What Matters Most - A Meta-Analysis of Automated Vehicles Acceptance Studies

Authors

Jennifer Wiefel, Technische Universität DarmstadtFollow

Abstract

In recent times, research has put much effort into investigating the factors relevant to the acceptance of automated vehicles (AVs). In order to identify the key influencing factors, we combine findings from extant literature in a meta-analysis assessing the main drivers of the behavioural intention to use automobiles with automated driving features. The analysis spanning 51 articles identifies attitude, perceived usefulness, efficiency, trust in AVs, safety, and subjective norms to correlate most strongly with the behavioural intention to use an automated car. On top of that, we analyse the moderating effects of design choices made in the considered primary studies. We investigate the influence of the study’s location, the vehicle’s level of automation, the ownership model, and applied methodology on the behavioural intention. Thereby, we identify moderating effects for almost all design characteristics. Our metaanalysis helps researchers and practitioners to determine the most relevant AV acceptance factors, and by conducting the moderator-analysis, we pave the way towards a more careful design of future studies.

Recommended Citation

Wiefel, Jennifer, "What Matters Most - A Meta-Analysis of Automated Vehicles Acceptance Studies" (2020). In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference, June 15-17, 2020.
https://aisel.aisnet.org/ecis2020_rp/25