UTAUT4-AV: An extension of the UTAUT model to study intention to use automated shuttles and the societal acceptance of different types of automated vehicles

Bellet, Thierry; Banet, Aurélie · 2023 · Crossref

DOI: 10.1016/j.trf.2023.10.007

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Summary

This study addresses the need for a robust model to predict and explain societal acceptance of Automated Shuttles (AS) and other automated vehicles (AV). Motivated by the limitations of existing technology acceptance models, which often overlook users' satisfaction with their current transportation methods, the authors developed UTAUT4-AV. This extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) aims to identify key factors influencing the intention to use AS, particularly within the context of the French ENA research program. The researchers conducted an online survey among a representative sample of 2,612 French citizens. The methodology utilized a comprehensive questionnaire comprising 166 items, including socio-demographic data, individual profiling factors (such as attitudes toward innovation and familiarity with technology), and the core UTAUT4-AV constructs. Responses were collected using 0–100 continuous visual analogue scales to enhance statistical precision and user engagement. The UTAUT4-AV model integrates ten specific constructs: Perceived Usefulness, Performance Expectancy compared to the Current Mean of Transport (CMoT), Effort Expectancy, Social Influence, Hedonic Motivations, Perceived Safety, Anxiety, Price Value of AS, Price Value of CMoT, and Satisfaction with CMoT. Additionally, the model accounts for individual factors such as age, gender, socio-professional category, residence size, current transport mode, and personality traits. The results demonstrate that the UTAUT4-AV model explains 89% of the variance in the intention to use automated shuttles, representing a 20–30% increase in explanatory power compared to existing models. This improved performance is attributed to the inclusion of constructs related to the user's current transport mode, specifically satisfaction with the CMoT and the comparative performance of AS against it. Furthermore, individual factors like attitudes toward new technologies significantly contributed to the model's predictive accuracy. The study also tested the model's robustness across three other AV types: Automated Cars, Robotaxis, and Autonomous Air Mobility Vehicles. The model successfully explained between 90% and 92% of the variance in the intention to use these alternative vehicles, indicating strong generalizability across different automated mobility solutions. The significance of this research lies in providing a highly effective framework for understanding public acceptance of automated transportation. By highlighting the critical role of current transport satisfaction and comparative performance, the study offers actionable insights for policymakers and developers aiming to deploy automated shuttles. The findings suggest that successful adoption depends not only on the perceived benefits of the new technology but also on the dissatisfaction or limitations of existing mobility options. While the model proves robust across various AV types, the authors note that further studies are required to validate its applicability across different countries and cultural contexts.

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• acceptance adoption

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• Empirical Findings: self report data