The Impact of Computerized Agents on Immediate Emotions, Overall Arousal and Bidding Behavior ...

30 Pages·2015·0.56 MB·English

ToappearinJournaloftheAssociationforInformationSystems(JAIS) 1
InstituteofInformationSystemsandMarketing(IISM)
The Impact of Computerized Agents on Immediate Emotions, Overall Arousal and
Bidding Behavior in Electronic Auctions
Timm Teubnera, *, Marc T. P. Adamb, Ryan Rioardanc
a KarlsruheInstituteofTechnology(KIT),Germany
b UniversityofNewcastle,Australia
c Queen’sUniversity,Ontario,Canada
ABSTRACT
The presence of computerized agents has become pervasive in everyday live. In this paper, we examine the impact of agency
onhumanbidders’affectiveprocessesandbiddingbehaviorinanelectronicauctionenvironment. Inparticular,weuseskinconductance
responseandheartratemeasurementsasproxiesfortheimmediateemotionsandoverallarousalofhumanbiddersinalabexperiment
withhumanandcomputerizedcounterparts. Ourresultsshowthatcomputerizedagentsmitigatetheintensityofbidders’immediate
emotionsinresponsetodiscreteauctionevents,suchassubmittingabidandwinningorlosinganauction,aswellasthebidders’overall
arousallevelsduringtheauction. Moreover,biddingbehavioranditsrelationtooverallarousalareaffectedbyagency: whereasoverall
arousalandbidsarenegativelycorrelatedwhencompetingagainsthumanbidders,thisrelationshipisnotobservableforcomputerized
agents. Inotherwords,lowerlevelsofagencyyieldlessemotionalbehavior. Theresultsofourstudyhaveimplicationsforthedesignof
electronicauctionplatformsandmarketsthatincludebothhumanandcomputerizedactors.
Keywords:
agency;auctions;e-commerce;NeuroIS;emotions;arousal;human-computerinteraction
1 | Introduction ing many of these tasks. As markets have automated and
increasedtheiroperatingspeeds,sohavetheparticipantsin
Informationtechnologyhasrevolutionizedmarkets. While thesemarkets. Theyrelyoncomputerizedagentstorepre-
traditionally, a market was a place where people came to- senttheirinterests,suchassnipingagentsoneBayemployed
gether to trade, a large portion of today’s trading activity “to avoid a bidding war” (Ariely et al., 2005). In modern
inmarketsisactuallyconductedbyandwithcomputerized financialmarkets,thechancesaregreatertotradewithan
tradingagents. Anecessaryprecursortothisdevelopmentis algorithmthanwithahumanbeing(Brogaardetal.,2014).
theubiquitousadoptionofelectronicmarketsinindustryand Demonstratingtheimportanceofcomputerizedtraders,Hen-
government (Bakos, 1991). Today, electronic markets are dershottandcolleaguesshowedthatalgorithmictraderswere
pervasiveandanintegralpartofoureverydaylife. Billionsof responsibleforalargeincreaseinliquidityavailableonthe
transactionstakeplaceinelectronicmarketsandplatforms NewYorkStockExchange(Hendershottetal.,2011). Taken
on a daily basis. They may be as small as the purchase of asawhole,computerizedtraderspresumablyareresponsible
anelectronicnewspaperoraslargeasinfinancialandspec- forover70%ofthevolumeinUSstockmarkets(Brownlees
trum auctions. In particular, auctions are frequently used etal.,2011). Asubsetofcomputerizedtraders,calledhigh
inelectronicconsumermarkets(e.g.,ebay.com,dubli.com, frequencytraders(HFT),makeupmorethan40%ofthetrad-
madbid.com). Regardlessofmarketsize,bidding,searching, ingvolumeonNasdaqandwereshowntobemoreinformed
matching,clearing,andsettlementprocessesareallsupported thannon-HFTs(Brogaardetal.,2014). Clearly,computerized
byITsystemsdesignedtoreducetransactioncosts,increase tradersplayanimportantroleinelectronicmarketstoday. As
the probability of finding trading partners, and to support part of this development, they also became competitors of
complexdecisionmaking. Forthemostpart,societyhascome humantraders. Theresearchontheimpactofcomputerized
toacceptthefactthathumansarenolongeractivelyperform- tradersonthehumantraders’affectiveprocessesandbehav-
*Correspondenceto:KarlsruheInstituteofTechnology,Kaiserstr.12,Bldg.01.80,76131Karlsruhe,Germany.Tel.:[phone];fax:[phone]
8399.E-Mail:[email protected]
2 ToappearinJournaloftheAssociationforInformationSystems(JAIS)
ioraswellasonoverallmarketefficiencyisstillinitsinfancy, thefollowingsection,weoutlinethetheoreticalbackground
and it is unclear how accepting market participants are of andhypothesesofourstudy. Thenextsectionthenoutlines
this trend. However, given the amount of negative public theexperimentaldesign. Intheresultssection,weanalyze
presssurroundingalgorithmicandhigh-frequencytradingin thebidders’immediateemotionsinresponsetodiscreteauc-
financialmarkets,itissafetosaythatsomeparticipantsare tionevents,aswellastheinterplayofagency,overallarousal,
unhappyaboutthesituation.1 Thisleadstothequestionhow biddingbehavior,andmarketefficiency. Finally,wediscuss
the increasing importance of computerized agents in elec- thetheoreticalandmanagerialimplicationsofthestudyand
tronicauctionsandthedegreetowhichusers’believethey presentourconclusions.
areinteractingwithhumanornon-humanactors(agency)
influencestheirdecisionmakingprocesses.
To study the impact of agency on market participants’ 2 | TheoreticalBackgroundandHypotheses
behavior,affectiveprocesses,andmarketefficiency,wecon-
ductaNeuroISlaboratoryexperimentinwhichparticipants Overthepastdecade,thepresenceofcomputerizedagents
bidagainstotherhumanparticipantsinonetreatment(high hasbecomepervasiveineverydaylive(Foxetal.,inpress).
agency), and against computerized bidding agents in the Wheretraditionallyhumansdirectlyinteractedwithotherhu-
othertreatment(lowagency). Thelevelofagencyistheonly mans,todaymanyusersinteractwithcomputerizedagents.
meaningful difference between treatments. Applying Neu- Thedomainofcooperativeandcompetitiveinteractionshas
roISmethodsisparticularlyinsightfulinthisstudy,asthey thus been extended from a purely human environment to
allow us to measure proxies for market participants’ affec- a ”mixed zone,” in which sentient human beings and arti-
tiveprocesseswhichmaypartiallybeunconsciousinnature. ficialagentsinteract. Therangeofexperiencesiscaptured
Moreover,NeuroISenablesustoassessthisdataatdifferent by the notion of agency, which is defined as the extent to
stagesoftheauctionprocesswithouthavingtointerruptthe whichauserbelievesthatheorsheis“interactingwithan-
participantsduringdecisionmaking(Riedletal.,2014;vom othersentienthumanbeing”(Guadagnoetal.,2007,p. 3).
