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Analysis and Design of Business-to-Consumer Online Auctions

Author

Listed:
  • Ravi Bapna

    () (Department of Operations and Information Management, U-41 IM, School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Paulo Goes

    () (Department of Operations and Information Management, U-41 IM, School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Alok Gupta

    () (Information and Decision Sciences Department, 3-365 Carlson School of Management, University of Minnesota, 321 19th Avenue South, Minneapolis, Minnesota 55455)

Abstract

Business-to-consumer online auctions form an important element in the portfolio of mercantile processes that facilitate electronic commerce activity. Much of traditional auction theory has focused on analyzing single-item auctions in isolation from the market context in which they take place. We demonstrate the weakness of such approaches in online settings where a majority of auctions are multiunit in nature. Rather than pursuing a classical approach and assuming knowledge of the distribution of consumers' valuations, we emphasize the largely ignored discrete and sequential nature of such auctions. We derive a general expression that characterizes the multiple equilibria that can arise in such auctions and segregate these into desirable and undesirable categories. Our analytical and empirical results, obtained by tracking real-world online auctions, indicate that bid increment is an important factor amongst the control factors that online auctioneers can manipulate and control. We show that consumer bidding strategies in such auctions are not uniform and that the level of bid increment chosen influences them. With a motive of providing concrete strategic directions to online auctioneers, we derive an absolute upper bound for the bid increment. Based on the theoretical upper bound we propose a heuristic decision rule for setting the bid increment. Empirical evidence lends support to the hypothesis that setting a bid increment higher than that suggested by the heuristic decision rule has a negative impact on the auctioneer's revenue.

Suggested Citation

  • Ravi Bapna & Paulo Goes & Alok Gupta, 2003. "Analysis and Design of Business-to-Consumer Online Auctions," Management Science, INFORMS, vol. 49(1), pages 85-101, January.
  • Handle: RePEc:inm:ormnsc:v:49:y:2003:i:1:p:85-101
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    File URL: http://dx.doi.org/10.1287/mnsc.49.1.85.12754
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    References listed on IDEAS

    as
    1. Yamey, B S, 1972. "Why 22,31,000 for a Velazquez?: An Auction Bidding Rule," Journal of Political Economy, University of Chicago Press, vol. 80(6), pages 1323-1327, Nov.-Dec..
    2. Slovic, Paul, 1972. "Psychological Study of Human Judgment: Implications for Investment Decision-Making," Journal of Finance, American Finance Association, vol. 27(4), pages 779-799, September.
    3. Randolph E. Bucklin & James M. Lattin, 1991. "A Two-State Model of Purchase Incidence and Brand Choice," Marketing Science, INFORMS, vol. 10(1), pages 24-39.
    4. Alvin E. Roth & Axel Ockenfels, 2000. "Last Minute Bidding and the Rules for Ending Second-Price Auctions: Theory and Evidence from a Natural Experiment on the Internet," NBER Working Papers 7729, National Bureau of Economic Research, Inc.
    5. Milgrom, Paul R & Weber, Robert J, 1982. "A Theory of Auctions and Competitive Bidding," Econometrica, Econometric Society, vol. 50(5), pages 1089-1122, September.
    6. Michael H. Rothkopf & Ronald M. Harstad, 1994. "Modeling Competitive Bidding: A Critical Essay," Management Science, INFORMS, vol. 40(3), pages 364-384, March.
    7. Patrick Bajari & Ali Hortacsu, 2000. "Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights from eBay Auctions," Econometric Society World Congress 2000 Contributed Papers 1927, Econometric Society.
    8. Milgrom, Paul, 1989. "Auctions and Bidding: A Primer," Journal of Economic Perspectives, American Economic Association, vol. 3(3), pages 3-22, Summer.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ravi Bapna & Paulo Goes & Alok Gupta, 2005. "Pricing and Allocation for Quality-Differentiated Online Services," Management Science, INFORMS, vol. 51(7), pages 1141-1150, July.
    2. Hur, Daesik & Mabert, Vincent A. & Hartley, Janet L., 2007. "Getting the most out of reverse e-auction investment," Omega, Elsevier, vol. 35(4), pages 403-416, August.
    3. Chen, Xi & Ghate, Archis & Tripathi, Arvind, 2011. "Dynamic lot-sizing in sequential online retail auctions," European Journal of Operational Research, Elsevier, vol. 215(1), pages 257-267, November.
    4. Alok Gupta & Stephen Parente & Pallab Sanyal, 2012. "Competitive bidding for health insurance contracts: lessons from the online HMO auctions," International Journal of Health Economics and Management, Springer, vol. 12(4), pages 303-322, December.
    5. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    6. repec:eee:indorg:v:54:y:2017:i:c:p:125-147 is not listed on IDEAS
    7. repec:bla:jindec:v:65:y:2017:i:3:p:597-622 is not listed on IDEAS
    8. Jason Kuruzovich & Siva Viswanathan & Ritu Agarwal, 2010. "Seller Search and Market Outcomes in Online Auctions," Management Science, INFORMS, vol. 56(10), pages 1702-1717, October.
    9. Jiang, Zhong-Zhong & Fang, Shu-Cherng & Fan, Zhi-Ping & Wang, Dingwei, 2013. "Selecting optimal selling format of a product in B2C online auctions with boundedly rational customers," European Journal of Operational Research, Elsevier, vol. 226(1), pages 139-153.
    10. repec:eee:joinma:v:24:y:2010:i:4:p:283-296 is not listed on IDEAS
    11. repec:eee:jouret:v:88:y:2012:i:1:p:131-144 is not listed on IDEAS

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