Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS

Artificial intelligence (AI)-powered autonomous vehicles (AVs) are one of the most disruptive technologies with potentially wide-ranging social implications, including improvements in passenger/driver safety, environmental protection, and equity considerations. The current research extends the UTAUT2 model in the context of fully AVs (level 5 automation) to determine and rank determinants of intention to adopt AVs. Collected data from 378 respondents were analysed by a hybrid approach employing partial least squares (PLS) complemented by the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) technique. According to the findings, five major determinants emerged: trust, hedonic motivation, social influence, compatibility, and effort expectancy. Furthermore, compatibility positively moderates the association between performance expectancy and intention to use AVs. The findings shed light on determinant factors, their level of importance, and the potential interplay between them in shaping individuals' intention to adopt and use AVs. Furthermore, the current research provides valuable insights to carmakers, technology developers, and practitioners on determinants of AVs adoption, assisting them in devising effective AVs-related strategies.

Automated summary

This study identifies the major determinants of intention to adopt autonomous vehicles, including trust, hedonic motivation, social influence, compatibility, and effort expectancy. The findings are valuable for devising effective strategies in the development and adoption of autonomous vehicles.