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Trust, risk perception, and intention to use autonomous vehicles: an interdisciplinary bibliometric review.

Naiseh, M., Clark, J., Akarsu, T., Hanoch, Y., Brito, M., Wald, M., Webster, T. and Shukla, P., 2024. Trust, risk perception, and intention to use autonomous vehicles: an interdisciplinary bibliometric review. AI and Society. (In Press)

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s00146-024-01895-2.pdf - Published Version
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DOI: 10.1007/s00146-024-01895-2


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.

Item Type:Article
Uncontrolled Keywords:Autonomous vehicles; Trust; Risk; Bibliometric analysis
Group:Faculty of Science & Technology
ID Code:39732
Deposited By: Symplectic RT2
Deposited On:24 Apr 2024 14:42
Last Modified:24 Apr 2024 14:42


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