Factors Influencing Users' Willingness to Adopt Connected and Autonomous Vehicles: Net and Configurational Effects Analysis Using PLS-SEM and FsQCA

To accelerate the widespread adoption of connected and autonomous vehicles (CAVs) and take full advantage of CAVs' transportation safety, efficiency, and pro-environment, a deep understanding of CAVs acceptance is needed. However, little is known about the combined effects of factors influencing CAVs acceptance using traditional statistical methods. We developed an integrated model to explore how various antecedent factors work together on CAVs' acceptance. The symmetric (Structure Equation Modeling) and asymmetric (Qualitative Comparative Analysis) techniques were utilized for analyzing data from 362 Chinese. PLS-SEM assesses the net effect of each antecedent on CAVs' adoption, while fsQCA provides supplementary analysis by revealing the configurations of causal conditions associated with CAVs' adoption. PLS-SEM results show that perceived usefulness, perceived ease of use, and initial trust directly influence users' willingness to adopt CAVs, while perceived risk, social influence, and facilitating conditions do not. Meanwhile, automation, ubiquitous connectivity, structural assurance, and corporation reputation indirectly influence CAVs adoption, while environmental performance and technological uncertainty have no statistically significant indirect effect. Interestingly, fsQCA reveals five configurations resulting in a high level of CAVs' acceptance, and seven configurations leading to the negation of CAVs' acceptance. The complementary analysis results provide insights into both theory and practice.