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How much is our online privacy worth? A comparison of the value of personal data to internet users and online platforms in Poland

Author

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  • Bylicki, Michał
  • Zawojska, Ewa
  • Łukasik, Krystian

Abstract

Data are a key resource in the modern economy. In exchange for access to their services, online platform operators collect data about users and their online behavior, which are then used to tailor the services and increase advertising revenue. In this study, we investigate potential welfare effects caused by increased protection of online privacy and related restrictions on personal data use by online platform operators: on one side, the increased protection could generate welfare benefits for society to the extent that users value online privacy, on the other side, the increased protection may cause welfare losses in a form of forgone revenues to the operators. In this paper, we assess the welfare changes by measuring and comparing these benefits and losses. To measure the benefits, we employ a non-market valuation approach—a stated preference discrete choice experiment, which help us estimate the value of increased online privacy protection for users. We approximate the potential losses to platform operators by using estimates of the revenues generated by access to the personal data of users. Our specific case study relates to the Facebook and Google Search platforms and their users in Poland. The empirical results suggest that the welfare benefits of increased protection of online privacy for users may outweigh the associated welfare losses measured as possible revenue losses to online platform operators. This indicates that alternative financing models for online platforms can be taken into consideration to enhance welfare.

Suggested Citation

  • Bylicki, Michał & Zawojska, Ewa & Łukasik, Krystian, 2025. "How much is our online privacy worth? A comparison of the value of personal data to internet users and online platforms in Poland," Technology in Society, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:teinso:v:83:y:2025:i:c:s0160791x25002040
    DOI: 10.1016/j.techsoc.2025.103014
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    1. Zhang, Kaige & Pang, Hua, 2026. "How does risk information exposure affect AI-driven technology users’ privacy protection? Combining social amplification of risk framework and technology threat avoidance theory," Technology in Society, Elsevier, vol. 84(C).

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