classificationandimprovementstrategyfordesignfeaturesof...

Research ArticleClassification and Improvement Strategy for Design Features ofMobile Tourist Guide Application A Kano-IPA Approach

Shun Li and Quan Xiao

School of Information Management Jiangxi University of Finance and Economics Nanchang 330032 China

Correspondence should be addressed to Quan Xiao xiaoquanfoxmailcom

Received 26 March 2020 Revised 28 April 2020 Accepted 11 May 2020 Published 31 May 2020

Academic Editor Jinan Fiaidhi

Copyright copy 2020 Shun Li and Quan Xiao is 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 isproperly cited

An increasing number of people are using mobile applications to obtain travel-related information and activities due to theprosperity of the internet and mobile technologies nowadays e design development and improvement of mobile tourist guideapplication (MTGA) are particularly important for travel-related companies As an emerging application scenario of mobiletechnologies in the field of tourism existing research onMTGA lacks analysis of its specific design especially from the perspectiveof users to investigate the microscopic design features and improvement strategies e Kano model was adopted by prior studiesto analyse product quality attribute while importance-performance analysis (IPA) was used to prioritize quality attributes forimprovement However due to the limitation of the Kano model in neglecting the attribute performance and importance and theweakness of IPA in considering only the one-dimensional quality attributes the use of single approach has its shortcomings foranalysing the design features of MTGAWe attempt to integrate Kano model and IPA to conduct a study on the classification andimprovement strategy issues for the design features of MTGA Particularly we identify design features of MTGA first propose amethod to classify them and determine their priorities for developing and improving as well An online questionnaire survey isconducted e paper extends research on Kano model and IPA into the domain of mobile application design and providesinsights into management strategies about the design of MTGA which also offers novel and important implications for travel-related companies to increase the usersrsquo satisfaction by optimizing their mobile application design

1 Introduction

In recent years the growing popularity of mobile technol-ogies has dramatically changed the ways in which onlineservice operates and delivers in tourism industry [1] eadvanced computing capability and ubiquitous features ofmobile device empower it to be an essential tool for travellers[2] e penetration of mobile technologies into tourismindustry changes the behavior of tourists [3 4] and alsoattracts attentions of scholars [5] For example touristrecommendation system [6] location-based smart tourisminformation system [7] smart audio tour guide system [8]and personalized location-based mobile tourism application[9] are hot topics of recent related studies Among themlocation-based services (LBS) are information and enter-tainment services that can be accessible with mobile devices

via the mobile network Taking advantage of the ability toutilize geographic location of mobile devices the MTGA is atypical case of location-based services [10] MTGA utilizesthe positioning function of mobile device and combinesvarious technical means such as network wireless com-munication and voice system to provide tourists with self-guided services In contrast to traditional tour guide serviceMTGA allows users to abandon or modify tours at any time[11] and it also provides more comprehensive accurate andconvenient experiences e emerging of MTGA not onlymakes up for the deficiency in traditional tour guide servicebut also meets the personalized needs of tourists [12]

rough a review on recent related literature we dis-cover that a number of existing researches on mobile ap-plications empirically investigate usersrsquo acceptance orintention to use mobile applications For example Lai [13]

HindawiMobile Information SystemsVolume 2020 Article ID 8816130 9 pageshttpsdoiorg10115520208816130

investigated touristsrsquo technological acceptance of a MTGAChen and Tsai [9] studied the factors affecting usersrsquo in-tention to use the mobile tourism application Lei et al [14]explored usersrsquo perceived technology affordance and value ofmobile applications Fang et al [1] empirically examined thefactors influencing usersrsquo engagement behavior of mobiletravel applications Information system literatures inform usthat the design of information system has a considerableimpact on the operation performance and success of en-terprises [15] Mobile application as a specific informationsystem is of great importance since it provides an effectivechannel for enterprises to communicate and forge intimaterelationships with their customers [1] In order to retaincommitted and engaged customers as well as increase salesenterprises should place particular emphasis on the design oftheir mobile applications

Although MTGA enables users to obtain tour guidanceinformation as needed anytime and anywhere [16] the is-sues such as complexity when operating lack of useful in-formation and information overload severely reduce itseffectiveness We address the fact that these problems areclosely related to the insufficiency of extant studies on designfeatures of MTGA Despite research on this topic studiesregarding user satisfaction and improvement strategies forthe design features of MTGA from userrsquos perspective havebeen few in number In terms of describing the relationshipbetween product or service design and user satisfactionKano model is a classic method As userrsquos expectations varyamong different design features of MTGA Kano model isappropriate to depict the asymmetric and nonlinear rela-tionship between design feature and user satisfaction [17]Based on the Kano model this paper complements theextant research by investigating usersrsquo satisfaction with thevarious design features of MTGA determining the Kanoclassification of design features and analysing their prior-ities to improve through combining with IPAis work alsoprovides implications for practitioners to build the devel-opment and optimization strategies for designing MTGAthat well satisfies the needs of users

2 Literature Review

21 Mobile Tourist Guide Applications While the topic ofmobile travel applications has been under research for manyyears it is only in the recent years that MTGA began toreceive attentions across the tourism industry Currentlythere are two main streams of literature on MTGA e firstis design-science-based one For example Chu et al [18]integrated geographic information system and global po-sitioning system technologies into a MTGA which canprovide tourists with LBS information at popular touristdestinations Smirnov et al [19] proposed a mobile appli-cation which generates recommendations for the touristabout interesting attractions around Kang et al [8] devel-oped the location-based audio tour guide application usingspeech synthesis technology which is helpful for enhancingself-guided tours of destinations Tarantino et al [20]proposed an interactive electronic guide application pro-totype which can recommend personalized tourist routes to

mobile users and evaluated the effectiveness of the proto-type through an experimentation Such studies heavily an-alyse the design development or implementation ofMTGAs and discuss their main advantages and limitationse second stream of MTGA research is empirical studyregarding user behavior Based on unified theory of ac-ceptance and use of technology Lai [13] identified ante-cedents and determinants influencing touristsrsquo acceptance ofa MTGA through a questionnaire survey of 205 touristsvisiting Macau Chen and Tsai [9] investigated usersrsquo in-tention to use a mobile tourism application by integratingthe technology acceptance model and the informationsystem success model e results showed that informationquality perceived ease of use and perceived usefulnesssignificantly affect the intention to use while informationquality and perceived convenience affect perceived useful-ness is kind of studies is mainly to empirically investigateuser behavior through questionnaire survey such as tech-nological acceptance and intention to use Actually MTGAis an IT artifact that consists of various design features butmost of the relevant literature fails to consider it from such amicroscopic perspective which leads to a lack of deep lookinto the user satisfaction with different design featuresCompared to existing research this study focuses on specificdesign features of MTGA and we expect to provide moreprecise and operable suggestions for the design and im-provement of MTGA from a finer granularity

