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What is the public really concerned about the AV crash? Insights from a combined analysis of social media and questionnaire survey

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  • Jing, Peng
  • Wang, Baihui
  • Cai, Yunhao
  • Wang, Bichen
  • Huang, Jiahui
  • Yang, Chenglu
  • Jiang, Chengxi

Abstract

Autonomous vehicles (AVs) will bring considerable benefits to individuals and society, while the process of AVs' popularity may not always be smooth. Sometimes crashes are unavoidable, which could hinder the widespread acceptance of AVs. Although previous studies have investigated factors that affect AV acceptance, few of them pay attention to the impact of accidents and not to mention quantitative analysis. This study aims to explore what the public is really concerned about the AV crash and whether it significantly affects the public's behavioral intention to use AVs utilizing a multi-method analytical approach, including latent Dirichlet allocation, structural equation model, and Bayesian network. We employed a qualitative exploratory analysis to identify the topics of social media data about AV crashes and proposed an integrated framework using variables originating from topics. We introduced perceived severity in AV acceptance research to cater to the context of AV crashes. The findings reveal how trust, attitude, knowledge, perceived risk, media exposure, and perceived severity affect the public acceptance of AVs and how differences exist between full automation and driver assistance. This research helps to provide theoretical contributions and practical implications to reduce the negative effect of AV crashes and improve public acceptance of AVs.

Suggested Citation

  • Jing, Peng & Wang, Baihui & Cai, Yunhao & Wang, Bichen & Huang, Jiahui & Yang, Chenglu & Jiang, Chengxi, 2023. "What is the public really concerned about the AV crash? Insights from a combined analysis of social media and questionnaire survey," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:tefoso:v:189:y:2023:i:c:s0040162523000562
    DOI: 10.1016/j.techfore.2023.122371
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