 with scoresrepresenting ldquono influencerdquo (1) ldquolow influencerdquo (2) ldquomediuminfluencerdquo (3) ldquohigh influencerdquo (4) and ldquovery high influencerdquo(5) They are asked to indicate the direct effect they believe afactor will have on factor 119895 as indicated by 119889
119894119895 The matrix D
of direct relations can be obtained
Step 2 Normalize the direct-influence matrix based on thedirect-influence matrix D the normalized direct relationmatrix N is acquired by using
N = VD
V = min 1max119894sum119899
119895=1 119889119894119895
1max119895sum119899
119894=1 119889119894119895
119894 119895 isin 1 2 119899
(1)
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 9
Step 3 Attaining the total-influence matrix T once thenormalized direct-influence matrix N is obtained the total-influence matrix T of NRM can be obtained
T = N+N2+ sdot sdot sdot +N119896 = N (IminusN)minus1 (2)
where T is the total influence-related matrix N is a directinfluence matrix and N = [119909
119894119895]119899times119899
lim119896rarrinfin
(N2+ sdot sdot sdot + N119896)
stands for an indirect influence matrix and 0 le sum119899
119895=1 119909119894119895 lt 1or 0 le sum
119899
119894=1 119909119894119895 lt 1 and only one sum119899119895=1 119909119894119895 or sum
119899
119894=1 119909119894119895 equalto 1 for forall119894 119895 So lim
119896rarrinfinN119896 = [0]
119899times119899 The (119894 119895) element 119905
119894119895of
matrix denotes the direct and indirect influences of factor 119894on factor 119895
Step 4 Analyze the result In this stage the row and columnsums are separately denoted by r and c within the total-relation matrix T through
T = [119905119894119895] 119894 119895 isin 1 2 119899 (3)
r = [119903119894]119899times1 =
[
[
119899
sum119895=1119905119894119895
]
]119899times1
(4)
c = [119888119895]1times119899 = [
119899
sum119894=1119905119894119895]
1times119899
(5)
where the r and c vectors denote the sums of the rows andcolumns respectively
Suppose 119903119894denotes the row sumof the 119894th row ofmatrixT
Then 119903119894is the sum of the influences dispatching from factor 119894
to the other factors both directly and indirectly Suppose that119888119895denotes the column sum of the 119895th column ofmatrixThen
119888119895is the sum of the influences that factor 119894 is receiving from
the other factors Furthermore when 119894 = 119895 (ie the sumof therow sum and the column sum) (119903
119894+ 119888119894) represents the index
representing the strength of the influence both dispatchingand receiving (119903
119894+ 119888119894) is the degree of the central role that
factor 119894 plays in the problem If (119903119894minus119888119894) is positive then factor 119894
primarily is dispatching influence upon the strength of otherfactors and if (119903
119894minus 119888119894) is negative then factor 119894 primarily is
receiving influence from other factors [83 96] Therefore acausal graph can be achieved by mapping the dataset of (119903
119894+
119904119894 119903119894minus 119904119894) providing a valuable approach for decision making
(see Phillips-Wren et al [90])Now we call the total-influence matrix TC = [119905
119894119895]119899times119899
obtained by criteria and TD = [119905119863119894119895]119899times119899
obtained by dimen-sions (clusters) fromTCThenwe normalize theANPweightsof dimensions (clusters) by using influence matrix TD asshown in Table 6
Step 5 The original supermatrix of eigenvectors is obtainedfrom the total-influence matrix T = [119905
119894119895] for example 119863
values of the clusters in matrix TD as (7) where if 119905119894119895 lt 119863then 119905119863
119894119895= 0 else 119905119863
119894119895= 119905119894119895 and 119905
119894119895is in the total-influencematrix
T The total-influence matrix TD needs to be normalized by
dividing by the following formulaThere we could normalizethe total-influence matrix and represent it as TD (Figure 3)
TD =
[[[[[[[[[[[[[[[[[[
[
11990511986311111198891
sdot sdot sdot11990511986311198951119895
1198891sdot sdot sdot
119905119863111989811198981198891
11990511986311989411198941119889119894
sdot sdot sdot119905119863119894119895
119894119895
119889119894
sdot sdot sdot119905119863119894119898
119894119898
119889119894
11990511986311989811198981119889119898
sdot sdot sdot119905119863119898119895
119898119895
119889119898
sdot sdot sdot119905119863119898119898119898119898
119889119898
]]]]]]]]]]]]]]]]]]
]
=
[[[[[[[[[[[[
[
1205721198631111 sdot sdot sdot 120572
11986311198951119895 sdot sdot sdot 120572
11986311198981119898
12057211986311989411198941 sdot sdot sdot 120572
119863119894119895
119894119895sdot sdot sdot 120572
119863119894119898
119894119898
12057211986311989811198981 sdot sdot sdot 120572
119863119898119895
119898119895sdot sdot sdot 120572119863119898119898119898119898
]]]]]]]]]]]]
]
(6)
where 120572119863119894119895119894119895
= 119905119863119894119895
119894119895119889119894 This research adopts the normalized
total-influence matrix TD (hereafter abbreviated to ldquothenormalized matrixrdquo) and the unweighted supermatrix Wusing (8) shows these influence level values as the basis of thenormalization for determining the weighted supermatrix
Wlowast
=
[[[[[[[[[[[[
[
1205721198631111 timesW11 120572
1198632121 timesW12 sdot sdot sdot sdot sdot sdot 120572
11986311989811198981 timesW1m
1205721198631212 timesW21 120572
1198632222 timesW22 sdot sdot sdot sdot sdot sdot
sdot sdot sdot 120572119863119895119894
119895119894timesWij sdot sdot sdot 120572
119863119898119894
119898119894timesWim
12057211986311198981119898 timesWm1 120572
11986321198982119898 timesWm2 sdot sdot sdot sdot sdot sdot 120572119863119898119898
119898119898timesWmm
]]]]]]]]]]]]
]
(7)
Step 6 Limit the weighted supermatrix by raising it tosufficiently large power 119896 as (8) until the supermatrix hasconverged and become a long-term stable supermatrix to getthe global priority vectors or called ANP weights
lim119896rarrinfin
(Wlowast)119896 (8)
According to the definition by Lu et al [97] the signifi-cant confidence level can be calculated by
11198992
119899
sum119894=1
119899
sum119895=1
10038161003816100381610038161003816119905119901
119894119895minus 119905119901minus1119894119895
10038161003816100381610038161003816
119905119901
119894119895
times 100 (9)
where 119899 denotes the number of criteria 119901 denotes to thenumber of experts and 119905119901
119894119895is the average influence of criterion
119894 on criterion 119895
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
10 Mathematical Problems in Engineering
Table 3 The evaluative results of dimensions based on fifteen experts by modified Delphi method
Gender WorkExperience
DimensionsPerformanceexpectancy
Effortexpectancy
Socialinfluence
Facilitatingconditions
Hedonicmotivations
Pricevalue Habit Use
intentionUse
behaviorMale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeMale 5sim10 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Disagree Agree Agree Agree Disagree Agree AgreeFemale 10sim15 Agree Disagree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Disagree Agree Agree Agree AgreeMale 5 Agree Agree Agree Agree Agree Agree Agree Agree DisagreeFemale 5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 5sim10 Agree Agree Agree Agree Agree Agree Disagree Agree AgreeMale 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree AgreeMale lt5 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree AgreeFemale lt5 Disagree Agree Agree Agree Agree Agree Agree Agree Agree
Agree 14 14 14 15 13 15 13 15 13Disagree 1 1 1 0 2 0 2 0 2Agree 9333 9333 9333 10000 8667 10000 8667 10000 8667
Disagree 667 667 667 000 1333 000 1333 000 1333
=
tD1111
middot middot middot tD1119895
1jmiddot middot middot t
D1119898
1m
tD1198941
i1middot middot middot t
D119894119895
ijmiddot middot middot t
D119894119898
im
tD1198981
m1middot middot middot t
D119898119895
mjmiddot middot middot t
D119898mmm
997888rarr d1 =
m
sumj=1
tD1119895
1j
997888rarr di =
m
sumj=1
tD119894119895
ij
997888rarr dm =
m
sumj=1
tD119898119895
mj
di =
m
sumj=1
tD119894119895
ij i = 1 middot middot middot mTD
Figure 3
4 Empirical Study
In this section the background of Phablet will be discussedin Section 41 Then the factors for predicting consumersrsquopreferences toward the Phablet will be summarized andconfirmed by experts using the modified Delphi method inSection 42 In Section 43 the DEMATEL method will beused to construct the causal network Then the influenceweights versus each dimension and criterion will be derivedby the DNP
41 Industry Background and Research Problem Descrip-tion The rapid emergence of smart mobile devices pushesdemandsMore andmore customers need a smart phonewitha large screenThus the integrated device consists of featuresof both smart phones and tablet PCs Such a concept was firstrealized by Samsungwhich released theGalaxyNote the firstcommercialized Phablet
According to the forecasts being provided by the Statistathe worldwide Phablet shipment will reach 2037 million
units in 2017 from 351million units in 2013Thus the Phabletmanufacturers should initiate the focus on designmarketingand product improvement of Phablet products Those com-panies which can dominate the Phablet market may obtainconsiderable benefits Nevertheless very few academic stud-ies researched on factors influencing consumersrsquo acceptancesof Phablets Further exploring consumer behaviors in theacceptance of some specific product is always a crucial taskfor Phablet manufacturers and marketers [98 99]
