Abstract
The existing studies suggest that sniping is an equilibrium strategy in hard-close online auctions, but not in soft-close ones. In this paper, we use a unique, large-scale data set from soft-close Overstock and hard-close eBay to document sniping phenomena under the two different closing rules. Estimation results show that sniping is prominent on both websites, but they are prevalent at different times. On eBay, sniping occurs right before the auction close, while on Overstock sniping happens predominantly in a short window of time before the triggering period, during which any additional high bid automatically extends the online auction. Furthermore, the revenue effect of sniping is significantly stronger on Overstock than on eBay.
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Notes
Overstock extends the auction for an additional 10 min when there is a new high bidder within 10 min of the scheduled auction closing time. Such a bid automatically extends the auction for another 10 min, and so on, until no more bids occur within the last 10 min, at which time the soft-close auction is closed.
We thank an anonymous referee for offering this explanation.
Interestingly, Overstock.com shut down its auction channel in 2011.
These listings could have had issues because they were canceled, ended prematurely by Buy-It-Now purchases, ended without any bid whatsoever, or simply contained technical errors.
We also code the first bid of the last bidder or all bidders as a dependent variable to conduct robustness checks.
We do not control the number of bidders, because no information about the current number of bidders or visitors is available for bidders at the time of making their bidding decision in real-life online auctions. Moreover, since bidders arrive to the auction at different times and never know the total realized number of bidders until the end of an online auction anyway, this variable is not reliable.
We have checked for the issue of multicollinearity. Please see the correlation matrix in the online appendix. Also it will be available upon request.
These regressions are omitted to save space, but they are available upon request.
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Acknowledgements
We thank two anonymous referees and the editor for their highly constructive and valuable comments and suggestions. The authors contributed equally to this paper. The usual caveat applies.
Funding
This research is supported by the China National Natural Science Foundation (71873036), HKU-Fudan IMBA joint research fund (JRF1718_0601), and Shanghai Pujiang Talent Program (13PJC009).
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Cao, W., Sha, Q., Yao, Z. et al. Sniping in soft-close online auctions: empirical evidence from overstock. Mark Lett 30, 179–191 (2019). https://doi.org/10.1007/s11002-019-09487-7
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DOI: https://doi.org/10.1007/s11002-019-09487-7