Internet auctions are characterized by the separation of

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1 Shibo Li, Kannan Sinivasan, & Baohong Sun Intenet Auction Featues as Quality Signals Intenet auction companies have developed innovative tools that enable selles to eveal moe infomation about thei cedibility and poduct quality to avoid the lemons poblem. On the basis of signaling and auction theoies, the authos popose a typology of Intenet auction quality and cedibility indicatos, adopt and modify Pak and Badlow s (2005) model, and use ebay as an example to examine empiically how diffeent types of indicatos help alleviate uncetainty. This empiical evidence demonstates how Intenet auction featues affect consume paticipation and bidding decisions, what modifies the cedibility of quality indicatos, and how diffeent buyes eact to indicatos. The signaling-based hypotheses povide coheent explanations of consumes bidding behavio. As the fist empiical study to evaluate the signaling ole of compehensive Intenet auction institutional featues in mitigating the advese selection poblem, this eseach povides evidence to claify the economic foundation behind innovative Intenet auction designs. Keywods: infomation asymmety, lemons poblem, winne s cuse, signaling, consume bidding stategies, Intenet auction, e-commece, ebay Intenet auctions ae chaacteized by the sepaation of buyes fom selles (Lucking-Reiley 1999). Typically, Intenet auction companies act as auctionees and do not assign esponsibility fo items listed on thei Web sites; the selles descibe, list, and ship the poduct and specify payment methods. Because shipment usually occus afte the payment has been eceived (Lucking-Reiley 2000), it is impossible fo biddes to inspect the goods befoe bidding, and buyes bea significant isks fo items not deliveed o those significantly misepesented by selles. The impesonal tansactions of Intenet auctions intoduce sevee infomation asymmety egading both poduct quality and selle cedibility. Thus, compaed with customes decisionmaking task in bicks-and-mota etail stoes, online auction consumes must make decisions unde moe sevee uncetainty (Cheema et al. 2005; Dewally and Edeington 2006). Without enough infomation to distinguish good fom bad poducts (i.e., uncetainty about poduct quality) and eputable fom diseputable selles (i.e., uncetainty about selle cedibility), buyes may choose not to paticipate in an auction. This dual uncetainty contibutes to a lemons maket, in which the potential of puchasing poo-quality poducts fom a diseputable selle dives buyes away fom the maket. As a esult, advese selection could eventually dive good-quality items fom the auction maket and cause lemons poblems (Akelof 1970). Shibo Li is Assistant Pofesso of Maketing, Kelley School of Business, Indiana Univesity ( [email protected]). Kannan Sinivasan is H.J. Heinz II Pofesso of Management, Maketing, and Infomation Systems ( [email protected]), and Baohong Sun is Associate Pofesso of Maketing ( [email protected]), Teppe School of Business, Canegie Mellon Univesity. The authos thank Young-Hoon Pak fo shaing his WinBUGs pogam fo this eseach. 2009, Ameican Maketing Association ISSN: (pint), (electonic) 75 As Huston and Spence (2002), Bajai and Hotacsu (2004), and Kazumoi and McMillan (2005) note, the lemons poblem stems fom impesonal tansactions and infomation asymmety and may be the geatest obstacle to the apid gowth of Intenet auction maketplaces. Despite thei soaing populaity, Intenet auctions emain fa fom mainsteam e-commece outlets. Accoding to Foeste Reseach (Johnson and Hult 2008), only one-thid of th Ameican households with Intenet connections made an auction puchase in Many consumes voice concens about tust and safety issues associated with Intenet tansactions (Pinke, Seidmann, and Vakat 2001), fueled by epots of poduct misepesentation, delivey failues, fee stacking, black-maket goods, and so foth. 1 In 2006, the Intenet Cime Complaint Cente (IC3 [a patneship between the Fedeal Bueau of Investigation and National White Colla Cime Cente]) eceived appoximately 207,492 Intenet faud complaints, with total losses fom these complaints exceeding $ million, and Intenet auction faud was the top complain on the list (IC3 2006). Theefoe, to attact moe uses to an auction site o a paticula auction, Intenet auction companies and selles must addess an impotant question: What is the best auction design to mitigate uncetainty and attact moe biddes? Since the emegence of the Intenet, Intenet auction companies have continued to develop innovative institutional featues that enable selles to eveal moe infomation about thei cedibility and poduct quality to potential buyes. Fo example, in 2001, selles wee given the option to post multiple pictues to ebay, fo a fee, to povide moe visual 1Buye-elated faudulent activities, such as nonpayment, tiangulation, multiple bidding, and shield bidding, also exist; howeve, we study how selles quality indicatos help mitigate buyes concens and affect bidding decisions. We leave the study of how to impove auction designs to ovecome buyes faudulent poblems to futhe eseach. Jounal of Maketing Vol. 73 (Januay 2009), 7592

2 infomation about thei listed poducts. Selles ae also allowed to specify a minimum bid (i.e., the smallest amount that can be bid fo a specific auction) o a eseve pice, which emains hidden fom biddes but must be exceeded befoe the selle is equied to sell. In 2002, ebay instituted the buy-it-now (BIN) option, which futhe allowed selles to specify the exact pice at which they wee willing to sell thei poducts immediately. Acquied by ebay in 2002, Pay- Pal offes ebay buyes coveage of up to $2,000 fo claims of nondelivey o significant misepesentation. Thus, selles can adopt a thid-paty payment system to educe the isks associated with the tansaction. Most Intenet auction companies offe a feedback system that enables both selles and buyes to ate each othe accoding to thei pefomance. The cumulative ating points ae displayed next to the paticipant s name, along with all pevious comments fom othe uses. As an impotant fomat fo electonic commece, the apid development of Intenet auctions demands additional eseach to undestand how such innovative auction featues affect consume paticipation and bidding decisions and the implications fo solving the lemons poblem. As Chakavati and colleagues (2002) and Heschlag and Zwick (2002) asset, eseach should investigate how the design of Intenet auction mechanisms o institutional featues influence bidding behavio and pice and evenue outcomes. Existing auction eseach in maketing and economics liteatue centes on modeling biddes specific behavios, such as late bids (Ockenfels and Roth 2006; Roth and Ockenfels 2002), sale pices (Aiely and Simonson 2003; Bajai and Hotacsu 2003; Dholakia 2005), eactions to minimum bids (Bole, Boatwight, and Kadane 2005; Kamins, Dèze, and Folks 2004; Lucking-Reiley 1999), willingness to pay (Badlow and Pak 2007; Nunes and Boatwight 2004; Pak and Badlow 2005), eactions to the BIN option (Budish and Takeyama 2001; Wang, Montgomey, and Sinivasan 2004), and bidde heteogeneity in bidding stategies (Sinha and Geenleaf 2000; Wilcox 2000). 2 To the best of ou knowledge, little eseach has empiically investigated how innovative auction featues affect biddes decisions when they suffe fom infomation asymmety o has evaluated whethe cuent Intenet auction designs help alleviate the lemons poblem. In this aticle, we ecognize the dual infomation asymmety and sepaate all obseved Intenet auction featues into potential diect poduct quality indicatos, indiect poduct quality indicatos, and selle cedibility indicatos. We daw fom signaling theoy (Akelof 1970; Milgom and Robets 1982; Spence 1973) to develop a typology of most cuently obseved Intenet auction featues and examine thei potential signaling oles. On the basis of signaling and auction theoy (Bajai and Hotacsu 2004; Milgom and Webe 1982; Rothkopf 1969; Wilson 1969), we hypothesize that diffeent auction featues affect biddes intedependent decisions, such as whethe to paticipate, who bids, when to bid, and how much to bid. Using field data collected fom ebay auctions, we test the hypotheses by applying a modifi- 2Fo a compehensive eview of Intenet auctions, see Bajai and Hotacsu (2004) and Bake and Song (2007). cation of Pak and Badlow s (2005) model, implemented in a hieachical Bayesian famewok, to descibe consumes dynamic bidding behavio in the fom of latent competition within auctions (Aiely and Simonson 2003; Bajai and Hotacsu 2004). 3 Ou empiical esults show that quality indicatos that diectly eveal infomation on poduct quality and selle cedibility (e.g., multiple pictue postings, money-back guaantee, selle s cumulative ating, thid-paty payment method) encouage biddes not only to paticipate but also to shade bids. The opposite is tue fo indiect quality indicatos (e.g., minimum stating bid, hidden eseve pice, the BIN option), which discouage paticipation but incease bidding amount. We futhe demonstate that the simultaneous use of quality indicatos and selle cedibility indicatos (e.g., selle s ating, thid-paty payment) stengthens the effects of quality indicatos. Moe expeienced consumes tend to make bette infeences about the oles of both cedibility and quality indicatos. On the basis of these estimation esults, we un a simulation to examine how bidding decisions may change when the auction featues appea moe fequently. Oveall, the signaling-based hypotheses ae consistent with both the estimation and the simulation esults and povide coheent explanations of consumes bidding behavio. Although pevious studies have documented quality signaling as a solution fo infomation asymmety (e.g., Andeson and Simeste 2001; Balachande and Sinivasan 1994; Chu 1992; Desai 2000; Desai and Sinivasan 1995; Kala and Li 2008; Kimani and Rao 2000; Moothy and Sinivasan 1995; Sobeman 2003; Spence 1973; Sinivasan 1991; Wenefelt 1988), we find no empiical tests of geneal signaling theoy in an Intenet auction setting (cf. Dewally and Edeington 2006). Theefoe, ou eseach attempts to fill this gap by ecognizing dual infomation uncetainties and investigating whethe the cuent design of Intenet auctions helps incease tust among buyes and selles and alleviate the lemons poblem. In doing so, we also shed light on the evaluation of auction designs. Reseach Hypotheses In this section, we fist classify commonly obseved auction featues into possible diect quality indicatos, indiect quality indicatos, and selle cedibility indicatos. We then poceed to detemine whethe each classification qualifies as a signaling tool, and we pedict how each might affect biddes decisions, such as whethe to bid, how much to bid, and when to bid. Classification of Auction Featues In the fist column of Table 1, we list the majo auction featues that many online auction houses adopt. The adoption of multiple pictue postings enables the selle to eveal diectly moe visual infomation about the poduct. Speci- 3When we collected ou data in 2001, featues such as poduct o selle cetification wee not yet available. Thus, ou empiical analysis is applied to a subset of the indicatos that we discuss in the Reseach Hypotheses section. 76 / Jounal of Maketing, Januay 2009

3 TABLE 1 Typology of Intenet Auction Featues Auction Featues Possible Diect Quality Indicatos Pictue postings Money-back guaantee Cetification of poduct Poduct desciption Classification of Quality Indicatos Uncetainty Poduct quality Type of Indicato Diect Qualification as Signaling Device Signaling Cost Single- Expected Effects on Bidding Behavio Cossing Upfont Futue Popety Enty Time Amount N.A. a N.A. N.A. Possible Indiect Quality Indicatos Minimum bid Hidden eseve pice BIN option Poduct quality Indiect Intenet Auction Featues as Quality Signals / 77 Possible Selle Cedibility Indicatos Selle s ating Thid-paty payment Cetification of selle Cedit cad payment Escow sevice Selle cedibility Diect apedictions ae not applicable to these auction featues because they ae not eligible to seve as quality indicatos N.A. N.A. N.A. N.A. N.A. N.A.