22 Kano Model Inspired by Herzbergrsquos two-factor theorythe Japanese scholar Noriaki Kano proposed the Kanomodel in 1984 e model divides product quality attributesinto five categories according to the relationship betweenobjective product performance and customer subjectivefeelings namely must-be quality one-dimensional qualityattractive quality indifferent quality and reverse quality[17] Figure 1 shows the relationship between quality real-ization degree and user satisfaction corresponding to theabovementioned five Kano categories e Kano model hasbeen applied to various research fields For instance Qi et al[21] applied the Kanomodel to the analysis of online reviewsto develop appropriate improvement strategies for productIlbahar and Cebi [22] classified the design parameters ofe-commerce websites according to consumer expectationsand evaluated the effectiveness of e-commerce websites Goand Kim [23] grouped travellers based on the frequency offlights per year to investigate the differences in the Kanoclassification of each airline service quality element in dif-ferent groups Velikova et al [24] applied the Kano model tothe management of festival activities and investigated thefactors affecting satisfaction with festival activities In thecontext of mobile internet development and the popularityof smart devices Yao et al [25] explored the quality attributeclassification of key functions in mobile security applicationsby using the Kano survey method to determine the im-portance ranking Kuo [26] used the Kano model to cate-gorize web-community service quality dimensions and theirelements and provided suggestions for future improvementof web-community service In the field of hospitality

2 Mobile Information Systems

services Chiang et al [27] used the Kano model to classifytechnological innovation attributes for hotels and providedsuggestions for managers to introduce innovative technol-ogies However Kano analysis method has several limita-tions despite its wide application For example the Kanomodel usually classifies the quality attributes while ignoringthe influence of the quality attribute on the increase orreduction of user satisfaction thus the priority of qualityattributes for improvement cannot be determined Hencethe present study attempts to improve the classificationmethod of the Kano model and combines with anothermethod ie IPA to make up for the identifiedshortcomings

23 Importance-Performance Analysis IPA is a structuralmethod that generates specific decision-making ideas byconsidering the usersrsquo perceived importance and perfor-mance IPA places the values of importance and perfor-mance of each attribute into a two-dimensional coordinateplane with their mean or median as a cross point to dividethem into four quadrants namely keep up the good work(high importance and high performance) concentrate here(high importance and low performance) low priority (lowimportance and low performance) and possible overkill(low importance and high performance) as shown in Fig-ure 2 Attributes in quadrant ldquokeep up the good workrdquo areconsidered good and should be maintained and ldquoconcen-trate hererdquo attributes should be improved with many re-sources Attributes in quadrant ldquolow priorityrdquo have lowpriority for improvement while those in quadrant ldquopossibleoverkillrdquo receive excessive resources that could better beallocated elsewhere IPA is a common business analysistechnique for understanding customer satisfaction anddeveloping improvement strategies for products or services[28] It has been applied in many related fields such as foodservices [29] tour guide services [30] hot springs tourism[31] information system [32] smartphone applications [33]

e-business [34] hotel [35ndash37] wildlife park [38] sustainabletourism [39 40] and nature-based tourism [41] As a simpleand effective method that helps determining the priority ofquality attributes for improvement IPA is instructive forproposing management strategies [42] In the field oftourism Coghlan [43] quantitatively analysed visitor satis-faction and its relation to tourism attributes on the GreatBarrier Reef through the IPA model determining high andlow impact attributes and the range of impact of each at-tribute Zhang and Chan [41] investigated the developmentof nature-based tourism in Hong Kong based on the IPAmodel from the perspectives of local residents and touristsand the results show a difference in the level of attention tothe factors affecting the tourism development between themerefore after the user satisfaction survey and IPA scarceresources can be well scheduled to achieve a high level ofuser satisfaction [31] Because of the weakness of consideringonly the one-dimensional quality attributes the IPA modelcannot obtain the accurate priority for improvement to allquality attributes To this end the combination of IPA withKano model in this study can make better use of the ad-vantages of both

24 Kano-IPA Integration Model To avoid the limitation ofthe Kano model in neglecting the attribute performance andimportance and exclude the drawback of IPA in consideringonly the one-dimensional quality attributes existing liter-ature has integrated the two approaches together to conductresearch in many fields For example Lin and Chan [44]integrated Kano model with IPA for the purpose of en-hancing service quality strategies and verified the validitythrough an online job agency case in Taiwan Kuo et al [45]combined Kano model with IPA to classify quality attributesof mobile value-added services and purpose appropriatemanagement strategies Pai et al [46] used Kano model andIPA approach to investigate the restaurant service qualityattributes in the chain restaurant industry Huang [47]identified the improved priority of the service quality at-tributes of mobile healthcare through combining Kano

Concentrate here Keep up the good work

Low priority Possible overkill

Hig

hIm

port

ance

Low

Low HighPerformance

Figure 2 Importance-performance analysis

Satisfaction

Attractive quality

One-dimensional quality

Indifferent qualityRealization degree

Must be quality

Reverse quality

Figure 1 Kano model

Mobile Information Systems 3

model with IPA Most of the relevant research obtained theimportance of design features through questionnaireswhich is often cumbersome and prone to subjective devi-ation erefore in the process of using the Kano-IPA in-tegration model this study quantitatively measures theimportance of design features through the reuse of data fromKano questionnaire

