Simplifying acceptance: A general acceptance factor predicting intentions to use shared autonomous vehicles

The primary aim of this study was to develop an accurate measure of acceptance for shared autonomous vehicles (SAVs) and to assess whether this measure can predict intentions to use SAVs. One leading model for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is extensive and has received numerous suggested extensions and revisions, even being developed into a Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). The challenge is to consolidate a model that effectively measures SAV acceptance and to determine which extensions capture the unique social situation within SAVs.

The current study used survey data from 1902 respondents. The sample was split into two: one half underwent a principal component analysis (PCA) and the other half a confirmatory factor analysis (CFA). We found that the 24 items we included were reducible to a single general acceptance factor (GAF), with three additional factors measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. 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. However, there is also an argument to be made that the other components of SAV acceptance may capture different nuances of the service, particularly relating to the social situation. Interaction terms show differences between genders in their rating of sociability and how this impacts intentions to use SAVs.

Our findings carry significant implications for future research in this field. They underscore the pivotal roles of trust and usefulness while corroborating the notion that SAV acceptance is best represented by a single latent component. However, further investigation is warranted to explore individual-level moderating effects on the other components, potentially offering novel insights for the design of future SAV services.