Public Acceptance of Driverless Buses in China: An Empirical Analysis Based on an Extended UTAUT Model

Jian Chen, Rui Li, Mi Gan, Zhiyan Fu and Fatao Yuan

Discrete Dynamics in Nature and Society, 2020, vol. 2020, 1-13

Abstract:

Driverless buses are expected to play a vital role in the future, and better public acceptance will provide a social foundation for its development. In this study, two new variables, personal innovativeness (PI) and perceived risk (PR), were incorporated into the integrated technology acceptance model (UTAUT, unified theory of acceptance and use of technology) to construct an extended model, which was then applied to explore the influencing factors for the public acceptance of driverless buses. The quality of this extended model was verified through survey data collected in Chongqing, China. The structural equation modeling (SEM) method was adopted to quantitatively describe the impact of each factor on acceptance intention (AI) as well as the mutual influence relationships between the factors. The moderating effects of demographic attributes (gender, age, and education level) on each factor in the model were also analyzed. The results showed that PI and PR are the most critical factors that affect the public’s acceptance intention; effort expectancy (EE), performance expectancy (PE), social influence (SI), and facilitating condition (FC) can also determine the acceptance intention to a certain extent; gender, age, and education level have exhibited significantly different moderating effects on the influencing factors. The explanatory power of the current research model for acceptance intention has reached 48%. This study has confirmed the applicability of the extended UTAUT model to the research of driverless bus acceptance and the research outcomes can serve as a reference basis for improving the service quality of driverless buses in China.

Date: 2020
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:4318182

DOI: 10.1155/2020/4318182

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