JOURNAL ARTICLE

Behavior-Based Pricing Under Informed Privacy Consent: Unraveling Autonomy Paradox.

  • Published In: Marketing Science (INFORMS), 2025, v. 44, n. 6. P. 1362 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Kim, Yunhyoung; Cui, Haitao; Zhu, Yi 3 of 3

Abstract

This paper investigates behavior-based pricing (BBP) under informed privacy consent, a regulatory framework requiring firms to obtain explicit consumer consent before collecting personal data. It reveals an autonomy paradox whereby empowering consumers to make autonomous privacy decisions can lead to a decrease in overall consumer surplus, despite consumers' strategic privacy choices and firms' provision of rewards for opting in. The study models a two-period duopoly where firms use consumers' purchase history for third-degree price discrimination, showing that informed privacy consent introduces an additional segment of opt-out consumers and limits firms' ability to poach rival customers, thereby softening price competition and increasing firms' profits relative to uniform pricing. Extensions considering ongoing privacy control, endogenous additional utility from personalized products, and data breach settlement agreements confirm the robustness of these findings. Overall, the research highlights that while informed privacy consent respects individual autonomy, it may unintentionally enable firms to exploit consumer privacy decisions, resulting in mixed welfare outcomes.

Additional Information

  • Source:Marketing Science (INFORMS). 2025/11, Vol. 44, Issue 6, p1362
  • Document Type:Article
  • Subject Area:Religion and Philosophy
  • Publication Date:2025
  • ISSN:0732-2399
  • DOI:10.1287/mksc.2024.0867
  • Accession Number:189190924
  • Copyright Statement:Copyright of Marketing Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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