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Analysis and Design of Business-to-Consumer Online Auctions

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  • 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)

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    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.

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    File URL: http://dx.doi.org/10.1287/mnsc.49.1.85.12754
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 49 (2003)
    Issue (Month): 1 (January)
    Pages: 85-101

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    Handle: RePEc:inm:ormnsc:v:49:y:2003:i:1:p:85-101

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    Related research

    Keywords: Online auctions; Dynamic pricing; Pricing mechanisms;

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    Cited by:
    1. Alok Gupta & Stephen Parente & Pallab Sanyal, 2012. "Competitive bidding for health insurance contracts: lessons from the online HMO auctions," International Journal of Health Care Finance and Economics, Springer, vol. 12(4), pages 303-322, December.
    2. 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.

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