4 fying a money-back guaantee eveals infomation about the selle s confidence in the poduct quality. Cetification of the auction item (e.g., pofessional appaisal of baseball cads) demonstates its tue quality level. Selles may also disclose the condition o quality level of thei auctioned items in the poduct desciption. These fou auction featues diectly eveal infomation about the quality of the auctioned poducts. Thus, we label them as potential diect quality indicatos. The minimum stating bid specifies the lowest bidding amount, and the hidden eseve pice places a lowe bound on the final sale pice. The BIN option spells out the pice at which the selle is willing to sell the auctioned item and to end the auction immediately (Budish and Takeyama 2001). Theefoe, these thee featues may eveal infomation about poduct quality as foms of pice signals (Milgom and Robets 1982; Moothy and Sinivasan 1995). Futhemoe, they eveal the values imposed by the selle and thus indiectly povide infomation about the poduct quality. We label these thee auction featues as potential indiect quality indicatos. The selle s feedback ating, thid-paty payment, and cetification of selle (i.e., by an independent tade association) eveal infomation about the cedibility of the selle and may incease the buye s tust. Adopting cedit cad payment methods and online escow sevices may also incease the buye s tust in both the selle and the tansaction because buyes eceive some faud potection fom the cedit cad issues o the escow sevice povides. 4 Theefoe, these auction featues could potentially eveal diect infomation on a selle s cedibility and thus ae all potential diect indicatos. We label these as selle cedibility indicatos. Although these auction featues contain infomation about poduct quality o selle cedibility, they do not necessaily function as signaling devices. As Spence (1973) and Kimani and Rao (2000) note, two conditions must be met befoe featues epesent signaling devices. Fist, it should be costly fo the selle to adopt the signaling device; in the teminology of signaling liteatue, the device must induce signaling costs. Second, the signaling costs must satisfy the single-cossing popety that such costs ae highe fo bad selles than fo good selles so that a sepaating equilibium occus. In such equilibium, consumes coectly infe the selle s tue type on the basis of the diffeent signaling stategies adopted. Next, we boow the signaling theoy that Spence (1973) poposes in the labo maket context to examine whethe each of these indicatos satisfies the two conditions, and we ely on both signaling and auction theoies to 4Companies offeing genuine escow sevices povide a fom of secue payment mechanisms that enable buyes to escow payments until they have inspected, appoved, and accepted the deliveed mechandise. Online escow sevices potect the inteests of buyes against faud and the isk of nonpefomance. They usually ae paid fo by the buyes and appea in high-value tansactions. As a elatively new featue, ebay ecommends using escow guaantees povided by Escow.com fo mechandise woth $500 o moe. motivate and povide an analogy fo the hypotheses, in which we pedict how each of the indicatos will affect consume paticipation and othe bidding decisions. In the emainde of Table 1, we summaize the qualification status and pedicted diection of each indicato s influence on whethe, when, o how much to bid. Diect Quality Indicatos The signaling costs of posting additional pictues equal the fixed fees the selle pays up font, as well as the sunk costs of any associated photogaphy and/o scanning equipment (Dewally and Edeington 2006). Fo example, ebay chages a nonefundable fee of $.15 fo each additional pictue afte the fist pictue posting. Because multiple pictue postings equie nonefundable costs, egadless of a sale, lowquality selles ae less likely to incu that cost, especially because multiple pictues may just eveal the tue (poo) quality of the poduct. Thus, it is moe costly fo lowquality selles to adopt this tool (Desai 2000; Kala and Li 2008; Moothy and Sinivasan 1995), and consumes can ely on this indicato to sepaate high-quality fom lowquality poducts. Selles can specify a money-back guaantee in the poduct desciption section. Because it is usually not enfoced by most Intenet auction companies, selles do not need to pay any fee fo thei vebal pomise. Howeve, this option incus some futue cost because of the existence of the IC3, which handles disputes between selles and buyes (Lutz 1989). That is, the signaling cost esults fom the potential futue efund (Moothy and Sinivasan 1995). Because lowquality poducts have highe etun ates, it is moe expensive fo selles with low-quality poducts to adopt moneyback guaantees. Cetifying auctioned items can also equie significant selle costs. Fo example, Dewally and Edeington (2006) note that it typically costs the selle $20$55 to have a comic book cetified by Comics Guaanty, and accoding to Jin and Kato (2006), pofessional gading of baseball cads costs $6$20 pe cad when pefomed by the Pofessional Spots Authenticato o Beckett Gading Sevice. These cetification costs epesent nonefundable, upfont signaling costs. Both studies show that the cetification of auctioned items satisfies the single-cossing popety because a lowquality poduct is less likely to ean a high ating. Selles may disclose quality-elated infomation (e.g., a condition that excludes a money-back guaantee) about the auctioned item in the poduct desciption. Howeve, this infomation disclosue is cheap talk and does not incu any cost to the selle (Bake and Song 2007; Jin and Kato 2006). Theefoe, it does not qualify as a signaling device. In summay, multiple pictue postings, money-back guaantees, and poduct cetification not only incu signaling costs but also satisfy the single-cossing popety, which qualifies them to seve as signaling devices. Next, we discuss how these tools can affect bidding decisions. Whethe to bid. Intuitively, posting multiple pictues, offeing money-back guaantees, and cetifying poducts should offe biddes moe infomation about the poduct and signal its quality. With moe infomation, consumes should be moe likely to paticipate in the auction (Gilkeson 78 / Jounal of Maketing, Januay 2009

5 and Reynolds 2003; Ottaway, Buneau, and Evans 2003). Theefoe, we expect that diect quality indicatos encouage consumes to paticipate in auctions. How much and when to bid. Knowing how the quality indicatos affect biddes bidding amount (how much to bid in each bidding occasion) and bidding time (when to submit each bid) duing the couse of an auction is also impotant. Unlike pivate-value auctions, in which the bidde knows the value of the item with cetainty and his o he valuation is independent of othe biddes valuations (Vickey 1961), a bidde s valuations of a poduct can depend on the pefeences of othes in an Intenet auction (Milgom and Webe 1982). As Bajai and Hotacsu (2003) and Milgom and Webe (1982) indicate, when biddes ae uncetain about thei own evaluations of a poduct, auctioned items may have an affiliated value that can be affected by othe biddes. Recent empiical wok (Dholakia and Soltysinski 2001; Gilkeson and Reynolds 2003) suppots the view that the affiliated-value model is the most desciptive type of Intenet auctions. When buyes decide how much to bid fo a poduct, they ae likely to ely on the bidding behavio of othe biddes to fom thei willingness-to-pay amount. Fo example, speculative investos who buy fo futue esale ae likely to adjust thei bidding amount accoding to the obseved bidding pices submitted by othe biddes. Ealy bids tansmit infomation about the item s value to competitive biddes and may dive up the final winning pice. In tun, too many biddes o too fequent ealy bids may ceate the winne s cuse, such that the winne of the auction ovebids o pays a highe pice than the tue value of the item (Thale 1988). Biddes ae ational (Bajai and Hotacsu 2003; Ockenfels and Roth 2006), such that when they expect moe biddes to be attacted by the use of quality indicatos, they ealize the incease of the affiliated value of the poduct and the potential winne s cuse. Seveal studies of Intenet auctions (i.e., Bajai and Hotacsu 2003; Jin and Kato 2006; Yin 2006) show that when they anticipate moe biddes, ational biddes stategically shade thei bids by educing the amount they submit on each bidding occasion. Specifically, Bajai and Hotacsu (2003) show that buyes of collectable coins bid 10% less than thei own pivate signals to compensate fo the winne s cuse. Adding anothe bidde educes those equilibium bids by 3.2%. Similaly, Yin (2006) finds that the bidding stategy in the common value second-pice auction model esponds negatively to an incease in the vaiance of the bidde s pivate valuation. Theefoe, we expect that consumes educe thei bidding amount duing the couse of the bidding pocess when they obseve the use of diect quality indicatos. Auction theoy futhe indicates that in auctions with common value, it is in the biddes best inteests to bid as infequently and/o as late as possible (Bajai and Hotacsu 2003; Ockenfels and Roth 2006). This is because moe fequent o ealie bids incease the common value of othe biddes o the chances of a bidding wa. Howeve, the use of diect quality indicatos educes infomation asymmety, which may make consumes less likely to depend on othe biddes bidding infomation and encouage them to bid thei maximum value ealie in affiliated-value auctions. Futhemoe, accoding to Rasmusen (2001), late bidding occus because biddes suffe uncetainty about thei valuation fo an item and want to economize the costs of acquiing infomation. In contast, with moe complete infomation about a poduct, biddes bid ealie. Theefoe, we pedict that the use of diect quality indicatos encouages ealy bidding. H 1 : Diect quality indicatos (e.g., multiple pictue postings, money-back guaantees, poduct cetification) encouage bid paticipation, decease biddes bidding amount, and encouage biddes to bid ealy. Indiect Quality Indicatos Indiect quality indicatos, such as minimum stating bid, hidden eseve pice, and BIN pice, may expeience two types of signaling costs. The fist type entails the publicly visible, nonefundable fees the selle pays that ae independent of the sale of the auctioned item. Fo example, ebay chages nonlinealy inceasing insetion fees to the selle accoding to the amount of the minimum stating bid, with a maximum of $4.80 (i.e., $.30 to set the stating bid between $.01 and $.99, $.35 fo stating bids between $1.00 and $9.99, and so on). In addition, ebay chages a eseve fee of eithe $.50 o $1.00 fo the use of a hidden eseve pice, which may be efunded if the item sells at a pice highe than the eseve pice. The use of the BIN pice on ebay costs the selle a fixed amount of $.05. The second type of signaling costs includes the potential loss of evenue when minimum stating bids and hidden eseve pices sceen out consumes with low willingness to pay. Wang, Montgomey, and Sinivasan (2004) demonstate that the BIN option may lowe the chances of winning a bid and discouages biddes, especially those who ae willing to bea paticipation costs. As Bajai and Hotacsu (2003, 2004) show, having fewe paticipating biddes lowes the evenues. Katka and Reiley (2006) suppot this claim by noting that a high secet eseve pice detes bidding and educes both the likelihood of sale and the final bid amount. The sum of the two types of signaling costs is geate fo low-quality selles, which nomally have lowe maginal costs (Desai 2000; Kala and Li 2008; Moothy and Sinivasan 1995). Although the nonefundable fees may be the same to both high-quality and low-quality selles, the potential evenue loss will be highe fo the low-quality selle because of the lowe maginal costs. That is, fo each unit loss incued by the high-quality selle that adopts these indicatos, the low-quality selle must incu moe than one unit of loss to mimic it (Moothy and Sinivasan 1995). In this sense, the signaling ole of such quality indicatos is simila to that of taditional pice signals, such that highe pices esult in a loss of evenue fo the high-quality selle that the low-quality selle must incu moe of if it wants to mimic the high-quality selle (Milgom and Robets 1982; Moothy and Sinivasan 1995). Theefoe, the thee possible indicatos satisfy the single-cossing popety and qualify as quality signaling devices. Intenet Auction Featues as Quality Signals / 79