Kotler and Keller [99] argued that the consumer pur-chase behavior is often involved with various factors suchas simple unexpected concrete credibility emotion andstoriesThose factors always influence the purchase behaviorConsumer electronics firms also understand the importanceof factors influencing consumersrsquo purchase of their productsThus investigating these factors and predicting consumersrsquopurchase motivation have become indispensable tasks
42 Deriving Factors for Phablet Acceptances by the ModifiedDelphi Method In order to derive the most suitable criteria
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 11
Table4Th
eevaluativer
esultsof
criteria
basedon
them
odified
Delp
himetho
d
(a)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
usefulness
Extrinsic
motivation
Jobfit
Relativ
eadvantage
Perceived
ease
ofuse
Com
plexity
Ease
ofuse
Subjectiv
eno
rmSo
cialfactor
Image
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
Agree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
1212
Disa
gree
02
30
00
00
33
Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
2000
(b)
Gender
Yearso
fwork
experie
nce
Criteria
Perceived
behavioralcontrol
Facilitating
cond
ition
sCom
patib
ility
Enjoym
ent
Interest
Curio
sity
Quality
Value
Price
1Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
2Male
10sim15
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
3Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
4Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
5Male
5Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
6Female
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
7Male
15sim20
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
8Male
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
9Female
5Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
10Male
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
11Male
10sim15
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Disa
gree
12Male
15sim20
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
13Male
lt5
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
14Female
5sim10
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
15Female
lt5
Agree
Agree
Disa
gree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
1513
1215
1515
1515
12Disa
gree
02
30
00
00
3Agree
10000
8667
8000
10000
10000
10000
10000
10000
8000
Disa
gree
000
1333
2000
000
000
000
000
000
2000
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
12 Mathematical Problems in Engineering
Table 5 The evaluative results of criteria based on the modified Delphi method
Gender Years of workexperience
Criteria
Pastbehavior
Reflexbehavior
Individualexperience
Repurchaseintentions
Positiveword-of-mouthcommunication
Servicequality
Usagetime
Usagefrequency
Usevariety
1 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree2 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree3 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree4 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree5 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree6 Female 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree7 Male 15sim20 Agree Agree Agree Agree Agree Agree Agree Agree Agree8 Male 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree9 Female 5 Agree Agree Agree Agree Agree Agree Agree Agree Agree10 Male 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree11 Male 10sim15 Agree Agree Agree Agree Agree Agree Agree Agree Agree12 Male 15sim20 Agree Agree Disagree Agree Agree Agree Agree Agree Agree13 Male lt5 Agree Agree Agree Agree Agree Agree Agree Agree Agree14 Female 5sim10 Agree Agree Agree Agree Agree Agree Agree Agree Agree15 Female lt5 Agree Agree Disagree Agree Agree Agree Agree Agree Agree
Agree 15 15 14 15 15 15 15 15 15Disagree 0 0 1 0 0 0 0 0 0Agree 10000 10000 9333 10000 10000 10000 10000 10000 10000
Disagree 000 000 667 000 000 000 000 000 000
for predicting acceptances of Phablets 28 possible criteriawill first be derived from the literature review results Thosecriteria will then be confirmed based on the modified Delphimethod Based on the modified Delphi method 75 wasrecognized as a minimum percentage of agreement foreach criterion [100] Tables 3ndash5 verified the percentage of
agreement of the nine dimensions and twenty-eight criteriaby experts All of the dimensions and criteria exceeding 75are recognized by experts as suitable for analyzing adoptionsof Phablets Then the factors being suitable for predictingPhablets can be derived
The average initial direct influence matrixD
D
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000413339333800440033334867273334002933420031333267460045334333486743333467460047334467253335333800413343334267
4467000036674467313332003533286730002933380028673000406739334000393342002667406736674067266728673600366736003467
4533466700004200406736003800286722671933366730672933413342004067380038002667366738004067260030673200373338673733
4200393341330000346738004067266734002733333330002800353334673600460038672667393339333800313333333933320036004000
3533306730672333000047333733260029332933333328672600420042002867353338002333446744674600446744672933366739333267
2667213324672267420000003867180018001733246728672200393339332867306730672267393339334067366740002600313329333533
3867346733332600446742000000260029332933333332002667393343333000393339332467460046004733446740673133393339333133
3000306730674067320026673600000039332933220029332267320032672600433343333467320032003200260033333067266730672667
3067293328004067300026673333280000003467233329332133366732673133353337333467280025332533313338673533273329332600
2800300027334267300025333267226734670000233323332267353330673000420040673467253326672933306736673267213323332200
4667266736672467446740004333226725331800000030674733460046003533333330672467393340674200313332002467420040673400
3533286724672200293336003467306736672800293300004867406740673267380034002733353335333533293332004000333328673467
2933246724001867333336673067213326002333333329330000280028002667400035333067420042004200220026672667293327332933
3667286730673667406735334400260038674000406736002933000043334200433345333400353340673800326740673533460038004200
3933286729333533400036004267260038003667406734672733426700004000420042673400353339333533320038003067380032673667
2800246720003867400035334400286741333867360031332733433346000000473345334200380035333533340038003067326732673533
2933253324673733320034673800220036003267340024002467366735332867000041334800333339333800320038003533320032674267
4133386736004667440032004600280043334000360032002800400038674000486700004467346746003467393346674467380038674533
3400326724003800280024673667213338003200260021332000273327332800473347330000240032002600380038003933320032003667
3000253322002667233318672133280025332733273325331867240023332400193320671800000016001533266725332733280028002400
3800246722672267360032673933280028002733393330673533360033333333260030002267346700003400353334003333400044673600
3267313323332800293323332867273330672933286728002133353332003200220022001600480046000000273328002533293329332667
4733406736674067446739334867400037333400380030002933433342003667480046673733453346004933000044674333426742004400
4667413336673533433342004733400037333400380032672800420042003933486748003400413344674000420000004333446745334333
2667220023332867220020672600340032672933273331332400320032003200373339333733240024002467286727330000226724002733
4467406734673267346727333667173326672267393329332333433344003133366742001933346745334333326733332600000044673467
4400446737333400346730673667173326672267406726672400433342673200393343332133380048674600313330002667420000003333
4267366735333133360026003800173322672267413325332133446743334000366742001867320044674533326732672800413343330000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(10)
Note Consider (1(119899 times (119899 minus 1)))sum119899119894=1
sum119899
119895=1(|119889119901
119894119895minus 119889119901minus1
119894119895|119889119901
119894119895) times
100 = 4823 lt 5 that is significant confidenceis 9518 where 119901 = 15 denotes the number of experts119889119901
119894119895is the average influence of 119894 criterion on 119895 and 119899
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 13
denotes number of criteria here 119899 = 28 and 119899 times 119899
matrixThe normalized direct influence matrix N
N
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0000003700350034003900300044002400300026003800280029004100410039004400390031004100420040002300320034003700390038
0040000000330040002800290032002600270026003400260027003600350036003500380024003600330036002400260032003300320031
0041004200000038003600320034002600200017003300270026003700380036003400340024003300340036002300270029003300350033
0038003500370000003100340036002400300024003000270025003200310032004100350024003500350034002800300035002900320036
0032002700270021000000420033002300260026003000260023003800380026003200340021004000400041004000400026003300350029
0024001900220020003800000035001600160016002200260020003500350026002700270020003500350036003300360023002800260032
0035003100300023004000380000002300260026003000290024003500390027003500350022004100410042004000360028003500350028
0027002700270036002900240032000000350026002000260020002900290023003900390031002900290029002300300027002400270024
0027002600250036002700240030002500000031002100260019003300290028003200330031002500230023002800350032002400260023
0025002700240038002700230029002000310000002100210020003200270027003800360031002300240026002700330029001900210020
0042002400330022004000360039002000230016000000270042004100410032003000270022003500360038002800290022003800360030
0032002600220020002600320031002700330025002600000044003600360029003400300024003200320032002600290036003000260031
0026002200210017003000330027001900230021003000260000002500250024003600320027003800380038002000240024002600240026
