Influence of Psychological and Socioeconomic Factors on Purchase Likelihood for Autonomous Vehicles: A Hybrid Choice Modeling Approach

Yunyi Liang, Jinjun Tang, Zhizhou Wu and Mei Jia ()
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Yunyi Liang: School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Jinjun Tang: School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Zhizhou Wu: College of Transportation Engineering, Tongji University, Shanghai 201804, China
Mei Jia: School of Business, Xinjiang University, Urumqi 830091, China

Sustainability, 2023, vol. 15, issue 21, 1-21

Abstract: This study looks into how psychological and socioeconomic factors interact to affect people’s propensity to purchase autonomous vehicles (AVs). Inspired by the Technology Acceptance Model, six psychological variables—social influence, convenience, perceived utility, perceived ease of use, perceived risk, and usage attitude—are proposed. Twenty-two measurement variables are introduced because it is difficult to measure these latent factors directly. To understand the link between the latent variables and calculate their factor scores, a structural equation model is created. The latent variables, along with observable socioeconomic attributes, are included as explanatory variables in a mixed logit model to estimate the purchase likelihood for AVs on different levels. A stated preference survey is conducted for data collection. We obtained 302 effective samples. The experiment results demonstrate that perceived usefulness has the most significant positive impact on purchase likelihood, followed by social influence and perceived ease of use. However, perceived risk has a significant negative impact on the purchase likelihood. Individuals with less driving experience and those without a motor vehicle driving license are more inclined to adopt autonomous vehicles. Additionally, there is a substantial correlation between the frequency of car use and the propensity to support the deployment of autonomous vehicles.

Keywords: hybrid choice model; purchase likelihood; autonomous vehicles; latent variables (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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