assesment of innovation process capability-based on ... · school of business and...

The Asian Journal of Technology Management Vol. 8 No. 2 (2015): 132-150

*Corresponding author. Email: [email protected]: September 21, 2015 ; Revised: November 13 2015, Accepted: December 02, 2015DOI: http://dx.doi.org/10.12695/ajtm.2015.8.2.5Print ISSN: 1978-6956; Online ISSN: 2089-791X.Copyright@2015. Published by Unit Research and KnowledgeSchool of Business and Management-InstitutTeknologi Bandung

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Assesment of Innovation Process Capability-Based on Innovation Value ChainModel in East Java Footwear Industry

Benny Lianto*, Rahman Dwi Wahyudi, Esti Dwi Rinawiyanti, and Aziera HernindaIndustrial Engineering Department, University of Surabaya Raya Kalirungkut, Surabaya 60293, Indonesia

Abstract. This study attempts to assess the innovation process based on innovation value chain model in footwear industry in East Java,Indonesia. A strength and weakness mapping analysis was performed and it included three factors related to company characteristics: operationscale based on number of employees, operational priod, and market orientation. The samples were 62 footwear industries, members of EastJava Indonesian Footwear Association (Aprisindo). The questionnaire was sent via email. Thirty industries (48.38%) sent the questionnaireback. A focus group discussion (FGD) was conducted with several representatives from footwear industry before the questionnaire was sent.The study found that companies are relatively good at idea conversion (42,30%) but the companies have a little difficulties at diffusion(50,80%) and at idea generation (55,80%). From the result respose show (see table.2) that the weakest links (the innovation processbottleneck) is cross-pollination activity [in which the people typically don't collaborate on projects across units, businesses, or subsidiaries(88,6%)], while the strongest links is selection activity [the companies have a risk- averse attitude toward investing in novel ideas (39,3%)].Based on p-value, the study found that company characteristics influencing a certain phase of innovation value chain significantly were companyperiod (age of company) and market orientation. Specifically, both of them influenced idea generation phase.

Keywords: Innovation process capability, Innovation value chain, footwear industry, company characteristics, strength and weaknessevaluation

1. Introduction

The world of business and industry iscurrently facing new order as characterized byhyper-competition, fast-changing technology,shortened product life cycle, and dynamicbusiness environment (O’Regan andGhobadian, 2005; Wang et al, 2008; Rejeb etal, 2008; Ishak et al, 2014; Hossein et al,2013). In such situation, several studiesconcluded that innovation-related activities isa key factor in topping the competition(Dervitsiotis 2010; Johanessen and Olsen,2009; Ren et al, 2010). Other research alsoshowed that innovation is an essential factorfor companies to keep their competitivenessand to increase growth significantly (Abidin etal, 2011; Chesbrough, 2003; EU European

Commission, 2004, Dervitsiotis 2010, Gamal,2011, Mohd Nizam et al, 2015).

On the other hands, in several cases,innovation did not affect companies’competitiveness significantly (Ahmed, 1998).Several studies analyzed the ineffectiveness ofthe innovation process and found that it wasdriven by a lack in innovation processmanagement, starting from idea pitching toproduct commercialization (EU EuropeanCommission, 2004; Salerno et al, 2015; Kempet al, 2003; Van der Panne et al, 2003; Tiddand Bessant, 2009). These studies highlight thepoint that innovation process is an importantand determining factor to increase innovationeffectiveness in a company. Hansen andBirkinshaw (2007) stated that innovationprocess, similar to business or production

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process; needs to be effectively managed inorder to maintain and increase the quality,production speed, and productivity. Severalstudies found that assessment process andinnovation process capability evaluationperformed continuously as well as making theassessment a base to manage the innovationprocess effectively will benefit company interms of competitiveness (Gupta et al, 2007;Tidd and Bessant, 2009; Ishak et al, 2014).

There are various research and studies usingdifferent models focusing on innovationprocess capability monitoring and evaluation.A literature study showed that innovationvalue chain model developed by Hansen andBirkinshaw (2007) is the most frequently usedmodel and has distinctive strength comparedto other models (Ishak et al, 2014; Taghizadehet al, 2014; Solerno et al, 2015). However, oneof the drawbacks in using innovation valuechain model is the lack of companycharacteristics as a variable, such asorganizational structure, human resources,form of work appraisal, and informationsystem (Salerno et al, 2010).

