Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis

被引:5
作者
Schandl, Franziska [1 ]
Fischer, Peter [1 ]
Hudecek, Matthias F. C. [1 ]
机构
[1] Univ Regensburg, Dept Expt Psychol, Univ Str 1, D-93040 Regensburg, Germany
关键词
Autonomous driving; Artificial intelligence; Latent profile analysis; Autonomous vehicle acceptance; User groups; AUTOMATED VEHICLE ACCEPTANCE; USER ACCEPTANCE; TRUST; VALIDATION; NUMBER; DETERMINANTS; PEOPLE; MODELS; CARS;
D O I
10.1007/s11116-023-10447-4
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Autonomous driving and its acceptance are becoming increasingly important in psychological research as the application of autonomous functions and artificial intelligence in vehicles increases. In this context, potential users are increasingly considered, which is the basis for the successful establishment and use of autonomous vehicles. Numerous studies show an association between personality variables and the acceptance of autonomous vehicles. This makes it more relevant to identify potential user profiles to adapt autonomous vehicles to the potential user and the needs of the potential user groups to marketing them effectively. Our study, therefore, addressed the identification of personality profiles for potential users of autonomous vehicles (AVs). A sample of 388 subjects answered questions about their intention to use autonomous buses, their sociodemographics, and various personality variables. Latent Profile Analysis was used to identify four personality profiles that differed significantly from each other in their willingness to use AVs. In total, potential users with lower anxiety and increased self-confidence were more open toward AVs. Technology affinity as a trait also contributes to the differentiation of potential user profiles and AV acceptance. The profile solutions and the correlations with the intention to use proved to be replicable in cross validation analyses.
引用
收藏
页码:1015 / 1038
页数:24
相关论文
共 89 条
[81]  
Venkatesh V, 2012, MIS QUART, V36, P157
[82]  
Wiggins B.C., 2000, DETECTING DEALING OU
[83]   Reliable Accuracy Estimates from k-Fold Cross Validation [J].
Wong, Tzu-Tsung ;
Yeh, Po-Yang .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 32 (08) :1586-1594
[84]   Putting the "Person" in the Center: Review and Synthesis of Person-Centered Approaches and Methods in Organizational Science [J].
Woo, Sang Eun ;
Jebb, Andrew T. ;
Tay, Louis ;
Parrigon, Scott .
ORGANIZATIONAL RESEARCH METHODS, 2018, 21 (04) :814-845
[85]   What drives people to accept automated vehicles? Findings from a field experiment [J].
Xu, Zhigang ;
Zhang, Kaifan ;
Min, Haigen ;
Wang, Zhen ;
Zhao, Xiangmo ;
Liu, Peng .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2018, 95 :320-334
[86]   Impact of Autonomous-Vehicle-Only Lanes in Mixed Traffic Conditions [J].
Yu, Hwapyeong ;
Tak, Sehyun ;
Park, Minju ;
Yeo, Hwasoo .
TRANSPORTATION RESEARCH RECORD, 2019, 2673 (09) :430-439
[87]   A theoretical investigation of user acceptance of autonomous public transport [J].
Yuen, Kum Fai ;
Choo, Ling Qian ;
Li, Xue ;
Wong, Yiik Diew ;
Ma, Fei ;
Wang, Xueqin .
TRANSPORTATION, 2023, 50 (02) :545-569
[88]   Statistical Modeling of Cultural Differences in Adopting Autonomous Vehicles [J].
Yun, Yongdeok ;
Oh, Hyungseok ;
Myung, Rohae .
APPLIED SCIENCES-BASEL, 2021, 11 (19)
[89]   Automated vehicle acceptance in China: Social influence and initial trust are key determinants [J].
Zhang, Tingru ;
Tao, Da ;
Qu, Xingda ;
Zhang, Xiaoyan ;
Zeng, Jihong ;
Zhu, Haoyu ;
Zhu, Han .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 112 :220-233