3 Research Method

31 Scoping Design Features of MTGA e research objectof this study is the design features of MTGA Due to thepremise that the study on the design feature of MTGA hasbeen rather scant we cannot recognize the specific designfeature of MTGA by analysing the existing literatureerefore we install current mainstream MTGAs that canbe downloaded from Android and IOS app stores to con-struct the MTGA repository for study After a thoroughdecomposition and analysis of design features for all of thedownloaded applications 12 major design features that usersinteract with in MTGA are identified e name and de-scriptive introduction of each design feature are shown inTable 1

32 Questionnaire Design Following the paradigm of Kanomodel a questionnaire consists of three parts is designede first part is Kano-based two-dimensional items in termsof the 12 design features of MTGA Respondents are re-quired to rate their perceptions considering both thepresence and absence of each design feature of MTGA eperceptions are measured on five levels of ldquolike must beneutral live with and dislikerdquo Second perceived impor-tance of each of the 12 design features are asked for by a scalefrom 1 being ldquovery unimportantrdquo to 5 being ldquovery impor-tantrdquo Perceived performance of each design feature is alsoinquired from 1 as ldquovery unsatisfiedrdquo to 5 as ldquovery satisfiedrdquoe third part of the questionnaire is questions about thedemographic characteristics of respondents which includegender age education level preference for travel wayspreference for scenic types and the number of averageannual travel

33 Classification of Design Features Based on the collectedquestionnaires a basic classification of each design featurefor each respondent is firstly conducted by the typical qualityclassification table of the traditional Kano model shown inTable 2

is traditional Kano method is able to classify designfeatures but incompetent for measuring their impacts on the

increasing of satisfaction or eliminating of dissatisfactionCorrespondingly this study proposes an improved designfeature classification method based on the concept of theuser satisfaction coefficient proposed by Berger et al [48]

331 Calculation of Better and Worse Indices e Betterand Worse indices of the design features are calculated byusing the typical quality classification results of the tradi-tional Kanomodele absolute values of which are between0 and 1 e Better index of design feature Fi is calculated as

Betteri Ai + Oi

Ai + Oi + Mi + Ii

(1)

where Ai Oi Mi and Ii represent the quantity of results fordesign feature Fi classified as A (attractive quality) O (one-dimensional quality)M (must-be quality) and I (indifferentquality) respectively e value of Betteri is positive therebyindicating the extent to which the provision of the designfeature improves user satisfaction A value close to 1 suggestsa strong improvement effect on user satisfaction

On the contrary the Worse index of design feature Fi iscalculated as in (2) e value of Worsei is negative therebydescribing the influence of exclusion of particular designfeature in reducing total user satisfaction A value close to minus1prompts a strong reduction effect

Worsei minusOi + Mi

Ai + Oi + Mi + Ii

(2)

332 Classification Rules After calculating the Better andWorse indices for each design feature the values are nor-malized to Betteri

prime and Worseiprime e average value of all

normalized Better indices is computed by (3) while theaverage of the absolute values of all normalized Worse in-dices is calculated as in (4)

Better 1n

1113944

n

i1Betteriprime (3)

|Worse| 1n

1113944

n

i1Worseiprime

11138681113868111386811138681113868111386811138681113868 (4)

e rule of classification for design features is describedas in (5) e type of each design feature is determinedaccording to the relationship between their normalizedBetter index as well as Worse index and their correspondingaverages C(Fi) represents the Kano type of design featureFi

C Fi( 1113857

O(One minus dimensional) Betteriprime ge Better and Worsei

prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|

A(Attractive) Betteriprime ge Better and Worsei

prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|

I(Indifferent) Betteriprime lt Better and Worsei

prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|

M(Must minus be) Betteriprime lt Better and Worsei

prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(5)


34 Calculating Importance of Design Features In the pastmost of the relevant research obtained the importance ofdesign features through questionnaires e process is oftentoo tedious and its survey content is too much increasingthe burden of questionnaire fillers thereby resulting in moredifficult questionnaire collection erefore this paperproposes a method to measure the importance of designfeatures quantitatively

e larger the Betteri and |Worsei| values of the designfeature are the greater the impact of realization degree of thedesign feature on user satisfaction or dissatisfaction is andthe more important the design feature is is study mea-sures the importance of design feature by calculating thedistance between the point of the two-dimensional planeand the coordinate origin and the farther the distance is themore importance it is e calculation of importance Wi ofdesign feature Fi is as shown in (6) Finally the calculationresults of the importance are normalized for further analysis

Wi

Better2i + Worsei

111386811138681113868111386811138681113868111386811138682

1113969

(6)

35 IPA of Design Features e importance measure rep-resents the vertical axis and the performance measurerepresents the horizontal axis of a two-dimensional matrixe importance and performance results of each designfeature are placed in a two-dimensional matrix with theaverage value as cross point to divide into four quadrantsen based on the quadrants in the IPA matrix of designfeatures corresponding managerial strategies are proposed

and their priorities to improve under the situation of limitedresources are determined

4 Data and Results

41 Data Collection e questionnaire survey was con-ducted through the professional survey platform ldquowjxcomrdquoA total of 214 questionnaires were collected and 197 validquestionnaires were obtained after screening yielding aneffective rate of 9206 e ages of the respondents aremainly between 18 and 39 years old In terms of gendermales account for 457 and females account for 543ose with bachelorrsquos degrees account for 594 of therespondents and those with graduate degrees account for340 Most of the respondents have more than 3 yearsrsquoexperience of using smartphone and 873 of the re-spondents prefer independent travel e Cronbach α co-efficients for Kano dimension importance dimension andperformance dimension are 0856 0827 and 0929 re-spectively indicating considerable reliabilities for furtheranalysis

42 Results e frequencies of the respondentsrsquo basicclassification results for each design feature are shown incolumns ldquoArdquosimldquoQrdquo of Table 3 Traditional Kano model de-termines classification of quality attribute based on the Kanocategory with the largest frequency which does not alwayswork well and the representation of which may be con-troversial [22 49] erefore this paper will adopt an im-proved method to determine the Kano categories of design

Table 1 Major design features of MTGA

Design feature name (tag) Descriptive introductionDownload offline (DO) Download data beforehand to save network traffic and be available without networkAutomatic tour (AT) Automatically trigger the audio or video guidance for scenic spot based on visitorrsquos positionSelf-drawn maps (SM) Hand-drawn maps of scenic spots with real-life effectsMultistyle explanations (ME) Explanations with different options of stylesRoute recommendation (RR) To recommend tour routes to users based on other travellers and tour guidesAI recognition (AIR) Scan exhibits or nameplates with the camera to identify and automatically show explanations