0033002600270033003600320039002300350036003600320026000000390038003900410030003200360034002900360032004100340038
0035002600260032003600320038002300340033003600310024003800000036003800380030003200350032002900340027003400290033
0025002200180035003600320039002600370035003200280024003900410000004200410038003400320032003000340027002900290032
0026002300220033002900310034002000320029003000210022003300320026000000370043003000350034002900340032002900290038
0037003500320042003900290041002500390036003200290025003600350036004400000040003100410031003500420040003400350041
0030002900210034002500220033001900340029002300190018002400240025004200420000002100290023003400340035002900290033
0027002300200024002100170019002500230024002400230017002100210021001700180016000000140014002400230024002500250021
0034002200200020003200290035002500250024003500270032003200300030002300270020003100000030003200300030003600400032
0029002800210025002600210026002400270026002600250019003200290029002000200014004300410000002400250023002600260024
0042003600330036004000350044003600330030003400270026003900380033004300420033004100410044000000400039003800380039
0042003700330032003900380042003600330030003400290025003800380035004400430030003700400036003800000039004000410039
0024002000210026002000180023003000290026002400280021002900290029003300350033002100210022002600240000002000210024
0040003600310029003100240033001600240020003500260021003900390028003300380017003100410039002900300023000000400031
0039004000330030003100270033001600240020003600240021003900380029003500390019003400440041002800270024003800000030
0038003300320028003200230034001600200020003700230019004000390036003300380017002900400041002900290025003700390000
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(11)
The normalized direct influence matrix TC
TC
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0171018601750187020501810218014601780162019401640156021802150195022102160168021102210212017301940186020002020199
0192013501590177017701640189013501600148017501480141019601920176019501970147018901930191015901720169017901790176
0195017601280176018701690193013601550141017501510142019801960178019601950148018801960193016001750167018101830179
0191017001630140018101700195013401640147017201500140019201900174020201950148018901970190016401770173017601800181
0184016101530159015001770190013301590148017001480137019601940166019101930144019202000195017401850163017901820173
0156013501310139016501180170011101310121014401310118017201700147016501650126016701730170014901610141015401530156
0191016801590165019201760162013601630151017401540141019902000171019901990149019802050201017801860169018501860177
0164014801400160016201450173009901540136014601360123017101700149018201820141016601720167014401610151015501590154
0162014401360157015801430168012101180138014401340120017201670151017301740139015901640159014701630152015301550150
0155014101320155015401380163011401440105014101250118016701610146017401720136015301610158014201570146014401460143
0194015801580159018801710195013001550138014101500156020001980171019001870145018801970192016301740159018301830175
0175015201410150016701600179013001580140015901160150018601840161018501810141017601830177015301660164016701640167
0155013501270133015501470160011101350124014801290097015901570142017001660131016601720168013401470139014901480148
0196016901620180019501770207014101760166018601630148017202070187021002110162019502080200017401920178019701910192
0191016301550173018801710199013601700157018001560142020101620179020102010156018801990190016701830168018301800181
0181015901460175018701690199013701720158017501520141020002000143020502030163018901950189016801830167017801790179
0172015101420165017101600184012401590145016401380131018401810160015401890159017501880181015701730162016801690175
0209018501730196020601810218014801880172019001660153021502110193022301810178020302210205018702050194019802000202
0169015001360159016001450175011801540139015001300121016901670152018701860112016001740164015601660159016101610163
0131011401060118012201090126009901130107012001060095013001280117012701280100010401240120011501220118012401250120
0175014601370148017001550180012601480137016501410137018001750159017201750134017301490174015601650156017101750165
0154013801240138014801320155011401360126014101250113016201570144015101510116016801720128013501450135014601470142
0220019301800197021301930227016301880173019801700160022502210197023002280177021902280225015902100198020902090208
0218019101780191021001930223016201860171019601700157022102190197022802270172021302250215019301690196020802100205
0147012801220137014001280150011801360124013701260113015701550141016301640133014401510147013401420111013801400141
0189016701540164017601570186012301530139017201460133019401920165018901930138018101970190016101720157014401830172
0191017201580167017901620189012401550141017601450135019601940168019301960141018602020194016101710160018201470173
0188016401550163017801560188012301500140017401430131019601920173018901930137017901970192016101720159018001820142
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(12)
43 The Causal Relationships and Weight Derivations by theDNP TheDEMA is a usefulmethod to illustrate the relation-ships between dimensions and criteria Researches belongingto various fields have applied the DEMATEL to solve real-world problems The DNP an MCDMmethod being derived
from the concept of the DEMATEL and the ANP can beapplied to construct the structure of a decision problem andderive weights being associated with the criteria based onthe total relationship matrix being derived by DEMATELIn this research the DNP method will be introduced for
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
14 Mathematical Problems in Engineering
Table 6 119903119894+ 119888119894and 119903119894minus 119888119894versus each dimension
Dimensions 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Performance expectancy (1198631) 1449 1599 3049 minus0150
Effort expectancy (1198632) 1548 1488 3036 0060
Social influence (1198633) 1277 1353 2630 minus0076
Facilitating conditions (1198634) 1321 1436 2757 minus0114
Hedonic motivations (1198635) 1601 1605 3206 minus0004
Price value (1198636) 1555 1536 3092 0019
Habit (1198637) 1642 1250 2891 0392
Use intention (1198638) 1466 1611 3077 minus0145
Use behavior (1198639) 1533 1515 3048 0018
constructing the structure of the decision problem and derivethe influence weights
At first the influence of each criterion on others canbe derived based on 15 lead usersrsquo opinions Initial 28 times
28 influence relation matrix D 28 times 28 can be constructed
accordingly (refer to Figure 4) Furthermore the significanceconfidence of questionnaires based on the fifteen expertsrsquoopinions can be derived by (9) The result equals 482which is less than 5 That is the significance confidence is9518 which is greater than 95 (see total average initialdirect matrix D) Afterwards the direct influence matrixD will be normalized according to (7) The normalizeddirect influence matrix N is depicted in (11) Then the totalinfluence matrix T can be calculated based upon (3) Thematrix T is demonstrated in (12) Meanwhile the causalnetwork of dimension is shown in (13)Moreover to illustratethe causal relationship network the 119903
119894and 119888119894can be derived by
using (4) and (5) which stand for the summation of row andcolumn versus each criterion and dimension Subsequentlythe (119903119894+ 119888119894) and (119903
119894minus 119888119894) can be derived as illustrated in Tables
6 and 7The total influence matrix TD
TD =
1198631 1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
1198631
1198632
1198633
1198634
1198635
1198636
1198637
1198638
1198639
[[[[[[[[[[[[[[[[[[[
[
017001860151015901930186019801720170
015801670139014601790170018901670158
014901560125013201620164016201510149
015301690136013801730166018001560153
017101880157016001840190019501760171
016701780150014901810174018601730167
013601440123012701500139014601390136
017501860158015901920191019601680175
016901750139015101860174019101640169
]]]]]]]]]]]]]]]]]]]
]
(13)
According to Table 6 the (119903119894minus 119888119894) value of the effort
expectancy dimension (119903119894minus 119888119894) has the highest positive value
Thus the effort expectancy (1198632) is the most importantdimension (1198632) which plays a dominant role in the causalnetwork This dimension has the most significant effect onother dimensions Given this the Phablet manufacturersand marketers should first take the effort expectancy (1198632)into consideration for enhancing consumersrsquo adoption ratesThe use intention dimension (119903
119894minus 119888119894) has the lowest (119903
119894minus
119888119894) value Therefore it is the least important dimension for
improving the Phablet from the dimension of the causalnetwork Hedonic motivation (119903
119894+ 119888119894) has the largest value
and it can be interpreted that it has themost crucial influentialrelationships with all other dimensions On the contrarysocial influence has the lowest (119903
119894+ 119888119894) value and it can be
interpreted that it is less important than other dimensionsIn light of the influential degrees versus each criterionthis finding implies that the social influence (1198633) can berecognized as the least influential dimension for predictingthe Phablet adoptions
According to the analytic results based on lead usersrsquoopinions the Phablet manufacturers should emphasize on
