Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?

Abstract

Shared autonomous vehicles (SAVs) are revolutionizing the future of urban mobility. This study aims to investigate the effects of artificial intelligence (i.e., autonomy level and anthropomorphic characteristics), human-related, environmental, and societal factors on public trust and acceptance. Structural equation modelling is used to analyze a valid survey sample of 451 participants. Results show that autonomy level can both directly and indirectly (via trust) increase public acceptance; Whereas, anthropomorphic characteristics cannot directly affect public acceptance, but can indirectly increase their acceptance via trust. The other human-related, environmental, and societal factors also positively contribute to public acceptance. Additionally, moderators, including age, gender, income, housing size, COVID-19 history, shared mobility experience, vehicle ownership, and driving experience are also examined. In theory, this study contextualizes the trust-in-automation three-factor model, UTAUT model, and trust theory and includes two domain-specific constructs (i.e., SAV anthropomorphism and SAV autonomy) to study public trust and acceptance towards SAVs. In practice, this study suggests the incorporation of some anthropomorphic features and relatively high autonomy level in SAVs to build public trust and acceptance.