6 Whethe to bid. The use of minimum bids and hidden eseve pices should discouage consumes paticipation by placing lowe and highe bounds on thei valuations, espectively (Budish and Takeyama 2001). Consumes with low valuations ae sceened fom an auction with a lowe bound, and the tansaction will not occu if the uppe bound is not met. Futhemoe, as Wang, Montgomey, and Sinivasan (2003) and Bajai and Hotaçsu (2003) ague, significant paticipation costs play impotant oles in biddes decisions to paticipate in Intenet auctions. Because the eseve pice is unknown to the biddes, they face highe paticipation costs and un the isk that selles will withdaw the poduct, making thei bidding effots obsolete. Impatient selles adopt the BIN option to avoid both the tansactional and the monetay costs of the bidding pocess (Wang, Montgomey, and Sinivasan 2004). The BIN option mixes dynamic picing with fixed picing and tempts buyes to accept the fixed pice without going though the bidding pocess. Thus, it lowes the chance of winning a bid and discouages biddes, especially those who ae willing to bea paticipation costs. Theefoe, in geneal, we pedict that indiect quality indicatos discouage auction paticipation. How much and when to bid. In contast to H 1, when consumes obseve the use of indiect quality indicatos and thus expect fewe paticipating biddes, they incease the bid amount with less concen about the potential winne s cuse. By evealing selles valuation of the poduct, indiect quality indicatos help consumes fom moe accuate valuations (Bajai and Hotacsu 2004; Bake and Song 2007). With less uncetainty, biddes can be moe deteministic about how much to bid and do not need to evise thei bidding pices by obseving competitive bids (Aiely and Simonson 2003). With moe complete infomation about a poduct, biddes bid ealie (Rasmusen 2001). Theefoe, we expect that indiect quality indicatos encouage them to bid ealy. H 2 : Indiect quality indicatos (e.g., minimum bid, hidden eseve pice, the BIN option) discouage bid paticipation, incease biddes bidding amount, and encouage ealy bidding. Selle Cedibility Indicatos Long-tem pefomance evaluations, such as feedback atings, motivate selles to be consistently tuthful in thei epesentations of poduct infomation and delivey (McDonald and Slawson 2002). A selle must devote time and effot to monito and build up a eputation and to eceive stong positive feedback fom buyes afte each tansaction. Again, the signaling costs ae elated to the futue evenue at stake. A poo eputation esulting fom inconsistent signals esults in lowe futue sales (Dholakia 2005; Dholakia and Soltysinski 2001; Gilkeson and Reynolds 2003; House and Woodes 2006; Lucking-Reiley 2000; Melnik and Alm 2002). In addition, because a low-cedibility selle must incu moe costs to attain the same eputation, the signaling costs ae geate, so this featue satisfies the single-cossing popety, and the selle s ating qualifies as a signaling device. Aiely and Simonson (2003) ague that a selle s investment in feedback atings is simila to that of band eputation. Thid-paty payment methods (i.e., PayPal) offe secue online payments. Afte an auction is completed, the winning bidde contacts the selle to pay fo the auctioned item though Paypal, on which both the selle and the buye open accounts (which is fee). The selle ships the item to the buye, and a signaling cost of the tansaction fees is chaged to the selle to eceive payment fom the buye. Fo example, PayPal chages 2.9% of the tansfeed funds plus $.30 pe tansaction to the selle to eceive payments fom buyes in the United States when the selle s monthly sales fall between $0 and $3,000. Again, this signaling cost is a futue cost that is applied at the end of the auction; it will be highe fo a low-quality selle because it invokes a geate pobability of disputes and being held accountable than fo the high-quality selle. Theefoe, a thid-paty payment method qualifies as a signaling device. Simila to the signaling ole of poduct cetification, selle cetification by an independent thid paty, such as a tade association, ceates costs fo the selle. These signaling costs ae highe fo low-cedibility selles because the chance of obtaining a high anking is lowe than it would be fo high-cedibility selles. Theefoe, selle cetification povides anothe signaling device. In contast, the adoption of cedit cad payment and/o online escow sevices do not qualify as signaling tools, because these appoaches do not cost the selle anything. Thus, no signaling cost exists, though both methods could povide some potection to the buye. Whethe, how much, and when to bid. In Intenet auctions, the selle s cedibility is elated to the delivey of the poduct and the accuacy of the poduct desciption. Fo easons simila to those we pose egading diect quality indicatos, we posit that selle cedibility indicatos encouage consumes to paticipate and bid ealy. Howeve, because the use of such indicatos may esult in moe biddes, it also may decease biddes willingness to bid (WTB) (Bajai and Hotacsu 2003; Jin and Kato 2006; Yin 2006). H 3 : Selle cedibility indicatos (e.g., selle ating points, thid-paty payment, selle cetification) encouage bid paticipation, decease biddes bidding amount, and encouage biddes to bid ealy. Although selles can adopt Intenet auction featues to signal poduct quality, biddes can question the cedibility of these indicatos. Selles ae motivated to send false indicatos if the (shot- and long-tem) cost of sending such signals is lowe than the pice pemium (Jin and Kato 2006). The cumulative ating system developed by ebay eveals the degee of consistency between the selles signals and the quality and delivey of all the poducts they have sold (McDonald and Slawson 2002). Thus, buyes can ely on the histoical pefomance of the selles to make infeences about the cedibility of thei indicatos. Highe selle atings imply highe consistency between selles signals and the tue quality of thei poducts. All else being equal, the signals sent by selles with highe atings can be deemed to be 80 / Jounal of Maketing, Januay 2009

7 tue than those fom selles with lowe atings. Similaly, the use of thid-paty payment methods and selle cetification may incease consumes tust because thei payment is secue and the selle must pay a tansaction fee to eceive the funds fom the buye, wheeas a PayPal account is fee fo the buye (Gilkeson and Reynolds 2003). In addition, PayPal offes ebay buyes coveage of up to $2,000 fo claims of nondelivey o significant misepesentation. All PayPal tansactions also ae coveed by 100% potection against unauthoized payments fom the buye s account. Theefoe, selle cedibility indicatos, such as selle ating points, thid-paty payment, and selle cetification, should amplify the effect of quality indicatos (both diect and indiect) on consume bidding behavio. Moe specifically, the simultaneous use of selle cedibility and poduct quality indicatos amplifies the signaling effect. H 4 : Selle cedibility indicatos (e.g., selle ating points, thid-paty payment, selle cetification) amplify the effects of quality indicatos. Thus, selle cedibility indicatos play dual oles: They povide veifiable infomation about the selle s cedibility o eputation, and they may lend cedibility to othe poduct qualityelated auction featues. te that we ae not inteested in the intaindicato-type amplification because, as Kimani and Rao (2000) show, if one signal has aleady evealed the tue quality, anothe signal of the same type is unnecessay. Impact of Bidde Expeience Empiical analysis eveals that when an Intenet auction stats, some biddes lack knowledge about the auction fomat, and the impact of biddes expeience on thei decisions is significant in Intenet auctions. Fo example, Wilcox (2000) shows that moe expeienced biddes bid late than less expeienced biddes. Expeimental studies also demonstate that inexpeienced biddes tend to ovebid and suffe fom the winne s cuse (Bajai and Hotacsu 2004; Kagel and Roth 1995; Wilcox 2000). Fo example, online chat ooms often featue expeienced biddes eminding othe biddes to pay attention to feedback. Biddes with moe expeience may undestand the auction mechanism and signaling oles of indicatos bette than less expeienced biddes. New to Intenet auctions, inexpeienced biddes ae still leaning about the design of the bidding system and the economic functions of indicatos. Theefoe, we expect that the impact of quality and cedibility indicatos on bidding decisions will be geate fo moe expeienced biddes. H 5 : The effects of indicatos in H 1 H 4 ae stonge fo expeienced biddes. Model To test ou hypotheses, we adopt and modify Pak and Badlow s (2005) model, which entails a geneal integated statistical famewok that captues consumes dynamic bidding behavio within auctions using a multistage pocess. The integated model of bidding behavio includes fou key components: (1) whethe consumes paticipate in an auction; (2) if so, who bids o becomes the actual obseved bidde at a paticula bidding occasion; (3) when the bid takes place; and (4) how much the consume bids duing the entie sequence of bids in the auction. The key latent constuct that integates all fou modules is consumes WTB at each bidding occasion. Thus, the famewok incopoates consumes endogenous enty and competition among potential biddes, including those not diectly obseved in the auction. We account fo consume heteogeneity with a hieachical Bayesian famewok. In summay, the integated modeling famewok enables us to examine how diffeent quality/cedibility indicatos affect biddes WTB and thei bidding decisions. A Consume s WTB A consume s WTB is a time-vaying stochastic valuation that can be updated fo a paticula item ove the couse of the auction and can change fom one bidding occasion to anothe because the consume s affiliated valuation of the auctioned item may vay accoding to the actions of othe biddes (Aiely and Simonson 2003). Intuitively, a consume s WTB should be detemined by peceived selle cedibility and poduct quality, as well as the auction and bidde chaacteistics (Badlow and Pak 2007; Chan, Kadiyali, and Pak 2007). Let WTB i epesent bidde i s WTB fo auction j in poduct categoy m at the th ound of bidding. Moe fomally, we model consume s WTB i as follows: 1 WTB i im0 i i β i1 () = β + C + Q + X + φ + η. Thus, C i and Q i ae consumes peceptions of selle cedibility and poduct quality, espectively, which ae unobsevable. Howeve, without diectly obseving the poduct o selle, biddes can assess the selle s cedibility and the poduct s quality using cues (auction featues) on the site. On the auction site, consumes can obseve public infomation published by the selle, including the selle s ating points (SRATING ), thid-paty payment (THIRD- PAY ), multiple pictue postings (PICTURE ), moneyback guaantees (MONEY ), minimum bids (MBID ), esevation pices (RESERVE ), and BIN options (BUY- ITNOW ). (te that we collected ou empiical data in 2001, and at that time, the use of poduct o selle cetification was not yet available.) Theefoe, we assume that consumes fom peceptions about selle cedibility and poduct quality accoding to the following equations: ( 2) C = φ SRATING + φ THIRDPAY,and () 3 Q i i1 i2 = φ PICTURE + φ MONEY + φ MBID i i3 i4 i5 + φ RESERVE + φ BUYITNOW i6 i7 i8 E + φ SRATING PICTUR + φ SRATING MONEY i9 i10 D + φ SRATING MBI + φ SRATING RESERVE i11 + φi 12SRATING BUYITNOW j i Intenet Auction Featues as Quality Signals / 81

