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Private Ordering on the Internet: The eBay Community of Traders

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  • Baron, David P.

Abstract

eBay provides an online auction venue for remote and anonymous members of its online community to realize gains from trade. As a venue it never sees the items sold, verifies the item listings, handles settlements, or represents the buyer or seller. Despite the associated market imperfections and incentive problems, over five million auctions are active on an average day. Trading is based on trust among members of the eBay community, and trust is supported by a multilateral reputation mechanism based on member feedback. eBay supplements the reputation mechanism with rules and policies that mitigate incentive problems, reduce transactions costs, and support trust among members and between members and the company. Reputations and the rules and policies provide a private ordering of eBay's community. This paper examines this private ordering in the context of the company's strategy and in the shadow of the public order.

Suggested Citation

  • Baron, David P., 2002. "Private Ordering on the Internet: The eBay Community of Traders," Business and Politics, Cambridge University Press, vol. 4(3), pages 245-274, November.
    • Handle: RePEc:cup:buspol:v:4:y:2002:i:03:p:245-274_00
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    Cited by:

    1. Weijia You & Hui Shu & Suyuan Luo, 2018. "Competition, cooperation, and performance: an empirical investigation of Chinese online sellers," Information Systems and e-Business Management, Springer, vol. 16(4), pages 743-760, November.
    2. Zhang, Juheng & Aytug, Haldun, 2016. "Comparison of imputation methods for discriminant analysis with strategically hidden data," European Journal of Operational Research, Elsevier, vol. 255(2), pages 522-530.
    3. Patrick Bajari & Ali Hortaçsu, 2004. "Economic Insights from Internet Auctions," Journal of Economic Literature, American Economic Association, vol. 42(2), pages 457-486, June.
    4. Juheng Zhang & Selwyn Piramuthu, 2018. "Product recommendation with latent review topics," Information Systems Frontiers, Springer, vol. 20(3), pages 617-625, June.
    5. Juheng Zhang & Haldun Aytug & Gary J. Koehler, 2014. "Research Note —Discriminant Analysis with Strategically Manipulated Data," Information Systems Research, INFORMS, vol. 25(3), pages 654-662, September.
    6. Juheng Zhang & Selwyn Piramuthu, 0. "Product recommendation with latent review topics," Information Systems Frontiers, Springer, vol. 0, pages 1-9.

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