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Underlying dimensions of benefit and risk perception and their effects on people’s acceptance of conditionally/fully automated vehicles

Author

Listed:
  • Yukari Jessica Tham

    (The University of Tokyo
    Japan Society for the Promotion of Science)

  • Takaaki Hashimoto

    (Toyo University)

  • Kaori Karasawa

    (The University of Tokyo)

Abstract

Automated vehicles (AVs) have garnered increasing attention since they have the potential to dramatically reshape our lives in the near future. At the same time, people are concerned about various risks associated with the new technologies. Thus, people’s attitudes toward AVs pose a major challenge to the wider adoption of them. Previous studies examined the effect of benefit/risk perception on people’s acceptance of AVs, but they did not address the multidimensionality of benefit/risk perception. We conducted a survey (n = 840) to reveal the underlying dimensions of how people construe the benefits and risks of conditionally/fully automated vehicles. Our results showed that there were two dimensions underlying benefit perception (i.e., the perception that AVs would increase convenience and reduce harm) and three dimensions underlying risk perception (i.e., the perception of risk to physical safety and comfort, cybersecurity, and ease of use). The perception that AVs would reduce harm positively impacted people’s intention to use both fully automated vehicles and conditionally automated vehicles. The perception that AVs would increase convenience and the perception that AVs would pose a risk to ease of use had a positive and negative effect, respectively, on intention to use fully automated vehicles. This study makes theoretical contributions by questioning the assumption that benefit/risk perception is a one-dimensional factor that impacts people’s acceptance of AVs. This study also has practical implications as it suggests an effective method for automobile manufacturers and policymakers to communicate with the public regarding the new technologies and diffuse them safely.

Suggested Citation

  • Yukari Jessica Tham & Takaaki Hashimoto & Kaori Karasawa, 2022. "Underlying dimensions of benefit and risk perception and their effects on people’s acceptance of conditionally/fully automated vehicles," Transportation, Springer, vol. 49(6), pages 1715-1736, December.
  • Handle: RePEc:kap:transp:v:49:y:2022:i:6:d:10.1007_s11116-021-10225-0
    DOI: 10.1007/s11116-021-10225-0
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    References listed on IDEAS

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    1. Greenwald, Judith M. & Kornhauser, Alain, 2019. "It’s up to us: Policies to improve climate outcomes from automated vehicles," Energy Policy, Elsevier, vol. 127(C), pages 445-451.
    2. Hohenberger, Christoph & Spörrle, Matthias & Welpe, Isabell M., 2016. "How and why do men and women differ in their willingness to use automated cars? The influence of emotions across different age groups," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 374-385.
    3. John Horn, 1965. "A rationale and test for the number of factors in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 30(2), pages 179-185, June.
    4. Nielsen, Thomas Alexander Sick & Haustein, Sonja, 2018. "On sceptics and enthusiasts: What are the expectations towards self-driving cars?," Transport Policy, Elsevier, vol. 66(C), pages 49-55.
    5. Chen, Yuche & Gonder, Jeffrey & Young, Stanley & Wood, Eric, 2019. "Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 134-145.
    6. Teresa Brell & Ralf Philipsen & Martina Ziefle, 2019. "sCARy! Risk Perceptions in Autonomous Driving: The Influence of Experience on Perceived Benefits and Barriers," Risk Analysis, John Wiley & Sons, vol. 39(2), pages 342-357, February.
    7. Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2017. "Psychological roadblocks to the adoption of self-driving vehicles," Nature Human Behaviour, Nature, vol. 1(10), pages 694-696, October.
    Full references (including those not matched with items on IDEAS)

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