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Overbidding in electronic auctions: factors influencing the propensity to overbid and the magnitude of overbidding

  • Original Empirical Research
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Abstract

This research investigates the buyer and seller characteristics that impact overbidding in electronic auctions. Specifically, the empirical analysis shows that customers with more total experience are less likely to overbid and that overbidding is less frequent for auction items with higher face values. When bidder experience pertains to the same product category, the frequency and magnitude of overbids are further reduced. This research also finds that consumers are more likely to overbid as the bidding environment becomes more competitive. Furthermore, auction item attributes, such as starting price, day/time of the auction, and shipping fees, are shown to impact the propensity to overbid and the magnitude of overbidding. The empirical results have important implications for understanding bidder behavior and for assisting online sellers in formulating more profitable selling strategies.

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Notes

  1. We note that some physical gift cards, e.g., uniquely-designed ones that are collected as memorabilia, may retain some residual value for some bidders even if the face value reaches zero. However, the current study, which utilizes data on standard gift cards issued by well-known retailers, assumes that a zero balance gift card is of negligible value to consumers.

  2. Jones (2011) also suggests that, given the general awareness of and easy access to outside purchase options, limited attention is unlikely to explain why bidders would overbid for Amazon.com gift certificates on a non-Amazon retail platform (i.e., eBay).

  3. The solution in this example is easy to find if the rival’s bid, X, is known. However, in practice, other participants’ future bids are unknown. Thus, the optimal bidding strategy will depend on the distribution of future bids, which is not known to auction participants.

  4. The interplay between bidder experience and the thrill of winning could provide an alternative explanation for H1. Due to satiation effects, as the number of auctions previously won rises (which would be correlated with bidder experience), a bidder is likely to get a smaller (if any) boost in utility from the thrill of winning.

  5. We note that 55.83% of all overbids are non-winning bids.

  6. We thank an anonymous reviewer for bringing the endogeneity issues to our attention.

  7. This approach evaluates the observed output (e.g., the observed bid from each auction winner) given a certain observed input (e.g., bid-determining characteristics). The error term of the stochastic frontier model can be decomposed into two parts: (1) a random error (which is similar to the error term in a standard OLS model) and (2) a non-random error (which is a measure of the gap between true and observed bids). Please refer to Kumbhakar and Lovell (2003) and Hofler and List (2004) for additional methodological details.

  8. Note that in one of our robustness checks, we extend Eq. 4 by incorporating a random-effects component to further control for bidder heterogeneity and apply generalized least squares to estimate a random effects panel model.

  9. For additional details about eBay bucks, please see http://pages.ebay.com/rewards/faq.html.

  10. Since our data range from November 2013 to February 2014, we categorize our data into four different groups according to the ending date of the auctions (i.e., November group, December group, January group, and February group). We then run a series of pairwise comparisons for our three focal dependent variables (i.e., Overbid, Current Magnitude, and Final Magnitude) using the Tukey-Kramer test. The results do not support the hypothesis that the mean value for the January group is significantly higher than for the other groups.

  11. We conducted a post-auction test to examine whether or not bidders who win by overbidding are satisfied with the transactions. The qualitative results show that winners who left feedback for the sellers seem to be satisfied with the transactions, even when overbidding. Sample comments included “Great seller! Awesome communication! A+++++”; “Super easy, fast transaction, and very fast shipping.... Thank You! A+++”; “Fast shipping and great packaging. Recommended”; and “A very smooth transition, thank you very much!”

  12. For example, a buyer paid $114.99 in a buy-it-now auction for a $100 Amazon gift card (http://www.ebay.com/itm/100-Amazon-com-Gift-Card-Amazon-Certificate-/390923867920?pt=US_Gift_Certificates&hash=item5b04e0d710&nma=true&si=UFfBooCigNPbssd6o4sqEvSCYuM%253D&orig_cvip=true&rt=nc&_trksid=p2047675.l2557).

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Acknowledgments

The authors would like to thank the JAMS review team for insightful comments and constructive suggestions. They would also like to thank Amiya Basu, Dinesh Gauri, Eunkyu Lee, Tridib Mazumdar, Debanjan Mitra, Eleonora Patacchini, S. P. Raj, Breagin Riley, and seminar participants at Syracuse University and 2013 Pricing and Retailing Conference for comments on earlier versions of the paper. Rui Gao, Qing Luo, and Kexin Xiang provided excellent research assistance. Cong Feng acknowledges the financial support from the Earl V. Snyder Innovation Management Center, Whitman School of Management, Syracuse University.

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  1. Whitman School of Management, Syracuse University, 721 University Avenue, Syracuse, NY, 13244, USA

    Cong Feng & Scott Fay

  2. College of Business and Economics, Lehigh University, 621 Taylor Street, Bethlehem, PA, 18015, USA

    K. Sivakumar

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Feng, C., Fay, S. & Sivakumar, K. Overbidding in electronic auctions: factors influencing the propensity to overbid and the magnitude of overbidding. J. of the Acad. Mark. Sci. 44, 241–260 (2016). https://doi.org/10.1007/s11747-015-0450-9

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