8 + φ THIRDPAY PICTURE + φ i13 i14 THIRDPAY MONEY + φ THIRDPAY MBID + φ i15 i16 i17 THIRDPAY As we discussed peviously, selles cumulative ating points and use of thid-paty payments may convey infomation about selle cedibility and elate diectly to poduct delivey. We include multiple pictue postings, money-back guaantee, minimum bid, esevation pice, and BIN option in the peceived quality component because they may signal poduct quality unde infomation asymmety. Recall ou discussion that selles ating points and thid-paty payment not only ae cedibility indicatos themselves but also detemine the effectiveness of the othe quality indicatos. We include the inteaction tems of selle ating points and thid-paty payment with the othe quality indicatos to captue whethe the selle cedibility indicatos amplify the effectiveness of the poduct quality indicatos. 5 We also examine the coelation of the independent vaiables in Equations 2 and 3 and find that the coelations ae low (i.e., the highest is equal to.45). Theefoe, multicolleaity is not a concen fo ou model. The quality/cedibility indicatos we investigate usually ae set at the beginning of the bidding pocess, befoe the bidde s paticipation o bidding decisions ae evealed, and do not change acoss bidding occasions. To account fo the time-vaying factos that affect WTB duing the couse of the bidding, we follow Pak and Badlow (2005) and include bid-specific chaacteistics at any paticula bidding occasion. The tem X is a vecto of covaiates fo the th bid-specific and auction-specific chaacteistics in auction j in poduct categoy m, including the following exogenous vaiables: (1) length of auction duation (BIDDAY ); (2) whethe the th bid occus on a weekend ( WEEKEND ); (3) the time emaining until the end of the auction ( REMAIN ); (4) the numbe of bids submitted befoe the th bid ( NUMBID ); (5) the bid ate, defined as ( 1) divided by the total elapsed time ( BIDRATE ); and (6) the ate of bid incements, detemined as the incemental bid amount in the pevious ound divided by its elapsed time ( AMTRATE ). These vaiables affect the bidde s WTB o the bidding amount as contol vaiables (Bake and Song 2007; Pak and Badlow 2005). The inclusion of NUMBID enables us to test whethe biddes vay thei bidding decisions accoding to the bidding behavio of othe biddes o the existence of affiliated value in Intenet auction. The eo tem ϕ j captues unobseved auctionspecific chaacteistics that affect WTB, which we assume 5We include the inteaction tem between SRATING (o THIRDPAY ) and othe quality indicatos to test H 4. We also tied some othe inteaction tems, such as PICTURE MONEY ; howeve, many of them wee not significant. Theefoe, we do not include them in the poposed model. RESERVE + φ THIRDPAY BUYITNOW. ae independently and identically distibuted acoss biddes, ϕ j N(0, σ2), and η ϕ i captues the unobseved vaiation affecting WTB and vaying with bidde, auction, and poduct with the i.i.d. distibution η N(0, σ2 i η ). In Equation 1, β im0 epesents consume i s intinsic WTB fo poduct categoy m, afte we contol fo the idiosyncatic diffeences acoss poduct categoies. The 17 1 paamete vecto Φ i = (φ i1, φ i2,, φ i17 ) captues the impacts of vaious indicatos on biddes WTB. Specifically, φ i12 measue the effectiveness of the cedibility indicatos with egad to WTB, φ i37 measue the main effects of the quality indicatos on WTB, and φ i817 indicate the effects of selles eputation and thid-paty payment on the cedibility of each quality indicato. The tem β i1 is a 6 1 vecto of paametes that measues the effects of the contol vaiables on WTB. Pak and Badlow (2005) use the latent constuct of WTB as the coe of thei poposed model of online bidding behavio that detemines the consume s bid speed, which in tun govens the decisions about whethe to bid, who bids, when to bid, and how much to bid. Specifically, bid speed, d i, of potential bidde i at bid occasion in auction j in poduct categoy m follows an exponential distibution. Aiely and Simonson (2003) show that consumes value assessment and decision dynamics diffe in the enty stage compaed with duing the auction. Theefoe, we allow the consume s WTB to have a diffeent impact on the bid speed in diffeent decision stages, as well as on the consume s decisions about whethe to bid, who bids, and when to bid (i.e., d ik, whee k = 1, 2, o 3, epesenting decisions whethe to bid, who bids, and when to bid, espectively). Fomally, ( 4) d ~ λ exp[ λ ( t t 1)], whee ( ) log( λ ) = α + α ( WTB BID 1) + ε 5 ik k0 k1 i In this equation, is potential bidde i s bidding time at bid occasion in auction j in poduct categoy m, as we explain subsequently. Let t 1 and BID 1 denote the obseved bidding time and obseved bidding amount in the pevious bid occasion ( 1) in auction j in poduct categoy m, espectively. The mean of the bidding speed equals Ed ( ik ) = 1/ λ ik, given the exponential distibutional assumption. Intuitively, Equations 4 and 5 note that bidde i s bidding speed at bid occasion fo item j in poduct categoy m is detemined by the bidding suplus fo item j ik ik t i ε 1 ) ik ~ N(, ). 0 σ2 ε (i.e., WTBi BID because the bidde s WTB must be geate than the obseved bidding amount in the pevious ound if the bidde is to paticipate in the cuent bid. In tun, α k0 and α k1 measue the bidde s intinsic ate of bidding speed and the impact of the bidding suplus on the bidding speed, espectively. If α k1 is negative, highe bidding ik suplus inceases bidding speed that is, given Ed ( ik ) = 1/ λ ik. The expectation of bidding speed is the invese of λ ik, which is detemined by the bidding suplus (i.e., ik i, WTB i 82 / Jounal of Maketing, Januay 2009

9 1 ). BID Theefoe, the impacts of quality o cedibility indicatos on the bidde s bidding speed can be captued by the evese sign of α k1 Φ i fo k = 1, 2, and 3, whee vecto Φ i efes to the impact of quality/cedibility indicatos on WTB as in Equations 2 and 3. Intuitively, bidding speed captues the bidde s ugency. By incopoating the bidde s peceptions of selle cedibility and poduct quality, we can investigate the potential effects of these indicatos on bidding behavio (Badlow and Pak 2007; Chan, Kadiyali, and Pak 2007; Pak and Badlow 2005). Whethe an Auction Receives a Bid Many listed auctions esult in no paticipation, so we assume that whethe an auction eceives a bid depends on 1 whethe the lowest bidding time (i.e., min[ t i ]) of all potential biddes at the fist potential bid lies within the = 1 auction duation. We use I to epesent the numbe of potential biddes fo item j in poduct categoy m duing ound 1 and T to denote the length of auction j in poduct categoy m. In othe wods, the pobability that auction j ends with no biddes equals the pobability that the lowest 1 bidding time ( min[ t fo all i I = 1 i ]) exceeds the auction duation T. The minimum of a set of exponentials is exponentially distibuted with the ate equal to the sum of the ates, so the pobability that auction j in poduct categoy m eceives no bids ove its whole T duation is I = 1 ( ) p min( t ) > T = exp λ i = i i The pobability that auction j attacts at least one bidde (i.e., WBID = 1) is ( 7) pob( WBID = 1) = 1 p min( t1 ) > T I = 1 = 1 exp λ1 i T. i = 1 That is, the enty pobability fo consume i in auction j is 1 elated positively to the consume s bid ate λ i fom Equation 7, and we can test the impacts of cedibility o quality indicatos on a consume s enty decisions with the sign of α 11 Φ i. Who Bids i T. Next, we model whose bids we obseve duing ound out of the I latent biddes in auction j. Accoding to Equations 4 and 5, the consume s bidding speed follows an exponential distibution with the ate detemined by his o he WTB. Theefoe, at each bidding occasion, intuitively consumes ae engaging in an exponential ace that matches ates elated to thei WTB, and the peson with the shotest time wins as the actual bidde at the th bid. Of the I latent biddes at bid in auction j in poduct categoy m, the bidde becomes the peson with the shotest bidding 1 time within the time inteval [ t, T ] with a pobability of 1 ( 8) p min( ti) = t t < min( ti) T, i I A peson cannot bid twice in a ow o outbid him- o heself in Intenet auctions; theefoe, we emove the ate of bid speed by the pevious bidde fom the denominato in Equation 8. In addition, in the bid speed ace, the obseved bidde at bid must be in the ace by definition, so his o he WTB must be tuncated below the outstanding bid. te that the inequality in Equation 8 enables the biddes to bid befoe o ight on the ending time of an auction. The bid pobability fo consume i in auction j in poduct categoy m at bid is elated positively to the consume s bid ate λi. Theefoe, we can test the impacts of cedibility o quality indicatos on a consume s competitive enty o paticipation decisions at bid accoding to the sign of α 21 Φ i. When to Bid At each bidding occasion, the bidde as the winne of the afoementioned exponential ace has the shotest bidding time. Theefoe, conditional on who has submitted a bid, the bidding time at bid is the minimum ode statistic of the bid speeds of the I latent biddes. The pobability of obseving is given by ( 9) p min( t ) t 1 t t 1 i = Δ < min( t i) = t T, i I = t Because the bidding time of bidde i at bid is the minimum ode statistic of the bid speeds among the I latent biddes, conditional on who has bid, the when-to-bid pobability is elated negatively to the consume s bid ate λ i fom Equation 9. Theefoe, the impacts of the cedibility o quality indicatos on a consume s bidding time decisions at bid ae elated negatively to the sign of α 31 Φ i. How Much to Bid AMT i I I λ i exp λij m Δt i = 1 i i 1. I 1 exp λ i 1 ( T t ) i = 1 i i 1 i = 1 i i 1 = i Let epesent the latent bidding amount at bid fo auction item j in poduct categoy m. Thus, the latent bidding amount should be detemined by WTB befoe this moment, following a nomal distibution with mean WTB i and vaiance σ2 ξ : ( 10) AMT, ~ ( 0, 2 i = WTBi +ξ i ξ i N σ ). ξ i i λi I = 1 1 i λ. Intenet Auction Featues as Quality Signals / 83

