Assessing public a priori acceptance of fully automated vehicles using an extended technology acceptance model and importance-performance analysis

Fully automated vehicles (FAVs) are anticipated to enter the passenger vehicle market soon. Given the uncertainties surrounding user adoption of this emerging technology, research is needed to understand their user acceptance. While most studies on the acceptance of automated vehicles have been conducted in upper-middle-income or high-income developed countries, similar research in middle-income countries is limited. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. Structural Equation Modeling was applied to confirm model validation by using data from 1026 drivers from different cities in Iran. A multi-group analysis was conducted to assess whether the relationships between model constructs vary across different demographic and background groups. Additionally, an importance-performance analysis was performed to gain a deeper understanding of the factors influencing behavioral intention. The findings of the model highlighted that beyond the original TAM scales, subjective norms were the strongest predictor in explaining drivers' intentions to use FAVs. Initial trust also moderately contributed to explaining user acceptance of FAVs. The results also revealed that among decision-making styles, thoroughness positively affected behavioral intention through trust, while social resistance had an indirect negative effect on intention. The findings showed that effect of the perceived safety risk on behavioral intention through the initial trust was confirmed; however, importance-performance map analysis revealed that a significant improvement was observed in the safety area that could still be obtained. It is suggested that policymakers start promoting the usefulness and ease of use of FAVs through advertisements, social media, public campaigns and autonomous vehicle test ride events to facilitate the adoption of FAVs when available in countries with similar sociocultural contexts. • Proposed an extended technology acceptance model to investigate the public intention to use fully automated vehicles in Iran. • Explored the role of “trust”, “perceived safety risk”, “subjective norms”, and “decision-making styles”. • “Subjective norms” were the strongest predictor of intention. • “Thoroughness” positively and “social resistance” negatively affected intention.