Privacy Decision-Making under Constrained Disclosure: IoV Evidence

Outlet Title

Journal of Computer Information Systems

Document Type

Article

Publication Date

2026

Abstract

The Internet of Vehicles (IoV) enables continuous data exchange among vehicles, manufacturers, infrastructure, and third-party services, generating extensive personal and behavioral driving data. Unlike discretionary digital technologies, IoV operates as essential mobility infrastructure in which users cannot meaningfully opt out of data collection without forfeiting core functionality. This constrained choice fundamentally alters privacy decision-making, rendering traditional acceptance models grounded in voluntary adoption insufficient. This study examines privacy decision-making under constrained disclosure, focusing on drivers’ intention to disclose personal driving data in IoV contexts by extending privacy calculus theory to conditions of constrained disclosure. Drawing on Protection Motivation Theory (PMT) and UTAUT2, we conceptualize a dual calculus in which perceived privacy risk (threat vulnerability, threat severity, response efficacy, and self-efficacy) and perceived benefits (performance expectancy and hedonic motivation) jointly shape disclosure intention when technology use is effectively mandatory. We further examine whether perceived driving status, a context-specific self-assessment of driving ability and safety, moderates these relationships. Survey data from 177 U.S. respondents were analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that perceived privacy risk negatively influences intention to disclose, while performance-related benefits exert a positive effect, supporting the operation of privacy calculus even under constrained choice. In contrast to findings from voluntary contexts such as social media and mobile applications, we find no evidence of the privacy paradox, identifying constrained disclosure as a boundary condition under which attitudinal—behavioral inconsistency diminishes. Moderating effects of perceived driving status are limited, suggesting that objective behavioral indicators may be required to capture situational vulnerability in essential technology settings. This study advances privacy theory by reconceptualizing acceptance as intention to disclose rather than intention to use in involuntary digital systems and by delineating the limits of the privacy paradox in essential infrastructures. Practically, the findings underscore the need for transparent data governance, privacy-by-design mechanisms, and regulatory oversight to sustain trust and legitimacy in connected mobility ecosystems.

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