The application of innovation value chainmodel as developed by Hansen andBirkinshow focuses limitedly on multinationalcompany with big scale industry (Salerno et al,2015). On the other hand, Fontana in hisresearch (2013) on 39 government companiesin Indonesia using input, process, and output(IPO) method concluded there is a positiveeffect of innovation input on innovation valuechain model implementation. His researchstressed that innovation process evaluationusing innovation value chain model needs aclear picture on how idea is transformed intocommercially sound product as an integratedprocess involving various factors, one of themis company characteristics. This studyattempts to perform an assessment ofinnovation process capability based oninnovation value chain model as developed byHansen and Birkinshaw in footwear industry

in East Java, Indonesia. The strength andweakness of innovation activities along theinnovation value chain was mapped, inputtingthree company characteristics as a variable:business or operation scale based on numberof employees, operational time, and marketorientation. This study aims to assessinnovation process capability by observingand identifying the weakest and strongest linksof innovation activities along the innovationvalue chain and testing the difference ofinnovation process capability with differentcompany characteristics.

Footwear industry was chosen because it isone of strategic and most prominentindustries in Indonesia. It is one of top exportcommodities and it contributes significantly toIndonesian income (Khair, 2014). In recentyears, the export value of Indonesian footwearproduct experiences a considerable decline. Itis caused by decrease in competitiveness andtight competition with similar industries inother Asian countries, such as India and China(Ragimun, 2014). Extensive effort inincreasing industrial competitiveness is beingconducted, specifically in East Java, Indonesia,in which footwear industry is selected as oneof priority industry to face ASEAN EconomicCommunity 2015 (kemenperin.go.id).

2. Literature Review

2.1. Innovation Process and Innovation Value ChainInnovation process capability plays animportant role in various research focusing oninnovation (Roman et al, 2011). Thesignificance of innovation process in acompany has been underlined by previousstudies (Hansen and Birkinshaw, 2007; Roperet al, 2006; Utterback, 1994; Li, Zhao & Liu,2006; Clark and Fijimoto, 1991). Gupta et al(2007) established that company depends oninnovation process to increase theircompetitiveness.

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Several concepts and models have been afocus for several researchers. Utterback (1971)found that innovation process is a managerialprocess consisting of main activities, i.e. ideageneration, problem solving, invention,implementation, and diffusion; in order tocreate major economical impact.

Basically, innovation process in a companycan be viewed as a cluster of activities in aninnovation value chain model. Several studiesdescribed main activities classification ininnovation value chain as follows: stage gatemodel with 5 steps innovation value chainprocess (Copper, 1988), 4 steps innovationvalue chain (Sundbo,1997), 3 steps and 6activities innovation value chain (Hansen &Birkinshaw, 2007). With increase in the role ofknowledge in innovation process, Roper andcoworkers (2008) included the knowledgeproduction function in the innovation valuechain approach. They declared 3 stepsinnovation value chain: accessing knowledge,building innovation, and commercializinginnovation. Goffin and Mitchell (2010)developed pentathlon model with 5-stepsprocess, while Management Center Consultant(2011) adopted 7 steps process value chainconcept from a Harvard study. Recent studiesfocused on innovation process usinginnovation value chain as developed byHansen and Birkinshaw.

The advantage of this model is on itscomprehensive framework with 3 stepsinnovation value chain: ideation, conversion,and diffusion; and its 6 innovation activities:internal sourcing, cross-unit sourcing, externalsourcing, selection, development, andcompany-wide spread of the idea. Thisframework allows company leaders to havecomprehensive view of the whole steps andactivities in innovation value chain, enablingthem to diagnose and assess the innovationprocess in a fast way. In general, variousinnovation value chain models describinginnovation process have the orientation of the

linear process from product development ornew process activities (Solerno et al, 2015)

In the context of innovation management, toimprove company ability to develop newprocess or product, company leaders needpertinent information related to its innovationprocess capability status. The informationincludes how company strength and weaknesscan be properly identified in order to developimprovement for its innovation processcapability (Hansen & Birkinshaw, 2007).

Neely and Hii (1998) concluded that internalinnovation process capability is one of threeimportant factor in determining innovationcapacity of a certain company to develop newproduct or process. On the other hand,Hansen and Birkinshaw (2007) asserted thatsimilar to business or production process,innovation process needs to be effectivelymaintained in order to improve innovationquality, speed, and productivity. Severalstudies concluded that innovation processcapability evaluation and assessmentperformed continuously as well as design it asa base in managing innovation processeffectively will induce a significant impact inimproving company competitiveness (Guptaet al, 2007; Tidd and Bessant, 2009; Ishak et al,2014). In a wider context, mapping andanalysis on process value chain is a startingkey in achieving the effectiveness of processmanagement (IMA, 1996).

