Koppel Sjaan, Logan David B, Zou Xin, Kaviani Fareed, McDonald Hayley, Hair Joseph F, St Louis Renée M, Molnar Lisa J, Charlton Judith L
Monash University Accident Research Centre, Monash University, VIC, Australia.
Monash University Accident Research Centre, Monash University, VIC, Australia.
J Safety Res. 2024 Dec;91:423-430. doi: 10.1016/j.jsr.2024.10.006. Epub 2024 Oct 28.
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.
Data from an online survey of 844 participants were analyzed using partial least squares structural equation modeling (PLS-SEM).
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).
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.
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.
本研究通过将技术准备指数扩展到技术接受模型，探讨了影响澳大利亚驾驶员对有条件自动驾驶汽车接受度的因素。
使用偏最小二乘结构方程模型（PLS-SEM）分析了来自844名参与者的在线调查数据。
感知有用性对行为意向的直接影响最强（0.469，p<0.001），其次是态度（0.318，p<0.001）。创新性对行为意向有正向影响（0.183，p<0.001），而不安全感有负向影响（-0.071，p<0.01）。乐观和不适不显著。感知有用性还通过态度（0.156，p<0.001）和信任（0.072，p<0.001）产生显著的间接影响。感知易用性通过感知有用性（0.306，p<0.001）、态度（0.102，p<0.001）、信任（0.047，p<0.001）及其组合间接影响行为意向。信任通过态度间接影响行为意向（0.130，p<0.001）。感知安全和隐私风险通过信任和态度产生间接负向影响（-0.035，p<0.001；-0.005，p<0.05）。
这些结果表明，培养技术不太熟练的个人的接受度可能有助于促进积极态度，增加有条件自动驾驶汽车的采用，并潜在地提高道路安全性。
这些发现表明需要有针对性的计划来提高感知有用性和信任，同时解决安全和隐私问题，最终通过采用有条件自动驾驶汽车为更安全的道路系统做出贡献。
