Understanding the antecedents of privacy fatigue in facial recognition-based m-Gov services: An empirical study from China

Abstract

Facial recognition technology has been adopted for the better delivery of m-government services, but the ease and frequency with which users' personal information is being accessed and misused have inevitably increased. This has recently triggered a new phenomenon of privacy fatigue in e-governance, which makes people feel powerless to protect their privacy. A comprehensive understanding of users' privacy fatigue is important, since negative user perceptions may lead to disengagement and the distrust of the government. Drawing on the Person-Environment fit theory and user agency perspective, this study examines the antecedents of privacy fatigue, manifested as emotional exhaustion and cynicism. A large-scale telephone survey allowed data to be collected from 3, 436 users of facial recognition-based m-Gov services in China. The Partial Least Squares (PLS) results reveal that: (1) privacy fatigue indeed occurs among the users of facial recognition-based m-Gov services in China; (2) the perceptions of privacy control is the core antecedent of users' privacy fatigue, which influences two dimension—emotional exhaustion and cynicism—in different directions; (3) the effectiveness of both privacy self-efficacy and government legislation positively influences perceived privacy control, while the effect of the effectiveness of privacy policies on it is insignificant. This study contributes to the research by empirically testing the notion and antecedents of privacy fatigue in the context of facial recognition-based m-Gov services, and provides a mechanism analysis with an overarching theoretical framework. In addition, the findings may generate new research avenues related to privacy fatigue under an AI-enabled government.