BrockeandLiang,2014). Inparticular,wemeasurepartici- Thereby,settingsinwhichusersknowinglyinteractwithcom-
pants’heartrates(HR)andskinconductanceresponses(SCR) puterizedagentsyieldlowagency,whereassettingsinwhich
asproxiesfortheiroverallarousalandtheirimmediateemo- usersknowinglyinteractwithotherhumansyieldhighagency
tions. Thesemeasuresarecombinedwithmarketresultsto (Guadagnoetal.,2007).
provideinsightsintoparticipants’affectiveprocessesduring While computer agents now also play an increasingly
auctionsandinresponsetodiscreteauctionevents,suchas importantroleinelectronicauctions(Arielyetal.,2005;Bro-
submittingabidandwinningorlosinganauction. Bycap- gaardetal.,2014),researchontheimpactofcomputeragents
turingparticipants’overallarousalandimmediateemotions on the human bidders’ affective processes and behavior is
indifferentscenarios(humanopponents/computeroppo- scant. Inthefollowing,wethereforeaimtocontributetoan
nents,i.e. highagencyandlowagency),weseektobetter improvedunderstandingofaffectiveprocessesandbehavior
understandrecentdevelopmentsinelectronicmarkets,and inelectronicauctionsbybuildingonestablishedresearchon
takeasteptowardsexplainingtheimpactofemotions. theroleofagencyindifferentcontextsofhuman-computer
Ourresultsshowthatparticipantsaresignificantlymore interaction. Thisresearchhasshownthatagencyhasadefi-
aroused in the high agency treatment (human opponents) niteinfluenceontheuser’saffectiveprocessesandbehavior,
than in the low agency treatment (computer opponents). whichinturndepends,amongotherfactors,onthetypeofthe
Moreover,participantssubmitlowerbidswhentheyexperi- taskandthecomputeragents’behavioralrealism(Blascovich
encehigherlevelsofoverallarousal. Whatisstrikingisthat etal.,2002;Foxetal.,2014;Guadagnoetal.,2007;Limand
therelationshipbetweenoverallarousalandbiddingbehav- Reeves,2010).2 Inparticular,whileitwasfoundthatagency
iorisonlypresentinthehighagencycondition. Inthelow hasaninfluenceinthedomainofcommunicativetasks(e.g.,
agencycondition,incontrast,bidsandoverallarousallevels persuasive communication (Guadagno et al., 2007, 2011),
are lower–and uncorrelated. Additionally, we observe par- self-introduction(NowakandBiocca,2003;vonderPütten
ticipants’immediateemotionsinresponsetoauctionevents. etal.,2010)andchatting(Appeletal.,2012)andcooperative
Again,wefindthatparticipantsexhibitstrongerreactionsin tasks(e.g.,tradingitems(LimandReeves,2010),bargaining
thehighagencycondition. Thisisthefirstpapertostudythe (Sanfeyetal.,2003),andtrustgames(Riedletal.,2014)),
interplayofagency,immediateemotions,overallarousal,and this influence seems to be even more pronounced in com-
economicbehavior(bidding). petitivetasks(Gallagheretal.,2002;LimandReeves,2010;
Theremainderofthisarticleisorganizedasfollows. In Polosanetal.,2011;WilliamsandClippinger,2002). Inthe
1See:“High-SpeedTradersRaceToFendOffRegulators,”WallStreetJournal,December28th,2012.
2Intheliterature,theinvestigationofagencyoftenalsoconsiderstheinfluenceofthecounterpart’sgraphicalrepresentation(e.g.,NowakandBiocca
(2003);Appeletal.(2012);Riedletal.(2014);Foxetal.(2014)).Asthoughoneofthefeaturesofonlineauctionsisthattheparties“remainanonymousand
transactionsbetweenpartiesareofanimpersonalnature”(Steinhartetal.,2013,p.48),wedeliberatelyfocusontheroleofagencyinanenvironmentwithout
graphicalrepresentations.WecomebacktothisaspectinmoredetailintheLimitationsandFutureResearchsection
ToappearinJournaloftheAssociationforInformationSystems(JAIS) 3
contextofcomputergames,forinstance,(LimandReeves,
2010) found that the differences in affective processes be-
tweenhighandlowagencysettingswereparticularlystrong
incompetitiveratherthancooperativeinteraction.
Asauctionsarecharacterizedbyaninherent“socialcom-
petition”(Delgadoetal.,2008,p. 1849),andthusfallinto
thecategoryofcompetitivetasks,weexpectthatagencyalso
playsanimportantroleinelectronicauctions. Inourstudy,
weemployfirst-pricesealed-bid(FPSB)auctionstoinvesti-
gatetheroleofagency. InFPSBauctions,eachbiddersubmits Figure1: ResearchModel
onesinglebidwithoutknowingtherespectiveotherbids,the
highestbidwinstheauction,andthewinningbidderpaysa
priceintheamountofhisorherbid(Engelbrecht-Wiggans TheImpactofAgencyonImmediateEmotionsandOver-
and Katok, 2008; Vickrey, 1961). Classical auction theory allArousalinElectronicAuctions(H1&H2)
assumesthatbiddinginanauctioncanessentiallybeunder-
Fromanevolutionarypsychologyperspective,engaginginco-
stoodasamaximizationofexpectedutility. Incontrast,our
operativeandcompetitiveinteractionwithotherconspecifics
studystartsfromtheintuitionthat(i)biddinginanelectronic
hasalwaysbeenanimportantfactorinsurvivalandoverall
auctionalsoinvolvesaffectiveprocesses(i.e.,experiencing
humansuccess(Decetyetal.,2004;Lochetal.,2006). To
intenseimmediateemotionssuchasthejoyofwinningand
succeedinsocialinteractions,humanshavedevelopedawide
the frustration of losing (Astor et al., 2013; Delgado et al.,
rangeofstrategies,buildingbothoncognitive(e.g.,analyti-
2008; Ding et al., 2005), and competitive arousal (Ariely
calandlogicalreasoning,perspectivetaking)andaffective
and Simonson, 2003; Ku et al., 2005), and that (ii) these
processes(e.g.,immediateemotions,overallarousal). Asthe
processesareinfluencedbyagency. Withrespecttoaffective
“humanbraindevelopedatatimewhenonlyhumanbeings
processes,weareparticularlyinterestedinthebidders’imme-
wereabletoshowsocialbehavior,”theseprocessesinherently
diateemotions,i.e.,short-livedsubjectiveexperiences(Rick
haveastrongfocusonhumancounterparts(vonderPütten
andLoewenstein,2008)inresponsetospecificauctionevents
et al., 2010, p. 1642). In order to assess and predict the
(Astoretal.,2013),aswellasinthebidders’overallarousal,
intentions, beliefs, and behaviors of others, humans make
i.e.,theintensityoftheoverallemotionalstate,duringthe
auctionprocess(Kuetal.,2005).3 inferences about their counterparts’ mental states; a core
human ability commonly referred to as “mentalizing” (De-
We thereby build on the advances in NeuroIS (Dimoka
cetyetal.,2004;FrithandFrith,2006)or“TheoryofMind”
etal.,2011;Riedletal.,2010,2014;vomBrockeandLiang,
(Polosanetal.,2011).