10 Let denote the obseved bid amount at the th bid in auction j in poduct categoy m. When an auction offes a BIN option with a pespecified pice BIN, the bidding pocess teminates if some biddes execise the option. In othe wods, when the bidding amount is geate than the BIN asking pice BIN, bidde i can eithe end the auction by bidding BIN o continue it by bidding less than BIN. To detemine whethe BIN is execised in auction j when is geate than BIN, we denote as the pobability of bidding BIN and model it in a logit famewok: ( 11) b AMT i p whee δ 0 and δ 1 ae coefficients to be estimated. The impacts of cedibility o quality indicatos on the consume s decisions to bid the BIN and end the auction ae elated positively to the sign of δ 1 Φ i. te that p i is a conditional pobability of bidding BIN, given that AMT i is geate than BIN. If the opposite is tue (i.e., AMT i < BIN ), in an auction with BIN pice, bidde i s bid amount is tuncated by BIN. Finally, in auctions without a BIN pice, bidde i can choose any level of bid amount based on his o he WTB. Taking all these cases into account, we model the bidde s bid amount as follows: ( 12) p( AMT = b ) I. p( AMT i BIN ) ( p i ). p( AMT II BIN ) i ( 1 p i ) φ b, b, BIN WTB, i = III. p( AMT < BIN ) i φ b, b 1, BIN WTBi, σ IV. φ b, b 1 2 ( WTB, i σ ξ ) i exp 0 1( WTB 1 i b ) = δ + δ 1 + exp δ 0 + δ 1 ( WTB 1), i b i ( 1 σ 2 ξ ) 2 ( ξ ) 1 2 AMT i p i whee φ( b, b, BIN WTBi, σ ξ ) is the nomal density with mean WTB and vaiance σ 2 i tuncated at 1 ξ, b j fom below and BIN fom above, and 1 2 φ( b, b WTBi, σ ξ ) is the tuncated nomal density 2 with mean WTBi and vaiance σ ξ, tuncated below by 1 b. te that thee ae fou cases in Equation 12: Cases I and II epesent bids when AMT i is geate than BIN, Case III efes to a bid when AMT i is lowe than BIN, and Case IV deals with a bidde s decision about how much to bid in auctions without a BIN pice. Moeove, Φ i diectly measues the impact of quality o cedibility indicatos on the consume s bidding amount, given Equation 10. In summay, the mechanism of how vaious quality/ cedibility indicatos affect consumes bidding decisions poceeds as follows: Fist, the indicatos diectly detemine the consume s WTB at each bidding occasion accoding to Φ i in Equations 13. Second, the consume s WTB affects his o he bidding speed though α k1 (whee k = 1, 2, o 3), in Equations 4 and 5. Thid, consumes engage in bidding speed aces in which thei bidding speeds detemine decisions of whethe to bid, who bids, and when to bid. Theefoe, we measue the impacts of the indicatos on these thee bidding decisions by the poduct of α k1 and Φ i fo k = 1, 2, o 3, espectively. Finally, conditional on who bids, the bidde s WTB detemines his o he bidding amount at each bidding occasion because the consume s bidding amount is mean centeed on his o he WTB, as in Equation 10. The impact of the indicatos on bidding amount is also diectly measued by Φ i. Unobseved Heteogeneity and Estimation Wilcox (2000) demonstates that expeienced biddes ae moe likely to bid late than inexpeienced ones. To account fo this heteogeneity (Allenby and Rossi 1999; Gönül and Sinivasan 1996), we wite the coefficients in Equation 1 as functions of the paticipating biddes expeiences. Let π i = (Φ i, β i ). Thus: ( 13) π = γ + γ BEXPER + μ, μ ~ N( 0, 2 ). i 0 1 i i i σ μ With the coefficient γ 1, we study how biddes expeiences modify the effect of cedibility o quality indicatos on bidding decisions. The hieachical stuctue we use to account fo consume heteogeneity diffes fom that of Pak and Badlow (2005), who specify consume unobseved heteogeneity only in the intecept of the WTB function fo simplicity. We diectly model the latent competition among a vaying numbe of potential biddes ( I ) ove the couse of an auction. To calculate I, we use the numbe of potential 1 biddes who have positive suplus (i.e., WTBi > b ) out of the maximal numbe of potential biddes (I ) fo auction j in poduct categoy m. We discuss how we appoximate I in the Data Desciption section. To estimate the model, we also intoduce a hieachical Bayesian appoach that accounts fo unobseved heteogeneity (Allenby and Rossi 1999). We then apply the Makov chain Monte Calo method (Gibbs sample) and data augmentation with Bayesian Infeence Using Gibbs Sampling (Win- BUGS) softwae to estimate the model paametes. We un two independent chains with 25,000 Makov chain Monte Calo iteations each and discad the fist 20,000 iteations as a bun-in peiod to ensue convegence. The last 5000 iteations fom both chains combine to calculate the estimates. We conduct standad eyeball scanning and Gelman and Rubin s (1992) F-statistic diagnostic to test the convegence of the chains (fo detailed infomation about the loglikelihood functions and estimation method, see Pak and Badlow 2005). To summaize, ou model is an adaptation of Pak and Badlow s (2005) integated modeling famewok that captues consumes dynamic bidding behavio (whethe to bid, who bids, when to bid, and how much to bid) within auctions. Howeve, we made the following adaptations: Fist, we explicitly model consumes peceptions of unobseved selle cedibility and poduct quality (i.e., C i and Q i ), wheeas these ae not the focus of Pak and Badlow s model. Second, we model consumes WTB acoss poduct 84 / Jounal of Maketing, Januay 2009

11 categoies, wheeas Pak and Badlow s model focuses on auctions within one poduct categoy. Thid, we make the model moe flexible by allowing the consume s WTB to have diffeent impacts on the bid speed in diffeent decision stages. In othe wods, we let the bid speed to be decision stage specific (d ik, whee k = 1, 2, o 3, epesenting decisions of whethe to bid, who bids, and when to bid, espectively). Accodingly, we allow bid speed to have diffeent effects on the consume s decisions of whethe to bid, who bids, and when to bid. Finally, we allow bidde pefeence heteogeneity to be explained by bidde expeience (Equation 13) using a hieachical Bayesian famewok. This is diffeent fom Pak and Badlow s model, which takes into account heteogeneity only in the constant tem. Empiical Applications Data Desciption We collect data fom two aeas paintings and silve plates of ebay s supe antiques categoy. We chose these aeas because as epesentatives of the supe antique categoy, thei values may be detemined by futue esale pices. Theefoe, we speculate that asymmetic infomation and the affiliated-value elements may be geate fo these poduct categoies, which is impotant in studies of advese selection issues (Bajai and Hotacsu 2004). Ou data contain 1324 auctions listed on ebay by 806 andomly selected paticipating biddes fom Apil 15, 2001, to May 6, We andomly selected 75% of ou sample fo estimation and used the emaining 25% to fom a holdout sample. In Table 2, we epot the definitions of all the vaiables we used and thei sample statistics. Thee wee seven quality indicatos adopted by ebay duing ou obsevation peiod. The fequencies of the adoption of quality indicatos ae 72%, 16%, 72%, 24%, and 9%, espectively, fo multiple pictue postings, money-back guaantee, thidpaty payment, hidden eseve pice, and the BIN option. The mean of the minimum stating bid pices is $92.7, and the mean and standad deviation of the selles cumulative ating points ae and , espectively. (We also tied the selles pecentage of positive ating points in the estimation. The esults emain simila.) The aveage numbe of days specified by the bidde is 7.35 days. The aveage bidding time is hous since the stat of the auction. The aveage bidding amount, which eflects a bidde s obseved bid amount, is $72.03, and 42% of the submitted bids occued on weekends. We follow the wok of Wilcox (2000) and measue bidde expeience by the bidde s cumulative feedback points, which appoximate the total numbe of auctions. The mean and standad deviation of biddes expeience ae and 203.9, espectively. Of the 1324 auctions, almost half esult in no bids at all (i.e., 49%). The aveage numbe of unique biddes fo each auction is 1.06, anging fom 0 to 9. The aveage numbe of bids is The mean of the BIN option pice is $10.50, with a minimum of $0 and a maximum of $1,399. The aveage values of emaining time to the end of the auction (REMAIN), numbe of bids submitted befoe the th bid (NUMBID), bid ate (BIDRATE), and the ate of bid incements (AMTRATE) ae 95.05, 2.65,.04, and.44, espectively. Finally, because we use two poduct categoies in the data, we mean-cente the categoy-specific vaiables by poduct categoy. TABLE 2 Definitions of Vaiables Selle s Options M (SD) SRATING Selle s cumulative ating point (796.86) THIRDPAY Whethe thid-paty payment is specified (1) o not (0).72 (.45) PICTURE Whethe thee ae two o moe pictues of the item (1) o not (0).72 (.13) MONEY Whethe money-back guaantees ae specified in the auction (1) o not (0).16 (.37) MBID Amount of minimum stating bid specified by the selle ( ) RESERVE Whethe the selle uses the hidden eseve pice (1) o not (0).24 (.43) BUYITNOW Whethe the selle uses the BIN option (1) o not (0).09 (.28) BIDDAY Numbe of days of an auction 7.35 (1.99) Bidde s Decisions/Chaacteistics BIDTIME Bidde s bidding time in hous since the stat of the auction (71.15) AMT Bidde s bidding amount (202.86) WEEKEND Whethe a bid is placed on a weekend (1) o not (0).42 (.49) BEXPER Bidde s bidding expeience (calculated fom cumulative ating point) (203.88) Othe Auction- o Bid-Specific Vaiables WBID Whethe thee is any bidde paticipating in the auction (1) o not (0).51 (.50) NBIDDER Numbe of unique biddes in an auction 1.06 (1.55) BIN The amount of BIN pice (68.72) REMAIN The emaining time to the end of the auction fo a paticula bid (157.65) NUMBID The numbe of bids submitted befoe a paticula bid 2.65 (3.82) BIDRATE The bid ate defined as ( 1) divided by the total elapsed time.04 (.09) AMTRATE The ate of bid incements as incemental bid amount at pevious ound divided by its elapsed time.44 (5.54) Intenet Auction Featues as Quality Signals / 85

12 We obseve the numbe of people who view a paticula auction and use it to appoximate the total numbe of potential biddes I fo auction j in poduct categoy m. We gathe this infomation with an automatic counte option that is fee and is set by selles; this was employed in 87% of the auctions in ou data. Fo auctions without such infomation, we impute the value using the mode of the vaiable (appoximately 72). This vaiable offes a good poxy of I because the automatic counte uses each Intenet use s cookie addess to identify unique biddes; thus, we have additional infomation to measue the latent competition among dynamically changing potential biddes duing the couse of an auction, compaed with Pak and Badlow s (2005) windowing pocedue, which lacks this poxy infomation. Results To evaluate the fit of ou model, we estimate two benchmak models. The fist model consides only obseved biddes and assumes that biddes ae homogeneous, simila to ou poposed model but without consideation of competition among latent biddes o bidde heteogeneity. The second model is simila to Pak and Badlow s (2005) and incopoates competition among latent biddes but ignoes bidde pefeence heteogeneity, except in the intecept. In Table 3, we epot the log of posteio maginal density (Kass and Rafety 1995); the hit ate fo the whethe-tobid model; and the mean absolute eos (MAEs) fo the numbe of unique biddes in each auction, fo when to bid, and fo how much to bid using both the estimation and the holdout samples. We also compute the Bayes factos (Kass and Rafety 1995) to assess model fit. The esults stongly favo ou poposed model ove the two benchmak models by odds of and 199.6, espectively. In addition, the poposed model pefoms bette than the two benchmak models on all othe dimensions fo both the estimation sample and the holdout sample, which indicates the impotance of ecognizing latent bidde competition with endogenous enty and bidde pefeence heteogeneity. Impact of diect quality indicatos: H 1. In Table 4, we epot the estimation esults fo the thee models, focusing on Model 3, o ou poposed model. As we expected, α k1 (k = 1, 2, 3) ae all estimated to be negative, which suggests that highe bidding suplus inceases bidding speed because of the geate ugency fo biddes with high suplus (Pak and Badlow 2005). The coefficient of PICTURE is significant and negative (φ i3 < 0) because multiple pictues eveal moe infomation about poduct quality and help biddes diffeentiate high- fom low-quality poducts. This eduction in incomplete infomation leads consumes to be moe willing to paticipate, which esults in moe auction paticipants (i.e., α 11 φ i3 > 0, and α 21 φ i3 > 0). Howeve, φ i3 < 0 suggests that this diect indicato educes consumes bidding amount because ational biddes expect moe paticipants and stategically shade thei bidding amount to counte the winne s cuse. In addition, α 31 φ i3 > 0 suggests that as consumes bid ates incease, they tend to bid ealy; because the quality indicatos povide moe infomation about poduct quality, biddes ae less likely to ely on competitive bids to detemine value, and theefoe biddes do not need to update thei bids constantly. Money-back guaantees (MONEY) have a significant and negative effect on consumes WTB and bidding amount (i.e., φ i4 < 0). They educe incomplete infomation, so consumes become moe likely to paticipate in the auction (α 11 φ i4 > 0, and α 21 φ i4 > 0) and bid ealy (α 31 φ i4 > 0). To counte the winne s cuse, consumes stategically shade thei bids (φ i4 < 0). Theefoe, fo both multiple pictue postings and money-back guaantees, we find suppot fo H 1. Impact of indiect quality indicatos: H 2. In contast to the diect quality indicatos, the indiect quality indicatos (RESERVE and BUYITNOW) incease WTB and bidding amount (φ i6 > 0 and φ i7 > 0), making consumes less likely to paticipate (given α k1 φ i67 < 0 fo k = 1, 2). Indiect quality indicatos discouage geneal paticipation because RESERVE and BUYITNOW eliminate buyes with low willingness to pay and high paticipation costs. Because fewe biddes paticipate, consumes become less concened about the winne s cuse and expeience inceased WTB and bidding amounts (φ i6 > 0 and φ i7 > 0). Thus, fo hidden eseve pices and BIN options, the empiical esults suppot H 2. Howeve, consumes tend to bid late afte TABLE 3 Compaison of Competing Models Homogeneous Pak and Model with Badlow (2005) Obseved Model with Sample Citeion Biddes Only Latent Biddes Poposed Model Estimation sample a Log-maginal density 868, , , Hit ate fo whethe to bid model MAE fo numbe of unique biddes MAE fo when to bid model MAE fo how much to bid model Holdout sample b Hit ate fo whethe to bid model MAE fo numbe of unique biddes MAE fo when to bid model MAE fo how much to bid model anumbe of auctions = 993, and numbe of obsevations = bnumbe of auctions = 331, and numbe of obsevations = / Jounal of Maketing, Januay 2009

