Staff View: Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach

Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach

The increasing popularity of blockchain technology has led to its adoption in various sectors, including higher education. However, the sustainability of blockchain in higher education is yet to be fully understood. Therefore, this research examines the determinants affecting blockchain sustainabili...

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Main Authors:	Alshamsi M., Al-Emran M., Daim T., Al-Sharafi M.A., Bolatan G.I.S., Shaalan K.
Other Authors:	57217736246
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2025
Subjects:	Automotive industry Blockchain Deep learning Driver training Engineering education Neural networks Sensitivity analysis Supply chain management Artificial neural network approach Block-chain Driver Fraud High educations Informatics Sem-ann Structural equation models Sustainable development
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id	my.uniten.dspace-37132
record_format	dspace
spelling	my.uniten.dspace-371322025-03-03T15:47:50Z Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach Alshamsi M. Al-Emran M. Daim T. Al-Sharafi M.A. Bolatan G.I.S. Shaalan K. 57217736246 56593108000 35565192000 57196477711 57430962600 6507669702 Automotive industry Blockchain Deep learning Driver training Engineering education Neural networks Sensitivity analysis Supply chain management Artificial neural network approach Block-chain Deep learning Driver Fraud High educations Informatics Sem-ann Structural equation models Sustainable development The increasing popularity of blockchain technology has led to its adoption in various sectors, including higher education. However, the sustainability of blockchain in higher education is yet to be fully understood. Therefore, this research examines the determinants affecting blockchain sustainability by developing a theoretical model that integrates the protection motivation theory and expectation confirmation model. Based on 374 valid responses collected from university students, the proposed model is evaluated through a deep learning-based hybrid structural equation modeling (SEM) and artificial neural network approach. The partial least squares-SEM results confirmed most of the hypotheses in the proposed model. The sensitivity analysis outcomes discovered that users' satisfaction is the most important factor affecting blockchain sustainability, with 100% normalized importance, followed by perceived usefulness (58.8%), perceived severity (12.1%), and response costs (9.2%). The findings of this research provide valuable insights for higher education institutions and other stakeholders looking to sustain the use of blockchain technology. ? 1988-2012 IEEE. Final 2025-03-03T07:47:49Z 2025-03-03T07:47:49Z 2024 Article 10.1109/TEM.2024.3365041 2-s2.0-85185370780 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185370780&doi=10.1109%2fTEM.2024.3365041&partnerID=40&md5=a3f5dd183b7f1e235870c0fbc9ace1b1 https://irepository.uniten.edu.my/handle/123456789/37132 71 8192 8208 Institute of Electrical and Electronics Engineers Inc. 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/
topic	Automotive industry Blockchain Deep learning Driver training Engineering education Neural networks Sensitivity analysis Supply chain management Artificial neural network approach Block-chain Deep learning Driver Fraud High educations Informatics Sem-ann Structural equation models Sustainable development
spellingShingle	Automotive industry Blockchain Deep learning Driver training Engineering education Neural networks Sensitivity analysis Supply chain management Artificial neural network approach Block-chain Deep learning Driver Fraud High educations Informatics Sem-ann Structural equation models Sustainable development Alshamsi M. Al-Emran M. Daim T. Al-Sharafi M.A. Bolatan G.I.S. Shaalan K. Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach
description	The increasing popularity of blockchain technology has led to its adoption in various sectors, including higher education. However, the sustainability of blockchain in higher education is yet to be fully understood. Therefore, this research examines the determinants affecting blockchain sustainability by developing a theoretical model that integrates the protection motivation theory and expectation confirmation model. Based on 374 valid responses collected from university students, the proposed model is evaluated through a deep learning-based hybrid structural equation modeling (SEM) and artificial neural network approach. The partial least squares-SEM results confirmed most of the hypotheses in the proposed model. The sensitivity analysis outcomes discovered that users' satisfaction is the most important factor affecting blockchain sustainability, with 100% normalized importance, followed by perceived usefulness (58.8%), perceived severity (12.1%), and response costs (9.2%). The findings of this research provide valuable insights for higher education institutions and other stakeholders looking to sustain the use of blockchain technology. ? 1988-2012 IEEE.
author2	57217736246
author_facet	57217736246 Alshamsi M. Al-Emran M. Daim T. Al-Sharafi M.A. Bolatan G.I.S. Shaalan K.
format	Article
author	Alshamsi M. Al-Emran M. Daim T. Al-Sharafi M.A. Bolatan G.I.S. Shaalan K.
author_sort	Alshamsi M.
title	Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach
title_short	Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach
title_full	Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach
title_fullStr	Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach
title_full_unstemmed	Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach
title_sort	uncovering the critical drivers of blockchain sustainability in higher education using a deep learning-based hybrid sem-ann approach
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2025
_version_	1826077595971616768
score	13.244413

Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach

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