2014;vomBrockeetal.,2013),usingSCRmeasurementsto
Mentalizing is defined as the “ability to read the men-
assesstheintensityofimmediateemotionsandHRmeasure-
tal states of other agents” (Frith and Frith, 2006, p. 531).
mentsforoverallarousal. NeuroISresearchhasdemonstrated
Gallagheretal.(2002)establishedthattheanteriorparacin-
thatthesemeasurescanprovidenovelinsightintotheaffec-
gulatecortex,abrainregionrepeatedlyfoundtobeactivated
tiveprocessesofusersinteractingwithinformationsystems.
when humans think about mental states (Frith and Frith,
In particular, SCR measurements have recently been used
2006), plays a critical role for mentalizing in competitive
toinvestigateimmediatestressreactionsofcomputerusers
human-humaninteraction. Theauthorsemployedanonline
(Riedletal.,2013)andHRmeasurementshavebeenused
versionofthegame“stone,paper,scissors”andfoundthat
toinvestigateusers’overallarousalinthecontextofISuse
the anterior paracingulate cortex was only activated when
patterns(OrtizdeGuineaandWebster,2013)andenterprise
participants believed to compete with human rather than
resource planning systems (Ortiz de Guinea and Webster,
computer opponents. The authors concluded that humans
2013).
adaptan“intentionalstance”whencompetingwithhumans,
Insummary,weinvestigatetheinterplayofagency,the whichisnotthecasewhencompetingwithcomputeragents.
bidders’affectiveprocesses,andbiddingbehaviorinaninte- While mentalizing primarily builds on cognitive processes,
gratedapproach. OurresearchmodelisdepictedinFigure suchasperspectivetaking,reflectingonpreviouslyacquired
1. Theunderlyingtheoreticalconceptsandhypothesesare knowledgeabouttheworld,andanticipatingwhataperson
outlined in detail in the following subsections. Related lit- is going to think and feel next, it is important to highlight
erature,specificallyconcerningexperimentalstudiesonthe thatmentalizingalsoincludesaffectiveprocesses(Frithand
impactofagencyonhumanaffectiveprocessesandbehavior, Frith,2006;LimandReeves,2010;Polosanetal.,2011). In
is summarized and structured in Table 1 at the end of this particular,humansseektosimulateandreenacttheircoun-
section. terparts’emotionsthroughthebrain’smirrorsysteminorder
3Thetermarousalcanbeusedfordescribingboththeintensityofimmediateemotions(phasicarousal)andtheintensityoftheoverallemotionalstate
(overallarousal).Inordertoavoidsuchambiguity,inthispaperweusethetermarousalonlytorefertooverallarousal.
4 ToappearinJournaloftheAssociationforInformationSystems(JAIS)
toassesstheiraffectiveprocessesandpredicttheirintentions. andtheintensityofimmediateemotionsshouldthusbelower
Aspartofthisprocessthesamebrainregionsareactivated whenhumansinteractwithcomputeragents.
“as when we experience the same emotion ourselves” (cf. Thislineofargumentationiswellsupportedbyempirical
simulation theory, Frith and Frith (2006, p. 531)). In an evidence. Sanfeyetal.(2003)andRillingetal.(2004),for
electronicauction,forinstance,biddersmighttrytoassess instance,foundthathumansubjectsexhibitedweakeracti-
theircompetitors’affectiveprocessesinordertopredicttheir vationofbrainregionsrelatedtoemotionswhenreceiving
bids. Eventhoughcomputeragentscanbedesignedtosimu- unfairoffersfromcomputeropponentsratherthanfromhu-
lateaffectiveprocessesandtakeontheroleof“socialactors” man counterparts. In the context of competitive computer
(NassandMoon,2000;Zadroetal.,2004,p. 84),itmustbe games, Lim and Reeves (2010), Weibel et al. (2008) and
expectedthatattemptingtoreenacttheaffectiveprocessesof Ravajaetal.(2006)foundthatplayersexperiencedlessover-
computeragentsislesspronouncedthansimulatingandreen- allarousalwhenplayingcompetitivegamesagainstcomputer
actingthoseofhumancounterparts(LimandReeves,2010; opponentsratherthanagainsthumanopponents. Interact-
Polosanetal.,2011). Affectiveprocessesarethusexpected ingwithhumanswasalsoreportedtoincreaseplayers’en-
tobeweakerwhenagencyislow. joymentcomparedtointeractionwithcomputers(Gajadhar
Inadditiontoassessingemotionsofothers,socialinter- etal.,2008),whereasWilliamsandClippinger(2002,p. 503)
actions also have a direct influence on our own emotional foundthatplayingaMonopolygamewithlowagency“gen-
states. Byweighingtheconsequencesofouractionsandfos- eratedsignificantlymoreaggressionintheparticipantsthan
teringsocialinteractions,emotions“guideouractionsinan playingagainstanotherperson.”EastinandGriffiths(2006),
adaptive fashion” (Wallin, 2007, p. 136) and enable us to however, foundnoeffectsofagencyonaffectiveprocesses
takeadvantageousdecisions(BecharaandDamasio,2005). and behavior in different types of computer games at all.