the degree of effort expectancy (1198632) and other dimensionsthan on the social influence (1198633) The causal networks ofthe total influence matrix based on dimensions and criteriaare depicted in Figure 4 as this figure demonstrates thatthe perceived usefulness (11988811) complexity (11988822) social fac-tors (11988832) perceived behavioral control (11988841) interest (11988852)quality (11988861) past behavior (11988871) service quality (11988883) andusage time (11988891) have the highest impacts on other criteriaunder each dimension including performance expectancyeffort expectancy social influence facilitating conditionshedonic motivation price value habit use intention and usebehavior respectively The causal relationship network beingestablished can serve as a basis for Phabletmanufacturers andmarketers to reduce the performance gaps in each dimensionFurthermore this causal relationship network model canbe used for recognizing and finding appropriate alternativesstrategies to approach the aspiration level
The DNP approach is broadly employed to derive theinfluence weights versus each criterion belonging to thecausal relation network Based on the DNP method theunweighted supermatrix W which stands for the degree ofimportance of the interacted network can be derived by
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 15
Perceived usefulness
Extrinsic motivation
Job fit
Relative advantage
minus006
minus003
000
003
006
125 130 135 140 145 150
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
092 098 104 110
Subjective
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Enjoyment
Interest
Curiosity minus005
000
005
104 108 112 116
Quality
Value Price
minus008
minus004
000
004
008
090
Past behavior
Reflex behavior Individual
experience minus008
minus004
000
004
008
012
078 083 088 093
Repurchase intentions
Positive word-of-mouth
communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Usage time Usage
frequency
Use variety minus002
minus001
000
001
099 101 103
(C22)
(C23)
(C71)
(C72)
(C52)
(C83)
(C11)
(C14)
(C12)
(C13)
(C91)
(C43)
(C33)
(C32)
norm (C31)
(C42)
(C41)
(C92)
(C93)
(C82)
(C81)
(C53)
(C51)
(C61)
(C62)
(C63)
(C21)
rminus
c
rminus
c
rminus
c
rminus
c
rminus
cr
minusc
rminus
c
rminus
c
rminus
c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
r + c
Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Use intention Use behavior
minus04
minus03
minus02
minus01
00
01
02
03
04
05
25 27 29 31 33
(D1)
(D2)
(D3)
(D4)
(D5)
(D6)
Habit (D7)
(D8)(D9)
rminus
c
r + c
r + c
Figure 4 The causal relationship network versus each dimension and criterion
(7) The results are demonstrated in (14) Given that thedifferent dimensional weights can affect criteria belonging toeach dimension the weighted supermatrix can be derivedbased on (8) The results are demonstrated in (15) Toconverge the weighted supermatrix Wlowast the power of the
weighted supermatrixWlowast will be raised to infinity Thus theinfluence weights versus each criterion can be derived anddemonstrated in Table 8
The unweighted supermatrixW
W
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0229025702600255034602930360034103360323037003340296034503370318031403800307028403800336037703720251033303360331
0278020202650255034702900363034103330326035503410304034503320323031003810309028603680346037703740250033203420326
0280025402050261034602950359034403330323037103300299035003350316031503840300028803740339037503710255033003390332
0275026102590205034303000357033803330329036003390301034203270331031703780305029203660342037603630261033203380330
0273023602490242029503260379034203330325036903270304034303300328031803840298027803860337037903730248033103350334
0264024002470249037602500374034103350324035803340307034203310328032903730298027603900334037603760248033103410329
0274023802430245036403250311034303330324036603350299034203290329031903770304027403910335037803720250033103350334
0265024502470243035002920358029603630341034903510300034003280332031803790303029203710336036903650267033203360332
0271024302360250035102900360037002830347034603520302034103280332031703750308028503720343036903650267033403380327
0271024802350246035202880360035903650277034403490308034403270329031803780304028903700341036903650266033203360332
0271024402450240034902940357033803350326031603540330034503320323032503770298028203870331037303690259033003360334
0268024102460245034103030356034403390317037902940327034503310323031903820299028503780337036403650271033603350329
0270024402450242034602980356034103320327038603730241034503290326032303780299027503970328037103650264033303380329
0271024302460239034603040350033503380327036603410292030003510350032503780297027603810344037303670260033103350334
0271024202470239034403020354034103350324036703410292036302850352032403780298027803810342037203680261033303350333
0270024402460240034303030354033403380328036103400299036703510281031603830301027903800342036803680264032703320342
0272024002400248034402970358034403270329034803390312034103270333027303960331028203820336037003670262033103380331
0269024502430243034702960357034403290326035003390310034303270329034003250335028203850333036903670265033103370332
0274024102420242034403020355033903340327034803380314033703250338035403960250028603830330036703580275033103390330
0272024302410243034503000355034603330320035503310313034103290330032603770297023503880377038003700251033103410328
0273024002430244034503000355034703300323035703310312034503310324032303790298027803360386037803720250033103390330
0270024302450242034503000355034503280326035803300312034503290326033003730297028404130303038303660251033003370333
0264024202440250034702970355033303380329036203410298034203280330031503730311028403840332032703970276033303340333
0270023902440246034803030349033503380327035803410302034403280327031803770305028203830335040303250273033403330333
0268024302400249034502990357033503390326034303550301034703270326031503760309028803820331039203880220033003360334
0271024302460239034502980357034303380319036703350299035303290319031903760305028203870331037603750249028403600356
0271024102460242034902940357034503370318036903310300034803270325031903760304027903920329037403750250035802870356
0270023902440247034303080349034303360320035703410302034803280324032403740302028003870333037503700255035303550292
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(14)
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
16 Mathematical Problems in Engineering
The weighted supermatrixWlowast
Wlowast
=
11986211 11986212 11986213 11986214 11986221 11986222 11986223 11986231 11986232 11986233 11986241 11986242 11986243 11986251 11986252 11986253 11986261 11986262 11986263 11986271 11986272 11986273 11986281 11986282 11986283 11986291 11986292 11986293
11986211
11986212
11986213
11986214
11986221
11986222
11986223
11986231
11986232
11986233
11986241
11986242
11986243
11986251
11986252
11986253
11986261
11986262
11986263
11986271
11986272
11986273
11986281
11986282
11986283
11986291
11986292
11986293
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
[
0027003000300030003800320039003500350033003900350031004100400037003600440035002700360031004600450030003900390039
0033002400310030003800320040003500340034003800360032004100390038003600440036002700340032004600450030003900400038
0033003000240031003800320039003500340033003900350032004100390037003600440035002700350032004500450031003800390039
0032003100300024003800330039003500340034003800360032004000390039003700440035002700340032004500440032003900390039
0033002800300029003200350041003400340033004000360033004200400040003600440034002600360031004600450030003700380038
0032002900300030004000270040003400340033003900370034004100400040003800430034002600360031004500450030003700380037
0033002900290029003900350033003500340033004000370033004200400040003700430035002500360031004500450030003700380038
0031002900290029003800320039002900360033003700370032004200400041003700440035002800360032004600450033003600370036
0032002900280029003800320039003600280034003700370032004200400041003700440036002700360033004600450033003600370036
0032002900280029003800310039003500360027003700370033004200400040003700440036002800360033004600450033003600370036
0033002900290029003900330040003400330033003300370035004200400039003700430034002700370032004500440031003800380038
0032002900300029003800340039003400340032004000310034004200400039003600430034002700360032004400440032003800380037
0032002900290029003800330039003400330033004000390025004200400040003600430034002600380032004500440032003800390037
0033002900300029003900340039003400340033004000370032003400400040003700430034002600360032004500440031003800390039
0033002900300029003900340040003400340033004000370032004200330040003700430034002600360032004500440031003900390039
0033002900300029003800340040003400340033003900370032004200400032003600430034002600360032004400440032003800380040
0032002900290030003800330039003600340035003700360033004200400041003100440037002500340030004600450032003700380037
0032002900290029003800320039003600350034003700360033004200400040003800360038002500350030004500450033003700380037
0033002900290029003800330039003600350034003700360034004100400041004000440028002600340030004500440034003700380037
0033002900290029004000350041003400330032003900360034004000390039003700430034002100340033004500440030003900400038
0033002900290029004000350041003400330032003900360034004100390038003600430034002500300034004500440030003800390038
0032002900300029004000350041003400320032003900360034004100390039003700420034002500370027004600440030003800390039
0031002800290029004000340041003400350034003800360032004100390040003700440037002700360031003700460032003700370037
0032002800290029004000350040003500350034003800360032004100390039003800450036002700360032004600370031003700370037
0032002900280029003900340041003500350034003600380032004200390039003700440036002700360031004500440025003700370037
0033002900300029003900330040003400330031003900360032004200400038003700440035002700370032004500450030003100390039
0033002900300029003900330040003400330031004000360032004200390039003700440035002700370031004500450030003900310039
