Personalized recommendation, behavior-based pricing, or both? Examining privacy concerns from a cost perspective

Chi Zhou, Danyang Bai, Tieshan Li and Jing Yu

Omega, 2025, vol. 133, issue C

Abstract: In the era of the big data, e-commerce increasingly adopts personalized recommendation and behavior-based pricing (BBP) strategies to enhance consumer experience, while also raising concerns about privacy. This study examines the impact of privacy costs on the effectiveness of those strategies using a two-period Hotelling model. The results indicate that retailers who combine personalized recommendation with BBP strategies can achieve higher prices and profits compared to those who do not employ these strategies, particularly when there are significant differences in privacy costs. Our study further reveals that relying solely on personalized recommendation without incorporating BBP may lead to decreases profit. Moreover, the accuracy of recommendations and variations in privacy costs significantly influence retailers’ strategy choices, emphasizing the importance of these factors in gaining a competitive advantage. This research provides valuable insights for online retailers on how to effectively position themselves in the market while addressing consumer privacy concerns, offering a new perspective on the comprehensive impacts of personalized recommendation and BBP strategies in the business landscape.

Keywords: Personalized recommendation; Behavior-based pricing; Privacy concerns; Privacy cost; Game theory (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.omega.2024.103223

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