Winner's Curse or Adverse Selection in Online Auctions: The Role of Quality Uncertainty and Information Disclosure

By Hou, Jianwei; Kuzma, Ann et al. | Journal of Electronic Commerce Research, August 1, 2009 | Go to article overview

Winner's Curse or Adverse Selection in Online Auctions: The Role of Quality Uncertainty and Information Disclosure


Hou, Jianwei, Kuzma, Ann, Kuzma, John, Journal of Electronic Commerce Research


ABSTRACT

The literature has shown empirical evidence for both the winner's curse and adverse selection in online auctions. Some researchers identify a higher online auction price than the offline/e-tailing market price for the same item, whereas others indicate the opposite. This remarkable inconsistency certainly demands further investigation. By using a controlled field experiment on a popular online auction site, this study was able to directly compare prices between online auctions and e-tailers. The experimental results indicate that both the winner's curse and adverse selection exist in online auctions. The extent to which either occurs depends on the level of online bidders' quality uncertainty about the auction item. This study also examined the role of information disclosure in determining the auction price. The results show that a cheap talk signal does not influence the price, while picture posting only affects price under a high level of quality uncertainty when there is no cheap talk signal. Implications and future research directions are discussed.

Keywords: online auctions, winner's curse, adverse selection, quality uncertainty, information disclosure, cheap talk

1. Introduction

Traditional auction theories suggest that when bidders are uncertain about the true value of an auction item and have to estimate it (i.e., a common value auction), the winner who is the one with the highest estimate often pays a price that is more than what the item is worth (i.e., the winner's curse). Empirical studies have demonstrated the presence of the winner's curse in online auctions by reporting that online auction prices are higher than their offline/e-tailing market prices [Amyx and Luehlfing 2006; Mehta and Lee 1999; Oh 2002].

In a market such as online auctions where buyers have difficulty assessing the product quality, however, it is likely that low quality products drive the high quality products out of the market because the high quality products cannot command a higher price [Akerlof 1970]. This often implies an adverse selection problem wherein buyers are reluctant to pay a high price due to their uncertainty about the product quality. Previous studies have also shown that online auction prices are lower than their offline/e-tailing market prices [Dewan and Hsu 2004; Huston and Spencer 2002].

In order to clarify the mixed empirical findings in the literature, the present study further investigated the existence of the winner's curse and/or adverse selection in online auctions. Most often, previous studies collected secondary data directly from online auction websites. One problem of this type of observational research, however, is the lack of control over potential confounding factors (e.g., different auction designs and differences among sellers). To avoid this problem, this study conducted a controlled field experiment on a popular online auction site. The experimental setting allowed us to combine the controls of laboratory experiments with the external validity of examining bidding behavior in a real marketplace.

The purpose of this study was twofold. First, this study compared prices between online auctions and e-retailers using a field experiment. Second, this study examined how different factors (i.e., quality uncertainty and information disclosure about auction items) might influence the existence of the winner's curse and adverse selection.

2. Literature Review

2.1. Auction Models

There are two general auction models of how bidders value an auction item: the Private Value Model and the Common Value Model [MacAfee and McMillan 1987; Milgrom and Weber 1982].

In a private value auction, each bidder knows exactly the value of the item and any bidder's valuation of the item is statistically independent from other bidders' valuations. For example, bidders often bid on computers for their personal consumption, thereby they know exactly how much they would like to pay for a computer and their valuations are also not likely to be influenced by how others bid (i. …

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