0033002900290030003800350039003400330032003800370033004200390039003800430035002700370032004500450031003900390032
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
]
(15)
From the perspective of DNP the influence weightsincluding local weights and global weights can be derivedThe global weight stands for the real influence weighs beingderived from the local weight The global weight can beregarded as a priority indicator for ranking these dimensionsand criteria The importance versus these dimensions andcriteria can thus be evaluated and ranked In this researchthe purpose of the DNP is to derive the dominant factorsinfluencing consumersrsquo acceptances of Phablets The futurePhablet products can also be enhanced in accordancewith thecausal relationship network being demonstrated in Figure 4
In general the findings demonstrate that both hedonicmotivation (1198635) and use behavior (1198639) are the most impor-tant dimensions in light of influence relationship Further-more the criteria of repurchase intention (11988881) and reflexbehavior (11988872) are the first considerations based on the globalweights being derived In contrast extrinsic motivation (11988812)and relative advantage are the least important (11988814) criteriafor influencing the Phablet acceptance Besides both positiveword-of-mouth communications and the value are the mostsignificant criteria The reasons are that consumer oftenconsiders product value andwhether the product iswith goodreputation or not when purchasing smart mobile productsIn conclusion DNP is an effective approach that can beemployed for the influence weight derivations in terms of thecausal network among the factors
5 Discussion
This study attempts to derive factors influencing consumerbehaviors and thus the adoptions of Phablets In this sectionbothmanagerial implications and advances in researchmeth-ods will be discussed
51 Managerial Implications In this study lead users meanexperts with more than 5-year experiences in smart mobiledevices company DEMATEL method was used based onlead usersrsquo opinions to construct the analytical frameworkThe results were integrated and shown in Figure 5 whichdemonstrates the differences between the original UTAUT2based theoretical framework versus the viewpoints beingderived from lead users
From the lead usersrsquo perspective the causal relationshipsare very complicated The causal relations revealed that thesecomplicated paths are being established in terms of thoseexperts (such asmanagers and senior engineers of the Phabletmanufacturers) whose opinions and innovative thinking areoften generated ahead of mass usersrsquo thoughts The leadusers expect to develop and introduce new ways to users Inother words generating new customersrsquo value proposition istheir purpose Thus Phablet manufacturers and marketersshould focus on the marketing and product developmentas well as negotiating effectively with engineers so thatthese paths being generated based on lead usersrsquo opinionscan be realized for the future products to be promoted tomass users According to Figure 5 influence paths exist instructural map based on the opinions of the lead usersSuch paths include the effort expectancy-use intention pricevalue-use intention habit-use intention and use intention-use behaviorWe also find that the habit has influence on pricevalue and performance expectancy and effort expectancy inlight of lead usersrsquo opinions Further the effort expectancy hasinfluences on both the performance expectancy and hedonicmotivation
On the other hand from the lead usersrsquo perspective thecausal relationship network of each dimension can further
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 17
Price value
Use intention Use behavior
Habit
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation
Performance expectancy
Effortexpectancy
Social influence
Facilitating conditions
Hedonic motivation Price value
Behavioralintention Use behavior
Habit
Figure 5 The original model versus the construct being derived by DEMATEL
Table 7 119903119894+ 119888119894and 119903119894minus 119888119894versus each criterion
Criteria 119903119894
119888119894
119903119894+ 119888119894
119903119894minus 119888119894
Perceived usefulness (11988811) 5016 5356 10373 minus0340
Extrinsic motivation (11988812) 4398 4812 9210 minus0415
Job fit (11988813) 4128 4858 8985 minus0730
Relative advantage (11988814) 4529 4847 9376 minus0318
Perceived ease of use (11988821) 4883 4795 9678 0088
Complexity (11988822) 4447 4140 8587 0308
Ease of use (11988823) 5161 4934 10095 0227
Subjective norm (11988831) 3595 4309 7904 minus0714
Social factor (11988832) 4358 4220 8578 0137
Image (11988833) 3992 4093 8085 minus0102
Perceived behavioral control (11988841) 4608 4797 9405 minus0189
Facilitating conditions (11988842) 4015 4530 8545 minus0515
Compatibility (11988843) 3748 4053 7801 minus0305
Enjoyment (11988851) 5227 5143 10370 0083
Interest (11988852) 5156 4922 10078 0234
Curiosity (11988853) 4603 4891 9494 minus0289
Quality (11988861) 5264 4584 9848 0680
Value (11988862) 5248 5402 10651 minus0154
Price (11988863) 4043 4341 8384 minus0298
Past behavior (11988871) 5019 3267 8286 1753
Reflex behavior (11988872) 5262 4443 9705 0819
Individual experience (11988873) 5088 3943 9031 1145
Repurchase intentions (11988881) 4423 5615 10038 minus1192
Positive word-of-mouthcommunication (119888
82) 4790 5542 10332 minus0752
Service quality (11988883) 4499 3867 8366 0632
Usage time (11988891) 4792 4687 9480 0105
Usage frequency (11988892) 4818 4758 9576 0061
Use variety (11988893) 4738 4696 9434 0042
be discussed In the performance expectancy dimension asillustrated in Figure 6 perceived usefulness (11988811) has directinfluences on extrinsic motivation (11988812) job fit (11988813) and rel-ative advantage (11988814) while the relative advantage (11988814) has anindirect impact on job fit (11988813) through extrinsic motivation(11988812) In practice managers may focus on improvement in
Table 8The influence weights versus each dimension and criterion
Dimensions Localweights Rank Criteria Local
weights Rank Globalweights
1198631
01194 3
11986211
02696 1 0032211986212
02421 4 0028911986213
02443 2 0029211986214
02440 3 00291
1198632
01110 611986221
03457 2 0038411986222
02987 3 0033211986223
03556 1 00395
1198633
01011 811986231
03408 1 0034511986232
03348 2 0033811986233
03244 3 00328
1198634
01071 711986241
03584 1 0038411986242
03387 2 0036311986243
03028 3 00324
1198635
01199 211986251
03439 1 0041211986252
03291 2 0039511986253
03270 3 00392
1198636
01148 411986261
03202 2 0036811986262
03769 1 0043311986263
03030 3 00348
1198637
00935 911986271
02809 3 0026311986272
03814 1 0035711986273
03377 2 00316
1198638
01203 111986281
03730 1 0044911986282
03686 2 0044311986283
02584 3 00311
1198639
01130 511986291
03316 3 0037511986292
03363 1 0038011986293
03322 2 00375
the perceived usefulness dimension If managers wanted toobtain high performance in terms of extrinsic motivation(11988812) and job fit (11988813) the Phablet manufacturers would getan improved priority for the 11988811 and 11988814 beforehand Thenthese two criteria can effectively enhance Phablet products toattract usersrsquo attentions
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
18 Mathematical Problems in Engineering
Perceived usefulness
Extrinsic motivation
Relative advantage
minus006
minus003
000
003
006
rminus
c
r + c
125 130 135 140 145 150
(C11)(C14)
Job fit (C13)
(C12)
Figure 6 The causal relationship network for performanceexpectancy
Perceived ease of use
Complexity
Ease of use
minus002
minus001
000
001
002
rminus
c
r + c
092 098 104 110
(C22)
(C21)
(C23)
Figure 7 The causal relationship network for effort expectancy
In the effort expectancy dimension the 11988823 is influencedby both the 11988822 and 11988821 In order to enhance the effortexpectancy managers and engineers may first improve thecomplexity of the Phablet products in comparison to otherbrands of Phablet devices The causal relationship is demon-strated in Figure 7
In the social influence dimension the 11988832 can enhancethe social influence If managers wanted to enhance thesocial influence (11988832) appropriate promotion and brandingstrategies should be adoptedThe causal relationship networkis shown in Figure 8 The illustration depicts that the socialfactor (11988832) and the image (11988833) should be improved before-hand and then the 11988831 would be enhanced
In the facilitating conditions dimension compatibility(11988843) is the most influential criterion which can influence11988842 and 11988841 directly (Figure 9) This implies that 11988843 has animproved priority comparing 11988842 and 11988841 By improving 11988843the performance of facilitating conditions can be enhanced
For the hedonic motivation dimension the interest (11988852)has direct impacts on enjoyment (11988851) and curiosity (11988853) Atthe same time the Enjoyment (11988851) influences interest (11988852)and curiosity (11988853) directly Based on the causal relationships(as shown in Figure 10) the interest (11988852) should be firstimproved then the hedonic motivation can be enhanced
For the price value dimension being demonstrated inFigure 11 the quality has the highest (119903
119894minus 119888119894) value which
means that quality (11988861) has the important impact on value
rminus
c
r + c
(C33)
(C32)
(C31)Subjective norm
Social factor
Image
minus0060
minus0040
minus0020
0000
0020
0040
0060
0700 0720 0740 0760 0780 0800
Figure 8 The casual relationship network for social influence
rminus
c
r + c
(C43)
(C42)
(C41)
Perceived behavioral
control
Facilitating conditions
Compatibility
minus01
00
01
075 080 085 090 095