Theyarethusanimportantelementofhumandecisionmak- While, in principle, affective processes are essential for all
ing, particularly in social interactions. According to social socialinteractions,theyseemtobeofparticularimportance
comparisontheory,engaginginsocialinteractionswithother for competitive interaction (Decety et al., 2004; Lim and
humans leads to comparing one’s status to those of others Reeves,2010;Weibeletal.,2008),i.e. wheresubjectsstrive
(Festinger,1954). Suchsocialcomparisonscanfueloverall fordivergentorevenmutuallyexclusivegoals. Here,social
arousal(Buunketal.,1990;LimandReeves,2010)andtrig- comparisonscancausesocialcompetitionamongindividuals.
gerimmediateemotions,suchasenvyandgloating,which Hence,differencesinaffectiveprocessingbetweenlowand
servetokeeptrackof“socialstatus”(Baultetal.,2008,p. 1). highagencyshouldbeparticularlypronouncedincompetitive
Forinstance,Baultetal.(2008)investigatedhowalottery scenarios.
player’s emotions are affected by the presence of a second
Inelectronicauctions,onlyonebiddercanwintheauction
player. Theyfoundthatwhenonlyoneoftheplayerscould
whileallotherslose(MalhotraandBazerman,2008). Hence,
win,theimmediateemotionsinresponsetowinningandlos-
electronicauctionsarecharacterizedbyaninherent“social
ingareexperiencedstrongerthanwhentherewasnosecond
competition”(Delgadoetal.,2008,p. 1849). Duringtheauc-
player. Thus,eventhoughthebehaviorofthesecondplayer
tionprocess,thissocialcompetitioncausesincreasedoverall
hadnoinfluenceonthefirstplayer’spayoffs,his/herpresence
arousalanda“desiretowin”(MalhotraandBazerman,2008).
introduced a social reference point which was reflected in
Theimmediateemotionstriggeredinresponsetowinningor
affectiveprocesses.
losinganauctionareusuallyreferredtoasthejoyofwinning
Computeragents,however,canhardlyserveassocialref- andthefrustrationoflosing,respectively(Astoretal.,2013;
erencepoints. Duetothedifferenceinnature,comparability– Delgadoetal.,2008;Dingetal.,2005).4 Naturally,winning
asonedriverforsocialcomparisonprocesses–ishardlygiven orlosinganauctionmaycauseemotions–regardlessofthe
(cf. Festinger’s third hypothesis). Also with regard to the opponents’type(humansorcomputers),sinceafterall,there
evolutionaryfunctionofsocialcomparisonprocessesamong ismoneyatstake. Aswehaveworkedoutbasedonmental-
membersofasociety,computeragents–eventhoughtheycan izingandsocialcomparisontheory,however,competingwith
infactbedesignedtoactlikesocialbeingsandhumansinturn computeragentsyieldsimportantdifferencestocompeting
haveevenshownsocialbehaviortowardscomputeragents withhumans. Wethushypothesizethataffectiveprocesses
(NassandMoon,2000;Zadroetal.,2004)–areyetnotequal arelesspronouncedinsettingswithlowagencyandthatthis
members of the social sphere in which we live, cooperate, isalsoreflectedinthephysiologicalcorrelatesofimmediate
compete,andcompare. Inviewoflackingcomparabilityand emotionsandoverallarousal. ResearchhypothesesH1and
socialnature,comparingone’sownsocialstatustothatofa H2state:
computeragentispointless(Engelbrecht-WiggansandKatok,
2008). Followingsocialcomparisontheory,overallarousal Hypothesis 1. The intensity of bidders’ immediate emo-
4Biddersmayalsoderivenegativeutilitywhentheyexperiencethe“winner’scurse,”i.e.,payingmoreforanitemthanitisactuallyworthbecauseof
overestimatingthetruevalueofthegood(Easleyetal.,2010).Moreover,dependingontheauctionandtheinformationprovided,abiddermayalsoexperience
winnerregretandloserregret(Engelbrecht-WiggansandKatok,2008).Inourexperiment,however,theseinformationeventsaredeliberatelyexcludedand
biddersknowtheexactvalueoftheitem.Thus,winnerregret,loserregret,andthewinner’scursearenegligibleinourstudy.
ToappearinJournaloftheAssociationforInformationSystems(JAIS) 5
tionsislowerinFPSBauctionswithlowagencythaninFPSB p. 44)6 inwhich“peopleenjoywinning–especiallyagainst
auctionswithhighagency. theirrivals–evenataprice”(MalhotraandBazerman,2008,
p. 80). Dueto thischaracteristicof auctions, “thethrillof
Hypothesis2. Thelevelofbidders’overallarousalislower bidding,theexcitementofwinning,andthestimulationof
inFPSBauctionswithlowagencythaninFPSBauctionswith beatingcompetitors”haveevenbeenidentifiedasreasonsfor
highagency. thepopularityofauctions(Leeetal.,2009,p. 77). Hence,
thesourceofadditionalutilityordisutilityisattributedtothe
inherentsocialcompetitionofauctions(Delgadoetal.,2008).
TheImpactofAgencyonBiddingBehavior(H3)
Inthatsense,biddersdonotjustbuycommodities–theywin
Previousresearchhasshownthatagencycandirectlyaffect orlosethemagainstotherbidders.
behavior,e.g.,impairingperformanceinnoveltasksinfront Correspondingly,ArielyandSimonson(2003)foundin
ofavirtualaudience(Hoytetal.,2003),orcausingdifferent an Internet survey that 76.8% of the survey respondents
evasiveactionswhenbeingapproachedbyavirtualcharacter perceivedotherbiddersascompetitorsandreferredtoauc-
(Bailensonetal.,2003). Thelackofanactualsocialcompeti- tionoutcomesas“winning”and“losing.”PalmerandForsyth
tioninauctionswithlowagencymightthusnotonlyaffect (2006,p. 236)concludedthat“auctionbehavioris,thus,a
humanbidders’affectiveprocesses(cf. H1,H2),but,since sociallyconstructedbehavior.”
