Interrelationships between influential factors and behavioral intention with regard to autonomous vehicles

Soon enough, autonomous driving systems may not need drivers at all. Ideally, a passenger can ride an autonomous vehicle (AUV) from one location to another without (either partial or complete) human intervention, inherently removing aberrant human driving behavior from the equation, requiring no or only a few parking spaces near a passenger’s destination, and possessing environmental advantages over traditional vehicles powered by internal combustion engines. The current study aims to investigate the interrelationships between influential factors and behavioral intention with regard to AUVs. The research was implemented with: (1) partial least squares structural equation modeling (PLS-SEM) to examine path relationships, (2) partial least squares multi-group analysis (PLS-MGA) to elaborate observed heterogeneity, and (3) partial least squares prediction orientation segmentation (PLS-POS) to study unobserved heterogeneity. The empirical results showed significant direct effects for Hypotheses H1-a ∼ H1-c (Attitude, Subjective norm, and Perceived Behavior Control on Behavior Intention) and for Hypothesis H3-a (Personal Innovativeness on Behavior Intention). Additionally, the results showed that observed heterogeneity does not exist by gender, and by city, and that unobserved heterogeneity can be best identified and divided into two segments by employing PLS-POS. A few final remarks concern the research findings and preparing for future AUV transportation.