Paper Number
1251
Paper Type
short
Description
There is increasing tension between the service quality improvements consumers receive when they allow their data to be analyzed by a firm, and the costs they incur in terms of privacy sacrifices. This is motivating firms to explore new models to attract and retain a new generation of privacy-active customers (i.e., customers who act in order to protect their privacy). This paper sets the foundation to solve the mechanism design problem for a firm that offers a continuous set of menus allowing its customers to chose their optimal combination of privacy and price. The solution of the problem shows that the relationship between prices and the amount of data shared is non-monotonic. This is a surprising result that may contribute to a better understanding of the privacy paradox, as well as to help scholars and practitioners to push the known boundaries of privacy-based versioning.
Recommended Citation
Parra-Moyano, José and Mehta, Sameer, "Privacy Versioning for Competitive Advantage" (2022). ICIS 2022 Proceedings. 2.
https://aisel.aisnet.org/icis2022/digit_nxt_gen/digit_nxt_gen/2
Privacy Versioning for Competitive Advantage
There is increasing tension between the service quality improvements consumers receive when they allow their data to be analyzed by a firm, and the costs they incur in terms of privacy sacrifices. This is motivating firms to explore new models to attract and retain a new generation of privacy-active customers (i.e., customers who act in order to protect their privacy). This paper sets the foundation to solve the mechanism design problem for a firm that offers a continuous set of menus allowing its customers to chose their optimal combination of privacy and price. The solution of the problem shows that the relationship between prices and the amount of data shared is non-monotonic. This is a surprising result that may contribute to a better understanding of the privacy paradox, as well as to help scholars and practitioners to push the known boundaries of privacy-based versioning.
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