itisinherentlyrelatedtoit,alsotheirbiddingbehavior. In Inauctionswithlowagency,winningperseislessimpor-
particular,ifthereisinfactapositiverelationshipbetween tant,becauseherethesocialcompetitiondoesnotexistatall
agencyandtheintensityofimmediateemotionsinresponse orisatleastlesssevere. Hence,biddersareexpectedtoplace
totheauctionoutcome,agencyshouldalsoaffecttheutility lower bids in such settings. For the case of common value
biddersderivefromwinningorlosinganauction,whichin auctions,forinstance,VandenBosetal.(2008)foundapos-
turncanleadtoachangeinbiddingbehavior.5 Ingeneral, itiveeffectofagencyonbids. Bidderssubmittedsignificantly
anticipatingajoyofwinninganauctioncanbereflectedinan higherbidsandwerepronetothewinner’scursewhencom-
additionalexpectedutilityandhenceanextramotivationfor petingagainstotherhumans,butnotiftheopponentswere
winningtheauction. Incontrast,anticipatingafrustration computers. Inthecontextofbargaining,Sanfeyetal.(2003)
oflosingcanbereflectedinanadditionalexpecteddisutility andvan’tWoutetal.(2006)foundthathumansweremore
andhenceanextramotivationfornotlosing,i.e.,again,win- likelytoacceptunfairoffersfromcomputerizedagentsthan
ningtheauction. Sincetheseemotionsarepresumablymore fromotherhumans,i.e.,showingahighertolerancetowards
intensewhenthereisfactualsocialcompetition,bothmecha- unfavorableallocationsduetoloweragency. Inauctionswith
nismsshouldcausebidderstoplacehigherbids(VandenBos lowagency,winningperseislessimportant,becausehere
etal.,2008)insettingswithhighagency. thesocialcompetitiondoesnotexistatallorisatleastless
Inbehavioraleconomicstheory,theinfluenceofsuchso- severe. Hence,biddersareexpectedtoplacelowerbidsin
cialcomparisonsonthedecisionmakers’utilityiscaptured such settings. For the case of common value auctions, for
byother-regardingorsocialpreferences. Suchpreferences instance, Van den Bos et al. (2008) found a positive effect
explicitlytakeintoaccountthathumansarenotonlyinter- of agency on bids. Bidders submitted significantly higher
ested in their own individual gains and losses, but also in bidsandwerepronetothewinner’scursewhencompeting
the payoffs of others who serve as social reference points against other humans, but not if the opponents were com-
(Baultetal.,2008;VandenBosetal.,2008). Relativepay- puters. In the context of bargaining, Sanfey et al. (2003)
offsandinterpersonalcomparisonshavebeenfoundtoplay andvan’tWoutetal.(2006)foundthathumansweremore
animportantroleineconomicbehavior(FehrandSchmidt, likelytoacceptunfairoffersfromcomputerizedagentsthan
1999). fromotherhumans,i.e.,showingahighertolerancetowards
Previousresearchhasdemonstratedthat,duetosuchin- unfavorableallocationsduetoloweragency.
terpersonalcomparisons(i.e.,the“socialnatureofauctions,” Associatedwithitseffectonaffectiveprocesses,wethus
(Van den Bos et al., 2008)), utility derived from succeed- expectagencytoinfluencebiddingbehavior. Formostbid-
ing in peer competition may even outweigh the monetary ders,winninganauctionagainstothersconstitutesvaluein
incentives(CooperandFang,2008). Thiseventuallycauses itself. This value, in turn, depends on the intensity of the
auction participants to overbid and pay more for an item social competition, i.e. whether the bidder competes in a
thanitisactuallyworthtothem(MalhotraandBazerman, settingwithhighorlowagency. Hypothesis3thusstates:
2008). Inthissense,highagencycausesmarketinteraction
tobeseenasa“play-to-wingame”(StaffordandStern,2002, Hypothesis3. InFPSBauctionswithlowagency,bidders
5Here,weassumethatwinninganauctionisrelatedtoemotionswithpositivevalence,whereaslosinganauctionisrelatedtoemotionswithnegative
valence(Dingetal.,2005;Delgadoetal.,2008;Astoretal.,2013).
6Correspondingly,in2007eBaylaunchedanadvertisementcampaigncalled“shopvictoriously,”stressingthecompetitivenatureofauctionswiththeslogan
“it’sbetterwhenyouwinit!”(eBay.com,2007).Inaddition,theplatformsendsemailstouserswhenanotheruserhastakenoverthestatusascurrentlyleading
bidderforaspecificgoodfromthem,suggestingtohitbackwithanevenhigherbid.
6 ToappearinJournaloftheAssociationforInformationSystems(JAIS)
placelowerbidsthantheydoinFPSBauctionswithhighagency. cause an increased willingness to take such risks in order
toachievehigherrewards(Arielyetal.,2006;Riversetal.,
2008). Theperspectiveofevolutionarypsychologyprovides
TheRelationshipbetweenOverallArousal&BiddingBe-
arationaleforthis,as“mostappetitivesystemsinthebrain,
havior(H4&H5)
includinghungerandthirst,aredesignedtoincreasemoti-
Beyondtheeffectofagencyonoverallarousalandbidding vation during times of opportunity” (Ariely et al., 2006, p.
behavior,weareinterestedintherelationshipbetweenoverall 88). Thissuggestsanegativerelationshipbetweenarousal
arousal and bidding behavior, and how this relationship is andbids,asarousalenhancesthemotivationaleffectsofre-
affectedbyagency. Previousresearchestablishedthatboth wards(Riversetal.,2008). Higherrewards(orthechanceon
affective and cognitive processes have a definite influence higherrewards,respectively)canusuallyonlyberealizedby
on human decision making (Bechara and Damasio, 2005). eitherhigherlevelsofeffort–orbytakingmorerisk. ForFPSB
Dependingonthesituation,however,theinfluenceofeither auctionswithmoneyatstake,arousalmaybeinterpretedas
affectiveorcognitiveprocessesonbehaviorcanbemorepro- acueforthechanceofmakingaprofit–whichisamplified
nounced(Arielyetal.,2006). Intheliterature, theroleof and results in striving for winning an even higher amount.
affectiveandcognitiveprocessesindecisionmakingisoften Increasingthepotentialprofit(valueofthegoodminusprice
conceptualizedin“dualsystem”modelswheretheaffective paid)inaFPSBauctioncanonlyberealizedbysubmitting
system is characterized as fast, automatic and emotionally lowerbids,whichconcurrentlyentailsalowerprobabilityof
charged while the cognitive system is characterized as ana- winningtheauction(Vickrey,1961).7 Lowerbidsmaythus
lytic,logicalandabstract(Leeetal.,2009;Steinhartetal., actuallybecausedbyhigherlevelsofarousal.
