Understanding user archetypes in fully autonomous vehicle adoption: insights from factor-based clustering

Fully autonomous vehicles (FAVs) are expected to render the driving environment safer and eco-friendlier, but their widespread adoption is highly dependent on the public’s acceptance. This study aimed to identify the key factors that impact perceptions of FAVs by surveying prospective users in Arlington, Texas, and evaluating the range of attitudinal and behavioral variables associated with the psychological and social determinants of receptivity to FAVs. An exploratory factor analysis classified primary acceptance of FAVs, anxiety about FAVs, driving enjoyment, and intrinsic trust in AVs as the four main factors of FAV adoption. The results from the factor analysis were analyzed using cluster analysis and identified the following three discrete classes: tech-trusting skeptics (34% of the sample), traditional drivers (37%), and pragmatic adopters (29%). The findings of this study describe the user archetypes of FAV adopters and discuss the importance of designing public policies that ensure a smooth transition from partially-to-FAVs.