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Seeing and experiencing: Heterogeneous acceptance pathways of autonomous vehicles across real-world exposure profiles
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DOI：10.1016/j.trf.2026.103600.png)
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Abstract
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Public acceptance is a critical barrier to the widespread deployment of autonomous vehicles (AVs), yet existing research inadequately addresses how experiential heterogeneity shapes acceptance mechanisms. This study investigates the differential acceptance of AVs across user groups with varying real-world exposure profiles by extending the Technology Acceptance Model (TAM) with perceived risk and personal innovativeness. A multi-group analytical framework was developed, classifying users into four experience-based categories. Data from 1199 respondents across 11 Chinese cities with operational AVs services were analyzed using multi-group structural equation modeling. Results reveal that perceived usefulness, attitude, and personal innovativeness significantly drive behavioral intention, but their effects vary across experience-profile groups: in indirect/no-experience modalities, perceived ease of use plays a more salient role, whereas in direct-experience modalities (hands-on use or ride experience), personal innovativeness exerts a stronger influence. Notably, perceived risk suppresses attitude but does not directly deter behavioral intention. The study shows that acceptance pathways differ across real-world exposure profiles, indicating profile-based heterogeneity in cognitive and behavioral responses to AVs adoption. These findings refine TAM's applicability in high-risk technology contexts and offer tailored strategies for policymakers and manufacturers to align AVs deployment with segmented user expectations. The research contributes a nuanced understanding of acceptance heterogeneity and provides actionable insights for accelerating AVs integration.
Keywords:
Autonomous vehicles
Technology acceptance model
Multi-group structural equation modeling
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