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A new framework to predict and visualize technology acceptance: A case study of shared autonomous vehicles

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  • Guo, Lirui
  • Burke, Michael G.
  • Griggs, Wynita M.

Abstract

Public acceptance is critical to the adoption of Shared Autonomous Vehicles (SAVs) in the transport sector. Traditional acceptance models, primarily reliant on Structural Equation Modeling, may not adequately capture the complex, non-linear relationships among factors influencing technology acceptance and often have limited predictive capabilities. This paper introduces a framework that combines Machine Learning techniques with chord diagram visualizations to analyze and predict public acceptance of technologies. Using SAV acceptance as a case study, we applied a Random Forest machine learning approach to model the non-linear relationships among psychological factors influencing acceptance. Chord diagrams were then employed to provide an intuitive visualization of the relative importance and interplay of these factors at both factor and item levels in a single plot. Our findings identified Attitude as the primary predictor of SAV usage intention, followed by Perceived Risk, Perceived Usefulness, Trust, and Perceived Ease of Use. The framework also reveals divergent perceptions between SAV adopters and non-adopters, providing insights for tailored strategies to enhance SAV acceptance. This study contributes a data-driven perspective to the technology acceptance discourse, demonstrating the efficacy of integrating predictive modeling with visual analytics to understand the relative importance of factors in predicting public acceptance of emerging technologies.

Suggested Citation

  • Guo, Lirui & Burke, Michael G. & Griggs, Wynita M., 2025. "A new framework to predict and visualize technology acceptance: A case study of shared autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:tefoso:v:212:y:2025:i:c:s0040162524007583
    DOI: 10.1016/j.techfore.2024.123960
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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Barbour, Natalia & Menon, Nikhil & Zhang, Yu & Mannering, Fred, 2019. "Shared automated vehicles: A statistical analysis of consumer use likelihoods and concerns," Transport Policy, Elsevier, vol. 80(C), pages 86-93.
    3. Felix Becker & Kay W. Axhausen, 2017. "Literature review on surveys investigating the acceptance of automated vehicles," Transportation, Springer, vol. 44(6), pages 1293-1306, November.
    4. Kenesei, Zsófia & Ásványi, Katalin & Kökény, László & Jászberényi, Melinda & Miskolczi, Márk & Gyulavári, Tamás & Syahrivar, Jhanghiz, 2022. "Trust and perceived risk: How different manifestations affect the adoption of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 379-393.
    5. Wendy De La Rosa & Eesha Sharma & Stephanie M. Tully & Eric Giannella & Gwen Rino, 2021. "Psychological ownership interventions increase interest in claiming government benefits," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(35), pages 2106357118-, August.
    6. Merfeld, Katrin & Wilhelms, Mark-Philipp & Henkel, Sven & Kreutzer, Karin, 2019. "Carsharing with shared autonomous vehicles: Uncovering drivers, barriers and future developments – A four-stage Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 66-81.
    7. Wang, Fei & Zhang, Zhentai & Lin, Shoufu, 2023. "Purchase intention of Autonomous vehicles and industrial Policies: Evidence from a national survey in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    8. Timo Liljamo & Heikki Liimatainen & Markus Pöllänen & Riku Viri, 2021. "The Effects of Mobility as a Service and Autonomous Vehicles on People’s Willingness to Own a Car in the Future," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    9. Pettigrew, Simone & Dana, Liyuwork Mitiku & Norman, Richard, 2019. "Clusters of potential autonomous vehicles users according to propensity to use individual versus shared vehicles," Transport Policy, Elsevier, vol. 76(C), pages 13-20.
    10. Cunningham, Mitchell L. & Regan, Michael A. & Horberry, Timothy & Weeratunga, Kamal & Dixit, Vinayak, 2019. "Public opinion about automated vehicles in Australia: Results from a large-scale national survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 1-18.
    11. Peng Liu & Run Yang & Zhigang Xu, 2019. "Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions," Risk Analysis, John Wiley & Sons, vol. 39(2), pages 326-341, February.
    12. Paul Orsot-Dessi & Arvind Ashta & Surender Mor, 2023. "The determinants of the intention to use autonomous vehicles," African Journal of Science, Technology, Innovation and Development, Taylor & Francis Journals, vol. 15(5), pages 650-660, July.
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