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Reputation, Trust, and Rebates: How Online Auction Markets Can Improve Their Feedback Mechanisms

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  • Lingfang (Ivy) Li

Abstract

"Reputation systems constitute an important institution, helping sustain trust in online auction markets. However, only half of buyers leave feedback after transactions, and nearly all feedback is positive. In this paper, I propose a mechanism whereby sellers can provide rebates (not necessarily in monetary form) to buyers contingent upon buyers' provision of reports. Using a game theoretical model, I show how the mechanism can increase unbiased reporting. There exists a pooling equilibrium where both good and bad sellers choose the rebate option, even though their true types are revealed through feedback. The mechanism also induces bad sellers to improve the quality of the contract." Copyright (c) 2010 Wiley Periodicals, Inc..

Suggested Citation

  • Lingfang (Ivy) Li, 2010. "Reputation, Trust, and Rebates: How Online Auction Markets Can Improve Their Feedback Mechanisms," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 19(2), pages 303-331, June.
  • Handle: RePEc:bla:jemstr:v:19:y:2010:i:2:p:303-331
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    Cited by:

    1. Luís Cabral & Lingfang (Ivy) Li, 2015. "A Dollar for Your Thoughts: Feedback-Conditional Rebates on eBay," Management Science, INFORMS, vol. 61(9), pages 2052-2063, September.
    2. Li, Lingfang (Ivy), 2010. "What is the cost of venting? Evidence from eBay," Economics Letters, Elsevier, vol. 108(2), pages 215-218, August.
    3. Robert Gazzale & Tapan Khopkar, 2011. "Remain silent and ye shall suffer: seller exploitation of reticent buyers in an experimental reputation system," Experimental Economics, Springer;Economic Science Association, vol. 14(2), pages 273-285, May.
    4. Lingfang (Ivy) Li & Steven Tadelis & Xiaolan Zhou, 2016. "Buying Reputation as a Signal of Quality: Evidence from an Online Marketplace," NBER Working Papers 22584, National Bureau of Economic Research, Inc.
    5. Christina Aperjis & Ramesh Johari, 2010. "Optimal Windows for Aggregating Ratings in Electronic Marketplaces," Management Science, INFORMS, vol. 56(5), pages 864-880, May.

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