Discussion Paper Number: JHD-2014-03 Discussion Paper End-User Collaboration for Process Innovation in Services: The Role of Internal Resources March 2014 Mona Ashok Henley Business School, University of Reading Rajneesh Narula Henley Business School, University of Reading Andrea Martinez-Noya University of Oviedo ii Ashok, Narula and Martinez-Noya, March 2014Theaimofthisdiscussionpaperseriesisto disseminatenewresearchofacademic distinction.Papersarepreliminarydrafts, circulatedtostimulatediscussionandcritical comment.HenleyBusinessSchoolistriple accreditedandhometoover100academic faculty,whoundertakeresearchinawiderange offieldsfromethicsandfinancetointernational business and marketing. dunning@henley.reading.ac.uk www.henley.reading.ac.uk/dunning Ashok, Narula and Martinez-Noya, March 2014 Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 20141 End-UserCollaborationforProcessInnovationinServices: The Role of Internal Resources Abstract This paper focuses on how to improve process innovation in service sectors. To do so, we analyse howtheinterplayofexternalknowledgesources(specifically,theintensityofend-user collaboration and the breadth of external collaboration) and the firms internal resources impact process innovation at the firm level. Survey data from 166 Information Technology Services firms providetheempiricaldata,whichistestedusingthepartialleastsquaresstructuralequation model. Our results demonstrate that benefits from collaboration are not automatic, as the firm's commitmentofinternalresourcesfullymediatestheimpactoftheintensityofend-user collaborationandbreadthofexternalcollaborationonprocessinnovation.Thus,internal resourcesbecomecriticaltomakeeffectiveuseoftheknowledgeresidingbothinternallyand externally,andkeymanagerialpracticesthatenableafirmtoextractbenefitsfromexternal collaboration are identified. Keywords end-usercollaboration,externalknowledgesources,internalresources,processinnovation, service industry JEL Classifications O32, M15, F230 Contacts Mona Ashok: jd101820@reading.ac.uk Rajneesh Narula: r.narula@henley.ac.uk Andrea Martinez-Noya: noya@uniovi.es John H Dunning Centre for International Business 2 Ashok, Narula and Martinez-Noya, March 2014 1Introduction Previousstudieshavehighlightedtheimportanceofinnovationintheservicesector(Howells and Tether,2004; OECD, 2005; Oliveira andvon Hippel, 2011), and that process innovation isa muchneglectedaspectofinnovationstudies(Davenport,1993;Keuppetal.,2012;Reichstein andSalter,2006).Indeed,priorresearchhasidentifiedaneedforbetterunderstandingoffirm-level factors that impact process innovation (Frishammar et al., 2012), specifically in the services context (Gopalakrishnan et al., 1999). In response to these research gaps, we analyse the impact (andinterplay)ofexternalknowledgesourcesandinternalresourcesonprocessinnovationin the services sector. In particular, we examine external knowledge sources as the intensity of end-user collaboration and the breadth of external collaboration. We argue that the intensity of end-usercollaborationplaysafundamentalroleforinnovationintheservicescontextparticularly whenservicefirmsarenotsupplierdominated.Usersareintegraltotheprovisionofaservice andarekeycollaborativepartners(HowellsandTether,2004).End-usersareknownto collaboratewithfirmstocommercialisetheirinnovations(vonHippel,2005),bothinthe services(OliveiraandvonHippel,2011),andtheprocesscontext(deJongandvonHippel, 2009).Further,breadthofexternalcollaborationisalsoexpectedtobeimportantforprocess innovation.Thisisbecausefirm-levelinnovationsaremorelikelytobecollaborativeand interactive, where the firm works with various external partners (OECD, 2005; Tether, 2002).However,firmsarenotexpectedtobenefitfromexternalknowledgesourcesjustbybeing exposedtothem.Instead,theymustdeveloptheabilitytorecognisethevalueofnewexternal knowledge,toassimilateit,andtoutiliseitforcommercialends(CohenandLevinthal,1990). Thus, firms need to commit internal resources to build competences, and to generate economic gains from innovation (Hamel, 1991; Teece et al., 1997). Internal resources are also necessary to supportlearning,encouragecollaborationwithpartners,andtranslatevaluableexternal knowledgeintocompetitiveadvantageforthefirm.Inthisstudy,weprovideabetter understandingofharnessingexternalknowledgesourcesandcommittinginternalresourcesin order to implement process innovation (Dahlander and Gann, 2010).Based on this, we consider the research questions: How do the external knowledge sources and internalresourcesimpactprocessinnovationintheservicescontext?andWhatroledothe