DECODING BEHAVIORAL INTENTIONS TOWARDS AUTONOMOUS VEHICLES: A META-ANALYSIS AND EMPIRICAL STUDY

This article presents a comprehensive analysis of behavioral intentions towards autonomous vehicles (AVs), addressing a critical gap in understanding AV adoption. Through a systematic review of existing literature, we develop a meta-framework that synthesizes previous findings and identifies key research gaps. Our article reveals distinct patterns of behavioral intention based on personal technology innovativeness. Innovative users are influenced by both utilitarian and hedonic factors, while laggards are primarily motivated by hedonic aspects. Importantly, technological fears, particularly data privacy concerns, significantly impact innovative users' intentions, whereas laggards experience more generalized technological anxiety. The article also explores the relationship between individual intentions and anticipated societal outcomes, such as equal mobility opportunities. Our findings highlight the crucial role of moderating variables in shaping AV adoption patterns and offer valuable insights for researchers and practitioners in the autonomous vehicle industry. This article contributes to the growing body of literature on AV acceptance and provides a foundation for future studies in this rapidly evolving field.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-13
DOI
https://doi.org/10.5281/zenodo.13756918
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

DECODING BEHAVIORAL INTENTIONS TOWARDS AUTONOMOUS VEHICLES: A META-ANALYSIS AND EMPIRICAL STUDY

Zenodo (CERN European Organization for Nuclear Research)
Human-Automation Interaction and Safety
article

DECODING BEHAVIORAL INTENTIONS TOWARDS AUTONOMOUS VEHICLES: A META-ANALYSIS AND EMPIRICAL STUDY

article en

Abstract

This article presents a comprehensive analysis of behavioral intentions towards autonomous vehicles (AVs), addressing a critical gap in understanding AV adoption. Through a systematic review of existing literature, we develop a meta-framework that synthesizes previous findings and identifies key research gaps. Our article reveals distinct patterns of behavioral intention based on personal technology innovativeness. Innovative users are influenced by both utilitarian and hedonic factors, while laggards are primarily motivated by hedonic aspects. Importantly, technological fears, particularly data privacy concerns, significantly impact innovative users' intentions, whereas laggards experience more generalized technological anxiety. The article also explores the relationship between individual intentions and anticipated societal outcomes, such as equal mobility opportunities. Our findings highlight the crucial role of moderating variables in shaping AV adoption patterns and offer valuable insights for researchers and practitioners in the autonomous vehicle industry. This article contributes to the growing body of literature on AV acceptance and provides a foundation for future studies in this rapidly evolving field.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 58%
Human-Automation Interaction and Safety
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DECODING BEHAVIORAL INTENTIONS TOWARDS AUTONOMOUS VEHICLES: A META-ANALYSIS AND EMPIRICAL STUDY · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS