Paper Number
ECIS2026-1980
Paper Type
SP
Abstract
AI-enabled technological enforcement, which leverages data analytics and automated decision-making to monitor and regulate driver behavior, has become an instrument for improving road safety. However, resistance among drivers remains pervasive, manifesting as active resistance, passive defiance, and instrumental avoidance. This study examines such resistance from an institutional legitimacy perspective in AI-enabled, non-voluntary enforcement contexts. Drawing on this perspective, we identify six governance practices that shape legitimacy perceptions: perceived enforcement benefit, inclusive decision-making, procedural justice, safety-framed communication, technology transparency, and procedural comprehensibility. These practices are proposed to strengthen pragmatic, moral, and cognitive legitimacy by activating evaluative processes related to self-interest, responsiveness, fairness, societal outcomes, and intelligibility. By enhancing legitimacy, these practices reduce different forms of resistance. This study shifts the focus from technology adoption to legitimacy formation in AI-enabled enforcement and provides actionable implications for governments deploying AI-driven systems that influence citizens’ rights and everyday behaviors.
Recommended Citation
Yeh, Gary Yu-Ho; Harry, Su-Han Huang; Brading, Ryan; and Hsu, Jack, "Why Drivers Resist AI- Enabled Technological Enforcement? An Institutional Legitimacy Perspective To Explain Traffic Violation" (2026). ECIS 2026 Proceedings. 10.
https://aisel.aisnet.org/ecis2026/is_adopt/is_adopt/10
Why Drivers Resist AI- Enabled Technological Enforcement? An Institutional Legitimacy Perspective To Explain Traffic Violation
AI-enabled technological enforcement, which leverages data analytics and automated decision-making to monitor and regulate driver behavior, has become an instrument for improving road safety. However, resistance among drivers remains pervasive, manifesting as active resistance, passive defiance, and instrumental avoidance. This study examines such resistance from an institutional legitimacy perspective in AI-enabled, non-voluntary enforcement contexts. Drawing on this perspective, we identify six governance practices that shape legitimacy perceptions: perceived enforcement benefit, inclusive decision-making, procedural justice, safety-framed communication, technology transparency, and procedural comprehensibility. These practices are proposed to strengthen pragmatic, moral, and cognitive legitimacy by activating evaluative processes related to self-interest, responsiveness, fairness, societal outcomes, and intelligibility. By enhancing legitimacy, these practices reduce different forms of resistance. This study shifts the focus from technology adoption to legitimacy formation in AI-enabled enforcement and provides actionable implications for governments deploying AI-driven systems that influence citizens’ rights and everyday behaviors.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.