internalresourcesplayinextractingvaluefromexternalknowledgesourcesforthebenefitof processinnovation?Toaddressthesequestionsweapplyrigoroustestingprocedures;we followedtheguidelinesprovidedbyGovindarajanandKopalle(2006)onreportingvalidand reliablemeasuresforinnovationstudies.Using theadvancedtechniqueofpartialleast squares-Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 20143 structural equation modelling (PLS-SEM) for model testing (Hair et al., 2012; Hulland, 1999), our studyshowsthatend-usercollaborationisimportantforprocessinnovationintheservice sector.However,externalknowledgesourcesonlycontributeoptimallytoprocessinnovation when the firm has committed internal resources. That means the firm's commitment of internal resourcesfullymediatestheimpactoftheintensityofend-usercollaborationandbreadthof externalcollaborationonprocessinnovation.Wealsocontributetomanagementpracticeby discussingtherelativeimportanceofexternalknowledgesourcesandinternalresourcesin improvingprocessinnovation;thusreducingtheinherentuncertaintyassociatedwithbusiness processchange(Halletal.,1993;O'NeillandSohal,1999).Finally,ourresultsprovideguidance that should enable managers to better control the factors that benefit process innovation in the service sector. 2Theory and hypotheses 2.1Process innovation and the strategic link to competitive advantage Althoughitiswellknownthattheintroductionofanewproduct,service,ororganisational changeisoftendependentoninnovationintheunderlyingprocesses,thestudyofprocess innovationhasreceivedlessacademicattention(Davenport,1993),particularlyintheservices industry (Gopalakrishnan et al., 1999). In response, we focus on process innovation in this study, andanalysethefirm-levelfactorsofexternalknowledgesourcesandinternalresourcesthat maximise the prediction of process innovation.Thetermprocessisdefinedasthetransformationofinputsthroughastructuredsetoftasks, donemanuallyorautomatically,withaviewtocreateanoutputthatisofsomevaluetothe users (Harrington, 1991). The term process innovation is defined as the implementation of a new orsignificantlyimprovedproductionordeliverymethodthatisofvaluetotheuser,where processinnovationincludessignificantchangesintechniques,equipmentand/orsoftware.The focus on value to user is added to the definition given by OECD (2005) because the users have tobewillingtopayfortheimprovedornewprocess,andend-uservalueprovidesasourceof competitive advantage (Woodruff, 1997). We adopt Vargo and Lusch's (2004, p. 2) definition of services,astheapplicationofspecialisedcompetences(knowledgeandskills)throughdeeds, processes, and performances for the benefit of another entity or the entity itself. Akeychallengeinundertakingprocessinnovationisthehighlevelofuncertainty.Thewider processchangeliteraturereportsambiguityoflong-termresultsofprocesschange,andfailure of a large proportion of process innovation projects to deliver their full potential (Baer and Frese, John H Dunning Centre for International Business 4 Ashok, Narula and Martinez-Noya, March 2014 2003).Despitethesechallenges,processinnovationenablesgenerationofeconomicrentsand developmentoforganisationalcapabilities(HuangandRice,2012;PisanoandWheelwright, 1995).Whenproperlyimplemented,processinnovationcandeliverimprovedefficiency, effectiveness, quality and reliability (Bauer and Leker, 2013; Davenport, 1993; Frishammar et al., 2012),whichhelpsthefirmtodelivervalueinternallyandtoitscustomers.Finally,process innovationcanleadtoasystemicchange,whichcannotbeeasilyimitatedbycompetitors;this provides a source of advantage to the service firms (Gopalakrishnan et al., 1999). Thus, the study offactorsthatmaximisethepredictivepowerof processinnovationisbeneficialfortheservice sector. 2.2The firm-level factors impacting process innovation Weinvestigatetheintensityandbreadthofknowledgesources,whereintensityisstudiedin relationtoend-usercollaborationandweanalysethediversityorbreadthofexternal collaboration.Takingtheservicecontext,end-usercollaborationisfundamentalastheyco-produce value (Vargo and Lusch, 2004). The breadth of external collaboration with partners like competitors,consultants,customers,government,groupcompanies,suppliers,anduniversities willalsobenefitprocessinnovation(HuangandRice,2012;Narula,2001;OECD,2005).Thisis becausethefirmisunlikelytopossessalltheskillsorcapabilitiestoinnovate,andsourcesof novel ideas are not restricted to the boundaries of the firm (Arundel and Kanerva, 2010; Powell et al., 1996). In this study, end-user is defined as the individual who is the consumer and co-producer of value, andexpectsbenefitfromusingaproductorservice(VargoandLusch,2004).Thefirmisthe