A number of research and studies related toinnovation process capability evaluation andassessment using various models has beenconducted, e.g. diamond model (Tidd et al,2005), Funnel Model (Gamal et al, 2011,Lamgdon Moris, 2008), Innovation ValueChain Model (Gunday, Ulusoy, Kilic &Alpkan, 2011; Wang, 2011; Hansen andBirkinshaw, 2007; Roper et al, 2006; Ishak etal, 2014; Seyedeh et al, 2014;). dan Oslomanual model (Oslo Manual, 2005). Salernoand coworkers (2015) stated that innovation


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value chain model developed by Hansen andBirkinshaw is a more comprehensive modelfocusing on innovation process and itsmanagement. This model integrated arelatively closed conventional model withwider approaches, particularly in relation todecision making. On the other hand, thisinnovation value chain model is equipped with

measurement instrument as a diagnostic toolfor the company to assess its innovationprocess capability (Hansen and Birkinshaw,2007). A description of innovation value chainmodel as developed by Hansen andBirkinshaw is displayed in Figure 1.

Figure 1. Innovation value chain model framework (Hansen and Birkinshaw, 2007)

Based on the model shown in Figure 1, thereare three steps in innovation value chain:ideation, conversion, and diffusion; as well as6 innovation activities: internal sourcing,cross-unit sourcing, external sourcing,selection, development, and company-widespread of the idea. Idea generation as the firststep is a mechanism to facilitate idea searchfrom internal and external sources.

The second step of this innovation value chainis the idea selection and development toconvert the ideas into a product, service, orprocess for the company. It is possible thatthis process is the bottleneck for the companywhen unsuitable decision is made. Roper andcoworkers (2008) found that in this step,multi-skilled teamwork is necessary. Besides,the managers need to consider a properdecision making process in determining abudget for new idea (Hansen & Birkinshaw,2007).


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There are two types of company policy inselection and budgeting process which canplay a role in the failure of idea conversioninto products (Ihsanuddin, 2009): (1) companywith strict budgeting policy and conventionalmanagement mindset and (2) company withless strict policy in its idea selection process.The company executes a number of projectsto convert the idea with various qualitieswithout tight selection. It also means a projectmay be conducted with sufficient resources. Acompany could also lose its sensitivity todetermine which initiative is related tocompany strategies. On the other hand, therealso companies choosing not to convert anyideas because they lack focus and toooccupied by other activities.

After idea selection and development process,the next step is diffusion process. Using thisapproach, the management could obtain twoimportant understandings to help themimproving innovation in the company. First,the best innovation process capability in thecompany that could be achieved is limited onthe weakest chain link in innovation valuechain. The management needs to identify theweakest link in the company to increase andimprove its innovation as well as determine asolution focused on the weakest link and notothers. Secondly, the strongest link ininnovation value chain can also be a weaknesssince the management tends to emphasize onthe link, making it worse (Ihsanuddin, 2009).On the other hand, this diffusion process isalso significantly affected by company focus inestablishing its brand strength and reputation(Roper, Du & Love, 2006).

Related to the identification of strength andweakness in innovation value chain, there arecompany profiles as observed from theweakest innovation value chain step(Ihsanuddin, 2009):(1) The idea-poor company, in which the main

problem is located on the idea mining andnot its execution. Company needs a

significant amount of time to develop andspread the idea but the result, in terms ofproduct or investment return, does notreflect the effort.

(2) The conversion-poor company, in which thecompany has numerous ideas but is notable to filter and develop the ideas well. Itoften occurs that the idea fails in budgetingsteps because the company chooses anincremental innovation with well measuredrisk. This type of company needs animprovement in doing idea screening andnot on the mining process.

(3) The diffusion-poor company, in which thecompany has the difficulty in marketing thedeveloped product, not the idea miningand development

In overall, the innovation process capabilityassessment using identification of activitystrength and weakness in the aforementionedvalue chain still lacks of companycharacteristics as a determining factor ininnovation value chain performance. Salernoand coworkers (2010) established thatinnovation process capability evaluation usinginnovation value chain overlooks the role ofcompany characteristics, such asorganizational structure, human resources,and appraisal system, as an input insupporting the activity performance. On theother hand, the application of innovationvalue chain model with the frameworkdeveloped by Hansen and Birkinshawfocuses more on multinational company witha large scale of business (Salerno et. al., 2015).

