外文翻译.docx

外文翻译.docx

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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