2013). Clearly, the conceptualization of such dual system The above reasoning thus speaks in favor of a negative
modelsis“undoubtedlyanoversimplificationandanimpre- relationshipbetweenarousalandbidheight. Thereare,how-
ciserepresentationofthecomplexhumanmind”(Leeetal., ever,alsodissentingtheoreticalapproaches. Accordingtothe
2009, p. 174). The overall distinction of decision making competitivearousalmodel,competitiveenvironmentsfuelthe
intosituationsinwhicheitheraffectiveorcognitiveprocesses desiretowininatwo-stepprocess(Kuetal.,2005;Malhotra
aremorepronounced,however,isyetusefulforinvestigat- andBazerman,2008). First,factorslikerivalry,timepressure,
ingemotionalbehavior(Leeetal.,2009). Inthefollowing orsocialfacilitationinducehigheroverallarousallevels. This
weoutlinethetheoreticalbasisforthemoderatingeffectof higherarousalthenfostersthedesiretowinagainsttheoppo-
agencyontherelationofoverallarousalandbiddingbehavior. nent,supersedingtheoriginalgoal(forinstance,generating
Wearguethat–asaffectiveprocessesareexpectedtobeless thehighestpossibleexpectedprofit),andbythismeansaf-
intenseinlowagencyauctions–thereisreasontobelievethat fectingbiddingbehavior. Sincehigherbidsareceterisparibus
also the relationship between overall arousal and bidding morelikelytowinanauctionthanlowerbids,thecompetitive
behavior is weaker when agency is low. We start off from arousalmodelsuggestsapositiverelationbetweenarousal
traditionalsettingswithhumancounterpartsand,thus,high andbidheight.
agency. There is, however, ample evidence for the prior line of
thought,stressingtheenhancingpowerofarousalonthemo-
HighAgency Forsuchsettings,itiscommonlyrecognized tivationaleffectsofrewards. Theempiricalobservationsthus
thathumanfinancialdecisionmakingtendstorelyonaffec- speakinfavorofanegativerelationshipbetweenarousaland
tive processes morestrongly when decision makersexperi- bidheight(Arielyetal.,2006;Mano,1994;Trimpop,1994).
encehigherlevelsofoverallarousal(Peterson,2007). Slovic Mano(1994),forinstance,investigatedtheimpactofarousal
etal.(2007)arguedthathumansseemtofollowan“affect on the willingness-to-pay for lotteries and insurances and
heuristic,” which guides their decision making through af- foundthathigherarousalwasrelatedtoahigherattractionto
fectiveprocessestriggeredbyinternalandexternalstimuli. thepossiblerewardsassociatedwithplayingalottery. More-
Ashumanbiddersareexpectedtoexperiencehigheroverall over, Rivers et al. (2008) reviewed decision making under
arousallevelsinthesocialcompetitionofauctionswithhigh theinfluenceofdifferentfactorssuchasage,impulsivity,and
agency(cf. H2),theassumptionofaffectheuristicssuggests arousal,andfoundarousaltobeanimpulsivity-promoting
amarkedrelationshipbetweenoverallarousalandbidding factor. Arielyetal.(2006)showedthat(sexual)arousalis
behaviorforhighagencyscenarios. capableofincreasingthesubjectivewillingnesstoengagein
Previous research found that the relationship between unsafeventures.
arousal and decision making is twofold. On the one hand, Takenasawhole,thetheoreticalperspectiveonarousal
situationsinvolvingriskareknowntotriggerarousal,asthey andbiddingthussuggestsanegativerelationbetweenarousal
canhavematerialconsequencesforthedecisionmaker(Trim- andbidsforhighagencyscenarios. Wethereforehypothesize
pop,1994). On theotherhand, however, arousalcan also thathigherlevelsofarousalareassociatedwithlowerbidsin
7Thisparticularlyholdsforauctionsinwhichbiddersknowtheirexactvaluationoftheauctionedoffcommodity(e.g.,independentprivatevalues).Inother
scenarios,asforinstanceincommonvalueauctions,thereisalsoadangerofpayingtoomuchandexperiencingthewinner’scurse(Easleyetal.,2010).Inour
study,however,thebiddersknowthevaluationandthusthewinner’scurseisnotpossible.
ToappearinJournaloftheAssociationforInformationSystems(JAIS) 7
FPSBauctions. H4states:
Hypothesis4. InFPSBauctionswithhighagency,higher
overallarousallevelsarerelatedtolowerbids.
LowAgency Whilethetenseatmosphereofsociallycom-
petingwithhumansestablishesacontextthatpossiblypro-
nouncestheroleofaffectiveprocesses(cf. H1,H2,andH4),
thereisreasontobelievethattheworkingprinciplesoflink-
ingarousalandbiddingbehavioraredifferentforlowagency.
First,asbidders’affectiveprocessesareexpectedtobeless
intenseinauctionswithlowagencyoverall,relyingonsuch
lowerimpulsescanbeexpectedtoplayalessimportantrole
too. Second, decision makers tend to rather rely on cogni-
tive processes in “depersonalized” and “asocial” situations
(Stanovich and West, 2000). As we have outlined above,
arousalisoftenattributeddirectlytothesocialcompetition
of auctions. In absence of an actual social competition in
auctions with computer agents, the bidders might thus fo-
cusonrational,analyticalthinking. Basedonthisreasoning,
we conjecture that low agency attenuates the relationship
betweenarousalandbids.
Previousresearchprovidessupportfortheargumentthat
thesocialcontextdefactoplaysanimportantroleintherela-
tionshipbetweenarousalandbehavior. Sanfeyetal.(2003),
for instance, considered ultimatum bargaining and found
thatunfairoffersbyhumansinducedstrongeractivationin
theanteriorinsula(interpretedastheperceptionofnegative
emotions)thandidthoseofcomputeragents,andthatunfair
offers by humans were rejected more often than identical
unfairoffersbycomputerizedagents. Theeffectofagency
ontherelationbetweenarousalandbehaviorisnotstated
explicitlyinthatstudy,butitcanbeassumedthatthegradi-
ent between arousal and rejection rates is steeper for high
agency. Inafollow-upstudy,van’tWoutetal.(2006,p. 565)
furtherinvestigatedthismatter,consideringtheinterplayof
agency,arousal,andeconomicdecisionsinultimatumgames.
Theauthorsfoundasignificantcorrelationbetweenarousal
andrejectionratesforhumanoffers,whereastherewas,in
fact,nosucheffectforcomputeroffers(onsubjectlevel). Put
differently,forhumanoffers,acceptancewasrelatedtolow,
whereasrejectionwasrelatedtohighskinconductancelevels.
Forcomputeroffers,bothacceptanceandrejectionshowed
intermediateandsimilarskinconductancelevels. Basedon
theabovereasoningandempiricalindication,wehypothesize
thatthepresenceofcomputeragentsmitigatestherelation-
shipbetweenarousalandbids. Hypothesis5states:
Hypothesis 5. The relationship between overall arousal
andbidsismitigatedbylowagency.
