Executive Summary
The impersonal transactions of Internet auctions introduce severe information asymmetry regarding both product quality and seller credibility. Many consumers voice concerns about trust and safety issues associated with Internet transactions. Without enough information to distinguish good from bad products (i.e., uncertainty about product quality) and reputable from disreputable sellers (i.e., uncertainty about seller credibility), buyers may choose not to participate in an auction. This dual uncertainty contributes to a “lemons” market, in which the potential of purchasing poor-quality products from a disreputable seller drives buyers away from the market. The lemons problem stems from impersonal transactions and information asymmetry and may be the greatest obstacle to the rapid growth of Internet auction marketplaces. Therefore, to attract more users to an auction site or a particular auction, Internet auction companies and sellers must address an important question: What is the best design of auction that will mitigate uncertainty and attract more bidders?
Since the emergence of the Internet, Internet auction companies have continued to develop innovative institutional features that enable sellers to reveal more information about their credibility and product quality to potential buyers. However, little research empirically investigates how innovative auction features affect bidders’ decisions when they suffer from information asymmetry or evaluates whether current Internet auction designs help alleviate the lemons problem.
On the basis of signaling and auction theories, the authors recognize the dual information asymmetry and propose a typology of Internet auction quality and credibility indicators to separate all observed Internet auction features into potential direct product quality indicators, indirect product quality indicators, and seller credibility indicators. The authors adopt and modify Park and Bradlow’s model and use eBay as an example to examine empirically how different types of indicators help alleviate uncertainty and affect bidders’ decisions, such as whether to participate and who bids, when to bid, and how much to bid.
The empirical results show that quality indicators that directly reveal information on product quality and seller credibility (e.g., multiple picture postings, money-back guarantee, seller’s cumulative rating, third-party payment method) encourage bidders to participate but also to shade bids. The opposite is true for indirect quality indicators (e.g., minimum starting bid, hidden reserve price, the buy-it-now option). The use of these indirect quality indicators discourages participation but increases bidding amount. The authors further demonstrate that the simultaneous use of quality indicators and seller credibility indicators (e.g., seller’s rating, third-party payment) strengthens the effects of quality indicators. More experienced consumers tend to make better inferences about the roles of both credibility and quality indicators. As the first empirical study to evaluate the signaling role of comprehensive Internet auction institutional features in mitigating the adverse selection problem, this research provides evidence to clarify the economic foundation behind innovative Internet auction designs.
Biography
Shibo Li is Assistant Professor of Marketing in the Kelley School of Business at Indiana University. He received a PhD in Industrial Administration (Marketing) from Carnegie Mellon University. His research interests are quantitative customer relationship management, Internet marketing, and analytical and empirical analysis of signaling models. He received the 2004 John A. Howard American Marketing Association Doctoral Dissertation Award and was a finalist for the 2004 John D.C. Little Best Paper Award. His research has appeared in Marketing Science, Journal of Marketing Research, Journal of Marketing, and Journal of Interactive Marketing.
Kannan Srinivasan is H.J. Heinz II Professor of Management, Marketing, and Information Systems in the Tepper School of Business at Carnegie Mellon University. He is an associate editor of Management Science and an area editor for Marketing Science and Quantitative Marketing and Economics.
Baohong Sun is Associate Professor of Marketing at Carnegie Mellon University. She develops empirical models to study rational consumer choice, evaluate promotion effect, and measure the impact on short-term and long-term sales. Her recent work has focused on developing dynamic structural models to investigate consumer response to cross-selling campaigns, service allocation, optimal design of subscription pricing, and dynamic customer relationship management. Her research has been published in Marketing Science, Journal of Marketing Science, Journal of Econometrics, and Quantitative Marketing and Economics, among other outlets. She serves on the editorial review boards of Marketing Science and Journal of Marketing.
Journal of Marketing, Volume 73, Number 1, January 2009
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