Trust, risk perception, and intention to use autonomous vehicles

An interdisciplinary bibliometric review

Bibliographic Data

ID20396296
AuthorsMohammad Naiseh (0000-0002-4927-5086, Bournemouth University), Jediah Clark, Jediah R Clark (0000-0002-1356-2462, University of Southampton), Tugra Akarsu (0000-0003-0491-3707, University of Southampton), Yaniv Hanoch (0000-0001-9453-4588, Coventry University), Mario Brito (0000-0002-1779-4535, University of Southampton), Mike Wald (University of Southampton), Thomas Webster, Thomas G Webster, Pravina Shukla (0000-0003-1957-8622, University of Southampton, corresponding author)
Year2025
Volume40
Issue2
Pages1091-1111
Publication date2025-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAI & Society (JOURNAL)
Journal identifiersISSN: 0951-5666 • E-ISSN: 1435-5655
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s00146-024-01895-2
PMID40191162
OpenAlexW4393164363
LanguageEN
Citations received10
References cited89

Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research

Conceptual framework · Data science · Knowledge management · Perception · Political science · Public relations · Risk perception · Social science · Sociology · Computer Science · Ethics and Social Impacts of AI · Human-Automation Interaction and Safety · Psychology · Risk Perception and Management

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