13 TABLE 4 Estimation Results Pak and Homogeneous Badlow Model with (2005) Model Covaiates Obseved with Latent Poposed Vaiable Types (Coefficients) Biddes Only Biddes Model Intecept INTERCEPT Silve plate (β i10 ).070 (.167).036 (.022) a (.162) INTERCEPT Painting (β i20 ) a (.541).911 a (.102) a (.234) Selle s cedibility indicatos SRATING (φ i1 ).240 (.638) a (.336).324 a (.019) THIRDPAY (φ i2 ) a (.729) a (.399).367 a (.014) Diect quality indicatos PICTURE (φ i3 ) (.623) a (.357).405 a (.010) MONEY (φ i4 ).031 (1.066).245 (.658).055 a (.014) Indiect quality indicatos MBID (φ i5 ).265 (.152).151 a (.041).021 (.028) RESERVE (φ i6 ).637 (.614).729 a (.349).036 a (.022) BUYITNOW (φ i7 ) (1.693) a (.761).197 a (.019) Inteactions between cedibility SRATING PICTURE (φ i8 ).225 (.293).142 (.264).146 a (.011) indicato and quality indicato SRATING MONEY (φ i9 ).370 (.325).473 (.261).125 a (.010) SRATING MBID (φ i10 ).599 (3.414) a (1.359).100 a (.054) SRATING RESERVE (φ i11 ).288 (.500).035 (.435).009 (.064) SRATING BUYITNOW (φ i12 ) (2.031) (.780).205 (.121) THIRDPAY PICTURE (φ i13 ) a (.757) a (.452).382 a (.010) THIRDPAY MONEY (φ i14 ).542 (1.217).385 (.812).097 a (.048) THIRDPAY MBID (φ i15 ) (3.753) a (2.161).455 a (.055) THIRDPAY RESERVE (φ i16 ) (.822) a (.516).001 (.014) THIRDPAY BUYITNOW (φ i17 ).811 (1.685).794 (.751).119 a (.062) Othe contol vaiables BIDDAY (β i1 ).208 a (.076).291 a (.045).002 (.002) WEEKEND (β i2 ) a (.461).535 a (.267).122 a (.017) REMAIN (β i3 ).014 (.025).001 (.010).016 a (.003) NUMBID (β i4 ).156 a (.032).015 (.012).022 a (.003) BIDRATE (β i5 ).892 (1.047).448 (.426).147 a (.027) AMTRATE (β i6 ).013 (.018).002 (.004).033 a (.007) Bid speed α a (.130).301 a (.059).629 a (.041) α a (.040).310 a (.067).686 a (.082) α (.134).014 (.022).100 a (.040) α a (.144).119 a (.025).019 a (.002) α a (.103).269 a (.075).613 a (.071) α a (.057).288 a (.069).674 a (.053) Bid BIN pice δ (6.982) (8.385) a (5.111) δ (6.878) (6.420) (9.295) azeo lies outside the 95% posteio pobability inteval. obseving the use of these two indicatos, conditional on being a bidde (α 31 φ i67 < 0), pehaps because they believe that the chance of winning such an auction is low and thus wait to bid (Budish and Takeyama 2001; Katka and Reiley 2006). The minimum stating bid (MBID) has an insignificant impact on consumes bidding amount, enty, and bidding time, though its coefficient is positive (φ i5 > 0), consistent with Standifid s (2001) findings. This esult may be due to the usual low values of minimum stating bids set by selles (e.g., a median of $15 with a minimum of $.01 in ou sample), which thus may not become constaints fo biddes. Theefoe, we find patial suppot fo H 2. Impact of cedibility indicatos: H 3. Cedibility indicatos should encouage bid paticipation, decease biddes bidding amount, and encouage biddes to bid ealy. In Table 4, we show the suppot we find fo H 3. The selle s feedback ating (SRATING) and the use of thid-paty payment methods help high-cedibility selles distinguish themselves fom low-cedibility selles by evealing thei tue cedibility. Consumes ae moe confident about paticipating in the auctions of selles that use thid-paty payment systems and thus become moe likely to paticipate in the auction (α k1 φ i12 > 0 fo k = 1, 2). Howeve, fom the consumes point of view, this willingness inceases the expected numbe of paticipating biddes, so they shade thei WTB (φ i12 < 0). The coefficient fo bid suplus in Equation 5 is negative (α 31 < 0), so α 31 φ i12 > 0, which indicates that as consumes bid ate inceases, they become moe likely to bid ealy, assuming that they bid. This finding is consistent with pevious esults in auction liteatue (Ba and Pavlou 2002; Binkman and Siefet 2001; Dewan and Hsu 2001; House and Woodes 2006; Livingston 2005). The eduction of quality uncetainty makes consumes less likely to depend on othe biddes bidding infomation. Because they do not need to obseve competitive bids to detemine value, biddes do not need to update thei bids constantly, in suppot of H 3. Inteaction between cedibility indicatos and quality indicatos: H 4. The coefficients of the inteaction tems Intenet Auction Featues as Quality Signals / 87

14 between SRATING o THIRDPAY and most of the quality indicatos ae significant with expected signs, which implies that selles cumulative ating points o thid-paty payment amplify the effects of quality indicatos on biddes decisions about enty, bidding time, and bidding amount. Highe accumulative atings and the use of thidpaty payment imply geate consistency between the selles signals and the tue quality of thei poducts. Theefoe, highe atings o thid-paty payment methods make selles indicatos moe cedible and amplify the effect of quality indicatos. Afte obseving the use of both cedibility and quality indicatos, ational consumes become moe likely to paticipate in the auction (α k1 φ i817 > 0 fo k = 1, 2), to shade thei bids (φ i817 < 0), and to bid ealy (α 31 φ i817 > 0). The only exception is the inteaction between THIRD- PAY and PICTURE, which is significant and positive, pehaps because these signals ae significantly diffeent, such that the fome is default contingent and the latte is default independent (Kimani and Rao 2000). Oveall, we find suppot fo H 4. Impact of bidde heteogeneity on the effects of cedibility indicatos and quality indicatos: H 5. As we show in Table 5, the effect of the indicatos diffes acoss biddes expeiences. Most heteogeneity coefficients ae significant; that is, the impacts of cedibility o quality indicatos on consumes bidding decisions ae stonge fo biddes with moe expeience. If we use multiple pictue postings as an example, we note that the negative effect of BEXPER on the pictue posting coefficient indicates that posting multiple pictues encouages auction paticipation and bid shading, and the effect is geate fo expeienced biddes, who make bette infeences about quality indicatos and ae less likely to suffe fom the winne s cuse, in suppot of pevious eseach (Bajai and Hotacsu 2004; Kagel and Roth 1995; Wilcox 2000). Similaly, the negative effect of BEXPER on the selle s cumulative ating points (SRATING) coefficient indicates that this cedibility indicato is moe effective fo expeienced biddes, encouaging them to shade thei bidding amount. Theefoe, in geneal, we find suppot fo H 5. Howeve, the enhancing impact of the bidde s expeience does not diffe accoding to the two types of indicatos (diect vesus indiect). Expeienced biddes undestand the auction mechanism and signaling oles of indicatos bette than less expeienced biddes, which enables them to make bette use of the infomation deliveed by the indicatos, which in tun inceases thei impact. This logic holds fo both diect and indiect indicatos. Othe auction context vaiables. The effects of othe auction context vaiables on biddes WTB include the intecept fo the painting categoy, which is geate than that fo the silve plate categoy, as we show in Table 4, pehaps because the value of auctioned items in the latte categoy is nomally lowe. The duation of an auction (BID- DAY) does not have a significant impact on WTB, bidding time, o bidding amount, in suppot of pevious eseach (Dholakia and Soltysinski 2001; Gilkeson and Reynolds 2003). If a bid occus ove a weekend (WEEKEND), consumes ae moe likely to incease thei WTB, pehaps because they have moe fee time on weekends and, theefoe, lowe psychological bidding costs. When moe time emains befoe the end of the auction (REMAIN), WTB tends to be lowe. Howeve, the highe the numbe of bids TABLE 5 Estimation Results fo Heteogeneity Equation Covaiates Intecept BEXPER INTERCEPT Silve plate a (.180) a (.252) INTERCEPT Painting a (.269).206 a (.039) SRATING.014 (.023) a (.042) THIRDPAY.311 a (.009).513 a (.064) PICTURE.351 a (.010).500 a (.032) MONEY.092 a (.017).346 a (.188) MBID.121 a (.021).933 a (.176) RESERVE.105 a (.014) a (.135) BUYITNOW.010 (.031) a (.231) SRATING PICTURE.030 a (.011) a (.032) SRATING MONEY.011 (.020) a (.129) SRATING MBID.518 a (.055) a (.117) SRATING RESERVE.034 (.024).237 (.567) SRATING BUYITNOW.129 a (.062) a (1.355) THIRDPAY PICTURE.293 a (.011).824 a (.044) THIRDPAY MONEY.175 a (.022) a (.321) THIRDPAY MBID.488 a (.059).304 a (.093) THIRDPAY RESERVE.035 (.023).316 a (.124) THIRDPAY BUYITNOW.060 (.046) a (.300) BIDDAY.008 a (.003).055 a (.014) WEEKEND.070 a (.016).490 a (.062) REMAIN.013 a (.004).028 a (.014) NUMBID.024 a (.004).021 (.018) BIDRATE.249 a (.031) a (.105) AMTRATE.020 a (.010).121 (.086) azeo lies outside the 95% posteio pobability inteval. 88 / Jounal of Maketing, Januay 2009

