Factors influencing behavioral intentions to use conditionally automated vehicles

Background: This study explored factors influencing the acceptance of conditionally automated vehicles among Australian drivers by extending the Technology Acceptance Model with the Technology Readiness Index. Method: Data from an online survey of 844 participants were analyzed using partial least squares structural equation modeling (PLS-SEM). Results: Perceived usefulness had the strongest direct effect on behavioral intention (0.469, p < 0.001), followed by attitude (0.318, p < 0.001). Innovativeness positively influenced behavioral intention (0.183, p < 0.001), while insecurity had a negative impact (-0.071, p < 0.01). Optimism and discomfort were not significant. Perceived usefulness also had significant indirect effects through attitude (0.156, p < 0.001) and trust (0.072, p < 0.001). Perceived ease of use indirectly influenced behavioral intention through perceived usefulness (0.306, p < 0.001), attitude (0.102, p < 0.001), trust (0.047, p < 0.001), and their combinations. Trust indirectly affected behavioral intention via attitude (0.130, p < 0.001). Perceived security and privacy risks had indirect negative effects through trust and attitude (-0.035, p < 0.001;-0.005, p < 0.05). Conclusion: These results suggest that fostering acceptance among less tech-savvy individuals may help promote positive attitudes, increase conditionally automated vehicle adoption, and potentially enhance road safety. Practical implications: These findings suggest a need for targeted programs to enhance perceived usefulness and trust while addressing security and privacy concerns, ultimately contributing to safer road systems through the adoption of conditionally automated vehicles.