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Revenue maximizing with return policy when buyers have uncertain valuations

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  • Zhang, Jun

Abstract

This paper examines the optimal mechanism design problem when buyers have uncertain valuations. This uncertainty can only be resolved after the actual transactions take place and upon incurring significant post-purchase cost. We focus on two different settings regarding how the seller values a returned object (salvage value). We first study the case where the salvage value is exogenously determined. We find that the revenue maximizing mechanism is deterministic and “separable”. We illustrate that the optimal revenue can be implemented by a mechanism with a “no-questions-asked” return policy. In addition, we show that “linear return policies” are suboptimal when the hazard rates of initial estimates are monotone. We next examine the case where the salvage value is endogenously determined. We demonstrate that “separability” no longer holds and the “recall” of buyers is necessary in the optimal mechanism.

Suggested Citation

  • Zhang, Jun, 2013. "Revenue maximizing with return policy when buyers have uncertain valuations," International Journal of Industrial Organization, Elsevier, vol. 31(5), pages 452-461.
  • Handle: RePEc:eee:indorg:v:31:y:2013:i:5:p:452-461
    DOI: 10.1016/j.ijindorg.2013.08.001
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    References listed on IDEAS

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    Cited by:

    1. Xiaomin Zhao & Shuhui Hu & Xiaoxiao Meng, 2020. "Who should pay for return freight in the online retailing? Retailers or consumers," Electronic Commerce Research, Springer, vol. 20(2), pages 427-452, June.
    2. Toomas Hinnosaar & Keiichi Kawai, 2020. "Robust pricing with refunds," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1014-1036, December.
    3. Hao Li & Xianwen Shi, 2017. "Discriminatory Information Disclosure," American Economic Review, American Economic Association, vol. 107(11), pages 3363-3385, November.
    4. Cadsby, C. Bram & Du, Ninghua & Wang, Ruqu & Zhang, Jun, 2016. "Goodwill Can Hurt: A theoretical and experimental investigation of return policies in auctions," Games and Economic Behavior, Elsevier, vol. 99(C), pages 224-238.

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    More about this item

    Keywords

    Auctions; Mechanism design; Return policies; Uncertain valuations;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L12 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Monopoly; Monopolization Strategies

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