Commentary--Bidders' Experience and Learning in Online Auctions: Issues and Implications

Kannan Srinivasan () and Xin Wang ()
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Kannan Srinivasan: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Xin Wang: International Business School, Brandeis University, Waltham, Massachusetts 02454

Marketing Science, 2010, vol. 29, issue 6, 988-993

Abstract: This study explores the implications of rejecting the sealed-bid abstraction proposed by Zeithammer and Adams [Zeithammer, R., C. Adams. 2010. The sealed-bid abstraction in online auctions. Marketing Sci. 29(6) 964-987]. Using a conditional order statistic model that relies on the joint distribution of the top two proxy bids of an auction, Zeithammer and Adams show that inexperienced bidders' reactive bidding is the main cause of the rejection of the sealed-bid abstraction. Their empirical study suggests that a large percentage of bidders reactively bid, and there is weak evolutionary pressure for bidders to converge to sealed bidding. We discuss theoretical implications of this rejection and the role of bidder experience, as well as inferences about bidder learning. Tracking an inexperienced bidder's bidding behavior over time, we show that bidders learn and their bidding strategy gravitates toward rational bidding. Potential biases in bidder experience measurement and bidder learning can be assessed using a cross-sectional, time-series data set that tracks a random sample of new eBay bidders. Learning speed is faster with their complete bidding history rather than feedback ratings or winning observations only. We highlight the importance of proper measures of bidder experience and its effect on bidding strategy evolutions, both of which play important roles in clarifying bidding behavior in online auctions.

Keywords: auctions; online; learning; bidding (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:29:y:2010:i:6:p:988-993

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