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Citation |
Xiao J, Goulias KG. Transp. Res. A Policy Pract. 2022; 161: 170-185. |
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Copyright |
(Copyright © 2022, Elsevier Publishing) |
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DOI |
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PMID |
unavailable |
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Abstract |
Understanding the mental process of public acceptance of autonomous vehicles (AVs) is important to the prediction and change of adoption behavior. We present a conceptual model to incorporate background factors such as demographic variables and travel behaviors attributes to the understanding of AV perceived usefulness and intention to adopt AVs. Using data from the 2019 California Vehicle Survey (CVS), we investigate the relationships between observed and latent variables with regard to AV acceptance via structural equation modeling (SEM) techniques. The results show that perceived usefulness is an important determinant of behavioral intention. Householders who are young, well-educated, and males perceive higher usefulness of AVs than other population segments. Households that have telecommuters, transit riders, transportation network company (TNC; e.g., Uber & Lyft) riders, and electric vehicles (EVs) owners, and households that own or plan to install photovoltaic cell (solar) panels also anticipate high benefits of AVs. Living or working at places with access to infrastructure such as EV charging stations and hydrogen fueling stations also add to positive perception of AVs' advantages. Controlling for perceived usefulness, households having higher annual income and EVs express a stronger interest in buying an AV but not in ridesharing. Young educated households with more TNC riders show a greater propensity to AV sharing services but not for owning AVs. The proposed conceptual model can help pinpoint how background factors such as socioeconomic status affects behavioral intention via its antecedent cognitive construct more accurately to represent the mental process of intention formation. The practical discoveries can assist policymakers identifying population segments that will be the first adopters of this technology. Language: en |
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Keywords |
Autonomous vehicles; Behavioral intention; Perceived usefulness; Structural equation modeling; Technology acceptance |