produceroftheproductorserviceofferingstomeetend-userorcustomerneeds.Theterm collaborationisdefinedasthejointcreationofvaluebythefirmanditspartners,andinvolves exchange,sharingandco-development(Gulati,1995;PrahaladandRamaswamy,2004).Thus, purecontractingoutwork,wherethereisnoactiveparticipationisnotregardedas [collaboration] (Tether, 2002, p. 949). We also study the role of internal resources on process innovation, as reflected by of the degree ofexecutivemanagementengagement,thefirmscontinuousinvestmentinresources,andits processbenchmarkingpractices.Firmsneedtocommitinternalresourcestobuild competences,anddelivercompetitiveadvantagethroughprocessinnovation.Wearguethat commitment of internal resources mediates the benefit of collaboration on process innovation. Toourknowledge,thisisthefirstattempttostudytheinterplayofcollaborationandinternal Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 20145 resourcesonprocessinnovationintheservicescontext.Theresearchmodeliscapturedin Figure 1, and we derive the hypotheses in the following sections. 2.3End-user collaboration End-users are substantial contributors of innovations in services (Oliveira and von Hippel, 2011), andareanintegralpartoftheservicefirmsoperations(Vargoetal.,2008).Theend-users contributionintheprocesscontextisfarfromnegligible.Alargescalesurveyconcludedthat over half of the end-user firms modified or developed new process equipment or software, and a quarteroftheseinnovationswerelatercommercialised(deJongandvonHippel,2009).We proposethathigherend-usercollaborationwill providethefocalfirmgreateropportunitiesfor knowledgecreation,testingtheapplicationofinnovation,andreducingprocessinnovation uncertainty (von Hippel, 2005). This is because firms benefit by co-ordinating and learning from end-users(VargoandLusch,2004).Weanalyseend-usercollaborationthroughthedimensions of end-user integration, end-user innovation drivers, and end-user influence (Figure 1). 2.3.1End-user integration Wearguethatend-userintegrationisacriticalaspectofend-usercollaboration,because internalteamandend-usercohesivenesswillpositivelyinfluenceprocessinnovation (Davenport,1993).Previousstudieshavedemonstratedthatjointinnovationwithend-users providesopportunitiesfordifferentiation,customisationandcompetitiveadvantage(Xieetal., 2008).Theintegrationofleading-edgeend-userscontributestowardsthegenerationof innovativeideas,alignmentofinnovationactivitiestouserneeds,improvementofcross-functionalteamworking,andreductionofriskoffailureofinnovationprojects(Lthjeand Herstatt, 2004). For these reasons, end-user integration reflects end-user collaboration. 2.3.2 End-user innovation drivers that support collaborative behaviour Currentacademicthinkingenumeratesthebenefitsofcollaboratingwithleading-edgeend-users, because they are highly innovative and show specific traits (Schreier and Prgl, 2008; von HippelandRiggs,1997).Theseend-usersarehighlymotivated,havepriorprocessexperience, andseebenefitsofparticipationinprocessinnovation;wecallthesetraitsend-userinnovation drivers.Theunderstandingofthesetraitsisimportant,becauseend-usersaremoreeffective thanproduceremployeesinthecontextofinnovationsdeveloped(Hienerthetal.,2014).End-usersarewillingtocollaboratewithfirmsbecausetheygainaccesstocompetencesand resources. Thus, we posit that these traits are a critical aspect of the end-user collaboration. John H Dunning Centre for International Business 6 Ashok, Narula and Martinez-Noya, March 2014 2.3.3End-users influence on the diffusion and adoption of process innovation Investments in innovation are plagued with uncertainty and poor results, and process innovation isnoexception(BaerandFrese,2003;O'NeillandSohal,1999).Wearguethatend-user influenceisacriticaldimensionofend-usercollaboration.Thisisbecauseinnovativeend-users are able to diffuse innovation through a variety of means (Fller et al., 2013). The integration of end-usersasproblemsolversimprovesthemarketacceptabilityofinnovation(Bilgrametal., 2008).Indeed,thesurvivalofinnovationisoftendependentonend-users,whoarewillingto experiment the new product (service or process) (Malerba et al., 2007). Thus, we posit that end-user influence on the diffusion and adoption of process innovation is a critical aspect of end-user collaboration for process innovation. Therefore,basedonthepreviousarguments,weproposethatend-usercollaborationas reflected by end-user integration in process innovation, end-user innovation drivers that support collaborativebehaviour,andend-userinfluenceonthediffusionandadoptionofprocess innovationwillbebeneficialfortheundertakingofprocessinnovationintheservicesector (Figure 1). Hypothesis1:End-usercollaborationwillpositivelyinfluenceprocessinnovation results. 