This condition has not been able to answerthe question on how the overview of strengthand weakness of value chain activity will differwhen the company characteristics are diverse.A number of research has been conducted toobserve how company characteristic mayaffect innovation value chain activity andoverall innovation management. Fontana, inhis research in 2013, studied 39 governmentcompanies in Indonesia with Input, Process,


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and Output (IPO) method and concluded thatthere is a positive effect of innovation inputon innovation value chain performance.

2.2. Innovation process capability and Business scaleThere are several studies performed to see theeffect of company characteristics on theinnovation value chain activity and overallinnovation management (Schmidt, 2010; Loveet al. 2010; Laforet, 2008; Bertschek andEntorf, 1996; Laforet and Tann, 2006;Avermaete et al, 2003). Those studiesconcluded that there is a difference ininnovation performance with different scale ofbusiness. Laforet and Tann (2006) studied theeffect of business scale of manufacturingcompanies on their ability to performinnovation activities. Their study showed thatcompanies with large scale of businessdisplayed better ability in their innovationprocess, as well as success in entering newmarket. The main reason was their capacity toinvest in new technology and equipments aswell as the ability to provide and train highlyqualified employee.

In another study, Avermaete and coworkers(2003) found that companies with small andmedium scale of business possess insufficientknowledge in conducting R&D activities sothey have difficulties in converting anddeveloping the ideas into an effectiveinnovation. Schmidt (2005) stated thatbusiness scale affects company effort indeveloping ideas from external sources.Companies with large business scale tend touse their own resources. White and coworkers(1988) found there was a difference ininnovation activity performance with differentbusiness scale. Companies with small businessscale have the advantage on its owner’sindividual strength, while big companies havebetter resources and system. Cao andcoworkers (2004) in their study in furnituremanufacturing company in China discovered adifference in innovation ability with differentbusiness scale (small: 0 – 100 employee,

medium: 1010 – 250, and big: >250). Theyfound that, in general, big scale companieshave greater innovation ability compared tomedium and small scale companies. Hansenand Birkinshaw (2007) analyzed that smallscale companies have more advantage inideation process, particularly in internalsourcing, while big companies have morestrength in diffusion step. This study attemptsto test the difference in strength and weaknessof innovation value activity in footwearindustry with different business scale.

2.3. Innovation process capability and Operationalperiod

Research about operational period or time of acompany in relation with its innovationperformance has been conducted by severalresearchers (Love et al. 2010; Hui et al, 2013;Hansen, 1992; Huergo and Jaumandreu, 2004;Coad et al , 2013; Balasubramanian et al,2008). The conclusion is that resources andinternal capacity of a company such asoperational period had an effect to innovationactivity. Hansen (1992) declared thatoperational period of a company affected acompany’s innovation level and output.Huergo and Jaumandreu (2004) also found theability of a company to innovate changed as itsoperational period continued. Longeroperational period tends to help a companycollect learning to support its innovationactivity. Balasubramanian also supports thestatement.

The aforementioned research analyzed theeffect of operational period of a company toits overall innovation performance. Detailstudy on the effect of operational period oneach innovation value chain has yet to beconducted. In this study, the effect ofdifferent operational period of footwearindustry on innovation value chain activity willbe investigated.


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2.4.Innovation process capability and MarketOrientationWhen it comes to market orientation, anumber of studies on the correlation betweenmarket orientation and innovationperformance of company have been published(Cao et al, 2004; Bernard et al, 2007; Lopez,2005; Ozsomer et al, 1997). Cao andcoworkers (2004) in their study in a furnituremanufacturing companies in China found thatthere is a difference in innovationperformance with different marketorientation. They discovered that companiesfocusing solely on export or import markethad less ability in performing innovationcompared to companies with diverse marketorientation. Ozsomer and coworkers (1997)concluded that there was a difference ininnovation ability between companies withdifferent market orientation. In general,companies focusing on export marker havemore proactive, aggressive, and competitivestrategies. This condition is the supportingfactor for the companies to di continuousdevelopment in their products and theirproduction method. However, the specificeffect of market orientation on innovationvalue chain has not been explored and it willbe performed in this study, specifically infootwear industry.

3. Research Methodology

This study is a survey research involving threesteps:1) Designing instrument for data harvesting.

A two-part questionnaire was created. Thefirst part was aimed to collect date relatedto company characteristics, while thesecond part was adopted from innovationvalue chain evaluation in an organization as

developed by Hansen and Birkinshaw(2007). The second part questionnaire isshown in Figure 2. The function of thesecond part was to collect date related tostrength and weakness of each activity andstep in innovation value chain.