15 submitted befoe each bid (NUMBID) o the highe the bid ate (BIDRATE), the highe is biddes WTB. This suppots ou agument that the affiliated value in these auctions emeges because consumes valuation is affected by competition. When the ate of bid incements (AMTRATE) inceases, biddes become moe cautious and stategically shade thei bids to avoid the winne s cuse, consistent with Pak and Badlow (2005). Because the coefficient of suplus δ 1 fo how much to bid in Equation 11 is insignificant, consumes valuations elative to the BIN pice do not have a significant impact on whethe to execise the BIN option. Because indiect quality indicatos dive up consumes WTB, selles with high feedback ating points can incease thei final bidding pices and pofit magins. Finally, both the vaiance of the unobseved auction-specific coefficient ϕ j (i.e., σ 2 ϕ =.135, SD =.030) and the vaiance of the pefeence heteogeneity σ2 ϕ (1.494, SD =.124) ae significant, which indicates thei significant impacts. Compaison with benchmak models. When we compae the esults fom ou poposed model with those of the benchmak models, we find that the coefficient estimates ae sensitive to the inclusion of latent competition among potential biddes and bidde pefeence heteogeneity. Fo example, in Table 4, the homogeneous model with obseved biddes only (fist benchmak model), which is simila to most models in existing empiical eseach, shows that the thee indiect indicatos have no significant impact on WTB o bidding amount. The second benchmak model takes into account the ole of latent biddes and thei unobseved bidding competition, and the impacts of these indicatos become significantly negative. This finding demonstates the impotance of modeling latent bidde competition. Futhemoe, when we incopoate biddes pefeence heteogeneity into the second benchmak model, the impacts of the indicatos become significantly positive, consistent with the findings in choice liteatue that the failue to account fo consumes unobseved pefeence heteogeneity leads to inconsistent and biased estimates (Allenby and Rossi 1999; Gönül and Sinivasan 1996). Oveall, we find suppot fo all five hypotheses. The poposed signaling-based hypotheses ae consistent with the estimation esults and povide coheent explanations of consumes bidding behavio. Simulations. To demonstate the implications of the signaling effect of ebay s featues on consumes bidding behavio moe intuitively, we conduct a seies of policy simulations based on estimates fom the poposed model. As we show in Table 6, we change the fequency of the use of each indicato and simulate the pobability of auction paticipation, aveage numbe of latent biddes, mean intebidding time since last bid, aveage final bid pice (i.e., last obseved bidding amount in an auction 6 ), and aveage bidding expeience of the paticipating biddes to detemine how these vaious indicatos might affect consumes bidding decisions. When we incease the pecentage of auctions with multiple pictue postings and money-back guaantees by 10% sepaately, paticipation pobabilities incease by 1.04% and.84%, espectively. If we incease the fequency of the use of the two indicatos by 20%, it encouages auction paticipation even moe (1.13% and 1.41%). These inceases also attact 3.53% and 1.34% moe latent biddes to a paticula bid on aveage, espectively. Theefoe, multiple pictues and money-back guaantees ae effective in attacting moe potential biddes because they educe the biddes quality uncetainty and encouage paticipation. Thus, diect quality indicatos help alleviate the lemons poblem. 6The last obseved bidding amount is the second-highest bidde s maximum bid because the winning bidde s maximum bid is unobsevable. At ebay, the bidde with the highest maximum bid wins the item and pays a pice equal to the second-highest bidde s maximum bid plus the bid incement. TABLE 6 Simulation Results Aveage Numbe of Intebidding Aveage Expeience of Value o Pobability of Unique Latent Time Since Final Bid Paticipating Indicatos Change Paticipation Biddes Last Bid Pice Biddes Cuent values % % % % Pictue postings 10% incease +1.04% +3.53% 4.78% 2.15% +1.91% 20% incease +1.13% %.34% 3.10% +2.93% Money-back guaantee 10% incease +.84% +1.34%.65% 1.21% +.36% 20% incease +1.41% +.06%.29% 1.35% +1.39% BIN 10% incease.82% 3.31% +2.77% +1.62% +.39% 20% incease 1.07% 3.29% +.44% +1.48% +1.36% Hidden eseve 10% incease 1.13% 1.58% +1.80% +3.10% +.44% 20% incease.57% 2.61% +.46% +2.29% +1.47% Minimum stating bid 10% incease.41%.04% +1.28% +.10% +.25% 20% incease.56% 1.69% +1.83% +.40% +.75% Selle s feedback ating 10% incease +.29% % 2.01%.55% +3.09% 20% incease +.37% % 3.13%.25% +4.43% Thid-paty payment 10% incease +1.17% % 2.02%.63% +2.30% 20% incease +1.54% % 4.17%.13% +3.54% Intenet Auction Featues as Quality Signals / 89

16 Inceasing the pecentage of auctions with multiple pictue postings and money-back guaantees by 10% sepaately also educes the bidde s intebidding time since the last bid (in hous) by 4.78% and.65%, espectively. Consumes shade thei final bid pice by 2.15% and 1.21%, espectively, because they obseve the significant incease in the numbe of latent biddes and shade thei bids to coect fo the potential winne s cuse. That is, the use of quality indicatos encouages ealy bidding but deceases the final bid pice. Howeve, bid shading is not vey damatic fo the poducts we study; the aveage obseved final bid pice is $ acoss all auctions. We do not attempt to make any conclusions about whethe biddes shade the bids enough, because we cannot obseve the tue values of the auctioned items. Futhe eseach should examine the impacts of quality indicatos on the degee of the winne s cuse by collecting tue value infomation about items (Bajai and Hotacsu 2003). The inceasing use of multiple pictue postings and money-back guaantees also attacts moe expeienced biddes by 1.91% and.36%, espectively, which suggests that moe expeienced biddes tend to undestand the indicatos bette and make bette use of such infomation. In contast, a 10% geate fequency of auctions with BIN options, hidden eseve pices, o minimum stating bids educes paticipation pobability by.82%, 1.13%, and.41%, espectively. In addition, they significantly decease the numbe of latent biddes by 3.31%, 1.58%, and.04% and encouage late bidding with the delay of 2.77%, 1.80%, and 1.28%, espectively. Thus, using indiect quality indicatos may wosen the lemons poblem because placing eithe lowe o uppe bounds on valuations discouages bidde enty. Howeve, having fewe latent biddes causes the final bid pice to incease by 1.62%, 3.10%, and.10%, espectively. Simila to the diect indicatos, these indicatos attact moe expeienced biddes at levels of.39%,.44%, and.25%, espectively. Theefoe, when consideing such quality indicatos, selles should balance the lowe auction paticipation ates against the highe final bid pice. Although indiect quality indicatos discouage paticipation, they may not significantly wosen the lemons poblem, because they disciminate in discouaging inexpeienced biddes who may be less seious about puchasing. Thus, the use of such indicatos may benefit selles with highquality poducts. The simulation esults finally show that the use of cedibility indicatos significantly inceases biddes auction enty pobability and encouages ealy bidding and bid shading. As we show in Table 6, when we incease a selle s feedback ating o the pecentage of auctions designed with thid-paty payment by 10%, the pobability of paticipation inceases by.29% and 1.17%; the aveage numbe of latent biddes jumps by a significant 11.10% and 10.43%; intebidding time deceases by 2.01% and 2.02%; the final bid pice deceases by.55% and.63%; and the aveage bidde expeience inceases by 3.09% and 2.30%, espectively. Again, bid shading is not damatic fo the poducts in this study, so cedibility indicatos significantly mitigate the lemons poblem. Cedibility indicatos can impove consumes tust in both auction selles and the auctioned items. Conclusion, Manageial Implications, and Futhe Reseach Many consumes eject Intenet auctions because of the physical sepaation of buyes fom selles. Since the emegence of the Intenet, auction companies have attempted to find the most efficient design fo thei auction mechanisms. The apid expansion of Intenet auctions to consume-toconsume and business-to-consume makets fo an inceasing numbe of consume poducts and sevices also demands eseach into how Intenet auction designs affect bidding behavio. We systematically evaluate the effect of auction featues (i.e., selles cumulative atings, thid-paty payment, pictue postings, money-back guaantees, minimum bidding pice, hidden eseve pice, and BIN option) on consume bidding behavios, and we povide implications fo how these featues can help solve the lemons poblem. We acknowledge dual infomation asymmety in Intenet auctions and theefoe develop a typology of Intenet auction indicatos of selle cedibility and poduct quality that consists of hypotheses about how these indicatos may help alleviate the lemons poblem. We adopt an appopiate modification of Pak and Badlow s (2005) model to test empiically how these quality indicatos affect biddes decisions about whethe to bid, who bids, when to bid, and how much to bid. By accounting fo the latent competition among potential biddes, consume heteogeneity, and the intedependence of bidding decisions, this poposed model simultaneously evaluates the signaling oles of intedependent bidding decisions and accounts fo consume heteogeneity. Specifically, we (1) examine whethe Intenet auction featues seve as effective cedibility o quality indicatos, (2) analyze factos that affect the eliability and cedibility of the indicatos, (3) study how diffeent types of indicatos affect bidding behavio and thei implications fo the lemons poblem, and (4) investigate how the signaling effect diffes acoss biddes with diffeent expeience. Reputable selles with high-quality poducts can use ebay s featues to signal thei cedibility and the poducts tue quality in each auction and to enhance thei eputation and thus distinguish themselves fom diseputable selles o selles of low-quality items. As a esult, biddes benefit fom educed uncetainty about both the selles cedibility and the quality of thei items, which diectly affects bidding behavio and WTB. Consistent with common intuition, cedibility o diect quality indicatos encouage biddes to paticipate and pompt bid shading in esponse to concens about the winne s cuse; the opposite is tue fo indiect quality indicatos. Howeve, the bid shading esulting fom diect indicatos is not vey damatic. Moeove, the simultaneous use of cedibility and quality indicatos stengthens the effects of quality indicatos. Moe expeienced consumes make bette infeences about the oles of both cedibility and quality indicatos. These esults ae even moe evident in the simulation esults. We find that the poposed signaling-based hypotheses ae consistent with the estimation esults and povide coheent explanations of consumes bidding behavio. Ou study has impotant manageial implications fo Intenet auctions sites and selles, which can use these find- 90 / Jounal of Maketing, Januay 2009