2.4Breadth of external collaboration Theinteractionbetweenafirmanditsenvironmentisknowntobeimportantforthefirmto exploitexternalsourcesofknowledgeforinnovation.Largescalesurveysconductedbythe EuropeanUnionsCommunityInnovationSurvey(EUCIS)andUKIS(2012)haveshownthat firmsundertakinginnovationbenefitbycollaboratingwithpartnerslikecompetitors, consultants,customers,government,groupcompanies,suppliers,anduniversities.Thus, collaborativepartnersarenotlimitedtoend-users.Wearguethattheexploitationofdiverse partnersknowledgewillbenefitprocessinnovationintheservicesector.Indeed,opennessto externalknowledgeismoreevidentinprocessinnovationthanproductinnovation(Robertson et al., 2012). The ability to use external sources of knowledge may also provide a differentiating capability for theinnovatingfirm(Frishammaretal.,2012).Firmsundertakingprocessinnovationarefaced with high levels of investment costs, uncertainty, and resource constraints.We argue that one of the ways firms can combat these challenges is to access knowledge across diverse partners. Thus, Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 20147 wepositthatthebreadthofexternalcollaborationwillbebeneficialfortheundertakingof process innovation in the service sector (Figure 1). Hypothesis2:Breadthofexternalcollaborationwillpositivelyinfluenceprocess innovation results. 2.5Internal resources as reflected by management practices Commitmentofinternalresourcesisnecessarytomanagethefrictionbetweenoldandnew knowledge,thereforeaidingprocessinnovationintheservicesector.Dedicatedinternal resourcesandunderlyingmanagementpracticeswillenablecapabilitydevelopment,resource adjustment, and the undertaking of process change. We build upon Foss et al.'s (2011) work and identify a mix of inward and outward-looking management practices. The first practice studied is the executive management engagement in process innovation (Kotter, 1995; Frishammar et al., 2012). The second practice examined is continuous investment, which has been a key subject of innovation studies (Zairi and Sinclair, 1995), and is an enabler of innovation (UKIS, 2012). Finally, theoutward-lookingpracticeofprocessbenchmarkinginvestigatesthefirmsstrategyto compareprocessperformance(AndersonandMcAdam,2004;Camp,1989).Thus,weexamine thedimensionsofinternalresourcesthroughthemanagementpracticesofexecutive management engagement, continuous investment, and process benchmarking (Figure 1). 2.5.1Executive management engagementThereisbroadconsensusthatexecutivemanagementgovernanceisaprecursortothesuccess ofprocessinnovation(Frishammaretal.,2012).Wearguethatexecutivemanagement engagementreflectscommitmentofinternalresources,becausemanagementrolesareknown tobekeyinitiators,leaders,andparticipantsinbusiness-wideprocessreengineeringprojects (ZairiandSinclair,1995).Executivesareresponsibleforpropagatinggoodpractices, encouragingcommunication,andenablingtransformationtoundertakeinnovation.In particular, executive management involvement is expected to be crucial for process innovation, becauseexecutivesareabletoguideinternalteamsandmaketheprocessinnovationproject attractive(Scott,1997).Theyarecriticalincreatingcollaborativebehaviourinternallyand sharedbeliefwithexternalpartners(Frishammaretal.,2012),andchallengingstatus-quo(Hall etal.,1993).Executivesareresponsiblefordrivingorganisationalvisionforprocesschange, addressingconflicts,governingmulti-functionprocesschange,motivatingstakeholders,and exploitingvaluefromexternalcontact(Kotter,1995).Therefore,wepositthatexecutive management engagement practice is intrinsic to the commitment of internal resources.John H Dunning Centre for International Business 8 Ashok, Narula and Martinez-Noya, March 2014 2.5.2Continuous investment practice Investmentstrategiesareattheheartofinnovationliterature.Ingeneral,poorinvestment practicesleadstofirm-levelinertia,andaninabilitytolearnfromnewknowledgeand technology(Narula,2002).Similarly,poorinvestmentstrategiescompromisethefirmsagility andresponsiveness(KogutandZander,1992),andafirmsrefusaltoinvestinupdatingits capabilitiescreateswhatLeonard-Barton(1992)callscorerigidity.Thus,wearguethat continuousinvestmentreflectsinternalresourcescommittedbythefirmbecauseafirms historicalinvestmentintolearning,skills,andcapabilitybuildingshapesthepathsitcantakein the future (David, 1985; Teece and Pisano, 1994). We posit that firms will benefit by investing in newprocessthinkingthatbreaksawayfromtheoldwayofworking.Continuousinvestment practiceshavethepotentialtoenhancecapabilities,toincreaseinteractionwiththe environment,andtoimproveresponsivenesstofluidconditions.Weexaminethefirms investmentpracticesinrelationtofactorsthatenableprocessinnovation,andarguethat continuous investment is intrinsic to the commitment of internal resources (Hall et al., 1993). 