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

Jian Chen,Fatao Yuan,Mi Gan,Zhiyan Fu,Rui Li,Tingsong Wang

doi:10.1155/2020/4318182

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.

Highlights

Autonomous or self-driving technology has received unprecedented attention since its inception
As reflected by the measurement variables, the construct has a good consistency, and the reliability is at the “very good” level. e factor loading coefficients of all measurement variables are greater than 0.7, and the average variance extracted (AVE) values are greater than 0.5, indicating that the measurement variables have a high degree of internal correlation, which can effectively reflect the latent traits of the latent variables
The proportion of variance explained by the latent variable is much higher than that explained by the measurement error, indicating that the measurement model has good convergent validity. e square root of AVE value of each latent variable is all greater than the Pearson correlation coefficient (PCC) value between this latent variable and the other ones, indicating that there are significant differences between the constructs of different latent variables, and the model has good discriminant validity

Summary

Introduction

Autonomous or self-driving technology has received unprecedented attention since its inception. The urban public transportation field—where the operating lines are relatively fixed, and the road traffic conditions are relatively regular—may be given priority for applying autonomous driving technology. Due to the comprehensiveness and complexity of transportation systems, there are still many constraints for officially launching large-scale operations of driverless public transportation to the public Such constraints are mainly manifested in terms of software technology, regulatory mechanisms, and public acceptance. A series of accidents—such as the world’s first AV-related fatal accident during a highway collision of a Tesla Model S in 2016, the collision between a Navya driverless bus and a truck in 2017, and the death of a pedestrian caused by one of the Uber’s driverless vehicles in 2018—have triggered the public to question the technology readiness level (TRL) of autonomous driving and the regulatory system

Objectives

Methods

Results

Conclusion