Willingness to pay for self-driving vehicles: Influences of demographic and psychological factors

By Peng Liu, Qianru Guo, Fei-fei Ren, Lin Wang, Zhigang Xu. Transportation Research Part C: Emerging Technologies, 2019

208 citations recorded by the source corpus.

Abstract Research on willingness to pay (WTP) can provide practical insights for assessing the value of self-driving vehicle (SDV) technology in the vehicle market. Are people willing to pay extra for the technology? What demographic and psychological factors can influence people’s WTP for this technology? These questions are not yet well investigated. We conducted surveys in two cities in China (total N = 1355) and examined WTP and its potential demographic determinants (familiarity, age, gender, education, and income) and psychological determinants (perceived benefit and risk of SDVs, anticipated perceived dread riding in SDVs, and trust in SDVs). About 26.3% of participants were unwilling to pay extra, 39.3% were willing to pay less than $2900, and the remaining 34.3% were willing to pay more than $2900. Younger and highly educated participants with higher-income were willing to pay more. Participants who had heard about SDVs before the survey reported higher WTP and higher trust and perceived higher benefits, lower risks, and lower dread. Trust and perceived benefit were positive predictors of WTP and perceived risk and perceived dread were negative predictors of WTP. Our results may offer practical implications for increasing the public’s acceptance and WTP of SDVs.

Subfields: Automotive Engineering, Renewable Energy, Sustainability and the Environment, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/0f994615e5968b3863e8b75325d92f4a48a0df62

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Citations in the corpus

Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries (2020, background): “Furthermore, research on the examination of the willingness to pay for automated driving technology and its determinants has grown in the past few years (Asgari & Jin, 2019; Liu et al., 2019; Tsouros & Polydoropoulou, 2020).”
A multi-level model on automated vehicle acceptance (MAVA): a review-based study (2019, background): “Safety is one of the basic human needs and one of the key drivers influencing AVA (Brell et al. 2019a; Cho et al. 2017; Kaan 2017; Liu et al., 2019; Overakker 2017; Piao et al.”
Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory (2020, background): “However, less attention has been placed on the indirect contributors and the interrelation-ships between the explanatory variables (Liu et al. 2019; Wang and Zhao 2019).”