2.5.3Process benchmarking practice The term benchmarking is defined as a continuous process of assessing a firms product (service), practices,workprocesses,andcorecompetencypositionagainstcompetitorsandindustry leaders,forthepurposeoforganisationalimprovement(AndersonandMcAdam,2004;Camp, 1989).Thus,benchmarkingassessesthefirmsprocessmaturityandperformancethrough internal and external surveillance (Potter, 2012; Tidd and Bessant, 2011). The practice of process benchmarking challenges status-quo, responds to a business problem through diligentenquiry, andhelpslong-termprocesscorrections.Benchmarkingisusedasacontinuousimprovement strategy in the service sector (Dattakumar and Jagadeesh, 2003). Thus, process benchmarking is anintegralpartoftheservicefirmscommitmentofinternalresourcesthatenablesprocess innovation.Therefore, taking all the previous arguments into account, we propose that internal resources asreflectedbythemanagementpracticesofexecutivemanagementengagement,continuous investment, and process benchmarking are expected to benefit process innovation (Figure 1). Hypothesis 3: Internal resources will positively influence process innovation results. Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 20149 2.6Extractingvaluefromcollaboration:themediatingroleofinternal resources Thebenefitsofexternalknowledgesourcesonprocessinnovationarenotexpectedtobe automatic, but dependent on the commitment of internal resources to extract value. Firms have totakeastrategicdecisiontosystematicallyorganisetheirmanagementpracticestobenefit fromcollaborativearrangements.Internalresourcessupportedbygoodmanagementpractices minimiserisks,improveindustrypositioning,andsupportcollaborationforinnovation(Teece, 1986).KogutandZander(1992)arguethatonlyafirmthatlearnstocreateandtranslate knowledgeefficientlywithinitsorganisationalcontextcandifferentiateitselffromits competitors.Thus,thebenefitsofend-usercollaborationandbreadthofexternalcollaboration areexpectedtobedependentonthecommitmentofinternalresources;wheretheinternal resourceshelptomakeeffectiveuseoftheknowledgeresidinginternallyandexternallyto maximise the economic effect of process innovation (Cohen and Levinthal, 1990). Inrelationtothis,wehavediscussedthatend-usersaresubstantialcontributorsofinnovations intheservicesectors,andareanintegralpartoftheservicefirmsoperations.However,alarge scalestudythatanalysedtheimpactofcustomercollaborationonproductinnovation performance concluded that this impact was mediated through organisational practices (Foss et al.,2011).Further,weknowthatthemanagementpracticeofprocessbenchmarkingisakey mechanismtocaptureend-userneedsandtranslatetheneedsintoprocessinnovationtargets (Davenport, 1993). Innovative end-users can choose which firm to co-operate with; and they are morelikelytocollaboratewithafirmwithhighmanagementcommitment,eagernessto continually investment, and inclination to benchmark process performance. Thus, we argue that theimpactofend-usercollaborationonprocessinnovationwillbemediatedthroughthe internal resources committed by the firm (Figure 1). Hypothesis4:End-usercollaborationwillpositivelyinfluenceinternalresources committedbythefirm,andinturninternalresourceswillpositivelyinfluence process innovation. In a similar vein, we argue that breadth of external collaboration will impact process innovation asmediatedthroughinternalresources.Thisisbecausecollaborativearrangementswith multiplepartnersrequireorganisationalintentandcommitment.Jointworkingwithpartners entailsanorganisationalatmosphereofcontinuousdialogue,transparency,andtrust(Prahalad and Ramaswamy, 2004). Further, under dynamic environments and changing market conditions, the firms management is responsible to maximise the value of knowledge to improve the firms John H Dunning Centre for International Business 10 Ashok, Narula and Martinez-Noya, March 2014 innovativeness(Esterhuizenetal.,2012).Thus,wearguethattheimpactofbreadthofexternal collaboration on process innovation will be mediated through the internal resources committed by the firm (Figure 1). Hypothesis5:Breadthofexternalcollaborationwillpositivelyinfluenceinternal resourcescommittedbythefirm,andinturninternalresourceswillpositively influence process innovation. 