2) Data collection. The samples were 62footwear industries, members of East JavaIndonesian Footwear Association (Aprisindo).The questionnaire was sent via email.Thirty industries (48.38%) sent thequestionnaire back. Most of therespondents were company leaders, such asplant manager, general manager, andproduction manager. A focus groupdiscussion (FGD) was conducted withseveral representatives from footwearindustry before the questionnaire was sent.The FGD aimed to explain and discuss theobjectives of the study.

3) Data processing and analysis. In this stagedescriptive statistics analysis, strength andweakness of innovation value chainanalysis, and multivariate analysis ofvariance (MANOVA) were employed inthis study. Descriptive statistics analysiswas chosen to describe the demography ofrespondent influencing the innovationvalue chain. The demography data werecompany characteristics consisting ofnumber of employees, age of company(operational period) and marketorientation. Furthermore, statistics analysiswould aid analysis of other results. Then,further data processing were strength andweakness of innovation value chainanalysis, and MANOVA. Detaileddiscussion of them would be showed inresult and discussion.


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Disagr

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Disagr

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Neither

Disagr

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Agree

Agree

Compl

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Activit

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1. Our culture makes it hard for people to put forward novelideas 1 2 3 4 5

2. People in our unit come up with very few good ideas oftheir own 1 2 3 4 53. Few of our innovation project involve team members fromdifferent units or subsidiaries 1 2 3 4 5

Cross-pollinationamong businesses

4. Our people typically don't collaborate on projects acrossunits, businesses, or subsidiaries 1 2 3 4 5

5. Few good ideas for new new products and business comeform outside the company 1 2 3 4 5

6. Our people often exhibit a " not invenred here' attitude-ideas from outside aren't considered as valuable as thoseinvented within 1 2 3 4 5

7. We have tough rules for investment in new projects- it'soften too hard to get ideas funded 1 2 3 4 58. We have a risk- averse attitude toward investing in novelideas 1 2 3 4 59. New product development project often don't finish ontime 1 2 3 4 5

10. Managers have a hard time getting traction developingnew businesses 1 2 3 4 511. We're slow to roll out new products and businesses 1 2 3 4 512.Compatitors quickly copy our product introductions andoften make preemptive launches in other countries 1 2 3 4 5

13. We don't penetrate all possible channels, customergroups, and regions with new products and service 1 2 3 4 5


External sourcing ofideas

Selection

Development

Diffusion

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Source: the authors, based on Birkinshaw (2007)

Figure 2. Questionnaire of innovation value chain evaluation (adoptep from Hansen and Birkinshaw)

4. Result and Discussion

4.1. Descriptive analysisBased on descriptive analyasis of 30 observedcompanies, the obtained data for number ofemployees was 13.33% of company employingless than 100 people, 3.33% of employing 101-

300 people, 16.67 % of employing 301-500people, 26.68% of employing 501-700 peopleand 40% of employing more than 700 people.Easing to compare the number, theinformation was graphically presented asFigure 3.

Figure 3. Number of employees


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In case that large industry was defined ascompany employing more than 100 peopleand medium-small industry was defined asopposite (Cao et al, 2004), the obtainedinformation was 86.67% of observedcompanies belonged to large company.Thisnumber of employees was suspected to beable to influence innovation value chain ofcompany. Probably the larger number ofemployees were employed, the more chanceof innovatioan idea would be generated andwould be easily realized. Surely, this allegationshould be tested by using MANOVA. Besidesnumber of employees, age of company(operational period) was the other suspectedfactor influencing innovation value chain.Based on this allegation, data of respondentwould be indentified based on age of companyand it was obtained that 16.67% of companyaged less 5 years, 10% of aged 6-10 years,6.67% of aged 11-15 years, 16.67% of aged16-20 years, and 50% of aged more than 20

years. Data showed that the majority ofobserved companies (73%) were companiesexperienced in footwear industry for morethan 10 years. Obviously, it was showed inFigure 4.

Meanwhile, the data of market orientationobtained from 30 repondents comprised 20%of domestic-oriented companies, 20% ofexport-oriented companies and 60% ofdomestic and export-oriented companies.Then, the domestic and export-oriented wouldbe mentioned as mix-oriented. This marketorientation was suspected to be able toinfluence innovation value chain.Furthermore, these three of input factorsshould be tested whether the allegation wasstatistically proved. Graphically, descriptivestatitics of market orientation was presented inFigure 5.

