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

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dc.citedby4
dc.contributor.authorAlshamsi M.en_US
dc.contributor.authorAl-Emran M.en_US
dc.contributor.authorDaim T.en_US
dc.contributor.authorAl-Sharafi M.A.en_US
dc.contributor.authorBolatan G.I.S.en_US
dc.contributor.authorShaalan K.en_US
dc.contributor.authorid57217736246en_US
dc.contributor.authorid56593108000en_US
dc.contributor.authorid35565192000en_US
dc.contributor.authorid57196477711en_US
dc.contributor.authorid57430962600en_US
dc.contributor.authorid6507669702en_US
dc.date.accessioned2025-03-03T07:47:49Z
dc.date.available2025-03-03T07:47:49Z
dc.date.issued2024
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TEM.2024.3365041
dc.identifier.epage8208
dc.identifier.scopus2-s2.0-85185370780
dc.identifier.spage8192
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185370780&doi=10.1109%2fTEM.2024.3365041&partnerID=40&md5=a3f5dd183b7f1e235870c0fbc9ace1b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37132
dc.identifier.volume71
dc.pagecount16
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Engineering Management
dc.subjectAutomotive industry
dc.subjectBlockchain
dc.subjectDeep learning
dc.subjectDriver training
dc.subjectEngineering education
dc.subjectNeural networks
dc.subjectSensitivity analysis
dc.subjectSupply chain management
dc.subjectArtificial neural network approach
dc.subjectBlock-chain
dc.subjectDeep learning
dc.subjectDriver
dc.subjectFraud
dc.subjectHigh educations
dc.subjectInformatics
dc.subjectSem-ann
dc.subjectStructural equation models
dc.subjectSustainable development
dc.titleUncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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