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.