Investigating trust leap with AI: a consumer's behavioural model on autonomous vehicle acceptance

Ludovica Burgese and Kyung Jin Cha

International Journal of Technological Learning, Innovation and Development, 2024, vol. 15, issue 4, 449-475

Abstract: Trust is crucial for automation acceptance across domains, yet exploration in the context of autonomous vehicles (AVs) remains limited. This study utilises an exploratory sequential mixed-design, combining qualitative and quantitative data analysis from an AV-focused survey, to investigate trust's role in shaping consumer acceptance of AVs. It validates the significance of trust in AI technology and provides unique empirical insights into the South Korean AV landscape. The study introduces novel dimensions of information transparency, including regulatory and individual factors, alongside conventional ones (benevolence, integrity, and competence), in shaping a source's trustworthiness. Findings reveal the heightened importance of regulatory transparency, particularly regarding data privacy handling and standards compliance, on consumers' perceptions of AV trustworthiness. South Korean consumer's prioritisation of personal safety over ethical values highlights the importance of considering heterogeneity in consumer behaviours and perceptions across different countries. Moreover, the research emphasises the significance of tailored digital literacy initiatives and infrastructure preparedness in fostering a conducive environment for AV acceptance, with direct implications for industry stakeholders, policymakers, and urban planners navigating emerging transportation technologies.

Keywords: autonomous vehicles; autonomous driving; intelligent transportation systems; technology acceptance models; user acceptance; behavioural models; emerging technologies; artificial intelligence; AI; trust; trust leap; human-machine interaction; information transparency; South Korea. (search for similar items in EconPapers)
Date: 2024
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