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User acceptance of autonomous vehicles: a mixed-methods study

Lookup NU author(s): Dr Davit MarikyanORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Purpose: This research aims to provide a comprehensive understanding of the factors influencing individuals' adoption of autonomous vehicles (AVs) and shed light on further development of technology acceptance theories in the backdrop of radical technological and societal changes. Design/methodology/approach: The study adopts a mixed-methods approach, combining a meta-analysis of 107 empirical studies and 20 in-depth interviews. The meta-analysis examines the predictive power of technology acceptance models, while the interviews offer qualitative insights to elaborate on and deepen the quantitative findings. The integration of these two methods allows for the development of meta-inferences and a more holistic view of AV adoption. Findings: The meta-analysis revealed that while traditional technology acceptance factors such as perceived usefulness and trust remain significant, the effects of perceived ease of use and perceived behavioral control are weak. New factors such as perceived loss of autonomy, loss of personal control,and informational social influence emerged as critical influences on AV adoption. The qualitative findings further elaborate on these factors, highlighting the need for updated conceptualizations. Originality: This research offers a novel contribution by integrating meta-analytic and qualitative findings to reassess the applicability of existing technology acceptance theories for AVs. It introduces new constructs, such as perceived loss of autonomy and informational social influence and emphasizes the need to update and refine traditional constructs like perceived usefulness and trust to reflect the unique features of AV technologies.


Publication metadata

Author(s): Chen X, Slade E, Wang X, Marikyan D

Publication type: Article

Publication status: Published

Journal: Industrial Management & Data Systems

Year: 2025

Pages: Epub ahead of print

Online publication date: 31/03/2025

Acceptance date: 07/03/2025

Date deposited: 12/03/2025

ISSN (print): 0263-5577

ISSN (electronic): 1758-5783

Publisher: Emerald Publishing Limited

URL: https://doi.org/10.1108/imds-11-2024-1097

DOI: 10.1108/IMDS-11-2024-1097

ePrints DOI: 10.57711/yeh6-c954


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