Information Sharing on Retail Platforms

40 Pages Posted: 23 Oct 2018

See all articles by Zekun Liu

Zekun Liu

Washington University in St. Louis - John M. Olin Business School

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School

Fuqiang Zhang

Washington University in St. Louis - John M. Olin Business School

Date Written: October 1, 2018

Abstract

Problem Definition: This paper studies the information sharing strategy for a retail platform on which multiple competing sellers distribute their products.

Academic/Practical Relevance: Due to the rapid growth of retail platforms in recent years, information sharing has become an increasingly important issue because retail platforms can gather enormous consumer information that may not be visible to the sellers. Understanding how to share consumer information with those sellers will provide useful implications both from the theoretical and practical perspectives.

Methodology: We develop a game-theoretic model where multiple sellers compete on a retail platform by selling substitutable products, and the platform charges a commission fee for each transaction. The platform owns superior demand information and can control the accuracy level when sharing the information with the sellers. There are two potential constraints on the platform's sharing strategy: privacy constraint (the platform cannot share the information collected from one seller with other sellers), and fairness constraint (the platform has to treat all sellers equally).

Results: We find that the platform always has incentives to share the information and such sharing is beneficial both to the platform and to all sellers. When there is no fairness constraint, the optimal strategy for the platform is to select a subgroup of sellers and truthfully share with them either the aggregate information (collected from all sellers) or the individual information (collected only from this seller). However, under the fairness constraint, the platform has incentives to reduce the accuracy of the shared information by adding random noises. Interestingly, although the privacy constraint sounds quite restrictive, it may not necessarily hurt the platform's profit. Finally, we show that when the sellers differ in their market power, the platform would prefer to share information truthfully with those sellers who have a lower impact on others.

Managerial Implications: The research findings provide useful implications for retail platforms on how to share information with sellers. They also offer insights into how the government should regulate retail platforms from an information-sharing perspective, especially when privacy or fairness is a concern.

Keywords: Information Sharing, Retail Platform, Privacy, Fairness

JEL Classification: C70, D83

Suggested Citation

Liu, Zekun and Zhang, Dennis and Zhang, Fuqiang, Information Sharing on Retail Platforms (October 1, 2018). Available at SSRN: https://ssrn.com/abstract=3258109 or http://dx.doi.org/10.2139/ssrn.3258109

Zekun Liu (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1156
St. Louis, MO 63130-4899
United States

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Fuqiang Zhang

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://www.olin.wustl.edu/faculty/zhang/

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