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1、Availableonlineatwww.sciencedirect.comElectronicCommerceResearchandApplications7(2008)399–410www.elsevier.com/locate/ecraTheeffectsofconsumerknowledgeonmessageprocessingofelectronicword-of-mouthviaonlineconsumerreviewsa,*bDo-HyungPark,SaraKimaKAIST(KoreaAdvancedInstituteofScience
2、andTechnology)BusinessSchool,207-43Cheongrangri-Dong,Dongdaemun-Gu,Seoul130-722,SouthKoreabUniversityofChicago,Chicago,IL,UnitedStatesReceived30November2006;receivedinrevisedform24December2007;accepted24December2007Availableonline8January2008AbstractThispaperinvestigatesonetypeo
3、felectronicword-of-mouth(eWOM),theonlineconsumerreview.Thestudyconsiderstwocom-ponentsofreviewstructure:thetypeandthenumberofreviews.Usingthecognitivefittheory,weshowthatthetypeofreviewscanbeakeymoderatingvariabletoexplaintheinconsistentrelationshipbetweenconsumerexpertiseandWOMi
4、npreviousresearch.Thisstudyexamineswhichtypeofreviewscognitivelyfitsconsumerswithahigh(low)levelofexpertise.Usingtheelaborationlikelihoodmodel(ELM),wealsoinvestigatethattheeffectsofthetypeofreviewsandthenumberofreviews.Thehypothesesweretestedusinga2(levelsofexpertise)2(typesofrev
5、iews)2(numberofreviews)mixeddesignincludingtwocontrolconditions.Theresultsshowthattheeffectofcognitivefit(thetypeofreviews)onpurchaseintentionisstrongerforexpertsthanfornoviceswhiletheeffectofthenumberofreviewsonpurchaseintentionisstrongerfornovicesthanexperts.Thispaperdeliversman
6、agerialimplicationsforonlinesellerspro-vidingconsumercreatedreviewsalongwithadvertisements.Ó2008ElsevierB.V.Allrightsreserved.Keywords:Electronicword-of-mouth;Onlineconsumerreviews;Expertise;Cognitivefittheory;Elaborationlikelihoodmodel1.Introductionsotheywanttogetinformationonbe
7、nefitstoevaluateaproduct.However,itisnoteasyformanagerstorecognizeAproductprogressesthroughasequenceofstagesfromwhenandhowtheyshouldchangetheirmethodofprovid-introductiontogrowth,maturity,anddecline[1].Thisingproductinformationbecausethestagesarenotdefi-sequenceisknownastheproduct
8、lifecycle.However,nitelydiscrete.Then,domarkete