Figure 9The causal relationship network for facilitating conditions
rminus
c
r + c
(C53)
(C51)Enjoyment
(C52)Interest
Curiosity minus005
000
005
104 108 112 116
Figure 10 The causal relationship network for hedonic motivation
rminus
c
r + c
(C61)
(C62)
(C63)
Quality
Value
Price
minus008
minus004
000
004
008
090
Figure 11 The causal relationship network for price value
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 19r
minusc
r + c
(C71)
(C72)(C73)
Past behavior
Reflex behavior
Individual experience
minus008
minus004
000
004
008
012
078 083 088 093
Figure 12 The causal relationship network for habit
(11988862) and price (11988863) These results further imply that Phabletmanufacturers should improve the quality (11988861) of futurePhablet products for satisfying future usersrsquo needs Then thehedonic motivation can be enhanced accordingly
Similarly in the habit dimension 11988871 should be prioritizedin improvement and the habit for Phablet use can beenhanced The causal structure is shown in Figure 12
Moreover in order to improve the use intention dimen-sion in Phablet adoptions the service quality of companies(11988883) should be first improved since the criterion would havedirect influences on positive word-of-mouse communica-tions and repurchase intensionsThe results are demonstratedin Figure 13
The causal network of the use behavior dimension asillustrated in Figure 14 indicates that the usage frequency(11988892) influences directly usage variety (11988893) and usage time(11988891) Likewise the usage time (11988891) also influences directly theusage time (11988892) and the usage variety (11988893)This result impliesthat the enhancement of the usage frequency of Phabletswill be a vital task for enhancing the adoptions of the futurePhablets Thus the usage frequency can be recognized asthe most important criterion for enhancing the use behaviordimension
In summary of Section 51 the influence weights versusthe criteria and dimensions can be derived by the DNPtechnique based on lead usersrsquo opinions Based on theweights being associated with each dimension the rankingof dimensions is shown as follows (from high to low)use intention hedonic motivation performance expectancyprice value use behavior effort expectancy facilitating con-ditions social influence and habit These results imply thatthe use intention hedonic motivation and price value arethe most particularly crucial factors for Phablet adoptionMore specifically from the use intention dimension therepurchase intentions and the positive word-of-mouth playessential roles in the acceptance of future Phablets Theseresults are consistent with the work by Kotler and Keller[101] and that of Keller et al [98] In price value dimensionPhablet quality value and price are recognized as crucialindicators for determining whether a consumer will adoptthe Phablet The findings were also consistent with prioracademic works on consumer behaviors and new productdevelopment strategies Thus practitioners and managers
rminus
c
r + c
(C81)
(C83)
Repurchase intentions
Positive word-of-mouth
(C82)communication
Service quality
minus010
minus005
000
005
010
015
08 10 12
Figure 13 The causal relationship network for use intention
rminus
c
r + c
(C91)
(C92)
(C93)
Usage time Usage
frequency
Usage variety minus002
minus001
000
001
099 101 103
Figure 14 The casual relationship network for use behavior
should focus on these regards for improving marketingmeans and enhancing the future Phablets For example theChina based Xiaomi Company designed smart phones withlow price and high quality and performance and adoptedhunger marketing strategies to commercialize their smartphones So far the marketing strategies have yielded hugeprofits and good word-of-mouth effect for them
52 Advances in Research Methods From the aspect ofadvances in research methods this research introduced theDNP based on the original UTAUT2 theoretical model Incontrast to the structure equation modeling (SEM) basedmethods for example the confirmatory factor analysis(CFA) which allows researchers to test the hypothesis for therelationships between the observed variables and their under-lying latent construct(s) exists the DNP based approachwhich can be used to derive the causal relationship with-out any assumptions on the existing relationships betweenvariables is apparently more suitable due to the followingtwo reasons (1)The available number of respondents is verylimited especially when the number of respondents is a verysmall number for example 5 to 10 (2)The research questionsmay not bemeasured as is Following are detailed discussionsfrom the two aspects
First from the aspect of expert availability for somespecific research questions like the definition of disruptive
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
20 Mathematical Problems in Engineering
or radical innovative products the definition of innovationpolicy tools for some emerging technology or service (egthe first authorrsquos earlier work [83] on the reconfigurationof the silicon intellectual property mall) the availabilityof respondents is very limited Therefore the traditionalstatistical analysis based approaches are not applicable due tothe violation of the central limit theorem in which statisticalanalysis approaches require a minimum sample size of 30 sothat the results can be statistically significant [102] For suchproblems decision making frameworks being constructedbased on the opinions of 6 to 10 experts will be much morereasonable and feasible since the total number of expertsbeing available is very limited
Second from the dimension that the research questionsmay not be measured as is numerous academic workssupport this viewpoint According to Suhr [103] for mostof the cases the researcher uses knowledge of the theoryempirical research or both postulates the relationship pat-tern a priori and then tests the hypothesis statistically [103]However it is becoming harder to find research articlesthat present only a simple confirmatory factor analysis on aset of variables [104] MacCallum and Austin [105] arguedthat a structural equation model is a hypothesis about thestructure of relationships among measured variables in aspecific population Researchers should explicitly define thepopulation of interest although this is often not done inpractice and should acknowledge that the generalizabilityof a model beyond that population may be uncertain [105]Therefore some research questions may not be measured asthe theoretic framework for example the UTAUT2 basedframework in this research being adopted
Based on the above discussion we argue that the DEMA-TEL based network process is more applicable in derivingthe causal relationship based on lead usersrsquo opinions A newanalytic framework based on the population of interest herethe group of lead users can be constructed without thepredefined path framework
In the future the proposed analytic framework can beapplied to the adoptions of any disruptive or radical innova-tive information technology products or services in whichthe features and performance of such products or servicesare very hard for consumersrsquo understanding Therefore theanalytic framework is especially useful for the informationtechnology hardware and software industries Possible appli-cations include the next generation smart eyeglasses smartwatches infrastructure as a service (IaaS) software as a ser-vice (SaaS) and platform as a service (PaaS) Usually normalconsumers cannot figure out the technical details as wellas possible applications Further the proposed DNP basedanalytic framework can also serve as a tool for improving theinformation technology products or services from the criteriawhich are the most influential from the dimension based onthe analytic results being derived by the DEMATEL
6 Conclusions
This research aims to derive the crucial factors for exploringusersrsquo acceptance of future Phablet based on the opinions
being provided by lead users To fulfill the abovementionedpurposes this research thereby proposes an evaluationmodelfor deriving the factors influencing Phablet acceptances Thefuture Phablets can be improved accordingly Based on theempirical study results the importance of understandingthe relationships between criteria among each dimensionhas been emphasized Based on the influence weights beingderived the factors with higher priority should be improvedFinally the proposed analytical framework can be utilizedfor enhancing future Phablet devices or other related smartmobile devices The analytic results can also serve as a basisfor marketing RampD and manufacturing strategy formula-tions in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors gratefully acknowledge the long term advices byDistinguished Chair Professor Dr Gwo-Hshiung Tzeng onthe research methods manipulations and analytic frameworkdevelopment Meanwhile the support of the Minister of Sci-ence and Technology Taiwan through Grants nos NSC100-2221-E-003-023 and NSC102-2511-S-003-061-MY3 is greatlyappreciated
References
[1] S A Vannoy and P Palvia ldquoThe social influence model oftechnology adoptionrdquo Communications of the ACM vol 53 no6 pp 149ndash153 2010
[2] A Y-L Chong F T S Chan and K-B Ooi ldquoPredictingconsumer decisions to adopt mobile commerce cross countryempirical examination between China and Malaysiardquo DecisionSupport Systems vol 53 no 1 pp 34ndash43 2012
[3] E M Rogers Diffusion of Innovations Simon and Schuster2010
[4] V Venkatesh J Y L Thong and X Xu ldquoConsumer acceptanceand use of information technology extending the unifiedtheory of acceptance and use of technologyrdquoMISQuarterly vol36 no 1 pp 157ndash178 2012
