Factors Affecting the Acceptance and Willingness-to-Pay of End-Users: A Survey Analysis on Automated Vehicles

Xiaobei Jiang, Wenlin Yu, Wenjie Li, Jiawen Guo, Xizheng Chen, Hongwei Guo, Wuhong Wang and Tao Chen
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Xiaobei Jiang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Wenlin Yu: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Wenjie Li: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Jiawen Guo: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Xizheng Chen: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Hongwei Guo: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Wuhong Wang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Tao Chen: Key Laboratory of Transportation Industry of Automotive Transportation Safety Enhancement Technology, Chang’an University, Xi’an 710064, China

Sustainability, 2021, vol. 13, issue 23, 1-12

Abstract: The emergence of automated vehicles (AVs) is expected to have a huge impact on traffic safety and environmental improvement. In order to promote the sustainable development of AVs, it is urgent to study the public’s acceptance of and willingness-to-pay for automated vehicles and their influencing factors. Based on a questionnaire survey and descriptive research, this paper investigates the public’s general views on AVs. A psychological model considering technical trust (TT), perceived benefit (PB), perceived risk (PR), and perceived ease of use (PU) was constructed to study the factors that influence the public’s acceptance of and willingness-to-pay for AVs. Logistic regression models based on demographic factors such as monthly income (MI) and driving experience (DE) and psychological factors were established to predict end-users’ acceptance and willingness-to-pay. The accuracy of the two models is 93.2% and 87.9%, respectively. Based on the results, the following policies can be put forward to promote the development of AVs: (1) more information to enhance TT; (2) pricing and easy maintenance based on PU; (3) education and training based on TT and PB; and (4) personalized sales based on DE and MI.

Keywords: automated vehicles; acceptance; willingness-to-pay; technical trust; perceived benefit; perceived risk; perceived ease of use; monthly income; driving experience (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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