Travel strategies (TS) Rich and practical travel tips and tactics in terms of scenic overview transportation routesaccommodation etc

Travel diaries (TD) Generate travel diaries with play tracksUtilities (UT) Tools for querying weather translation exchange rate etcFacility navigation (FN) Navigation of important places such as scenic entrances and exits restaurants shops ATMs toilets etcOnline review (OR) Posting and reading online reviews regarding travel experiencesRelated history and culture(RHC) Introduction to the history and culture with regard to scenic spots

Table 2 Typical quality classification table of traditional Kano model

Customer response Dysfunctional questionLike Must be Neutral Live with Dislike

Functional question

Like Q A A A OMust be R I I I MNeutral R I I I MLive with R I I I MDislike R R R R Q

Mmdashmust-be quality Omdashone-dimensional quality Amdashattractive quality Imdashindifferent quality Rmdashreverse quality and Qmdashquestionable quality


features According to the classification method of designfeature proposed in Section 33 after calculating the Betterand Worse indices for each design feature combined withthe classification rule the coordinate plane for classifyingdesign features is shown in Figure 3 and the determinedcategories for each design feature are show in columnldquoCategoryrdquo of Table 3 Based on the method in Section 34the calculated importance of each design feature is shown incolumn ldquoImportancerdquo in Table 3 followed by the perfor-mance of each design feature obtained in the questionnairee importance and performance results of each designfeature are placed in a two-dimensional coordinate planewith the average value as cross point the IPA is conductedand the results are presented in Figure 4 and the last columnin Table 3

As demonstrated in Figure 4 under the framework ofIPA design features RR TS UT and FN are placed in thequadrant of ldquokeep up the good workrdquo indicating that MTGAoperators should endeavour to maintain the quality of thesedesign features Design feature AIR is in the quadrant ofldquoconcentrate hererdquo meaning that MTGA operators shouldconcentrate their resources on improving this design featureat first Design features AT SMME TD OR and RHC lie inthe quadrant of ldquolow priorityrdquo indicating no urgent need forimprovement Design feature DO is located in the quadrantof ldquopossible overkillrdquo so reducing the resources allocated forthis design feature may be a considerable policy

43 Validity Test In order to verify the validity of theproposed method it is necessary to compare our resultswith those of the existing methods is study also sets upquestion items directly inquiring userrsquos evaluation of theimportance of each design feature when designing thequestionnaire (eg ldquoWhat do you think of the importanceof design feature ldquoonline reviewrdquo in mobile tourist guideapplicationsrdquo) whereby 5-point Likert scales are usedfrom 1 being ldquovery unimportantrdquo to 5 being ldquovery im-portantrdquo After averaging all respondents the directlymeasured importances of the 12 design features are 38733782 3655 3533 4264 3898 4325 3431 4025 43963701 and 3893 respectively en an IPA matrix withdirectly measured importance is drawn as shown in

Figure 5 After comparing between the IPA matrix fordesign features of MTGA with our proposed method inFigure 4 and the IPA matrix with directly measured im-portances in Figure 5 it can be found that the quadrantaldivisions of all 12 design features are consistent across thetwo matrices suggesting the validity of our method It isworth mentioning that our method reuses the Kanoquestionnaire data to generate the importances whicheffectively reduces the workload of respondents for an-swering additional questions of the importance

Table 3 Results of design featuresrsquo classification and IPA

Tag A O M I R Q Category Importance Performance IPA quadrantDO 33 14 8 137 0 5 I 0282 3706 CHAT 30 17 5 138 0 7 I 0290 3523 LPSM 34 7 4 145 0 7 I 0073 3497 LPME 30 7 2 150 2 6 I 0005 3426 LPRR 45 21 5 117 2 7 O 0590 3807 KUAIR 46 17 4 123 0 7 A 0482 3543 POTS 38 31 9 109 1 9 O 0832 3751 KUTD 29 7 4 150 1 6 I 0000 3492 LPUT 37 21 5 128 0 6 O 0470 3726 KUFN 41 39 6 105 2 4 O 1000 3701 KUOR 20 13 10 149 1 4 I 0222 3599 LPRHC 38 15 3 134 2 5 A 0310 3604 LPKUmdashkeep up the good work CHmdashconcentrate here LPmdashlow priority and POmdashpossible overkill

RRTS

UT

FN

AIR

RHC

DOAT

SM

METD OR

1

0 1

Bette

r

|Worse|

Attractive quadrant One-dimensional quadrant

Indifferent quadrant Must-be quadrant

Figure 3 Plane division diagram for design feature classification

DOAT

SMME

RR

AIR

TS

TD

UT

FN

OR

RHC

Keep up the good work

Concentrate here

Possible overkill

Low priority

0

02

04

06

08

1

Impo

rtan

ce

35 36 37 38 3934Performance

Figure 4 IPA matrix for design features of MTGA


5 Discussions and Managerial Strategies

We derive several findings and managerial implicationsfrom the above processes and results To interpret the resultswithin Kano model framework the Better index and ab-solute values of the Worse index of design features RR TSUT and FN are higher than average indicating that usersatisfaction will be improved when these design features areprovided in the MTGA while reduced when absent esefour design features are ldquoone-dimensionalrdquo design featureswhose realization degree is approximately linear with overalluser satisfaction so MTGA that optimizes the design ofthese four design features may get a payback of higher usersatisfaction

e Better index values of design features AIR and RHCare higher than the average value while their absoluteWorseindex values are lower than average So the user satisfactionwill be higher when these design features are found inMTGA but user satisfaction may not significantly reducewhen these design features are missed If the operator plansto further enhance the user satisfaction with the MTGAattentions should be paid to the development and optimi-zation of these two design features Such ldquoattractiverdquo designfeatures will enable MTGA operators to benefit from dif-ferentiated competition with competitors

As the Better index and absolute values of the Worseindex of design features DO AT SM ME TD and OR areboth lower than the average value little changes in usersatisfaction occur whether these design features provided inMTGA or not manifesting their ldquoindifferentrdquo nature

From the perspective of IPA the design features in thequadrant ldquokeep up the good workrdquo and ldquopossible overkillrdquoshould adopt the holding strategy e user satisfactions ofsuch design features are high so their performance shouldbe maintained Further considering their importances whendeploying system development and design resources theldquokeep up the good workrdquo design features should hold greaterpriority over the ldquopossible overkillrdquo ones On the other handthe design features located in quadrant ldquoconcentrate hererdquoand quadrant ldquolow priorityrdquo should adopt the improvement

strategy e performance of these design features should beimproved in general due to their low level of user satis-faction whereas ldquoconcentrate hererdquo design features would beprior to ldquolow priorityrdquo ones regarding their importancesConsidering the quality classification of Kano model co-herently the priorities of must-be quality attributes arehighest and one-dimensional quality attributes the secondfollowed by attractive quality attributes and finally come theindifferent quality attributes Synthesizing the principles ofIPA and Kano we provide managerial strategies for designfeatures of MTGA as follows

(1) Priority of Improvement Strategy e design featuresof MTGA that adopt improvement strategy includeAT SM ME AIR TD OR and RHC e prioritiesof the attractive quality design feature are higherthan the indifferent feature as ruled and because thedesign feature AIR is located in the quadrantldquoconcentrate hererdquo its priority should be higher thanRHC in the quadrant ldquolow priorityrdquo Design featuresAT SM ME TD and OR which lie in the quadrantldquolow priorityrdquo have the lowest priorities

(2) Priority of the Holding Strategy e design featuresDO RR TS UT and FN of MTGA adopt theholding strategy As the priorities of the one-di-mensional quality design feature are ruled to behigher than that of the indifferent quality feature thepriority of design features RR TS UT and FNshould be higher than the design feature DO It isobserved that a quadrant may include multiple de-sign features in this case the priority order of designfeatures within the same quadrant can be determinedafter trading-off the importance performance as wellas the preferences and available resources of theMTGA operator

6 Conclusions

MTGA has been widely adopted by tourists and generatedcommon concerns from academics and industries Howeverif a MTGA is poorly designed the effectiveness of service itdelivers to users will be considerably reduced Referring tothe framework of the Kano model and IPA this study in-vestigates the design of MTGA from a relatively microscopicperspective To the best of our knowledge we are among thefirst few studies to focus on the design of MTGA on the levelof design features e present study makes substantialtheoretical contributions to the literature and provides in-spiring managerial implications for practitioners Firstly theresults of online samples support the nonlinear relationshipbetween the design features of MTGA and user satisfactionas Kano model suggests erefore facing with resourceconstraints MTGA operators would be of more benefit touser satisfaction with less cost when designing or optimizingMTGA if they take the nonlinear nature of design featureinto consideration Secondly based on the Kano model andIPA this paper classifies the 12 major design featuresextracted from mainstream MTGAs determines the pri-orities of these design features for improvement and further

DOAT

SM

ME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

34

36

38

4

42

44

Impo

rtan

ce


Figure 5 IPA matrix with directly measured importances


advances managerial strategies which provide instructiveimplications for practitioners to reap usersrsquo satisfaction byimproving the design of MTGA irdly we provide apossible idea for retrenching the procedure of IPA byquantitatively measuring the importance of design featuresbased on the positions on the coordinate plane e con-sistent result of comparison with existing literature thatmeasures importance through direct measurement itemsdemonstrates the validity of our method We reuse the Kanoquestionnaire data to generate importances of design fea-tures which effectively reduces the workload of respondentsto answer additional questions regarding importance

e present study also has certain limitations whichprovide possible directions for future research First types ofscenic spots (eg natural cultural or thematic) are notdistinguished when assessing user needs for design featuresof MTGA Actually usersrsquo informational needs may differacross different types of scenic spots Future research canfurther examine and integrate these differences into theresearch model Second the preferences of different usergroups on the design features of MTGAmay also vary due todifferent demographic characteristics eg age gendereducation level and occupation In the future the impacts ofuser profiles can be empirically studied to induce moreaccurate and effective strategies for the development andstrategies of MTGA design Finally the traditional Kanomodelrsquos two-dimensional questionnaire is not efficient andconducive to capture the accurate perceptions of respon-dents future research can be done via methods such asregressions or mining user needs from objective datasets

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

is work was partially supported by the grant of the Na-tional Natural Science Foundation of China (no 71861014)China Postdoctoral Science Foundation (no 2019M652272)Priority Postdoctoral Research Projects of Jiangxi Province(no 2018KY10) the Science and Technology Project ofJiangxi Education Department (no GJJ60458) and theSocial Science Project of Jiangxi Province (no 17BJ31)

References

[1] J Fang Z Zhao C Wen and R Wang ldquoDesign and per-formance attributes driving mobile travel application en-gagementrdquo International Journal of InformationManagement vol 37 no 4 pp 269ndash283 2017

[2] J E Dickinson K Ghali T Cherrett C Speed N Davies andS Norgate ldquoTourism and the smartphone app capabilities

emerging practice and scope in the travel domainrdquo CurrentIssues in Tourism vol 17 no 1 pp 84ndash101 2014

[3] S P Singh and P Singh ldquoDesign and implementation of alocation-based multimedia mobile tourist guide systemrdquoInternational Journal of Information and CommunicationTechnology vol 7 no 1 pp 40ndash51 2015

[4] M Kenteris D Gavalas and D Economou ldquoAn innovativemobile electronic tourist guide applicationrdquo Personal andUbiquitous Computing vol 13 no 2 pp 103ndash118 2009

[5] R Law D Buhalis and C Cobanoglu ldquoProgress on infor-mation and communication technologies in hospitality andtourismrdquo International Journal of Contemporary HospitalityManagement vol 26 no 5 pp 727ndash750 2014

[6] R Anacleto L Figueiredo A Almeida and P NovaisldquoMobile application to provide personalized sightseeingtoursrdquo Journal of Network and Computer Applications vol 41pp 56ndash64 2014

[7] H-C Kim and Y-S Kim ldquoSmart tourism information systemusing location-based technologyrdquo International Journal ofSoftware Engineering and Its Applications vol 10 no 11pp 11ndash24 2016

[8] K B Kang J W Jwa and S D E Park ldquoSmart audio tourguide system using TTSrdquo International Journal of AppliedEngineering Research vol 12 no 20 pp 9846ndash9852 2017

[9] C-C Chen and J-L Tsai ldquoDeterminants of behavioral in-tention to use the personalized location-based mobile tourismapplication an empirical study by integrating TAM withISSMrdquo Future Generation Computer Systems vol 96pp 628ndash638 2019

[10] X Bao H Bie and M Wang ldquoIntegration of multimedia andlocation-based services on mobile phone tour guide systemrdquoin Proceedings of the 2009 IEEE International Conference onNetwork Infrastructure and Digital Content pp 642ndash646IEEE Beijing China November 2009

[11] R Kramer M Modsching K Ten Hagen and U GretzelldquoBehavioural impacts of mobile tour guidesrdquo Information andCommunication Technologies in Tourism 2007 SpringerBerlin Germany pp 109ndash118 2007

[12] S Li X Duan Y Bai et al ldquoDevelopment and application ofintelligent tour guide system in mobile terminalrdquo in Pro-ceedings of the 2015 7th International Conference on MeasuringTechnology and Mechatronics Automation pp 383ndash387 IEEENanchang China June 2015

[13] I K W Lai ldquoTraveler acceptance of an app-based mobile tourguiderdquo Journal of Hospitality amp Tourism Research vol 39no 3 pp 401ndash432 2015

[14] S I Lei D Wang and R Law ldquoPerceived technologyaffordance and value of hotel mobile apps a comparison ofhoteliers and customersrdquo Journal of Hospitality and TourismManagement vol 39 pp 201ndash211 2019

[15] Y Ji S Kumar V S Mookerjee S P Sethi and D YehldquoOptimal enhancement and lifetime of software systems acontrol theoretic analysisrdquo Production and OperationsManagement vol 20 no 6 pp 889ndash904 2011

[16] X Shi T Sun Y Shen et al ldquoTour-guide providing location-based tourist information on mobile phonesrdquo in Proceedingsof the 2010 IEEE International Conference on Computer ampInformation Technology IEEE Bradford UK June 2010

[17] N Kano N Seraku F Takahashi et al ldquoAttractive quality andmust-be qualityrdquo Journal of the Japanese Society for QualityControl vol 14 no 2 pp 39ndash48 1984

[18] T-H Chu M-L Lin and C-H Chang ldquomGuiding (mobileguiding)mdashusing a mobile GIS app for guidingrdquo Scandinavian


Journal of Hospitality and Tourism vol 12 no 3 pp 269ndash2832012

[19] A Smirnov A Kashevnik N Shilov et al ldquoMobile appli-cation for guiding tourist activities tourist assistant-TAISrdquo inProceedings of 16th Conference of Open Innovations Associ-ation FRUCT pp 95ndash100 IEEE Oulu Finland October 2014

[20] E Tarantino I De Falco and U Scafuri ldquoA mobile per-sonalized tourist guide and its user evaluationrdquo InformationTechnology amp Tourism vol 21 no 3 pp 413ndash455 2019

[21] J Qi Z Zhang S Jeon and Y Zhou ldquoMining customerrequirements from online reviews a product improvementperspectiverdquo Information amp Management vol 53 no 8pp 951ndash963 2016

[22] E Ilbahar and S Cebi ldquoClassification of design parameters forE-commerce websites a novel fuzzy Kano approachrdquo Tele-matics and Informatics vol 34 no 8 pp 1814ndash1825 2017

[23] M Go and I Kim ldquoIn-flight NCCI management by com-bining the Kano model with the service blueprint a com-parison of frequent and infrequent flyersrdquo TourismManagement vol 69 pp 471ndash486 2018

[24] N Velikova L Slevitch and KMathe-Soulek ldquoApplication ofKano model to identification of wine festival satisfactiondriversrdquo International Journal of Contemporary HospitalityManagement vol 29 no 10 pp 2708ndash2726 2017

[25] M-L Yao M-C Chuang and C-C Hsu ldquoe Kano modelanalysis of features for mobile security applicationsrdquo Com-puters amp Security vol 78 pp 336ndash346 2018

[26] Y-F Kuo ldquoIntegrating Kanorsquos model into web- communityservice qualityrdquo Total Quality Management amp Business Ex-cellence vol 15 no 7 pp 925ndash939 2004

[27] C F Chiang W Y Chen and C Y Hsu ldquoClassifyingtechnological innovation attributes for hotels an applicationof the Kano modelrdquo Journal of Travel amp Tourism Marketingvol 36 no 7 pp 796ndash807 2019

[28] J A Martilla and J C James ldquoImportance-performanceanalysisrdquo Journal of Marketing vol 41 no 1 pp 77ndash79 1977

[29] S E Sampson and M J Showalter ldquoe performance-im-portance response function observations and implicationsrdquogte Service Industries Journal vol 19 no 3 pp 1ndash25 1999

[30] H Q Zhang and I Chow ldquoApplication of importance-per-formance model in tour guidesrsquo performance evidence frommainland Chinese outbound visitors in Hong Kongrdquo TourismManagement vol 25 no 1 pp 81ndash91 2004

[31] W Deng ldquoUsing a revised importance-performance analysisapproach the case of Taiwanese hot springs tourismrdquo TourismManagement vol 28 no 5 pp 1274ndash1284 2007

[32] W Skok A Kophamel and I Richardson ldquoDiagnosing in-formation systems success importance-performance maps inthe health club industryrdquo Information ampManagement vol 38no 7 pp 409ndash419 2001

[33] M-M Chen H C Murphy and S Knecht ldquoAn importanceperformance analysis of smartphone applications for hotelchainsrdquo Journal of Hospitality and Tourism Managementvol 29 pp 69ndash79 2016

[34] N M Levenburg and S R Magal ldquoApplying importance-performance analysis to evaluate e-business strategies amongsmall firmsrdquo E-Service Journal vol 3 no 3 pp 29ndash48 2004

[35] R K S Chu and T Choi ldquoAn importance-performanceanalysis of hotel selection factors in the Hong Kong hotelindustry a comparison of business and leisure travellersrdquoTourism Management vol 21 no 4 pp 363ndash377 2000

[36] H Wilkins ldquoUsing importance-performance analysis to ap-preciate satisfaction in hotelsrdquo Journal of Hospitality Mar-keting amp Management vol 19 no 8 pp 866ndash888 2010

[37] K-Y Chen ldquoImproving importance-performance analysisthe role of the zone of tolerance and competitor performancee case of Taiwanrsquos hot spring hotelsrdquo TourismManagementvol 40 pp 260ndash272 2014

[38] R H Taplin ldquoCompetitive importance-performance analysisof an Australian wildlife parkrdquo Tourism Management vol 33no 1 pp 29ndash37 2012

[39] A Sorensson and Y von Friedrichs ldquoAn importance-per-formance analysis of sustainable tourism a comparison be-tween international and national touristsrdquo Journal ofDestinationMarketing ampManagement vol 2 no 1 pp 14ndash212013

[40] B B Boley N G McGehee and A L Tom HammettldquoImportance-performance analysis (IPA) of sustainabletourism initiatives the resident perspectiverdquo Tourism Man-agement vol 58 pp 66ndash77 2017

[41] S Zhang and C-S Chan ldquoNature-based tourism develop-ment in Hong Kong importance-performance perceptions oflocal residents and touristsrdquo Tourism Management Perspec-tives vol 20 pp 38ndash46 2016

[42] E Hansen and R J Bush ldquoUnderstanding customer qualityrequirements model and applicationrdquo Industrial MarketingManagement vol 28 no 2 pp 119ndash130 1999

[43] A Coghlan ldquoFacilitating reef tourism management throughan innovative importance-performance analysis methodrdquoTourism Management vol 33 no 4 pp 767ndash775 2012

[44] S P Lin and Y H Chan ldquoEnhancing service quality im-provement strategies by integrating Kanorsquos model with im-portance-performance analysisrdquo International Journal ofServices Technology andManagement vol 16 no 1 pp 28ndash482011

[45] Y F Kuo J Y Chen and W J Deng ldquoIPAndashKano model anew tool for categorising and diagnosing service quality at-tributesrdquo Total Quality Management amp Business Excellencevol 23 no 7-8 pp 731ndash748 2012

[46] F Y Pai T M Yeh and C Y Tang ldquoClassifying restaurantservice quality attributes by using Kano model and IPA ap-proachrdquo Total Quality Management amp Business Excellencevol 29 no 3-4 pp 301ndash328 2018

[47] J C Huang ldquoApplication of Kano model and IPA on im-provement of service quality of mobile healthcarerdquo Inter-national Journal of Mobile Communications vol 16 no 2pp 227ndash246 2018

[48] C Berger R Blauth and D Boger ldquoKanorsquos methods forunderstanding customer-defined qualityrdquo Center for QualityManagement Journal vol 2 no 4 pp 3ndash36 1993

[49] L-F Chen ldquoA novel approach to regression analysis for theclassification of quality attributes in the Kano model anempirical test in the food and beverage industryrdquo Omegavol 40 no 5 pp 651ndash659 2012


investigated touristsrsquo technological acceptance of a MTGAChen and Tsai [9] studied the factors affecting usersrsquo in-tention to use the mobile tourism application Lei et al [14]explored usersrsquo perceived technology affordance and value ofmobile applications Fang et al [1] empirically examined thefactors influencing usersrsquo engagement behavior of mobiletravel applications Information system literatures inform usthat the design of information system has a considerableimpact on the operation performance and success of en-terprises [15] Mobile application as a specific informationsystem is of great importance since it provides an effectivechannel for enterprises to communicate and forge intimaterelationships with their customers [1] In order to retaincommitted and engaged customers as well as increase salesenterprises should place particular emphasis on the design oftheir mobile applications

Although MTGA enables users to obtain tour guidanceinformation as needed anytime and anywhere [16] the is-sues such as complexity when operating lack of useful in-formation and information overload severely reduce itseffectiveness We address the fact that these problems areclosely related to the insufficiency of extant studies on designfeatures of MTGA Despite research on this topic studiesregarding user satisfaction and improvement strategies forthe design features of MTGA from userrsquos perspective havebeen few in number In terms of describing the relationshipbetween product or service design and user satisfactionKano model is a classic method As userrsquos expectations varyamong different design features of MTGA Kano model isappropriate to depict the asymmetric and nonlinear rela-tionship between design feature and user satisfaction [17]Based on the Kano model this paper complements theextant research by investigating usersrsquo satisfaction with thevarious design features of MTGA determining the Kanoclassification of design features and analysing their prior-ities to improve through combining with IPAis work alsoprovides implications for practitioners to build the devel-opment and optimization strategies for designing MTGAthat well satisfies the needs of users

2 Literature Review

21 Mobile Tourist Guide Applications While the topic ofmobile travel applications has been under research for manyyears it is only in the recent years that MTGA began toreceive attentions across the tourism industry Currentlythere are two main streams of literature on MTGA e firstis design-science-based one For example Chu et al [18]integrated geographic information system and global po-sitioning system technologies into a MTGA which canprovide tourists with LBS information at popular touristdestinations Smirnov et al [19] proposed a mobile appli-cation which generates recommendations for the touristabout interesting attractions around Kang et al [8] devel-oped the location-based audio tour guide application usingspeech synthesis technology which is helpful for enhancingself-guided tours of destinations Tarantino et al [20]proposed an interactive electronic guide application pro-totype which can recommend personalized tourist routes to

mobile users and evaluated the effectiveness of the proto-type through an experimentation Such studies heavily an-alyse the design development or implementation ofMTGAs and discuss their main advantages and limitationse second stream of MTGA research is empirical studyregarding user behavior Based on unified theory of ac-ceptance and use of technology Lai [13] identified ante-cedents and determinants influencing touristsrsquo acceptance ofa MTGA through a questionnaire survey of 205 touristsvisiting Macau Chen and Tsai [9] investigated usersrsquo in-tention to use a mobile tourism application by integratingthe technology acceptance model and the informationsystem success model e results showed that informationquality perceived ease of use and perceived usefulnesssignificantly affect the intention to use while informationquality and perceived convenience affect perceived useful-ness is kind of studies is mainly to empirically investigateuser behavior through questionnaire survey such as tech-nological acceptance and intention to use Actually MTGAis an IT artifact that consists of various design features butmost of the relevant literature fails to consider it from such amicroscopic perspective which leads to a lack of deep lookinto the user satisfaction with different design featuresCompared to existing research this study focuses on specificdesign features of MTGA and we expect to provide moreprecise and operable suggestions for the design and im-provement of MTGA from a finer granularity

