Governing biometric privacy in ride-sharing: an institutional–cognitive model of trust, distrust and continuance intention

Purpose Ride-sharing platforms are increasingly adopting biometric technologies to enhance trip safety. While such technologies aim to strengthen security, they also raise governance challenges regarding the collection and management of sensitive and irreversible biometric data. In this context, the critical issue is not merely the existence of privacy risks, but how users interpret institutional safeguards designed to manage those risks and how these perceptions influence their willingness to continue using ride-sharing services. Building on the Antecedent–Privacy Concern–Outcome (APCO) framework and integrating institutional logic theory, trust theory and privacy calculus theory, this study develops an institutional–cognitive model to examine how perceived governance mechanisms shape biometric privacy concerns, how trust and distrust jointly influence continuance intention and whether perceived benefits condition these relationships. Design/methodology/approach We collected 354 samples from ride-sharing service users and then analyzed the model using a mixed structural equation modeling (SEM) – artificial neural network (ANN) approach. Findings We found that trust and distrust have an indirect effect on continuance intention by influencing users' biometrics privacy concern; government regulation and social supervision are effective approaches to reduce users' biometrics privacy concern, while enterprise self-restraint played the opposite role; perceived benefits moderate the relationship between trust, distrust and continuance intention. ANN analysis revealed that enterprise self-restraint was the most important predictor of distrust, while distrust and trust were the strongest drivers of biometric privacy concerns. For continuance intention, perceived benefits emerged as the most influential factor, followed by biometric privacy concerns. Practical implications This study calls on the government, industry, society and companies to enhance appropriate privacy regulations and implement trust mechanisms to alleviate users' biometrics privacy concern while ensuring the robust growth of the ride-sharing industry. Originality/value This study advances the APCO framework by conceptualizing biometric privacy as a governance-driven cognitive process rather than merely a risk perception outcome. It develops an institutional–cognitive dual-path model that differentiates structural privacy concerns from relational evaluations (trust and distrust). Moreover, by positioning perceived benefits as a conditional amplifier instead of a compensatory factor, the study refines privacy calculus theory and clarifies the boundary conditions under which benefit perceptions intensify trust-related effects.