3Research design 3.1The sampleThe data used for analyses came from 166 firm-level responses to a web-based survey. We chose to test the hypotheses in a specific service industry the Information Technology Services (ITS), because it provides the perfect setting for the factors analysed. The ITS industry plays akey role intheeconomy;althoughitcontinuallyfacesvolatileend-userneedsandtechnological advances(BlackmoreandHale,2012b;HaleandBlackmore,2012).Additionally,firmsfrom otherindustriesdependonITSfirmstoachievetransformation,undergostructuralchangeand deliverefficiency(Pavitt,1984).WeadoptedGartnerstaxonomytoidentifyfirmsoperatingin theITSindustry;thesefirmsareinvolvedinConsulting,Implementation,ITOutsourcing, BusinessProcessOutsourcing,andSoftwareSupportandHardwareSupport(Blackmoreand Hale,2012a).Finally,althoughpreviousstudiesalreadyshowthatend-userintegrationis especiallyimportantforITSfirmsbecausethesefirmsareknowledge-intensiveandbusiness servicesoriented(Hertog,2000),aswellashigh-technologyservicesoriented(Tether,2002); wealsospoketonumerousITSbusinessleaderstovalidatetheimportanceofresearchinthe area of process innovation for the industry. Aweb-basedquestionnairedesignwasusedtocollectthedata,andCampbells(1955) guidelineswerefollowedtoconfirmtheinformantscompetence.Theinformantsrolewas evaluatedusinganonlineprofile,andonlyfunctionalheadsandtopmanagementwerechosen to respond to the survey on behalf of their firms. We took various steps to improve the accuracy oftheresponses.ThequestionnairesweresentoutinthreewavesinMay,July,andOctober 2012,andtheinformantswereaskedtoreflectuponthesurveyquestions(onbehalfoftheir organisation)forthelastoneyear,andtocompletethesurveyattheBusinessUnitlevelif respondents performed various roles.Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 201411 Thesampleforthisstudywasobtainedfromtwosources,aprofessionalnetworkingsiteand fromexperiencednetworkmembers.Thechoiceofthesesourceswasrelevantfortheindustry ofourstudyforthefollowingreasons.Socialnetworksandcommunicationchannelsare fundamentalintheITSindustrytoattractskilledworkforce,preparetheorganisationssocial mediapractices,connectwithitsonline-activeend-users,andexploitvaluableknowledge exchangedonsuchnetworks(Ridder,2013;RidderandCohen,2013).Giventhesampling method,wetestedforbiasusingacceptedprocedures.Wecomparedresponsesbasedonthe time taken to complete the survey, the wave when the surveys were sent out, and the early and late responses across the waves (Armstrong and Overton, 1977). No evidence of difference was found in these three tests.Aftercleaning1theresponses,atotalof166usableresponseswereobtainedfromthe612 requestsforparticipation.Wegotaresponserateof27percent,whichiscomparabletoother studies using web-based survey design (Baruch and Holtom, 2008; Foss et al., 2011; Sauermann and Roach, 2013). A sample size of 166 is adequate to conduct the analysis in this study because ofthefollowingreasons.Samplesizeasadeterminationofalphalevel0.05,forasevenpoint scale,andacceptablemarginoferrorof0.03is118(Bartlettetal.,2001,p.46).Thecriteriaof samplesizetoindicatorvariablesratioof5:1wasalsomet(Hairetal.,2013a).Samplesize requirementsusingTabachnickandFidells(2001)guidelinesformultiplecorrelationand testingindividualpredictorswerealsomet.Inaddition,weundertooknumerousstepsto improveresponsequality.Wecontactedtherespondentsviaemailtoexplainthecontextand potential business implications of the survey, assured confidentiality, and promised a copy of the summaryresults.Somecriticismsofonlinesurveysarethatrespondentsmaylackonline experience, or may be excluded because they have no internet access (Evans and Mathur, 2005); theseissuesdonotapplytoourresearchcontext.Descriptiveanalysisofthesamplewas undertaken; the sample showed that small firms represented six percent of the sample with less thanequalto50employees,17percentofmediumfirmswithgreaterthan50,butlessthan equal to 1000 employees, and 77 percent of large firms with greater than 1000 employees. The respondentsindicatedthecountrypertainingtotheprocessinnovationexperience,where13 percentcamefromEurope,19percentfromNorthAmerica,28percentfromIndia,29percent from UK, and 11 percent from other countries. An analysis of external partners collaborated with

