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Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis

Author

Listed:
  • Franziska Schandl

    (University of Regensburg)

  • Peter Fischer

    (University of Regensburg)

  • Matthias F. C. Hudecek

    (University of Regensburg)

Abstract

Autonomous driving and its acceptance are becoming increasingly important in psychological research as the application of autonomous functions and artificial intelligence in vehicles increases. In this context, potential users are increasingly considered, which is the basis for the successful establishment and use of autonomous vehicles. Numerous studies show an association between personality variables and the acceptance of autonomous vehicles. This makes it more relevant to identify potential user profiles to adapt autonomous vehicles to the potential user and the needs of the potential user groups to marketing them effectively. Our study, therefore, addressed the identification of personality profiles for potential users of autonomous vehicles (AVs). A sample of 388 subjects answered questions about their intention to use autonomous buses, their sociodemographics, and various personality variables. Latent Profile Analysis was used to identify four personality profiles that differed significantly from each other in their willingness to use AVs. In total, potential users with lower anxiety and increased self-confidence were more open toward AVs. Technology affinity as a trait also contributes to the differentiation of potential user profiles and AV acceptance. The profile solutions and the correlations with the intention to use proved to be replicable in cross validation analyses.

Suggested Citation

  • Franziska Schandl & Peter Fischer & Matthias F. C. Hudecek, 2025. "Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis," Transportation, Springer, vol. 52(3), pages 1015-1038, June.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:3:d:10.1007_s11116-023-10447-4
    DOI: 10.1007/s11116-023-10447-4
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