17 ings to impove auction site designs and to povide moe cedibility and quality indicatos to impove site use and taffic. Reputable online selles of high-quality poducts should combine vaious cedibility and quality indicatos to eveal thei tue cedibility and quality, to encouage auction paticipation, and to enjoy highe closing pices. Thus, we empiically establish the ole of Intenet auction featues as quality o cedibility indicatos. This eseach is subject to seveal limitations that povide avenues fo futhe eseach. Fist, because ou intent was to focus on how quality signals help solve the lemons poblem, we modeled fou intedependent bidding decisions whethe to bid, who bids, when to bid, and how much to bid based on the assumption of affiliated-value auctions. Additional eseach might conside pivate-value auctions and compae them with affiliated-value auctions to bette undestand the ole of the Intenet auction indicatos. Second, it would be wothwhile to study the dynamics of bidding behavio acoss multiple auctions ove time (e.g., Zeithamme 2006, 2007). Fo example, how do fowadlooking biddes evise thei bidding stategies when they obseve multiple listing of simila items at the same time? Moe stuctual models might enable biddes to update thei fomation of affiliated value in a Bayesian manne, on the basis of peiodically obseved decisions of othe biddes within and acoss auctions. Thid, we focused on consume bidding behavio and teated the selles decisions as exogenous, but expeienced selles might be stategic in adopting cedibility o quality indicatos. Theefoe, it would be wothwhile to incopoate the stategic behavio of selles to daw implications about the best stategy to maximize selles pofits as pat of the custome elationship management effots. The modeling appoach that Bajai and Hotacsu (2003) use seems pomising in this venue. Finally, we povide empiical evidence about how Intenet auction featues signal poduct quality and selle cedibility and how these featues affect consumes bidding behavio. Analytical models can be developed to examine the ationale behind these stategies and to compae diffeent indicatos in tems of signaling costs. REFERENCES Akelof, Geoge A. (1970), The Maket fo Lemons : Quality Uncetainty and the Maket Mechanism, Quately Jounal of Economics, 84 (3), Allenby, Geg M. and Pete E. Rossi (1999), Maketing Models of Consume Heteogeneity, Jounal of Econometics, 89 (vembe), Andeson, Eic T. and Duncan Simeste (2001), Ae Sale Signs Less Effective When Moe Poducts Have Them? Maketing Science, 20 (2), Aiely, Dan, and Itama Simonson (2003), Buying, Bidding, Playing, o Competing? Value Assessment and Decision Dynamics in Online Auctions, Jounal of Consume Psychology, 13 (12), Ba, Sulin and Paul A. Pavlou (2002), Evidence of the Effect of Tust Building Technology in Electonic Makets: Pice Pemiums and Buye Behavio, MIS Quately, 26 (3), Bajai, Patick and Ali Hotacsu (2003), Winne s Cuse, Reseve Pices, and Endogenous Enty: Empiical Insights fom ebay Auctions, RAND Jounal of Economics, 34 (2), and (2004), Economic Insights fom Intenet Auctions, Jounal of Economic Liteatue, 42 (June), Bake, Jeff and Jaeki Song (2007), A Review of Single-Item Intenet Auction Liteatue and a Model fo Futue Reseach, Jounal of Electonic Commece in Oganization, 5 (1), Balachande, Subamanian and Kannan Sinivasan (1994), Selection of Poduct Line Qualities and Pices to Signal Competitive Advantage, Management Science, 40 (July), Bole, Shaad, Pete Boatwight, and Joseph B. Kadane (2005), The Pivate/CommonValue Continuum: An Empiical Investigation Using ebay Online Auctions, woking pape, Teppe School of Business, Canegie Mellon Univesity. Badlow, Eic T. and Young-Hoon Pak (2007), Bayesian Estimation of Bid Sequences in Intenet Auctions Using a Genealized Recod Beaking Model, Maketing Science, 26 (Mach Apil), Binkman, U. and M. Siefet (2001), Face-to-Inteface: The Establishment of Tust in the Intenet: The Case of e- Auctions, Jounal of Sociology, 30 (1), Budish, Eic B. and Lisa N. Takeyama (2001), Buy Pices in Online Auctions: Iationality on the Intenet? Economics Lettes, 72 (Septembe), Chakavati, Dipanka, Eic Geenleaf, Atanu Sinha, Ama Cheema, James Cox, Daniel Fiedman, et al. (2002), Auctions: Reseach Oppotunities in Maketing, Maketing Lettes, 13 (3), Chan, Tat Y., Vinda Kadiyali, and Young-Hoon Pak (2007), Willingness to Pay and Competition in Online Auctions, Jounal of Maketing Reseach, 44 (May), Cheema, Ama, Pete T.L. Popkowski-Leszczyc, Rajesh Bagozzi, James Cox, Utpal M. Dholakia, Eic A. Geenleaf, et al. (2005), Economics, Psychology, and Social Dynamics of Consume Bidding in Auctions, Maketing Lettes, 16 (34), Chu, Wujin (1992), Demand Signaling and Sceening in Channels of Distibution, Maketing Science, 11 (4), Desai, Peyas (2000), Multiple Messages to Retain Retailes: Signaling New Poduct Demand, Maketing Science, 19 (4), and K. Sinivasan (1995), Demand Signaling Unde Unobsevable Sevice in Fanchising: Linea and nlinea Pice Contacts, Management Science, 41 (Octobe), Dewally, Michael and Louis Edeington (2006), Reputation, Cetification, Waanties, and Infomation as Remedies fo Selle- Buye Infomation Asymmeties: Lessons fom the Online Comic Book Maket, Jounal of Business, 79 (2), Dewan, Sanjeev and Venon Hsu (2001), Tust in Electonic Makets: Pice Discovey in Genealist Vesus Specialty Online Auctions, white pape, Univesity of Washington Business School. Dholakia, U.M. (2005), Concept Discovey, Pocess Explanation, and Theoy Deepening in e-making Reseach: The Case of Online Auctions, Maketing Theoy, 5 (1), and K. Soltysinski (2001), Coveted o Ovelooked? The Psychology of Bidding fo Compaable Listings in Digital Auctions, Maketing Lettes, 12 (3), Gelman, Andew and Donald B. Rubin (1992), Infeence fom Iteative Simulation Using Multiple Sequences, Statistical Science, 7 (4), Gilkeson, J.H. and K. Reynolds (2003), Deteminants of Intenet Auction Success and Closing Pice: An Exploatoy Study, Psychology & Maketing, 20 (6), Gönül, F. and K. Sinivasan (1996), Estimating the Impact of Consume Expectations of Coupons on Puchase Behavio: A Dynamic Stuctual Model, Maketing Science, 15 (3), Intenet Auction Featues as Quality Signals / 91

18 Heschlag, Miiam and Rami Zwick (2002), Intenet Auctions: Popula and Pofessional Liteatue Review, Quately Jounal of Electonic Commece, 1 (2), House, Daniel and John Woodes (2006), Reputation in Auctions: Theoy and Evidence fom ebay, Jounal of Economics and Management Stategy, 15 (2), Huston, John H. and Roge W. Spence (2002), Quality, Uncetainty and the Intenet: The Maket fo Cybe Lemons, Ameican Economist, 46 (1), IC3 (2006), Intenet Cime Repot: Januay 1, 2006Decembe 31, 2006, National White Colla Cime Cente and the Fedeal Bueau of Investigation. Jin, Ginge Z. and Andew Kato (2006), Pice, Quality, and Reputation: Evidence fom an Online Field Expeiment, RAND Jounal of Economics, 37 (4), Johnson, Caie and Pete Hult (2008), Tapping into U.S. Online Auction Buyes, eseach epot, Foeste Reseach (August 13). Kagel, John and Alvin Roth (1995), The Handbook of Expeimental Economics. Pinceton, NJ: Pinceton Univesity Pess. Kala, Ajay and Shibo Li (2008), Signaling Quality Though Specialization, Maketing Science, 27 (2), Kamins, Michael A., Xavie Dèze, and Valeie S. Folks (2004), Effects of Selle-Supplied Pices on Buyes Poduct Evaluations: Refeence Pices in an Intenet Auction Context, Jounal of Consume Reseach, 30 (Mach), Kass, Robet E. and Adian E. Rafety (1995), Bayes Factos, Jounal of the Ameican Statistical Association, 90 (430), Katka, Rama and David H. Reiley (2006), Public Vesus Secet Reseve Pices in ebay Auctions: Results fom a Pokemon Field Expeiment, Advances in Economic Analysis & Policy, 6 (2), Aticle 7. Kazumoi, Eichio and John McMillan (2005), Selling Online Vesus Live, Jounal of Industial Economics, 53 (4), Kimani, Amna and Akshay R. Rao (2000), Pain, Gain: A Citical Review of the Liteatue on Signaling Unobsevable Poduct Quality, Jounal of Maketing, 64 (Apil), Livingston, Jeffey (2005), How Valuable Is a Good Reputation? A Sample Selection Model of Intenet Auctions, Review of Economics and Statistics, 87 (3), Lucking-Reiley, David (1999), Using Field Expeiments to Test Equivalence Between Auction Fomats: Magic on the Intenet, Ameican Economic Review, 89 (5), (2000), Auctions on the Intenet: What s Being Auctioned, and How? Jounal of Industial Economics, 48 (3), Lutz, Nancy A. (1989), Waanties as Signals Unde Consume Moal Hazad, RAND Jounal of Economics, 20 (2), McDonald, Cynthia G. and V. Calos Slawson J. (2002), Reputation in an Intenet Auction Maket, Economic Inquiy, 40 (4), Melnik Mikhail I. and James Alm (2002), Does a Selle s ecommece Reputation Matte? Evidence fom ebay Auctions, Jounal of Industial Economics, 50 (3), Milgom, Paul and John Robets (1982), Limit Picing and Enty Unde Incomplete Infomation: An Equilibium Analysis, Econometica, 50 (2), and R.J. Webe (1982), A Theoy of Auctions and Competitive Bidding, Econometica, 50 (5), Moothy, Sidha and K. Sinivasan (1995), Signaling Quality with a Money-Back Guaantee: The Role of Tansaction Costs, Maketing Science, 14 (4), Nunes, Joseph C. and Pete Boatwight (2004), Incidental Pices and Thei Effect on Willingness to Pay, Jounal of Maketing Reseach, 41 (vembe), Ockenfels, Axel and Alvin E. Roth (2006), Late and Multiple Bidding in Second Pice Intenet Auctions: Theoy and Evidence Concening Diffeent Rules fo Ending an Auction, Games and Economic Behavio, 55 (2), Ottaway, T.A., C.L. Buneau, and G.E. Evans (2003), The Impact of Auction Item Image and Buye/Selle Feedback Rating on Electonic Auctions, Jounal of Compute Infomation Systems, 43 (3), Pak, Young-Hoon and Eic T. Badlow (2005), An Integated Model fo Whethe, Who, When, and How Much in Intenet Auctions, Jounal of Maketing Reseach, 42 (vembe), Pinke, Edieal J., Abaham Seidmann, and Yaniv Vakat (2001), The Design of Online Auctions: Business Issues and Cuent Reseach, Simon Business School Woking Pape. CIS 01-05, Univesity of Rocheste. Rasmusen, Eic (2001), Stategic Implications of Uncetainty ove One s Own Pivate Value in Auctions, woking pape, Kelley School of Business, Indiana Univesity. Roth, Alvin and Axel Ockenfels (2002), Last-Minute Bidding and the Rules fo Ending Second-Pice Auctions: Evidence fom ebay and Amazon Auctions on the Intenet, Ameican Economic Review, 92 (4), Rothkopf, Michael H. (1969), A Model of Rational Competitive Bidding, Management Science, 15 (7), Sinha, Atanu R. and Eic A. Geenleaf (2000), The Impact of Discete Bidding and Bidde Aggessiveness on Selles Stategies in Open English Auctions: Reseves and Covet Shilling, Maketing Science, 19 (3), Sobeman, David (2003), Simultaneous Signaling and Sceening with Waanties, Jounal of Maketing Reseach, 40 (May), Spence, Michael A. (1973), Job Maket Signaling, Quately Jounal of Economics, 87 (3), Sinivasan, Kannan (1991), Multiple Maket Enty, Cost Signaling and Enty Deteence, Management Science, 37 (12), Standifid, S.S. (2001), Reputation and e-commece: ebay Auctions and the Asymmetical Impact of Positive and Negative Ratings, Jounal of Management, 27 (3), Thale, Richad H. (1988), Anomalies: The Winne s Cuse, Jounal of Economic Pespectives, 2 (1), Vickey, W. (1961), Countespeculation Auction and Competitive Sealed Tendes, Jounal of Finance, 16 (1), 837. Wang, Xin, Alan L. Montgomey, and Kannan Sinivasan (2004), When Auction Meets Fixed Pice: A Theoetical and Empiical Examination of Buy-It-w Auctions, Teppe School of Business Woking Pape E19, Canegie Mellon Univesity. Wenefelt, Bige (1988), Umbella Banding as a Signal of New Poduct Quality: An Example of Signaling by Posting a Bond, Rand Jounal of Economics, 19 (3), Wilcox, Ronald T. (2000), Expets and Amateus: The Role of Expeience in Intenet Auctions, Maketing Lettes, 11 (4), Wilson, R.B. (1969), Communications to the Edito: Competitive Bidding with Dispaate Infomation, Management Science, 15 (7), Yin, Pai-Ling (2006), Infomation Dispesion and Auction Pices, Havad Business School Woking Pape , Havad Univesity. Zeithamme, Robet (2006), Fowad-Looking Bidding in Online Auctions, Jounal of Maketing Reseach, 43 (August), (2007), Stategic Bid-Shading and Sequential Auctioning with Leaning fom Past Pices, Management Science, 53 (9), / Jounal of Maketing, Januay 2009

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