Figure 4. Age of the companies

Figure 5. Range of Age


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4.2. Comprehensive strength and weakness ofinnovation value chain analysis

Since the demographic of observed companieshad been presented, the further dataprocessing was strength and weakness ofinnovation value chain analysis. This analysisaimed to understand of companies’ situationregarding to innovation value chain. Thecompanies’ situation was explored byanswering the questionnairre consisting ofstatements related to innovation value chainactivities. The aforementioned activities werein-house idea generation, cross-pollinationamong businesses, external sourcung of ideas,selection, development, and diffusion. Thequestionnairre was designed by refering tofigure 2. Every respondent was asked to giveagreement for each given statement in likert

scale of 1 to 5. The larger scale representedthe stronger agreement meaning that thestatement was suitable with respondent’ssituation. After collecting the data, total scorecalculation of each statement was conducted.At the rest, aforementioned total score wouldcontribute the total score of innovation valuechain of observed companies. The calculationof total score was conducted by usingweighted sum of likert scale given by 30respondents. Percentage calculation wascoducted as well to aid doing interpretationamong the statements. Maximum score wasachieved by multiplying the maximum likertscale (5) and the number of respondent (30).Therefore the maximum score achieved was150. The example of calculation given wasexplained as followed.

Table 1. Footage of Data Collecting

Statement

Likert Scale

CompletelyDisagree

(1)Disagree

(2)

NeitherDisagreenor Agree

(3)

Agree(4)

CompletelyAgree

(5)

Our culture makes it hard forpeople to put forward novelideas

4 11 9 4 2

Table 1 showed that 30 respondents consistedof 4 people answering 1 or completelydisagree, 11 people answering 2 or disagree, 9people answering 3 or neither disagree noragree, 4bpeople answering 4 or agree and 2people answering 5 or completely agree. Basedon those data, total score calculation wasconducted by weighted sum such as followed.Total score = (1x4) + (2+11) + (3+9) +(4x4) + (5+2) = 79

Therefore, the percentage for first statementwas 79/150 equaled to 52,6%. By doing thesame way dor each statement, total scores ofall statement was presented on table 2.

Based on table 2, the condition of observedcompanies regarding to activities related toinnovation value chain was known. Thehighest total score was 88,6% indicating thatemployee typically did not collaborate onprojects across units, businesses, orsubsidiaries in doing innovation process. Thisphenomenon revealed that innovationfunction was imposed to a certain function orunit. Meantime, the lowest total score was39,3% indicating that company consideredabout the risk arised due to new idea.Considering the risk revealed that there wasany doubt of successful in implementing thenew idea. In some cases, company oftenpreferred waiting for the competitor to do the


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innovation and traced it fast. Therefore, thefeared risk had been learned. This analysis was

reinforced by total score of statement 12which obtained 66%.

Table 2. Total score of statement regarding to activity

Statement Totalscore

% out ofMaximum

score

Activity Phase

1. Our culture makes it hard forpeople to put forward novelideas

79 79/150=52,6%

in-house ideageneration

High scores indicatethat companies may beidea-poor

2. People in our unit come up withvery few good ideas of theirown

78 78/150 =52%

3. Few of our innovation projectinvolve team members fromdifferent units or subsidiaries

61 61/150 =40,6%

Cross-pollinationamongbusinesses4. Our people typically don't

collaborate on projects acrossunits, businesses, or subsidiaries

133 133/150 =88,6%

5. Few good ideas for new newproducts and business comeform outside the company

77 77/150 =51,3%

Externalsourcing ofideas

6. Our people often exhibit a " notinvenred here' attitude-ideasfrom outside aren't consideredas valuable as those inventedwithin

75 75/150 =50%

7. We have tough rules forinvestment in new projects- it'soften too hard to get ideasfunded

63 63/150 =42%

Selection High scores indicatethat companies may beconversion-poor

8. We have a risk- averse attitudetoward investing in novel ideas

59 59/150 =39,3%

9. New product developmentproject often don't finish ontime

70 70/150 =46,6%

Development

10. Managers have a hard timegetting traction developing newbusinesses

62 62/150 =41,3%

11. We're slow to roll out newproducts and businesses

63 63/150 =42%

Diffusion High scores indicatethat companies may be


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Statement Totalscore

% out ofMaximum

score

Activity Phase

12.Compatitors quickly copy ourproduct introductions and oftenmake preemptive launches inother countries

99 99/150 =66%

Diffusion-poor

13. We don't penetrate all possiblechannels, customer groups, andregions with new products andservice

67 67/150 =44,6%

Refering to figure 2, every statement was usedto explore a certain activity related toinnovation value chain. So that, thestatement’s total score should be unified withothers statement’s total score into certainactivity measured. Therefore, the total score ofeach activity related to innovation value chainwould be showed. Furthermore, company wasable to prioritize controling activities in termsof innovation value chain improvement basedon the obtained total score. Its score wasobtained by summing the certain statement’stotal scores up. Since, activities were explored

by different number of statements, thepercentage calculation should be conducted tomake them comparable. The example ofcalculation given was total score calculationfor idea generation explored by statement 1and 2. The score of 157 was obtained bysumming 78 and 79. Likewise, the maximumscore was obtained by summing 150 and 150.Therefore, the percentage obtained for ideageneration was 157/300=52,3%. By using thesame way, the obtained total score for othersactivites was presented on Table 3.

