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Exploring the Factors Affecting Telemedicine Adoption by Integrating UTAUT2 and IS Success Model: A Hybrid SEM-ANN Approach

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dc.citedby20
dc.contributor.authorThabet Z.en_US
dc.contributor.authorAlbashtawi S.en_US
dc.contributor.authorAnsari H.en_US
dc.contributor.authorAl-Emran M.en_US
dc.contributor.authorAl-Sharafi M.A.en_US
dc.contributor.authorAlqudah A.A.en_US
dc.contributor.authorid57848419100en_US
dc.contributor.authorid58298730300en_US
dc.contributor.authorid58298356600en_US
dc.contributor.authorid56593108000en_US
dc.contributor.authorid57196477711en_US
dc.contributor.authorid57222525211en_US
dc.date.accessioned2025-03-03T07:48:28Z
dc.date.available2025-03-03T07:48:28Z
dc.date.issued2024
dc.description.abstractTelemedicine adoption has steadily grown due to its ability to provide accessible and cost-effective healthcare services. However, individuals' adoption rate still faces challenges, necessitating a comprehensive understanding of the factors influencing their adoption. This article explores the factors affecting telemedicine adoption by integrating Unified Theory of Acceptance and Use of Technology, the Information System success model, and perceived security. The integrated model is evaluated using a hybrid structural equation modeling-artificial neural network (ANN) technique based on data collected from 152 individuals. The results showed that performance expectancy, hedonic motivation, perceived security, and user satisfaction significantly drive telemedicine adoption. Additionally, user satisfaction is affected substantially by information quality, system quality, and service quality. However, effort expectancy, social influence, and facilitating conditions do not significantly impact telemedicine adoption. The ANN findings revealed that user satisfaction is the most important driver for telemedicine adoption, with a normalized importance of 100%. This article contributes to telemedicine literature by providing a comprehensive framework that combines two well-established theories, offering insights into the multifaceted factors affecting telemedicine adoption. The findings also provide practical implications for decision-makers, policymakers, telemedicine service providers, software companies, and developers, emphasizing the importance of addressing the identified factors to promote widespread telemedicine adoption and ensure its long-term success. ? 1988-2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TEM.2023.3296132
dc.identifier.epage8950
dc.identifier.scopus2-s2.0-85165866686
dc.identifier.spage8938
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85165866686&doi=10.1109%2fTEM.2023.3296132&partnerID=40&md5=9a78ce06c15fcbe3746c5f73e5372115
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37191
dc.identifier.volume71
dc.pagecount12
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Engineering Management
dc.subjectBehavioral research
dc.subjectCost effectiveness
dc.subjectDecision making
dc.subjectDigital storage
dc.subjectInformation systems
dc.subjectInformation use
dc.subjectNetwork security
dc.subjectReliability theory
dc.subjectTelemedicine
dc.subjectArtificial neural network
dc.subjectBehavioral science
dc.subjectInformation systems success models
dc.subjectMedical services
dc.subjectPLS?structural equation modeling
dc.subjectPrivacy
dc.subjectSecurity
dc.subjectStructural equation models
dc.subjectTelemedicine adoption
dc.subjectThe unified theory of acceptance and use of technology(UTAUT)
dc.subjectUnified theory of acceptance and use of technology (UTAUT2)
dc.subjectNeural networks
dc.titleExploring the Factors Affecting Telemedicine Adoption by Integrating UTAUT2 and IS Success Model: A Hybrid SEM-ANN Approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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