Authors Task,Description DependentVariables IndependentVariables Med./Mod. NeuroIS VL coop comp GR $ #
(year) andCVs Method
Gallagher Playing“stone,paper,scissors” Anteriorparacingulate Framingtowardsmen- - fMRI x 9
etal.(2002) againstacomputer/ahuman; cortex,inferior talizing/rulesolving
guessingtheother’schoice frontalcortex,and
cerebellumactivation
Williams PlayingMonopolyagainst Aggression Human/computeropponents - - x x x 54
and human/computeroppo-
Clippinger nents,withinsubjectdesign
(2002)
Bailenson Approachingacharacter Distancetovirtual Participantgender,agency,virtual - - x x 80
etal.(2003) inavirtualenvironment character,social charactergender,gazebehavior 80
presence,affect,memory
Hoytetal. Socialfacilitationandsocial Taskperformance Perceivedagencyofvirtual Mediator: - 39
(2003) inhibitioninpatternrecognition observers(agents,avatars,none), co-presence,
andcategorizationtasks tasktype(novel,well-learned) control:task
inavirtualenvironment novelty/anxiety
Nowak Presentoneself,describeskills, Presence,co-presence, - - - x x x 134
andBiocca gettothepartner;effectof socialpresence
(2003) agencyandanthropomorphism Anthropomorphism,
onvariousmeasuresofpresence perceivedagency
Sanfeyetal. Respondingtooffersinan Economic/behavioral Fairnessofoffer,typeof - fMRI x x x 19
(2003) ultimatumbargaininggame; response(acceptance co-player(human/com-
differentoffers(fair/unfair)and rate);perceptionof puter),acceptancerate
proposers(human/computer) fairness,bilateralante-
riorinsula,dorsolateral
prefrontalcortex
Rillingetal. UltimatumGameand Behavior,brainactivity Human/computer - fMRI x x 19
(2004) Prisoners’Dilemmawith partners,typeofoffer
human/computerpartners
Zadroetal. Balltossgame,2x2be- Levelsofbelonging,con- Ostracism/inclusion,hu- - - x 62
(2004) tweensubject(design trol,self-esteem,mean- man/computerco-players, 77
ostracism/inclusionxhu- ingfulexistence,mood scripted/unscriptedbehavior
man/computerco-players)
Eastinand Hostilityandpresencein Hostility,presence Gametype(fighting,shooting, - - x 219
Griffiths differentformsofcomputer driving),human/com-
(2006) gamesamongmaleplayers puteropponents,virtual
reality/standardconsole
Mandryk Playingasportscomputer Boredom,frustration, Opponent(friend/com- - HR, x x 8
etal.(2006) gameunder1)differ- fun,ease,engage- puter),difficulty(beginner, SCR,R 10
entlevelsofdifficulty,2) ment,challenge, easy,medium,difficult)
againstafriend/computer excitement,HR,SCR,R
Ravajaetal. Effectsofdifferentopponent Anticipatedthreat, Natureofopponent(com- - HR x x 33
(2006) typesonspatialpresence, challenge,spatial puter,friend,stranger)
emotionalresponses,threat presence,arousal
andchallengeappraisals (HR),engagement,
valence,arousal
(allself-reported)
van’tWout Respondingtooffersinan SCR,perceptionof Human/computeroffers - SCR x x 30
etal.(2006) ultimatumbargaininggame; fairness,Economic/
differentoffers(fair/unfair)and behavioralresponse
proposers(human/computer) (rejectionrate)
Guadagno Listento&evaluatecommu- Perceivedrealism, Virtualgender(m/f), - - x x 65
etal.(2007) nicationagent/avatarina attitudechange,agent participantgender(m/f), 174
virtualenvironment;effect likingandcredibility, behavioralrealism(h/low)
ofgender,behavioralrealism qualityofpresentation
Authors Task,Description DependentVariables IndependentVariables Med./Mod. NeuroIS VL coop comp GR $ #
(year) andCVs Method
Gajadhar Playing“WoodPong”against Gameexperience, Socialsetting(virtual, Mediator: - x x 42
etal.(2008) human/computeropponents aggressionstate mediated,co-located), socialpresence
familiarity(friends/strangers),
Performance(winners/losers)
VandenBos Winner’scursein Bids Agency - - x x 47
etal.(2008) electronicauctions 48
Weibeletal. Playingonlinegamesagainst Participants’feelings Typeofopponent Control: - x 70
(2008) computer-vs.human- ofpresence,flow, (human/computer) age,gender,
controlledopponents andenjoyment effortwhile
playing,time
spentplaying
computergames
Limand Fight/tradeinWorldof Arousal(HR,SCL) Agent/avatar,cooper- - HR,SCL, x x x 32
Reeves Warcraft;agency(avatar, andemotions(SCR), ation/competition SCR
(2010) agent)andtypeofinteraction valence,presence,
(competition,cooperation) likingtheco-player
vonder Answeringquestionstoaconver- Socialpresence, Agency(agent/avatar), - - x x 83
Püttenetal. sationalagent;effectofagency/ emotionalstate(PANAS), behavioralrealism(showing
(2010) behavioralrealismonsocial rapport,perception feedbackbehavior/nobehavior)
presenceandemotionalstate ofvirtualcharacter,
#words,pos./neg.affect
Guadagno Smiles/nosmilesandagency. Counselorempathy Moderator:interaction - - x x 38
etal.(2011) task:talktoacounselor Agency,smiling partnertype(agency)
inavirtualenvironment
Polosan Playingacompetitiveversionof superiorandmiddle Agency,wordcongruence - fMRI x 14
etal.(2011) theStrooptaskagainstdifferent frontal,anterior
opponents(human/computer) cingulate,insula
andfusiformgyrus
Appeletal. Text-chatwithanotherpartici- PANAS,person #words,revealedcharacteristics Agency(agent/ - - x x 90
(2012) pant,effectofagency/socialcues perception,rapport, avatar),number
socialpresence, ofsocialcues
Riedletal. Playingatrustgamewitha Moneytransfers, Human/avatarpic- - fMRI x x x 18
(2014) human/computerpartner; trustworthiness tures;trustworthiness
assessingtrustworthiness prediction,medial
frontalcortexactivation,
learningrates
Thisstudy Impactofcomputerized Bids,immediate Agency Mediator: HR,SC x 103
agentsonoverallarousal, emotions,overallarousal overallarousal;
immediateemotionsandbidding moderator:
behaviorinelectronicauctions agency;control:
riskaversion,
gender,IPV
Note:VL:valence;COOP:cooperative;COMP:competitive;GR:graphicalrepresentation;$:monetaryincentives;#:numberofobservations;
SCL:skinconductancelevel;SCR:skinconductanceresponse;HR:heartrate;R:respiration.
Table1: RelatedLiteratureontheImpactofAgencyonAffectiveProcessesandBehavior.
