User acceptance of autonomous vehicles: a mixed-methods study

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/value 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.

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Publication Details

Journal
Industrial Management & Data Systems
Published
2025-03-28
DOI
https://doi.org/10.1108/imds-11-2024-1097
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

User acceptance of autonomous vehicles: a mixed-methods study

Davit Marikyan, Xiaojun Wang, Emma Slade, Xiaofan Chen
Industrial Management & Data Systems
Human-Automation Interaction and Safety
article

User acceptance of autonomous vehicles: a mixed-methods study

Davit Marikyan, Xiaojun Wang, Emma Slade, Xiaofan Chen
article en

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/value 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.

Industrial Management & Data SystemsVol. 125(6)
Fudan University (CN), University of Bristol (GB), Xi’an Jiaotong-Liverpool University (CN), University of Birmingham (GB), Newcastle University (GB)
Openalex Percentile: Top 93%
Human-Automation Interaction and Safety
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