supply chain management

Malhotra,Gosain,andElSawy:LeveragingSEBIstoEnableAdaptiveSupplyChainPartnershipsInformationSystemsResearch18(3),pp.260–279,©2007INFORMSTable3MeasurementPropertiesofConstructs

ConstructMean(SD)12345

1.Adaptivecreationknowledge 40 2798 0.77

2.Mutualadaptation 31 9193 0.330.84

ebusinessofstandardinterfaceselectronic 51 8662 0.210.420.87

4.Collaborativeexchangeinformation 51 0108 0.250.150.310.80

5.Cooperativenorm 41 6512 0.240.170.400.130.87

Note.SquarerootofAVEisshownalongthediagonal.

Table3,theAVEforeachconstructwashigherthantheconstructs’correlationwithotherconstructsasrequiredforvalidatingdiscriminantvalidity(Barclayetal.1995).Table3alsoprovidesthemeanandstan-darddeviationvaluesforallconstructs.

monMethodBiasAssessmentWetriedtominimizetheconcernofcommonmethodbiasbyrequiringtheRosettaNetchampionsateachenterprisetodrilldownwithintheirenterpriseand ndtheexecutive(“keyinformant”)responsiblefortheday-to-dayfunctioningofthepartnershipunderinvestigation(adifferentkeyinformantforeachrela-tionshipifmultiplesurveyswere lledbyacom-pany).Thekeyinformantthenassignedvarioussectionsofthesurveytobecompletedbymanagerswhotheyfeltweremostlikelytoprovideaccurateresponsesforalineofquestioning.So,ineffect,differentrespondentswereassignedto lloutdifferentportionsofourquestionnaire.How-ever,therewasstillsomeconcernaboutthepossi-bilitythatasinglerespondentmayhavecompletedthewholesurvey.Toallaysuchconcern,wecon-ductedtheHarmon’sone-factortest.Anexploratoryfactoranalysisrevealeda ve-factorstructure(Eigenvalue>1)whereallitemsdidnotloadonthe rstfactorandthe rstfactordidnotexplainmostofthevarianceinthedata(～40%oftheoverall76%varianceexplained).Therefore,theconcernaboutcommonmethodbiasinthedatacollecteddoesnotseemtobeanissue.Further,notallthehypothe-sizedpathsweresigni cantinthemodel,andthesigni cantpathsvaryintheirlevelofsigni cance.271Therefore,thecommonmethodbiasseemstobeevenlessofaconcern(Patnayakunietal.2006).4.3.MediationAnalysisOurresearchmodel(Figure1)proposesmediatedimpacts.Therefore,wefollowedtheprocessoutlinedbyPatnayakunietal.(2006)andSubramani(2004)totestformediationeffects.Westartedoutbycompar-ingourresearchmodel,whichproposesadirecteffectandmediatedeffectoftheuseofstandards,againstacompetingmodelthatproposesfullmediation(i.e.,theeffectoftheuseofSEBIsisfullymediatedthroughCIE).Theaimofsuchanalysisistostatisticallytestwhetherthedirecteffectoftheindependentvariables(IVs)explainsadditionalvarianceinthedependentvariable(DV)aboveandbeyondthemediatedeffectsthroughthemediatingvariable(MV).Theproposedfullmodel(directandmediatedeffects—partialmedi-ation)canbecomparedagainstthenestedmodel(fullmediation—mediatedeffectsonly)statisticallyusingPLSresults(Patnayakunietal.2006,Chinetal.2003,Subramani2004).AscanbeseeninTable4,theR2foradaptiveknowledgecreation(DV1 andMA(DV2 inthepartiallymediatedmodels(modelsthatincludedirecteffectsofuseofstandardinterfaces)were0.245and0.287,respectively,comparedwithR2of0.067and0.199inthealternatenested(fullymediated)models.Thisdifferentialeffectinpartialandcompletemedia-tioncanbefurtherinvestigatedbyaproceduresimilartostepwiseregression(Chinetal.2003,Patnayakunietal.2006).Apseudo-FstatisticcanbecalculatedbyusingthedifferenceinR2betweenthefullmodelandthenestedmodel.3Thef2statistic(calculatedbasedonthedifferenceinR2betweenfullandnestedmodel)forthetwodependentvariables(DVofSEBIs(directpaths)1andDV2 ,withrespecttouseis0.06and0.304.Therefore,thepseudo-Fstatisticis2.07(notsigni cantat0.05level)and10.34(signi cantat0.05level).Thisanalysissuggeststhattheaddi-tionaldirectpathfromtheuseofSEBIs(STD)toMAexplainsadditionalvarianceandaddssigni cantlytotheexplanatorypowerofthemodel.However,thereisalackofevidencesupportingadirectrelationship3F=f2 n k 1 ,with1, n k degreesoffreedom.n=samplesize,k=numberofconstructsinthemodel,andf2= R2partial

mediation R2fullmediation / 1 R2partialmediation).