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Jump Bidding Strategies in Internet Auctions

Author

Listed:
  • Robert F. Easley

    (Department of Management, Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556-5646)

  • Rafael Tenorio

    (Department of Economics, DePaul University, 1 East Jackson Boulevard, Suite 6200, Chicago, Illinois 60604)

Abstract

Abidding strategy commonly observed in Internet auctions is that of "jump bidding," or entering a bid larger than what is necessary to be a currently winning bidder. In this paper, we argue that the cost associated with entering online bids and the uncertainty about future entry---both of which distinguish Internet from live auctions---can explain this behavior. We present a simple theoretical model that includes the preceding characteristics, and derive the conditions under which jump bidding arises in a format commonly used for online trading, the ascending-price auction. We also present evidence, recorded from hundreds of Internet auctions, that is consistent with some of the basic predictions from our model. We find that jump bidding is more likely earlier in an auction, when jumping has a larger strategic value, and that the incentives to jump bid increase as competition increases. Our results also indicate that jump bidding is effective: Jump bidders place fewer bids overall, and increased early jump bidding deters entry later in the auction. We also discuss possible means of reducing bidding costs and evidence that Internet auctioneers are pursuing this goal.

Suggested Citation

  • Robert F. Easley & Rafael Tenorio, 2004. "Jump Bidding Strategies in Internet Auctions," Management Science, INFORMS, vol. 50(10), pages 1407-1419, October.
  • Handle: RePEc:inm:ormnsc:v:50:y:2004:i:10:p:1407-1419
    DOI: 10.1287/mnsc.1040.0286
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    References listed on IDEAS

    as
    1. Isaac, R. Mark & Salmon, Timothy C. & Zillante, Arthur, 2007. "A theory of jump bidding in ascending auctions," Journal of Economic Behavior & Organization, Elsevier, vol. 62(1), pages 144-164, January.
    2. Lawrence M. Ausubel & Peter Cramton & Marek Pycia & Marzena Rostek & Marek Weretka, 2014. "Demand Reduction and Inefficiency in Multi-Unit Auctions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1366-1400.
    3. Alvin E. Roth & Axel Ockenfels, 2002. "Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet," American Economic Review, American Economic Association, vol. 92(4), pages 1093-1103, September.
    4. Bajari, Patrick & Hortacsu, Ali, 2003. "The Winner's Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions," RAND Journal of Economics, The RAND Corporation, vol. 34(2), pages 329-355, Summer.
    5. Rafael Tenorio, 1997. "On Strategic Quantity Bidding in Multiple Unit Auctions," Journal of Industrial Economics, Wiley Blackwell, vol. 45(2), pages 207-217, June.
    6. Lucking-Reiley, David, 2000. "Auctions on the Internet: What's Being Auctioned, and How?," Journal of Industrial Economics, Wiley Blackwell, vol. 48(3), pages 227-252, September.
    7. Christopher Avery, 1998. "Strategic Jump Bidding in English Auctions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 185-210.
    8. McAfee, R. Preston & McMillan, John, 1987. "Auctions with a stochastic number of bidders," Journal of Economic Theory, Elsevier, vol. 43(1), pages 1-19, October.
    9. Tenorio, Rafael, 1997. "On Strategic Quantity Bidding in Multiple Unit Auctions," Journal of Industrial Economics, Wiley Blackwell, vol. 45(2), pages 207-217, June.
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