Sharing Retailers’ Demand Information with Privacy Protection

Guoyue Pan ()
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Guoyue Pan: Nanjing University

A chapter in LISS 2024, 2025, pp 1254-1260 from Springer

Abstract: Abstract Problem definition: This paper investigates the issue of sharing private demand information between two complementary retailers considering information protection. Academic/practical relevance: In the existing literature, firms decide whether to share their private information. However, they cannot determine the precision of shared information. Methodology: We develop a game-theoretic model with two retailers, endogenize the degree of information sharing, and solve for the game's equilibrium. We consider three scenarios: no sharing (NN), full sharing (SS), and partial sharing (SN). Results: We show that under ex-ante information sharing, scenario NN and scenario SS will be Nash equilibria, while under ex-ante information sharing, all three scenarios can be equilibrium information-sharing outcomes. What is counterintuitive is that consumer welfare is better when sharing information if complementary is strong and privacy cost is low. Managerial implications: Our results provide novel political implications that the application of federated learning can create a win-win situation, which not only benefits firms’ profit but also improves consumer welfare.

Keywords: information sharing; privacy; federated learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_96

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DOI: 10.1007/978-981-96-9697-0_96

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