Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis
-
Published:2023-12-01
Issue:
Volume:
Page:
-
ISSN:0049-4488
-
Container-title:Transportation
-
language:en
-
Short-container-title:Transportation
Author:
Schandl Franziska,Fischer Peter,Hudecek Matthias F. C.
Abstract
AbstractAutonomous 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.
Funder
Federal Ministry for Digital and Transport Universität Regensburg
Publisher
Springer Science and Business Media LLC
Subject
Transportation,Development,Civil and Structural Engineering
Reference89 articles.
1. Angermeier, W.F., Bednorz, P., Hursh, S.R., Dinsmoor, J.A., Eider, S.T., Elsmore, T.F., Galbicka, G., Hörster, W., Hursh, S.R., Lashley, J.K., Raslear, T.G., Redmon, W.K., Staddon, J.E.: Operantes Lernen: methoden, ergebnisse, anwendung. Ein Handbuch. Reinhardt, Waleska (1994) 2. Araújo, A.M., Assis Gomes, C.M., Almeida, L.S., Núñez, J.C.: A latent profile analysis of first-year university students’ academic expectations. Anales De Psicología 35(1), 58–67 (2018). https://doi.org/10.6018/analesps.35.1.299351 3. Bauer, D.J., Curran, P.J.: The integration of continuous and discrete latent variable models: potential problems and promising opportunities. Psychol. Methods 9(1), 3–29 (2004). https://doi.org/10.1037/1082-989x.9.1.3 4. Beierlein, C., Kovaleva, A., Kemper, C.J., Rammstedt, B.: Ein Messinstrument zur Erfassung subjektiver Kompetenzerwartungen: allgemeine Selbstwirksamkeit Kurzskala (ASKU). GESIS (2012). 5. Benleulmi, A.Z., Blecker, T.: Investigating the factors influencing the acceptance of fully autonomous cars. In: Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL) (Vol. 23, pp. 99–115). Epubli GmbH, Berlin (2017). https://doi.org/10.15480/882.1449
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|