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外文翻译
Effectsofareputationfeedbacksystemonanonlineconsumer-to-consumerauctionmarket
JianYanga,1,XiaoruiHub,2,HanZhangc,*
aDepartmentofIndustrialandManufacturingEngineering,NewJerseyInstituteofTechnologyNewark,NJ07102,UnitedStates
bJohnCookSchoolofBusinessSt.LouisUniversity,St.Louis,MO63108,UnitedStates
cCollegeofManagement,GeorgiaInstituteofTechnology,Atlanta,GA30332,UnitedStates
Received23February2006;receivedinrevisedform26February2007;accepted11March2007
Abstract
Thisresearchestablishesadynamicgame-theoreticmodelthatinterpretsthemechanismofreputationfeedbacksystemsinonlineconsumer-to-consumer(C2C)auctionmarkets.Basedonthemodel,anumericalstudyisconductedtorevealtheeffectsoffeedbacksystemsonauctionmarkets.ThestudyshowsthattheexistenceoffeedbacksystemsgreatlyimprovestheperformanceofonlineC2Cauctionmarkets:
buyersaremorewillingtotradeandgainmorebenefitfromthetransactions;sellers'honestbehaviorisencouraged,ashonestsellers'gainsareincreasedanddishonestsellers'gainsarereduced.Italsoofferspracticalinsightsonthedesignofafeedbacksystem:
rewardinganhonestly-behavingsellerislesseffectiveonpromotingmarketperformancethanpunishingacheatingseller.
Keywords:
Gaming;Internet;Auction/bidding;Feedbacksystem
1.Introduction
Westudy,fromboththeoreticalandnumericalangles,reputationfeedbacksystems(hence-forwardfeedbacksystems)inonlineconsumer-to-consumer(C2C)auctionmarkets.Ourpurposeistoshowthatreasonably-designedfeedbacksystemscanpromotetrustandmitigatefraud,andhelpensurethehealthydevelopmentoftheonlinemarkets.Wealsointendournumericalstudytooffermanagerialinsightsonthemarketimpactsoffeedbacksystems'designfeatures.
WhileonlineC2Cauctionmarketshavebeengrowingrapidlyinrecentyears,fraudinthesemarketsisalsoontherise.Theaveragelossperclaiminonlineauctionfraudsjumpedfrom$895in2004to$1917in2005[14].Inonlinemarkets,interactingwithstrangersisinevitable,andmosttransactionsbetweenbuyersandsellersareone-timedeals.Meanwhile,auctionsitesserveasmarketmakersforbuyersandsellerstomeet,butclaimnoliabilityforanyfraudulenttransactions.Forexample,eBayclaimsthatthey“havenocontroloverthequality,safetyorlegalityoftheitemsadvertised,thetruthoraccuracyofthelistings”[10].Thus,onlineauctionparticipantshavetofaceamarketwheregoodsarepurchasedbeforeonecanassurethequality.Therefore,promotingtrustbetweenstrangersandreducingtheuncertaintyandriskforonlinetradersarethecriticalissuesfacingcurrentonlineC2Cauctionmarkets.
VariousmechanismshavebeendesignedandutilizedinC2Cauctionmarketstopromotetrustandreducerisk.Specificonlinepaymentsystems(e.g.,PayPal)havebeenimplementedtoprovidesecureandinstantaneousonlinetransactionsforsmallonlinemerchantsandauctionbuyers.Onlineauctionsites(e.g.,eBay)havealsobeenofferinglimitedinsuranceorguaranteestoprotectauctionparticipantsandcreateasaferenvironmenttotrade.FeedbacksystemshavealsobeenofferedbymostoftheC2Cauctionsitestoreduceonlinetraders'uncertaintiesandtherisksassociatedwithonlinetrading.Forinstance,eBay's“FeedbackForum”isaformofcommunityenforcement.IneBay,aftereachtrade,bothbuyersandsellersareencouragedtoleavecommentsabouttheirtradingpartnersbasedontheirexperience.Commentsabouttradersarekeptundereachtrader'sprofile,andcanbeaccessedbyeveryonewhovisitseBay.Thisway,thesystemtriestodeterdishonestbehaviorbyconveyingfactsandopinionsaboutpasttrades.
Doesthefeedbacksystemworkasadvertised?
Thereissubstantialresearchthatsaysitdoes.Kollock[15]conceptuallysummarizesonlinereputationsystemsandconcludesthattheireffectivenesstomanagetherisksofunsecuredtradesseemstobeimpressive.Resnicketal.[20]reviewtheonlinereputationsystemsandarguethatthereputationsystemsappeartoperformreasonablywelldespitetheirtheoreticalandpracticaldifficulties.ResnickandZeckhauser[19]empiricallyexaminealargedatasetfromeBayandclaimthatthereputationsystemappearstobeeffective.BaandPavlou[1]empiricallyexploretheextenttowhichtrustcanbeinducedbyproperfeedbackmechanismsinelectronicmarketsandfindthatfeedbacksystemscangeneratepricepremiumsforreputablesellers.Resnicketal.[21]conductacontrolledexperimentoneBaytoassessreturnstoreputation.
