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ConjointAnalysis,RelatedModeling,andApplicationsAuthors:JohnR.Hauser&VithalaR.RaoPresenter:jellyloverContents0308091820PageIntroductionBasicelements&theirevolutionSummary&conclusionsOriginschallenges12345R.DuncanLuceandJohnW.Tukey,“SimultaneousConjointMeasurement:ANewTypeofFundamentalMeasurement,”JournalofMathematicalPsychology,February1964,p.I.

Thefirstmarketing-orientedpaperonconjointmeasurementwasbyPaulE.GreenandVithalaR.Rao,“ConjointMeasurementforquantifyingjudgmentalData,”JournalofMarketingResearch,August1971,p.355.Accordingly,itnowseemsusefultoadoptthename,“conjointanalysis”tocovermodelsandtechniquesthatemphasizethetransformationofsubjectiveresponsesintoestimatedparameters.Green,PaulE.andV.Srinivasan(1978),“ConjointAnalysisinConsumerResearch:IssuesandOutlook,”JournalofConsumerResearch,5,2,(September),103-123.Origins:papers123IntroductionExample:ProcessesofConjointAnalysisIntroductionConsideranexample…Price?Brandname?Onlyavailableonline?1brandnameretailprice(yuan)onlyavailableonlineRespondents'evaluation(ranknumber)1mi1699Y32Motorola1699Y13HTC1699Y24mi1999Y125Motorola1999Y116HTC1999Y107mi2499Y148Motorola2499Y159HTC2499Y1310mi1699N611Motorola1699N412HTC1699N513mi1999N914Motorola1999N715HTC1999N816mi2499N1717Motorola2499N1618HTC2499N18ExperimentalDesignforevaluationofaXiaoMi2Weassumedthattheoverallpreferencewasanadditivesumofthe“partworths”ofthefeatures,representedeachfeaturebyaseriesofdummyvariables,usedmonotonicregressiontoestimatethecontributionofeachfeaturetooverallpreference.3FormoreinformationaboutMONOTONICREGRESSION,pleasereferto:Green,PaulE.andV.Srinivasan(1978),“ConjointAnalysisinConsumerResearch:IssuesandOutlook,”JournalofConsumerResearch,5,2,(September),103-123.OnlyAvailableOnlineRetailPriceBrandnameBrandnameRetailPriceOnlyAvailableOnlineUtilityUtilityUtilityUtilityRangesResultsofcomputeranalysisofexperimentaldata4Green,PaulE.andJerryWind(1975),“NewWaytoMeasureConsumers’Judgments,”HarvardBusinessReview,(July-August),107-117.ForMoreInformation,pleaserefertothepaperblew:CONTENTSBasicelements&theirevolutionPart1DecomposingtheProduct

andServicePart2RepresentationStimuliPart3HaveMercyonthe

RespondentPart4FormatsofDataCollectionPart5EstimationMethodsPart1DecomposingtheProductandServiceElements

intowhichtheproductisdecomposedAscompleteasfeasibleUnderstandable

torespondentsUseful

toproduct-developmentteamAsseparableasfeasibleConsideration1SelectingCriteriaThefunction

bywhichtheelementaldecompositionismappedontooverallpreferenceConsideration2PreferentialindependenceTheconjointfunctioncanberepresentedbyanadditive(ormultiplicative)decompositionSatisfiedN.S.TheconjointfunctionismoredifficulttoestimateandinteractionsarenecessaryPreferenceisdefinedoverriskyfeaturesPart2RepresentationofStimuliCombinationsofphysicalmodels,photographsandverbaldescriptions(Wind,etal.,1989)Virtualprototypestoweb-basedrespondents(DahanandSrinivasan2000)VerbalandpictorialdescriptionsoncardsVideotapes(Vavra,Green,etal.,1999)RepresentationofStimuliEvolutionPart3HaveMercyontheRespondents1.Itisdifficultforcustomerstorankmorethanadozenprofiles2.TheaccuracydegradesasthenumberofquestionsincreasesRealize

tradeoffanalysisTwostagesHierarchicalintegrationself-explicatetasks……Part4FormatsofDataCollectionFullProfileEvaluationsFormatsofDataCollectionFormatsofDataCollectionPartialProfileEvaluationsFormatsofDataCollectionStatedPreferenceFormatsofDataCollectionSelf-explicatedMethodsFormatsofDataCollectionConfiguratorshttp:///Example：Part5EstimationMethodsEstimationMethodsRegression-basedMethodsSimplicityandAvailabilityofsoftwarewithwhichtoperformestimationsRequiremoreobservations**EstimationMethodsRandom-UtilityModelsHierarchicalBayesEstimationDirectComputationBasedonSelf-ExplicatedImportanceEstimationBasedonNewOptimizationMethodsChallengesPragmaticIssuesAnalysisoftradeoffsbetweencomplexityofanalysis,costanddifficultyofdatacollection,andmanagerialapplication.AnalysismethodsVSNewformsofdataMeta-analysesofthevariedapplicationsunderavarietyofmanagerialproblemsExternalvalidity&comparativestrengthsofthevariousmethodsMethodstohandlelargenumbersoffeaturesChallengesConceptualIssuesRelativemeritsofthemeritsofthevariousdata-collectionmethodsPriceCompetitors→Equilibrium,non-equilibriummodelsDiffusionScenarioproblemChallengesMethodologicalIssuesAnticipatefurtherexplorationofhybridmethodsthatcombinedatafrommultipledatasour