[5] H Baumgartner and J-B E M Steenkamp ldquoExploratory con-sumer buying behavior conceptualization and measurementrdquoInternational Journal of Research in Marketing vol 13 no 2 pp121ndash137 1996
[6] J Richetin A Sengupta M Perugini et al ldquoA micro-level sim-ulation for the prediction of intention and behaviorrdquo CognitiveSystems Research vol 11 no 2 pp 181ndash193 2010
[7] A Mansur and T Kuncoro ldquoProduct inventory predictionsat small medium enterprise using market basket analysisapproach-neural networksrdquo Procedia Economics and Financevol 4 pp 312ndash320 2012
[8] D Booth and R Shepherd ldquoSensory influences on foodacceptancemdashthe neglected approach to nutrition promotionrdquoNutrition Bulletin vol 13 pp 39ndash54 1988
[9] D L Loudon andA J Della BittaConsumer Behavior Conceptsand Applications McGraw-Hill New York NY USA 1993
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 21
[10] P Kotler Marketing Management Analysis Planning Imple-mentation and Control Prentice Hall Englewood Cliffs NJUSA 1997
[11] M de Mooij Consumer Behavior and Culture Consequences forGlobal Marketing and Advertising Sage Thousand Oaks CalifUSA 2010
[12] M D de Bellis E van Voorhees S R Hooper et al ldquoDiffusiontensor measures of the corpus callosum in adolescents withadolescent onset alcohol use disordersrdquoAlcoholism Clinical andExperimental Research vol 32 no 3 pp 395ndash404 2008
[13] V Ratten ldquoEntrepreneurial and ethical adoption behaviour ofcloud computingrdquoThe Journal of High Technology ManagementResearch vol 23 no 2 pp 155ndash164 2012
[14] K M Elliott M C Hall and J G Meng ldquoStudent technologyreadiness and its impact on cultural competencyrdquo CollegeTeaching Methods amp Styles Journal vol 4 pp 11ndash22 2011
[15] M S Summak M Baglıbel and M Samancıoglu ldquoTechnologyreadiness of primary school teachers a case study in TurkeyrdquoProcediamdashSocial and Behavioral Sciences vol 2 no 2 pp 2671ndash2675 2010
[16] M A Kamins F Alpert and L Perner ldquoHow do consumersknow which brand is the market leader or market pioneerConsumersrsquo inferential processes confidence and accuracyrdquoJournal of Marketing Management vol 23 pp 590ndash611 2007
[17] R TMoriarty andT J Kosnik ldquoHigh-techmarketing conceptscontinuity and changerdquo Sloan Management Review vol 30 no4 pp 7ndash17 1989
[18] J-H Lee T C Garrett D R Self and C S C FindleyMusgrove ldquoExpressive versus instrumental functions on tech-nology attractiveness in the UK and Koreardquo Journal of BusinessResearch vol 65 no 11 pp 1600ndash1605 2012
[19] J J Mohr S Sengupta and S F Slater Marketing of High-Technology Products and Innovations Prentice Hall 2009
[20] M C Schuhmacher and S Kuester ldquoIdentification of lead usercharacteristics driving the quality of service innovation ideas rdquoCreativity and Innovation Management vol 21 no 4 pp 427ndash442 2012
[21] J B A Haller A C Bullinger and K M Moslein ldquoInnovationcontestsrdquoBusinessamp Information Systems Engineering vol 3 no2 pp 103ndash106 2011
[22] K T Ulrich Product Design and Development Tata McGraw-Hill Education 2003
[23] D Leonard and J F Rayport ldquoSpark innovation throughempathic designrdquo Harvard Business Review vol 75 no 6 pp102ndash113 1997
[24] E A von Hippel ldquoHas a customer already developed your nextproductrdquo Engineering Management Review vol 6 no 3 1976
[25] V Bilgram A Brem and K-I Voigt ldquoUser-centric innovationsin new product development systematic indentification of leadusers harnessing interactive and collaborative online-toolsrdquoInternational Journal of Innovation Management vol 12 no 3pp 419ndash458 2008
[26] C Hienerth and C Lettl ldquoExploring how peer communitiesenable lead user innovations to become standard equipmentin the industry community pull effectsrdquo Journal of ProductInnovation Management vol 28 no 1 pp 175ndash195 2011
[27] C Kraft ldquoInnovating with lead usersrdquo in User ExperienceInnovation pp 131ndash135 Springer New York NY USA 2012
[28] E von Hippel ldquoLead users a source of novel product conceptsrdquoManagement Science vol 32 no 7 pp 791ndash805 1986
[29] E von Hippel ldquoHorizontal innovation networksmdashby and forusersrdquo Industrial and Corporate Change vol 16 no 2 pp 293ndash315 2007
[30] N Franke and S Shah ldquoHow communities support innovativeactivities an exploration of assistance and sharing among end-usersrdquo Research Policy vol 32 no 1 pp 157ndash178 2003
[31] C Luthje and C Herstatt ldquoThe Lead User method an outlineof empirical findings and issues for future researchrdquo R and DManagement vol 34 no 5 pp 553ndash568 2004
[32] F M Belz andW Baumbach ldquoNetnography as amethod of leaduser identificationrdquoCreativity and InnovationManagement vol19 pp 304ndash313 2010
[33] E Von Hippel ldquoDemocratizing innovation the evolving phe-nomenon of user innovationrdquo International Journal of Innova-tion Science vol 1 pp 29ndash40 2009
[34] V Venkatesh M G Morris G B Davis and F D Davis ldquoUseracceptance of information technology toward a unified viewrdquoMIS Quarterly Management Information Systems vol 27 no 3pp 425ndash478 2003
[35] N Hennigs K-P Wiedmann and C Klarmann ldquoConsumervalue perception of luxury goods a cross-cultural and cross-industry comparisonrdquo inLuxuryMarketing pp 77ndash99 Springer2013
[36] S S Kim and N K Malhotra ldquoA longitudinal model ofcontinued IS use an integrative view of four mechanismsunderlying postadoption phenomenardquo Management Sciencevol 51 no 5 pp 741ndash755 2005
[37] M Limayem S G Hirt and C M K Cheung ldquoHow habitlimits the predictive power of intention the case of informationsystems continuancerdquoMISQuarterly Management InformationSystems vol 31 no 4 pp 705ndash737 2007
[38] D-H Shin Y-J Shin H Choo and K Beom ldquoSmartphonesas smart pedagogical tools implications for smartphones as u-learning devicesrdquo Computers in Human Behavior vol 27 no 6pp 2207ndash2214 2011
[39] E Park and A P del Pobil ldquoTechnology acceptance model forthe use of tablet PCsrdquo Wireless Personal Communications vol73 no 4 pp 1561ndash1572 2013
[40] C-Y Huang Y-F Lin and G-H Tzeng ldquoA DEMATEL basednetwork process for deriving factors influencing the acceptanceof tablet personal computersrdquo in Proceedings of the 3rd Inter-national Conference on Intelligent Decision Technologies (IDTrsquo2011) pp 355ndash365 Springer July 2011
[41] F D Davis ldquoPerceived usefulness perceived ease of use anduser acceptance of information technologyrdquoMISQuarterly vol13 no 3 pp 319ndash340 1989
[42] A Y-L Chong ldquoMobile commerce usage activities the roles ofdemographic and motivation variablesrdquo Technological Forecast-ing and Social Change vol 80 no 7 pp 1350ndash1359 2013
[43] T S H Teo V K G Lim and R Y C Lai ldquoIntrinsic andextrinsic motivation in Internet usagerdquoOmega vol 27 no 1 pp25ndash37 1999
[44] R L Thompson C A Higgins and J M Howell ldquoPersonalcomputing toward a conceptual model of utilizationrdquo MISQuarterly vol 15 no 1 pp 125ndash143 1991
[45] D J-F Jeng and G-H Tzeng ldquoSocial influence on the use ofclinical decision support systems revisiting the unified theoryof acceptance and use of technology by the fuzzy DEMATELtechniquerdquo Computers and Industrial Engineering vol 62 no 3pp 819ndash828 2012
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
22 Mathematical Problems in Engineering
[46] B Kijsanayotin S Pannarunothai and S M Speedie ldquoFactorsinfluencing health information technology adoption in Thai-landrsquos community health centers applying the UTAUT modelrdquoInternational Journal of Medical Informatics vol 78 no 6 pp404ndash416 2009
[47] T Casey and E Wilson-Evered ldquoPredicting uptake of technol-ogy innovations in online family dispute resolution services anapplication and extension of the UTAUTrdquoComputers in HumanBehavior vol 28 no 6 pp 2034ndash2045 2012
[48] G CMoore and I Benbasat ldquoDevelopment of an instrument tomeasure the perceptions of adopting an information technologyinnovationrdquo Information Systems Research vol 2 no 3 pp 192ndash222 1991
[49] C-L Hsu and H-P Lu ldquoWhy do people play on-line gamesAn extended TAM with social influences and flow experiencerdquoInformation and Management vol 41 no 7 pp 853ndash868 2004
[50] I Ajzen andT JMadden ldquoPrediction of goal-directed behaviorattitudes intentions and perceived behavioral controlrdquo Journalof Experimental Social Psychology vol 22 no 5 pp 453ndash4741986
[51] H C Triandis ldquoValues attitudes and interpersonal behaviorrdquoin Proceedings of the Nebraska Symposium on Motivation 1979
[52] R P Bagozzi F D Davis and P RWarshaw ldquoDevelopment andtest of a theory of technological learning and usagerdquo HumanRelations vol 45 pp 659ndash686 1992
[53] S-Y Hung Y-C Ku and J-C Chien ldquoUnderstanding physi-ciansrsquo acceptance of theMedline system for practicing evidence-based medicine a decomposed TPB modelrdquo InternationalJournal of Medical Informatics vol 81 no 2 pp 130ndash142 2012
[54] C-C Lee S-P Lin S-L Yang M-Y Tsou and K-Y ChangldquoEvaluating the influence of perceived organizational learningcapability on user acceptance of information technology amongoperating room nurse staffrdquo Acta Anaesthesiologica Taiwanicavol 51 no 1 pp 22ndash27 2013
[55] R M Ryan and E L Deci ldquoSelf-determination theory andthe facilitation of intrinsic motivation social development andwell-beingrdquo American Psychologist vol 55 no 1 pp 68ndash782000
[56] M Magni M S Taylor and V Venkatesh ldquoldquoTo play or notto playrdquo a cross-temporal investigation using hedonic andinstrumental perspectives to explain user intentions to explore atechnologyrdquo International Journal of Human Computer Studiesvol 68 no 9 pp 572ndash588 2010
