Unveiling the Dynamics of Shared Autonomous Vehicle Acceptance: Insights into the General Acceptance Factor, Social Preferences, and Design Components

Abstract

The technology behind shared autonomous vehicles (SAVs) is developing rapidly and may revolutionize public transport in metropolitan areas. To take full advantage of the potential benefits, it is paramount to understand the public acceptance of this new technology. One of the leading models for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is vast and has received numerous suggested extensions and revisions, even being developed into the Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). More research is needed to consolidate the model to best measure the acceptance of SAVs, and to determine which extensions capture the unique social situation arising within SAVs. The current study used survey data from 1902 respondents to perform a principal component analysis (PCA) of key constructs suggested by the MAVA. We found that these items were reducible to a single general acceptance factor (GAF), with three additional constructs measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The overlap between GAF and intention to use may suggest that these are best conceptualized as a single component. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. There is, however, also an argument to be made that the other three components of SAV acceptance may be important for capturing different nuances of the service. Interaction terms show that there is differences between genders in their rating of sociability, and how this impacts intentions to use SAVs. Our results have important implications for future research within the field. It cements the importance of trust and usefulness and corroborates the claim that acceptance of SAVs is best represented by a single latent component. However, more research should investigate the individual level moderating effects on the other components, as this may unlock new insights about how best to design a future SAV service.