1 Responses of Not Applicable and Dont Know treated as system-defined missing data. Responses with lessthan25%missingdataareused,outliersaredeleted,andspecialattentionisgiventomissingdata analysis(Enders,2001;Rubin,1976).Thisisbecausemissingdatacreatesanissueforfurtheranalysis using structural equation modelling (Allison, 2003). We conclude that the data is missing completely at randombecauseLittlesMCARtestshowsp>0.05.EMalgorithmisusedtoreplacemissingdata (Dempsteretal.,1977),whichisanacceptedpracticeforlatentvariablemodelsandfactoranalysis (Schafer and Graham, 2002). John H Dunning Centre for International Business 12 Ashok, Narula and Martinez-Noya, March 2014 showedthat41percentofrespondentsreportedcollaborationwithCompetitors,42percent withUniversities,45percentwithGovernment,66percentwithConsultants,73percentwith Groupbusinesses,75percentwithSuppliers,and86percent(highest)withClientsor customers. 3.2Variables and measures The research instrument contained 31 reflective scale indicators, of which 6 indicators measure thedependentconstructprocessinnovation,andtheremaining25indicatorsmeasure independentconstructs,seeAppendixAforalistofindicators.Asevenpointscalewasused, becauseitallowsthestudyofgreatervariationofresponsesascomparedtoafivepointscale (Lietz,2010).Onlytheextremitieswerelabelled;scaleonewaslabelledasStronglyAgreeand scale seven as Strongly Disagree. Further, based on the instrument testing feedback, options of NotApplicableandDontKnowwerealsoadded2.Newscalesforalltheseconstructswere developed in line with Churchill (1979) and Rossiters (2002) guidelines, as appropriate existing scales were not found.We validated the measures throughdiscussionswith ITS executives, and furtherpilot-testedthesurvey.Wepresentareflectivemodel;wearguethatthedirectionof causalityisfromtheconstructtomeasuredindicators,asindicatedbyfacevalidityofthe measuredindicators.Further,theinstructionsusedonthequestionnairewouldlead respondentstoperceivethesequestionsasreflectionsorperceptionsoftheconstruct.The construct and measurement development is captured in Table 1. 3.2.1Second-order constructs Theconstructsofend-usercollaborationandinternalresourcesarestudiedassecond-order constructs(Figure1).Thesecond-orderconstructdesignisdrivenbytheobjectivesof theoreticalparsimony,whichassiststheconstructionofthehypotheses,andfutureresearch (Edwards, 2001). We study the commonality of first-order constructs through the second-order constructs,whichreducesmodelcomplexity.Itisimportantthatthesecond-orderconstructs are examined through their relationship with other constructs in the model (Law et al., 1998). In thisstudy,thehigher-orderconstructsarenotdesignedinvacuumbutusedinthemodelas predictors of the dependent variable process innovation, additionally end-user collaboration is a predictor of internal resources (Chin, 1998a). Thefirstorderconstructsdonotshowanycause-and-effectrelationshipwiththesecondorder factor. However, the causal flow is from the second-order construct to the first-order construct.

2 Studies show that when Dont Know choice is offered, the probability of its use increases (Lietz, 2010). Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 201413 The repeated indicators approach was taken, therefore items reflecting the first-order constructs were repeated at the second-order construct level in order to run PLS-SEM (Hair et al., 2013a). 3.3Method of analysis We analysed the data in SmartPLS (Ringle et al., 2005), which uses PLS-SEM. We inferred that PLS, as against covariance based (CB)SEM, was more suitable for our study based on the discussion onthetopicbyWold(1982),Hairetal.(2013a),andHairetal.(2013b).Akeydifference betweentheSEMmodelsisthatPLStradesinoptimalityforconsistencyinthestatistical inference. PLS-SEM is a preferred method for our study for the following reasons. Our research is aimed at maximising prediction of the endogenous factor, and analysing theory in its early stage ofdevelopment.PLSisdistribution-free,andachieveshigherstatisticalpowerwithsmaller samples(Henseleretal.,2009).However,asdiscussedinSection3.1(Thesample),ourstudy metwiththesamplerequirement,andtheindicatorsmetwiththeskewnessandkurtosis conditionswiththeexceptionofCI4thatshowedkurtosisvalueof-1.0827(indicatorsare describedinAppendixA).Inaddition,PLSestimatesremainconsistentforlargenumberof indicators per latent variable, PLS supports a complex model design, and is therefore relevant for ourstudy(Ringleetal.,2012).WestartedtheanalysisusingCronbachs(1951)discussionon redundancyissuesamongindicators3.Weevaluatedtheindicatorouterloadings,firstto ascertainindicatorsthatpoorlyreflecttheconstruct.Next,wecheckedforhighinter-item correlationforindicatorsataconstructlevel;wemaintainedonlyoneindicatoranddeleted other highly correlated indicators to address the redundancy issue (see Appendix A for indicator loadings).ThenweevaluatedthePLSpathcoefficientsoftheindicatorsfortheirfirstand second-order constructs as applicable. 3.4Potential sources of bias in the analyses The results should be viewed in light of potential sources of bias in the study. Our sample shows high(overthree-quarters)representationoflargeITSfirms.Allrespondentsaresenior managementITSexecutives;hencecorroborationoffirm-levelresponses(asperceivedbythe management) through the study of additional factors will benefit future research. Factors like ITS firmsgeographicspread,culturaldifferences,processinnovationteamcomposition,approach to unsuccessful versus successful projects, and level of diversity promoted will be beneficial. We tookacross-sectionalviewfortheresearch;applicationofthemodelinalongitudinalresearch

