Beyond the Personalization–Privacy Paradox: Privacy Valuation, Transparency Features, and Service Personalization

TLDR

Digital services rely on consumer data to improve quality and revenue, yet it is unclear how to configure services to encourage data sharing, with prior work highlighting individual traits, transparency features, and personalization. This study investigates how interactions among privacy valuation, transparency features, and personalization affect consumers’ willingness to disclose information. Using information boundary theory, we ran an experiment with 286 participants on a data‑intense digital service. We found that transparency features do not increase disclosure, privacy valuation strongly inhibits information provision overall, personalization only persuades low‑privacy‑concern users, and providers should align designs with privacy preferences.

Abstract

Digital services need access to consumers’ data to improve service quality and to generate revenues. However, it remains unclear how such services should be configured to facilitate consumers’ willingness to share personal information. Prior studies discuss an influence of selected individual traits or service configurations, including transparency features and service personalization. This study aims at uncovering how interactions among individuals’ privacy valuation, transparency features, and service personalization influence their willingness to disclose information. Building on information boundary theory, we conducted an experimental study with 286 participants on a data-intense digital service. In contrast to our expectation, we found no indication that providing transparency features facilitates individuals’ information disclosure. Relative to the personalization–privacy paradox, individuals’ privacy valuation is a strong inhibitor of information provision in general, not only for personalized services. Personalization benefits only convince consumers who exhibit little focus on privacy. Thus, service providers need to align their service designs with consumers’ privacy preferences.