Community Driving-Safety Deterioration as a Push Factor for Public Endorsement of AI Driving Capability

TL;DR

This study investigates how community safety concerns influence public endorsement of AI driving, revealing a complex dual-pathway mechanism with both positive and negative effects on AI acceptance.

Contribution

It introduces a moderated mediation model showing community safety concerns can both promote and suppress AI driving evaluation through different psychological pathways.

Findings

Community safety concern has a small positive direct effect on AI evaluation.

Community safety concern suppresses general AI enthusiasm, which strongly predicts AI evaluation.

Net effect of safety concern on AI endorsement is near zero due to opposing pathways.

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.