10 ToappearinJournaloftheAssociationforInformationSystems(JAIS)
3 | ExperimentalDesign tobeauctioned.8 ThisIPVisindependentlydrawnforeach
bidderfromauniformdistributionwithsupportonthedis-
Ourexperimentincludestwotreatments. First,inthehigh creteintegerinterval{11,12,...,109,110}andisexpressed
agency(HA)treatment,theparticipantsinteractwithhuman inmonetaryunits(MU).Thebiddersonlyknowthatthere
opponentsonly. Second, inthelowagency (LA)treatment, are3biddersineachauction,theirownIPVandthegeneral
theparticipantsinteractwithcomputerizedopponentsonly. distributionofIPVs,whichisthesameforallbidders. The
ThecomputerizedbiddersintheLAtreatmentreplicatedthe bid of participant i is denoted by b. The winning bidder i
i
bidsofthehumanbidders. TheLAtreatmentsessionswere receivesapayoffequaltothewinningbidminushisorher
conducted one week after the HA treatment sessions. By individualvaluationforthecommoditybeingauctioned(v -
i
replicatingthehumanbids,weavoidinfluencingtheresults b). Allotherbiddersreceiveapayoffofzero. Theequilibrium
i
duetodifferencesinbiddingbytheagents. Thismakesthe biddingstrategyb(v)* forbidderiinanauctionwiththree
i
resultscomparableacrosstreatments(seeVandenBosetal. riskneutralbiddersintotalandthecommondistributionof
(2008)forasimilarapproach). ParticipantsintheLAtreat- IPVs denoted above is given by b(v)* = 2 ·(v −10)+10
i 3 i
ment thus faced the exact same bids from their opponents (Krishna,2002).
asparticipantsintheHAtreatment. Therefore,theonlydif- Inordertoexcludemissed-opportunityandmoney-left-
ferencebetweentreatmentsisthatbiddersknowthatthey on-the-tableeffects(Engelbrecht-WiggansandKatok,2008),
interactwithhumanopponents(HA)orcomputeropponents the bidders are presented a minimal information environ-
(LA),respectively. Therearenographicalrepresentationsof ment,inwhichtheyareneitherinformedaboutthehighest
thebidders. Notethattoavoidordereffectsourexperiments nor the second-highest bid (Van den Bos et al., 2008). At
is based on a between-subjects design, i.e., subjects either theendofanauction,biddersonlyreceiveinformationon
participatedintheHAorLAtreatment,butnotboth. whethertheyhavewontheauctionornot,andtheirpayoff.
During the experiment, each bidder takes part in a se- Thebidders’identitiesarenotrevealed. Inordertocapture
quenceof30FPSBauctionswith2otherbidders. TheFPSB thephysiologicalreactionstospecificeventsthroughoutthe
auctionisparticularlysuitedforourstudy,as(i)itbelongs auctionprocess,informationisprovidedintimedintervalsof
to the class of static auctions and thus enables us to main- atleastfiveseconds(Sanfeyetal.,2003). Inparticular,we
tainahighlevelofcontrolwithlittlepathdependence,(ii) investigatetheintensitiesofthebidders’immediateemotions
theimpactofcomputerizedagentscanbeinvestigatedina in response to three specific events in the auction process
scenario with little interaction, and (iii) the FPSB auction (E1, E2, and E3). More specifically, we first assess the bid-
formatisfrequentlyusedinmarketsworld-wide. IntheHA ders’ physiological responses to placing a bid (E1). Then,
treatment,participantsarerandomlyreassignedtogroupsof thebiddersseeaninformationscreenthatinformsthemthat
3biddersbeforeeverysingleauctionperiod(randomstranger theauctionoutcomewillberevealedsoon(E2). Finally,the
matching). Thus,asubjectdoesnotknowwhichotherpartic- biddersfindoutwhethertheyhavewonorlosttheauction
ipantsarecurrentlyparticipatinginthesameauction. Each (E3). TheauctionprocessissummarizedinFigure2.
groupthenplaysasingleFPSBauctionindependentlywith
3bidders(seeEngelbrecht-WiggansandKatok(2008);Ka-
Procedure
tokandKwasnica(2008),andAstoretal.(2013)forsimilar
approaches). After each period, the participants were re- Altogether, 27 female and 93 male participants (6 partici-
matchedintodifferentgroupsof3,whichwascommunicated pants per session, 120 in total, mean age = 23.16 years)
intheinstructions,sothatnoinsightsaboutspecificpartici- participated in 20 sessions. There were 12 sessions in the
pantscouldbegainedandcarriedovertothenextinteraction HAtreatment,and8sessionsintheLAtreatment(N =72,
HA
with a specific participant. In the LA treatment, every par- N =48). Theexperimentwasconductedat[anonymized]
LA
ticipantwasmatchedwith2computerizedbiddingagents, andinaccordancewiththeuniversity’sethicsguidelines. It
whichreplicatedthehumanbidsfromtheHAtreatmentone wasimplementedusingthez-Treeenvironment(Fischbacher,
week earlier. Here also, every participant was re-matched 2007). Theparticipantswererecruitedfromapoolofunder-
intoadifferentgroupwith2computerizedbiddingagents graduate students using the ORSEE software environment
aftereachauctionperiod. (Greiner,2004). Therewasnolumpsumpayment. Theex-
perimentalcurrencywasmonetaryunits(MU)with16MU
being equivalent to €1.00. Depending on their individual
AuctionProcess
performance,allofthegainsandlossesaccumulatedduring
Beforeanauctionstarts,eachbidderiisinformedabouthis theauctionswenttothebidders’individualaccounts,which
orherindependentprivatevalue(IPV)v forthecommodity wereindividuallypaidoutincashtotheparticipantsatthe
i
8TheIPVmodeldatesbacktotheseminalworkofVickrey(1961)andisfrequentlyusedinauctionexperiments(seeKatokandKwasnica(2008);
Engelbrecht-WiggansandKatok(2008),andAstoretal.(2013)forsimilarapproaches).AnIPVcorrespondstoabidder’sindividualvaluationoftheauctioned
commodity.Thisisprivateinformation,i.e.,abidderonlyknowsherownIPVbutnottheIPVsoftheotherbidders,andthevaluationisindependent,i.e.,
knowingone’sownIPVprovidesnoadditionalinformationonotherbidders’IPVs.Thebidderthenhastoweighherchancesofwinningagainstthenominal
payoffincaseofwinningtheauction.ThisisbasedonherownIPVandtheavailableinformationonthedistributionoftheotherbidders’IPVs.