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Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach

With their cost-effective performance, potential to encourage environmentally friendly behavior, and increased sustainability, autonomous vehicles (AVs) are expected to lead to significant changes in the economy, society, and the environment. This study investigates factors predicting green behavior...

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Main Authors: Arpaci I., Al-Sharafi M.A., Mahmoud M.A.
Other Authors: 35728204400
Format: Article
Published: Elsevier Ltd 2025
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spelling my.uniten.dspace-361422025-03-03T15:41:26Z Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach Arpaci I. Al-Sharafi M.A. Mahmoud M.A. 35728204400 57196477711 55247787300 With their cost-effective performance, potential to encourage environmentally friendly behavior, and increased sustainability, autonomous vehicles (AVs) are expected to lead to significant changes in the economy, society, and the environment. This study investigates factors predicting green behavior and environmental sustainability in AVs. The study developed a research model based on the ?Innovation Resistance Theory? (IRT). The proposed model was evaluated with data obtained from 1266 participants through a deep learning-based ?artificial neural network? (ANN) and the ?partial least squares structural equation modeling? (PLS-SEM) approach. The findings indicated a positive relationship between green behavior and environmental sustainability with AVs. A positive relationship is also found between green behavior and motivators, including environmental benefits, environmental concerns, economic benefits, and technophilia. In contrast, cost barriers, along with security and privacy concerns, negatively predict green behavior. The sensitivity analysis using the ANN approach revealed that economic benefits were the most crucial factor in predicting green behavior. These results offer important insights into understanding the key barriers and drivers predicting the acceptance of AVs. The findings contribute to stakeholders making informed decisions, developing effective strategies, and contributing to AVs' sustainable and successful integration into social life. ? 2024 Elsevier Ltd Final 2025-03-03T07:41:26Z 2025-03-03T07:41:26Z 2024 Article 10.1016/j.rtbm.2024.101228 2-s2.0-85206897088 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206897088&doi=10.1016%2fj.rtbm.2024.101228&partnerID=40&md5=91b0588ea36283465069dd0f994a934b https://irepository.uniten.edu.my/handle/123456789/36142 57 101228 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description With their cost-effective performance, potential to encourage environmentally friendly behavior, and increased sustainability, autonomous vehicles (AVs) are expected to lead to significant changes in the economy, society, and the environment. This study investigates factors predicting green behavior and environmental sustainability in AVs. The study developed a research model based on the ?Innovation Resistance Theory? (IRT). The proposed model was evaluated with data obtained from 1266 participants through a deep learning-based ?artificial neural network? (ANN) and the ?partial least squares structural equation modeling? (PLS-SEM) approach. The findings indicated a positive relationship between green behavior and environmental sustainability with AVs. A positive relationship is also found between green behavior and motivators, including environmental benefits, environmental concerns, economic benefits, and technophilia. In contrast, cost barriers, along with security and privacy concerns, negatively predict green behavior. The sensitivity analysis using the ANN approach revealed that economic benefits were the most crucial factor in predicting green behavior. These results offer important insights into understanding the key barriers and drivers predicting the acceptance of AVs. The findings contribute to stakeholders making informed decisions, developing effective strategies, and contributing to AVs' sustainable and successful integration into social life. ? 2024 Elsevier Ltd
author2 35728204400
author_facet 35728204400
Arpaci I.
Al-Sharafi M.A.
Mahmoud M.A.
format Article
author Arpaci I.
Al-Sharafi M.A.
Mahmoud M.A.
spellingShingle Arpaci I.
Al-Sharafi M.A.
Mahmoud M.A.
Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach
author_sort Arpaci I.
title Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach
title_short Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach
title_full Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach
title_fullStr Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach
title_full_unstemmed Factors predicting green behavior and environmental sustainability in autonomous vehicles: A deep learning-based ANN and PLS-SEM approach
title_sort factors predicting green behavior and environmental sustainability in autonomous vehicles: a deep learning-based ann and pls-sem approach
publisher Elsevier Ltd
publishDate 2025
_version_ 1825816163808968704
score 13.244413

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