Table 3. Total score of activities related to innovation value chain

IVC Activity TotalScore

% out of Maximumscore Phase

In-house idea generation 157 157/300 = 52,3% High scores indicatethat companies may

be idea-poorCross-pollination among businesses 194 194/300 = 64,6%External sourcing of ideas 152 152/300 = 50,6%

Selection 122 122/300 = 40,6% High scores indicatethat companies maybe conversion-poor

Development 132 132/300 = 44%

Diffusion 229 229/450 =50,8%High scores indicatethat companies maybe Diffusion-poor

The presented total scores above showed thatthe higher total score of activity was cross-pollination. It revealed that almost of theobserved companies had weak collaboration

with cross units in innovation. Whereas, agood collaboration with other unit was able toease innovation process such as finding ideaso idea generation phase would be better.


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Commonly, key performance indicator usuallyused to measure the success of crosspollination was Number of high-quality ideasgenerated across units. Meantime, the lowest totalscore was achieved by selection activitymeaning that the awareness of company toscreen the new idea had been emphasized.Based on total score presented in table 3, thepriority of activities which were able to beimproved by footwear industries in East Javato improve innovation value chain should bediscussed. Successively, the priority ofactivities to be improved were cross-

pollination among businesses, in-house ideageneration, diffusion, external sourcing ofideas, development and selection. Asmentioned in literature review, somereferences did not discuss in detail that thephases of innovation value chain werecontributed by some activites. Therefore, totalscores of acitivities displayed in table 3 shouldbe unified into related innovation phaserefering to figure 1. The result of calculationwas presented in table 4.

Tabel 4. Total score of innovation value chain

IVC Activity TotalScore

% out ofMaximum

scorePhase


IdeaGenerating 503 503/900 =

55,8%

High scores indicate thatcompanies may be idea-

poor

Cross-pollinationamongbusinessesExternalsourcing ofideas

Selection Conversion 254 254/600 =42,3%

High scores indicate thatcompanies may beconversion-poor

Development

Diffusion Diffusion 229 229/450=50,8%

High scores indicate thatcompanies may be

Diffusion-poor


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Figure 6. Innovation Value Chain Performance

The figure 6 showed that firms were relativelygood at idea conversion (42,30%) but thecompanies had a little difficulties at diffusion(50,80%) and at idea generation (55,80%).From the result response showed (see table.2)that the weakest links (the innovation processbottleneck) was cross-pollination activity [inwhich the people typically did not collaborateon projects across units, businesses, orsubsidiaries (88,6%)], while the strongest linkswas selection activity [The companies had arisk- averse attitude toward investing in novelideas (39,3%)]. Based on observation to someindustries, it was observable that there wasweak collaboration between units. It wascaused by tight target. Almost of employeehad to work with tight schedule to fullfil thepromised schedule. Based on the FocusGruoup Discussion, almost idea of productdevelopment was sourced from the owner.This situation made the companies brave totake some risk and strong to involve in valuechain innovation activites. Involvement andleverage of owner are able to support the nextprocess.

4.3. Multivariate Analysis of Variance(MANOVA)

The general purpose of MANOVA is todetermine whether multiple levels ofindependent variables on their own or incombination with one another have an effect

on the dependent variables. In this section,MANOVA was employed to convey theinfluence of company characteristics toinnovation value chain comprising ideageneration phase, conversion phase anddiffusion phase. Company characteristicsthought to affect each phases were number ofemployees, age of company, and marketorientation. In additional information, ideageneration phase comprised in-house ideageneration, cross-pollination amongbusinesses, external sourcing of idea;conversion phase comprised selection anddevelopment and diffusion phase comprisedspread activity. Build hypothesis is the firststep to employ MANOVA. Generally,hypothesis is built as follow:H0: The means of all groups in dependent variable are

equalHi: At least there is a pair of mean of group in

dependent variable which is significantly different

In this case, nine pairs of hypothesis would bebuilt and be tested for each independentvariables suspected to influence each phases.Decision in aforementioned test was acceptedby comparing significance value and alphavalue. Popularly, there are four measureswidely used to calculate significance value inMANOVA. They are wilks’ Lambda, Pillai’strace, Hotteling-Lawley trace and Roy’s largestroot . The fundamental difference among


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them is the way to analyze dependent variablescombination causing the amount of variancein the data. Pillai’s trace is considered the mostreliable of the multivariate measure and offersthe greatest protection against error. By usingSPSS software the results of MANOVA wereobtained and the example given for the test asfollowed:

Objective of the testThe test were conducted to convey whethernumber of employees worked on Ideageneration comprising in-house ideageneration, cross-pollination amongbusinesses and external sourcing of idea. Baseon the aforementioned statement, it wasknown that number of employees would beindependent variable and idea generationwould be dependent variable.

HypothesisH0: The means of in-house idea generation, cross-pollination among businesses and external sourcing ofidea are equal.Hi: At least there is a pair of mean of group in ideageneration which is significantly different.

ResultThe output of SPSS software for MANOVAwas presented in Table 5.

Decision and interpretationRejecting or do not rejecting null hypothesis(H0) was accepted by comparing alpha valueand significance value of Pillai’s Tracepresented on Multivariate Testsc table. In thiscase, obtained significance value was 0.164which was greater than alpha value of 0.05.Therefore, the decision of test was do notreject H0 meaning that the means of in-houseidea generation, cross-pollination amongbusinesses and external sourcing of idea areequal.

Table 5. Multivariate Testsc

Effect Value F Hypothesis df Error df Sig.Intercept Pillai's Trace 0,954 69,607a 6,000 20,000 0,000

Wilks' Lambda 0,046 69,607a 6,000 20,000 0,000Hotelling's Trace 20,882 69,607a 6,000 20,000 0,000Roy's LargestRoot

20,882 69,607a 6,000 20,000 0,000

Employee

Pillai's Trace 1,034 1,336 24,000 92,000 0,164Wilks' Lambda 0,248 1,451 24,000 70,982 0,116Hotelling's Trace 2,032 1,566 24,000 74,000 0,074Roy's LargestRoot

1,521 5,830b 6,000 23,000 0,001

a. Exact statisticb. The statistic is an upper bound on F that yields a lower bound on the significancelevel.c. Design: Intercept + Employee

By following the same step above, significancevalue of MANOVA test for each independent

variable and dependent variables waspresented on Table 6.


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Table 6. Manova Result

Company Characteristics IdeaGeneration Conversion Diffusion

Business Scale (Number of Employees) 0.164 0.889 0.324

Operational period (Age of Company) 0.033 0.824 0.293

Market Orientation 0.044 0.84 0.185

Based on p-value presented above, it wasconcluded that company characteristicsinfluencing a certain phase of innovation valuechain significantly were age of company andmarket orientation. Specifically, both of theminfluenced idea generation phase. Age ofcompany would consider about company’slearning process in business including ininnovation process. It related to the experiencein understanding, finding and realizing the newidea which was proper with customerexpectation and market competition.

Therefore, company was able to provideexpeditious resources in innovation proccess.Meantime, market orientation would considerabout strategy determination which was properwith various characteristics of market such asexport market or domestic market. Age ofcompany and market orientation were inputfactor belonging to company charactheristicswhich were influencing the initial phase ofinnovation value chain. Conversion anddiffusion were not statistacally influenced sinceboth of them probably only kept pace withpolicy related to realization of new ideadetermined before. Therefore, ideagenerationwas critical point in harmonizingbetween innovation and company internalwhich were suitable with companycharacteristics. Meanwhile, there were noenough evidence to conclude that number ofemployees would influence the innovatioanvalue chain.

5. Conclusions

The study found that that the weakest links(the innovation process bottleneck) is cross-pollination activity [in which the peopletypically don't collaborate on projects acrossunits, businesses, or subsidiaries (88,6%)],while the strongest links is selection activity[the companies have a risk- averse attitudetoward investing in novel ideas (39,3%)].Based on observations in several industriesappear that collaboration and cooperationamong units within the company is very weak.This is due to the worker must work to a verytight work schedule to meet the targetcompletion time of products. Based on theresults of focus group discussions, most of theproduct development ideas come from theowner of the company. This condition causesthe company the courage to take risks inproduct development becomes very high andbecame the company's strength in innovationvalue chain activities. Based on p-value, thestudy found that company characteristicsinfluencing a certain phase of innovation valuechain significantly were company period (ageof company) and market orientation.Specifically, both of them influenced ideageneration phase.

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