22 Kano Model Inspired by Herzbergrsquos two-factor theorythe Japanese scholar Noriaki Kano proposed the Kanomodel in 1984 e model divides product quality attributesinto five categories according to the relationship betweenobjective product performance and customer subjectivefeelings namely must-be quality one-dimensional qualityattractive quality indifferent quality and reverse quality[17] Figure 1 shows the relationship between quality real-ization degree and user satisfaction corresponding to theabovementioned five Kano categories e Kano model hasbeen applied to various research fields For instance Qi et al[21] applied the Kanomodel to the analysis of online reviewsto develop appropriate improvement strategies for productIlbahar and Cebi [22] classified the design parameters ofe-commerce websites according to consumer expectationsand evaluated the effectiveness of e-commerce websites Goand Kim [23] grouped travellers based on the frequency offlights per year to investigate the differences in the Kanoclassification of each airline service quality element in dif-ferent groups Velikova et al [24] applied the Kano model tothe management of festival activities and investigated thefactors affecting satisfaction with festival activities In thecontext of mobile internet development and the popularityof smart devices Yao et al [25] explored the quality attributeclassification of key functions in mobile security applicationsby using the Kano survey method to determine the im-portance ranking Kuo [26] used the Kano model to cate-gorize web-community service quality dimensions and theirelements and provided suggestions for future improvementof web-community service In the field of hospitality








Hig

hIm

port

ance

Low

Low HighPerformance


Satisfaction

Attractive quality



Must be quality

Reverse quality

Figure 1 Kano model



3 Research Method







Betteri Ai + Oi

Ai + Oi + Mi + Ii

(1)



Worsei minusOi + Mi

Ai + Oi + Mi + Ii

(2)




Better 1n

1113944

n

i1Betteriprime (3)

|Worse| 1n

1113944

n

i1Worseiprime

11138681113868111386811138681113868111386811138681113868 (4)


C Fi( 1113857


prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|


prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|


prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|


prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(5)




Wi

Better2i + Worsei

111386811138681113868111386811138681113868111386811138682

1113969

(6)



4 Data and Results









Functional question










RRTS

UT

FN

AIR

RHC

DOAT

SM

METD OR

1

0 1

Bette

r

|Worse|




DOAT

SMME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

0

02

04

06

08

1

Impo

rtan

ce












6 Conclusions


DOAT

SM

ME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

34

36

38

4

42

44

Impo

rtan

ce






Data Availability




Acknowledgments


References




























































Hig

hIm

port

ance

Low

Low HighPerformance


Satisfaction

Attractive quality



Must be quality

Reverse quality

Figure 1 Kano model



3 Research Method







Betteri Ai + Oi

Ai + Oi + Mi + Ii

(1)



Worsei minusOi + Mi

Ai + Oi + Mi + Ii

(2)




Better 1n

1113944

n

i1Betteriprime (3)

|Worse| 1n

1113944

n

i1Worseiprime

11138681113868111386811138681113868111386811138681113868 (4)


C Fi( 1113857


prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|


prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|


prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|


prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(5)




Wi

Better2i + Worsei

111386811138681113868111386811138681113868111386811138682

1113969

(6)



4 Data and Results









Functional question










RRTS

UT

FN

AIR

RHC

DOAT

SM

METD OR

1

0 1

Bette

r

|Worse|




DOAT

SMME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

0

02

04

06

08

1

Impo

rtan

ce












6 Conclusions


DOAT

SM

ME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

34

36

38

4

42

44

Impo

rtan

ce






Data Availability




Acknowledgments


References























































3 Research Method







Betteri Ai + Oi

Ai + Oi + Mi + Ii

(1)



Worsei minusOi + Mi

Ai + Oi + Mi + Ii

(2)




Better 1n

1113944

n

i1Betteriprime (3)

|Worse| 1n

1113944

n

i1Worseiprime

11138681113868111386811138681113868111386811138681113868 (4)


C Fi( 1113857


prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|


prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|


prime1113868111386811138681113868

1113868111386811138681113868lt |Worse|


prime1113868111386811138681113868

1113868111386811138681113868ge |Worse|

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(5)




Wi

Better2i + Worsei

111386811138681113868111386811138681113868111386811138682

1113969

(6)



4 Data and Results









Functional question










RRTS

UT

FN

AIR

RHC

DOAT

SM

METD OR

1

0 1

Bette

r

|Worse|




DOAT

SMME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

0

02

04

06

08

1

Impo

rtan

ce












6 Conclusions


DOAT

SM

ME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

34

36

38

4

42

44

Impo

rtan

ce






Data Availability




Acknowledgments


References
























































Wi

Better2i + Worsei

111386811138681113868111386811138681113868111386811138682

1113969

(6)



4 Data and Results









Functional question










RRTS

UT

FN

AIR

RHC

DOAT

SM

METD OR

1

0 1

Bette

r

|Worse|




DOAT

SMME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

0

02

04

06

08

1

Impo

rtan

ce












6 Conclusions


DOAT

SM

ME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

34

36

38

4

42

44

Impo

rtan

ce






Data Availability




Acknowledgments


References




























































RRTS

UT

FN

AIR

RHC

DOAT

SM

METD OR

1

0 1

Bette

r

|Worse|




DOAT

SMME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

0

02

04

06

08

1

Impo

rtan

ce












6 Conclusions


DOAT

SM

ME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

34

36

38

4

42

44

Impo

rtan

ce






Data Availability




Acknowledgments


References






























































6 Conclusions


DOAT

SM

ME

RR

AIR

TS

TD

UT

FN

OR

RHC


Concentrate here

Possible overkill

Low priority

34

36

38

4

42

44

Impo

rtan

ce






Data Availability




Acknowledgments


References
























































Data Availability




Acknowledgments


References






















































classificationandimprovementstrategyfordesignfeaturesof...

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