Problem
Research questions and friction points this paper is trying to address.
autonomous vehicle acceptance
driving-safety concern
AI driving capability
push factor
human error
Community Driving-Safety Deterioration as a Push Factor for Public Endorsement of AI Driving Capability
🤖 AI Summary
This study addresses the overlooked role of deteriorating neighborhood traffic safety in shaping public acceptance of autonomous vehicles, a factor neglected by prior research overly focused on technology-driven determinants. It proposes a moderated mediation model demonstrating that concerns about local driving safety influence evaluations of AI driving capability through dual pathways: a modest direct positive effect and a stronger indirect negative effect mediated by diminished general attitudes toward AI, resulting in a near-zero total effect—evidence of a “risk spillover” mechanism. Individual driving frequency moderates this mediation process. Employing weighted structural equation modeling, WLSMV-based confirmatory factor analysis, bias-corrected bootstrapping, and multiple robustness checks—including Imai sensitivity analysis, E-values, and propensity score matching—the study offers a novel perspective on how socio-contextual factors shape AI acceptance.
📝 Abstract
Road traffic crashes claim approximately 1.19 million lives annually worldwide, and human error accounts for the vast majority, yet the autonomous vehicle acceptance literature models adoption almost exclusively through technology-centered pull factors such as perceived usefulness and trust. This study examines a moderated mediation model in which perceived community driving-safety concern (PCSC) predicts evaluations of AI versus human driving capability, mediated by Generalized AI Orientation and moderated by personal driving frequency. Weighted structural equation modeling is applied to a nationally representative U.S. probability sample from Pew Research Center's American Trends Panel Wave 152, using Weighted Least Squares Mean and Variance Adjusted (WLSMV)-estimated confirmatory factor analysis on ordinal indicators, bias-corrected bootstrap inference, and seven robustness checks including Imai sensitivity analysis, E-value confounding thresholds, and propensity score matching. Results reveal a dual-pathway mechanism constituting an inconsistent mediation: PCSC exerts a small positive direct effect on AI driving evaluation, consistent with a domain-specific push interpretation, while simultaneously suppressing Generalized AI Orientation, which is itself a strong positive predictor of AI driving evaluation. Conditional indirect effects are negative and statistically significant at low, mean, and high levels of driving frequency. These findings establish a risk-spillover mechanism whereby community driving-safety concern promotes domain-specific AI endorsement yet suppresses domain-general AI enthusiasm, yielding a near-zero net total effect.
Innovation
Methods, ideas, or system contributions that make the work stand out.
moderated mediation
community driving-safety concern
Generalized AI Orientation
structural equation modeling
autonomous vehicle acceptance
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