3Cronbach(1951,p.330)summarisesthisissuesas,Asetofredundantitemscancarrymuchless information than a set of independent items. John H Dunning Centre for International Business 14 Ashok, Narula and Martinez-Noya, March 2014 settingwillenabletheunderstandingofhowfirm-levelfactorsimpactprocessinnovationover time. 4Results We adopted the guidelines set in literature to test a reflective outer and inner model using PLS-SEM.4.1Outer (measurement) modelReflectivemeasurementmodelwasevaluatedthroughtheassessmentofreliabilityandvalidity oftheconstructs.Reflectiverelationshipbetweentheconstructanditsindicatorsiscalled ModelA(Wold,1982).ThetraditionalCronbachsalphaissaidtounderestimatetheinternal consistencyreliabilityandassumesequalreliabilityofallconstructindicators,thereforethe compositereliabilitymeasurewasusedfortheassessment(Chin,1998b).Allconstructsinour modelreportedgreaterthan0.9compositereliabilityvalues,soexceedingthe0.7criterion (Henseleretal.,2009).Next,theconstructsconvergentvaliditywasassessedthroughthe average variance extracted (AVE) scores. AVE scores of greater than 0.5 is preferable so that the construct explains at least 50 percent of each of its indicators variance, and all constructs in our modelmetthiscriterion(Henseleretal.,2009).Finally,discriminantvaliditywastested;by qualitative assessment of face validity, by checking indicator construct crossloadings (Appendix B),andbyexaminingthatthesquarerootofconstructsAVEwasgreaterthanthecorrelation withtheotherconstructs(fortheinnermodel,andthefirst-orderconstructsinthestudy,see AppendixC)(Henseleretal.,2009).Bootstrappinganalysiswasundertakentoascertaincross-loadings, using 5000 sub-samples as prescribed by Hair et al. (2013a). 4.2Inner (structural) model Theinnermodelspathassessmentwasundertakenafterevaluationoftheoutermeasurement model.First,thesecond-orderreflectiveconstructswereanalysed,andtheresultsprovide theoretical confirmation. All path coefficients are significant. Two of the three paths for end-user collaborationandallthreepathsforinternalresourcesshowgreaterthan0.7pathcoefficients, therebymeetingChin(1998a)guidelineforusingsecond-orderconstructs.Thepath assessment,t-statisticandsignificancelevelsarecapturedinFigure2.Thecontrolvariablesof firmsizeandcountrypertainingtotheprocessinnovationexperienceshownon-significant impact on process innovation, the path coefficients for both control variables are 0.00 (Figure 2). Henley Discussion Paper Series Ashok, Narula and Martinez-Noya, March 201415 Inaliteraturereview,Damanpour(2010)foundthattheimpactoffirmsizeonprocess innovation was inconsistent across studies. Next,thehypotheseswereassessedusingone-tailedtests,becausethepathcoefficientsare hypothesisedtobepositive.Thelinkagesignswereassessedfirstforthedirecteffects.H1,H2, and H3 show partial empirical validation with the correct signs (Henseler et al., 2009). H4 results indicatedthatthepreviouslysignificantandpositivelinkagefromend-usercollaborationto processinnovationhadasignchangeandwasinsignificantwhenmediatedthroughinternal resources.Similarly,H5resultsconcludedthatthepreviouslysignificantandpositivelinkage frombreadthofexternalcollaborationtoprocessinnovationwasinsignificantwhenmediated through internal resources. Sobels (1982) test for indirect effect was undertaken using the path assessment results. The z-values for indirect effects of breadth of external collaboration and end-usercollaborationonprocessinnovationthroughinternalresourceswere3.75and6.23 respectively4;weconcludethattheindirecteffectinbothcaseswasnotequaltozero.Further, usingBaronandKennys(1986)threestepguidelinestotestmediation,weconcludedthat internalresourcesfullymediatestheeffectofbreadthofexternalcollaborationandend-user collaborationonprocessinnovation.PLS-SEMdoesnothaveaglobalgoodness-of-fitindex thereforevarioustestswereundertaken(Table2).Wetestedformulti-collinearityofthe predictor constructs and all VIF values are