How dataveillance shapes user behavior: The role of perceived value in disclosure and discontinuation

Abstract

The rapid rise of video recommender systems has transformed user experiences by offering highly personalized content. However, this growth has sparked debates over the boundaries of data collection practices and user control. This study investigates how perceived dataveillance and prior dataveillance experiences influence user behaviors, with a focus on the mediating role of perceived information disclosure value. Grounded in privacy calculus theory and chilling effect, the study proposes an integrated model that examines both preventive behaviors (e.g., restricting information disclosure) and problem-solving behaviors (e.g., discontinuing service use). The findings, based on Partial Least Squares (PLS) analysis, reveal that perceived dataveillance diminishes information disclosure value and negatively impacts information disclosure behavior. Furthermore, prior dataveillance experiences intensify perceived dataveillance, reduce users' willingness to share information, and increase the likelihood of discontinuation. Additionally, perceived information disclosure value plays a pivotal dual role, encouraging information disclosure while mitigating discontinuation intentions. These insights contribute to theoretical advancements by integrating risk and benefit considerations into a unified framework and offer practical guidance for designing user-centric recommender systems.