Intherelatedeconomicsliteratureonrepeatedgamesinvolvingreputation,mostpapersdealwithasituationwhereplayersarenotstrangerstoeachotherandaplayer'sreputationisequivalenttotheentirehistoryofhisactions.ThereadermayrefertoKrepsandWilson[16],MilgromandRoberts[17],FudenbergandLevine[11,12],CrippsandThomas[6],Celentanietal.[5],BattigaliandWatson[3],etc.foraglimpseofthisbodyofliterature.InanonlineC2Cauctionmarket,playersarestrangerstoeachother,andwithoutapropermechanismbeinginstalled,theyknowverylittleabouteachotherwhentheytrade.Moreover,itisunrealisticforanymechanismtorequirethateverytrader'sentirehistoryberemembered(stored).Afeedbacksystem,ontheotherhand,offersaplatformwheremerelyafunction,usuallymany-to-one,ofeverytrader'sentirehistoryneedstobestored.
DellarocasandBakoshaverecentlystudiedfeedbacksystemsfromatheoreticalperspective.Dellarocas[7]providesanoverviewofrelevantpastresearchonreputationmechanismsbasedontherepeated-gamesetting,andpointsoutseveralfutureresearchdirectionsconcerningonlinefeedbacksystems.BakosandDellarocas[2]studyatradingsysteminvolvingasinglesellerwhorepeatedlytradeswithbuyers.Theauthorsshowthateventhemostprimitivefeedbacksystem,asincarnatedbythebinaryscore,canprovideaneconomicallymoreefficientsolutionthanthethreatoflitigation.Dellarocas[9]examinesasimilarmodelwithamoresophisticatedscoringsystem.Theauthorisabletoobtainaclosed-formsolutiontotheproblem,whichshowsthatsustainablecooperationsbetweenbuyersandthesellerareachievableaslongasthereturn–costratiotothesellerishighenough.Dellarocas[8]showsthatinacertainenvironment,thecombinationoflistingfeesandbinary-scorefeedbackscaninducesellerstoannouncethetruequalityoftheirproductsandatthesametimemaximizetheaveragesocialwelfare.Fromanotherangle,Miller,Resnick,andZeckhauser[18]examinetheelicitationofproperbuyers'feedbackwritingbehaviorthatmakesafeedbacksystemfunction.
AsBakosandDellarocas[2]andDellarocas[9]havedone,thispaperstudiesthemeritsofonlinefeedbacksystemsinarepeated-gamesettingwherebuyers'reputationscoreupdatingbehaviorismadeexogenous,andsellersareassumedtobeofdifferenttypes,witheachtypepertainingtoaspecifictendencytowardscheating.Itdiffersfromthetwoaforementionedpapersmostsalientlyintwoaspects:
1)multiplesellertypesareconsidered,sothatthescoreassociatedwithasellerinfershistypeinadditiontohisfuturebehavior.Becauseofthis,featuresofBayesianlearningandthemarriageofperceivedandactualsellerdistributionsappearintheformulation;and2)itcontainsageneral,asopposedtostylized,feedbacksystemandspeculatesthatasetofstochasticorderingrelationshipsinitsevolutioniswhatmakesthesystemwork.
Specifically,weproposeadynamicgame-theoreticframeworktomodelthemechanismofafeedbacksysteminanonlineC2Cauctionmarket.Intheframework,weassumethatallbuyersarehonestwhilesellersareofdifferenttypeswithdifferentpropensitiesforcheating.Thefeedbacksystemiscomprisedofscoresassociatedwithsellers,whichareupdatedbytheirrespectivetradingpartnersinwaysthataredependentonthetreatmentthepartnershavereceived.Asthetradinggameisplayedoverasufficientlylongperiodoftime,buyerswillformassociationsbetweensellers'scoresandtheirtypesonthebasisofBayesianlearningfrompastexperiences.Forexample,asellerwithmorecheatinghistory(higherscore)isconsideredmorepronetocheatingthanonewithlesscheatinghistory,andbuyerswilltreatsellerswithdifferentscoresdifferentially.Knowingthedifferentpossibletreatmentsfrombuyers,sellerswillweightheirdecisionsaboutcheating/playinghonestlybasednotonlyontheirimmediateone-timegainbutalsoontheirfuturebusinessopportunities,whichareaffectedbyhowtheirscoresareupdatedbytheirrespectivetradingpartners.
Followingthetheoreticalanalysis,anumericalstudyispresented.Thenumericalresultsverifythebenefitsintroducedbythefeedbacksystem.Ourresultsalsoshowthatevenwithfeedbacksystemsinplace,dishonestbehaviorfromtraderswithexcellentreputationratingsoccurs,butatthesametime,feedbacksystemsdoenhancetheoveralllevelofhonestyinonlineauctionmarkets.
Therestofthispaperisstructuredasfollows:
inSection2weestablishthedynamicgame-theoreticmodel;inSection3wediscusstheanticipatedpropertiesofvariousmarketperformances;inSection4weconductanumericalstudybasedonthemodelandanalyzethefindingsfromthestudy;andinSection5weconcludethepaper.Wehaverelegatedmaterialsofsecondaryimportancetoourappendices.Thelatterwillbeavailableuponrequest.
2.Problemformulation
2.1.Thestage-gamesetting
Eachsellerischaracterizedbyaprone-to-cheatingfactorz≥0,whichreflectshisaveragegainwhilecheatinginatransaction.Buyersareallofthe