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Tat Y. Chan Vrinda Kadiyali, and Young-Hoon Park
Executive Summary
Are sellers on auction sites capturing the full extent of the winning bidder's willingness to pay (WTP) for any item? Would the winning bidder have been willing to pay more for the item? How do types of items, sellers, market competition conditions, and type of bidder affect WTP?
The authors answer these questions in this article by building a model of bidder WTP from observed bidding data for any item. Specifically, they model any bidder's WTP as being at least as high as his or her winning bid and below the winning bid (otherwise, they can counterbid). That is, the authors specify the bounds within which WTP must lie. Next, they model the actual level of WTP as a function of item characteristics, bidder and seller characteristics, competition among items and bidders, prior prices for similar items, and the presence of a buy-it-now (BIN) option.
The authors estimate the model on data from a leading Korean auction site in the laptop computer category. They find that for these data, the winning bid is slightly higher than the second-highest WTP. This is compatible with savvy bidding; this might also imply that the winning bidder's WTP is not being fully extracted or that the second-highest WTP bidder is similar to the highest WTP bidder. They also find that WTP is most sensitive to similar items of the same brand being offered and to the presence of a buy-it-now option. They demonstrate the usefulness of their model for sellers to determine how competition among items can reduce their expected revenues. They also estimate their model for data from eBay for the same category (i.e., laptop computers). They find that for even a market with as many similar items as eBay, competition among similar items remains the most important driver of WTP.
The managerial usefulness of this model is threefold. Specifically, the results suggest (1) how to use bidding data to calculate a bidder's unobserved preferences, specifically the unobserved WTP for an item; (2) how to understand the role of competition among observed and "latent" bidders (i.e., bidders who are not currently active in the focal item auction but might be following the auction process for any number of reasons); and (3) how to understand the impact of competition among items on reduced winning bids. Critically, because the model is flexible enough to incorporate various seller and bidder behaviors, the managerial insights that can be derived from this article are robust.
Biography
Tat Y. Chan is Assistant Professor in Marketing in the Olin School of Business at Washington University, St. Louis. He received a PhD in Economics from Yale University. His research interests are in modeling consumer demand and firms strategies using econometric methodologies that include both traditional and dynamic optimization techniques. His research also includes evaluating people's learning and private information in randomized clinical experiments, measuring managerial expectations and strategy in nonprofit environments, and the analysis of the household online browsing preferences and information search behavior.
Vrinda Kadiyali is an Associate Professor of Marketing and Economics in the Johnson Graduate School of Management at Cornell University. She has a PhD in Economics from Northwestern University. Her primary research interest is in competition and firm strategic behavior. She has used various methodologies to study competitive pricing, advertising, location, product release timing, and entry issues in diverse industries. She has published in marketing and economics journals and serves on the editorial boards of various marketing journals.
Young-Hoon Park is Assistant Professor of Marketing in the Johnson Graduate School of Management at Cornell University. He earned a PhD in Marketing from the Wharton School of the University of Pennsylvania. His research interests focus on customer value analysis, database marketing, online auctions, and marketing–operations interfaces. His research has appeared or is appearing in Journal of Marketing Research, Management Science, and Marketing Science. |