[57] Y Lu T Zhou and B Wang ldquoExploring Chinese usersrsquoacceptance of instant messaging using the theory of plannedbehavior the technology acceptance model and the flowtheoryrdquo Computers in Human Behavior vol 25 no 1 pp 29ndash39 2009
[58] E M Pilke ldquoFlow experiences in information technology userdquoInternational Journal of Human Computer Studies vol 61 no 3pp 347ndash357 2004
[59] M Koufaris ldquoApplying the technology acceptance model andflow theory to online consumer behaviorrdquo Information SystemsResearch vol 13 no 2 pp 205ndash223 2002
[60] T Zhou and Y Lu ldquoExamining mobile instant messaging userloyalty from the perspectives of network externalities and flowexperiencerdquo Computers in Human Behavior vol 27 no 2 pp883ndash889 2011
[61] E-C Chang and Y-F Tseng ldquoResearch note E-store imageperceived value and perceived riskrdquo Journal of BusinessResearch vol 66 no 7 pp 864ndash870 2013
[62] H-Y Wang and S-H Wang ldquoPredicting mobile hotel reser-vation adoption insight from a perceived value standpointrdquoInternational Journal of Hospitality Management vol 29 no 4pp 598ndash608 2010
[63] V A Zeithaml ldquoConsumer perceptions of price quality andvalue a means-end model and synthesis of evidencerdquo TheJournal of Marketing vol 52 no 3 pp 2ndash22 1988
[64] L Zhao Y Lu L Zhang and P Y K Chau ldquoAssessing the effectsof service quality and justice on customer satisfaction andthe continuance intention of mobile value-added services anempirical test of a multidimensional modelrdquo Decision SupportSystems vol 52 no 3 pp 645ndash656 2012
[65] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009
[66] I Soltani and J Gharbi ldquoDeterminants and consequences of thewebsite perceived valuerdquo 2008
[67] W B Dodds K B Monroe and D Grewal ldquoEffects of pricebrand and store information on buyersrsquo product evaluationsrdquoJournal of Marketing Research vol 28 pp 307ndash319 1991
[68] J F Petrick ldquoDevelopment of a multi-dimensional scale formeasuring the perceived value of a servicerdquo Journal of LeisureResearch vol 34 no 2 pp 119ndash134 2002
[69] P E Boksberger and L Melsen ldquoPerceived value a criticalexamination of definitions concepts and measures for theservice industryrdquo Journal of Services Marketing vol 25 no 3pp 229ndash240 2011
[70] H Aarts B Verplanken and A Van Knippenberg ldquoPredictingbehavior from actions in the past repeated decision making ora matter of habitrdquo Journal of Applied Social Psychology vol 28no 15 pp 1355ndash1374 1998
[71] T L Webb P Sheeran and A Luszczynska ldquoPlanning to breakunwanted habits habit strength moderates implementationintention effects on behaviour changerdquo British Journal of SocialPsychology vol 48 no 3 pp 507ndash523 2009
[72] H-W Kim H C Chan and S Gupta ldquoValue-based adoptionofmobile internet an empirical investigationrdquoDecision SupportSystems vol 43 no 1 pp 111ndash126 2007
[73] A N Giovanis P Tomaras and D Zondiros ldquoSupplierslogistics service quality performance and its effect on retailersrsquobehavioral intentionsrdquo Procedia-Social and Behavioral Sciencesvol 73 pp 302ndash309 2013
[74] R R Burke ldquoTechnology and the customer interface whatconsumers want in the physical and virtual storerdquo Journal of theAcademy of Marketing Science vol 30 no 4 pp 411ndash432 2002
[75] V Venkatesh and F D Davis ldquoA theoretical extension of thetechnology acceptance model four longitudinal field studiesrdquoManagement Science vol 46 no 2 pp 186ndash204 2000
[76] K Mathieson ldquoPredicting user intentions comparing the tech-nology acceptance model with the theory of planned behaviorrdquoInformation Systems Research vol 2 no 3 pp 173ndash191 1991
[77] S S Al-Gahtani G S Hubona and J Wang ldquoInformationtechnology (IT) in Saudi Arabia culture and the acceptance anduse of ITrdquo Information and Management vol 44 no 8 pp 681ndash691 2007
[78] N Dalkey and O Helmer ldquoAn experimental application of theDelphi method to the use of expertsrdquoManagement Science vol9 pp 458ndash467 1963
[79] J W Murry and J O Hammons ldquoDelphi a versatile methodol-ogy for conducting qualitative researchrdquo The Review of HigherEducation vol 18 pp 423ndash436 1995
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Mathematical Problems in Engineering 23
[80] W C Sung ldquoApplication of Delphi method a qualitative andquantitative analysis to the healthcare managementrdquo Journal ofHealthcare Management vol 2 pp 11ndash19 2001
[81] Y S Lee J C Huang and Y S Hsu ldquoUsing modified DelphiMethod to explore the competition strategy for software com-panies of Taiwanrdquo Journal of Informatics amp Electronics vol 13pp 39ndash50 2008
[82] J Jones and D Hunter ldquoConsensus methods for medical andhealth services researchrdquo British Medical Journal vol 311 no7001 pp 376ndash380 1995
[83] C-Y Huang J Z Shyu and G-H Tzeng ldquoReconfiguring theinnovation policy portfolios for Taiwanrsquos SIP Mall industryrdquoTechnovation vol 27 no 12 pp 744ndash765 2007
[84] C-Y Huang G-H Tzeng Y-T Chen and H Chen ldquoPerfor-mance evaluation of leading fabless integrated circuit designhouses by using a multiple objective programming baseddata envelopment analysis approachrdquo International Journal ofInnovative Computing Information and Control vol 8 no 8 pp5899ndash5916 2012
[85] H A Linstone and M Turoff The Delphi Method Techniquesand Applications Addison-Wesley Reading Mass USA 1975
[86] N C Dalkey ldquoTheDelphi method an experimental applicationof group opinionrdquo in Studies in the Quality of Life Delphi andDecision-Making N C Dalkey D Rourke L R Lewis and DSnyder Eds Lexington Mass USA Lexington Books 1972
[87] R C Judd ldquoForecasting to consensus gathering delphi growsup to college needsrdquo College and University Business vol 53 pp35ndash38 1972
[88] K W Brooks ldquoDelphi technique expanding applicationsrdquoNorth Central Association Quarterly vol 54 pp 377ndash385 1979
[89] C-H Liu G-H Tzeng and M-H Lee ldquoImproving tourismpolicy implementationmdashthe use of hybrid MCDM modelsrdquoTourism Management vol 33 no 2 pp 413ndash426 2012
[90] G Phillips-Wren L C Jain K Nakamatsu and R J HowlettAdvances in Intelligent Decision Technologies Proceedings of the2nd KES International Symposium IDT 2010 Springer NewYork NY USA 2010
[91] A Gabus and E Fontela World Problems an Invitation toFurther Thought Within the Framework of DEMATEL BatelleGeneva Research Center Geneva Switzerland 1972
[92] C-Y Huang and J Z Shyu ldquoDeveloping e-commerce businessmodels for enabling silicon intellectual property transactionsrdquoInternational Journal of Information Technology and Manage-ment vol 5 no 2-3 pp 114ndash133 2006
[93] C-Y Huang G-H Tzeng and W-R J Ho ldquoSystem on chipdesign service e-business value maximization through a novelMCDM frameworkrdquo Expert Systems with Applications vol 38no 7 pp 7947ndash7962 2011
[94] G-H Tzeng and C-Y Huang ldquoCombined DEMATEL tech-nique with hybrid MCDM methods for creating the aspiredintelligent global manufacturing amp logistics systemsrdquo Annals ofOperations Research vol 197 no 1 pp 159ndash190 2012
[95] T L Saaty The Analytic Hierarchy Process Planning PrioritySetting Resource Allocation McGraw-Hill 1980
[96] M Tamura H Nagata and K Akazawa ldquoExtraction andsystems analysis of factors that prevent safety and securityby structural modelsrdquo in Proceedings of the 41st SICE AnnualConference vol 3 pp 1752ndash1759 Osaka Japan 2002
[97] M-T Lu S-W Lin and G-H Tzeng ldquoImproving RFIDadoption in Taiwanrsquos healthcare industry based on a DEMATELtechnique with a hybrid MCDM modelrdquo Decision SupportSystems vol 56 no 1 pp 259ndash269 2013
[98] K L Keller M Parameswaran and I Jacob Strategic BrandManagement Building Measuring andManaging Brand EquityPearson Education India New Delhi India 2011
[99] P Kotler and K L Keller Marketing Management PearsonEducation 14th edition 2011
[100] J W Murry Jr and J O Hammons ldquoDelphi a versatilemethodology for conducting qualitative researchrdquo Review ofHigher Education vol 18 pp 423ndash436 1995
[101] P Kotler and K L Keller Framework for Marketing Manage-ment Pearson Education India 2012
[102] M Longnecker and R Ott An Introduction to StatisticalMethods and Data Analysis Duxbury Press Belmont CalifUSA 2001
[103] D D Suhr Exploratory or Confirmatory Factor Analysis SASInstitute Cary NC USA 2006
[104] W W Chin ldquoCommentary issues and opinion on structuralequation modelingrdquoMIS Quarterly vol 22 no 1 1998
[105] R C MacCallum and J T Austin ldquoApplications of structuralequation modeling in psychological researchrdquo Annual Reviewof Psychology vol 51 pp 201ndash226 2000
[106] E von Hippel S Thomke and M Sonnack ldquoCreating break-throughs at 3MrdquoHarvard Business Review vol 77 no 5 pp 47ndash183 1999
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of
Submit your manuscripts athttpwwwhindawicom
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical Problems in Engineering
Hindawi Publishing Corporationhttpwwwhindawicom
Differential EquationsInternational Journal of
Volume 2014
Applied MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Mathematical PhysicsAdvances in
Complex AnalysisJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OptimizationJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Operations ResearchAdvances in
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Function Spaces
Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of Mathematics and Mathematical Sciences
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Algebra
Discrete Dynamics in Nature and Society
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Decision SciencesAdvances in
Discrete MathematicsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Stochastic AnalysisInternational Journal of