PRIVACY PROTECTION: PETS BY INDIVIDUALS, PDPS BY FIRMS

Abstract

This thesis examines the role individuals and firms play in privacy protection. Study 1 investigates the impact of end-user privacy enhancing technologies (PETs) on firms’ analytics capabilities. I propose an inductively derived framework which qualitatively shows that PETs induce measurement error and/or missing values with regards to attributes, entities, and relationships in firms’ databases. The simulation experiments quantitively find that consumers’ adoption characteristics (adoption rate and pattern) and PETs characteristics (protection mechanism and intensity) significantly affect the performance of recommender systems. Study 2 investigates the economic implications of privacy dark patterns (PDPs). The analytical modeling results show that PDPs make users weakly worse off and the seller weakly better off. Nevertheless, the seller has incentives to not utilize any PDPs under some conditions. A welfare maximizing social planner would allow the presence of PDPs when users' privacy cost is sufficiently low.