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A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective

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dc.citedby28
dc.contributor.authorAlkawsi G.A.en_US
dc.contributor.authorAli N.en_US
dc.contributor.authorMustafa A.S.en_US
dc.contributor.authorBaashar Y.en_US
dc.contributor.authorAlhussian H.en_US
dc.contributor.authorAlkahtani A.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorEkanayake J.en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid54985243500en_US
dc.contributor.authorid57218103026en_US
dc.contributor.authorid56768090200en_US
dc.contributor.authorid55430817100en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid7003409510en_US
dc.date.accessioned2023-05-29T09:09:19Z
dc.date.available2023-05-29T09:09:19Z
dc.date.issued2021
dc.descriptionDigital storage; Electric power systems; Energy efficiency; Internet of things; Smart meters; Analytic approach; Electrical power supply; Internet of thing (IOT); Literature reviews; Neural network method; Neural network model; Structural equation modelling (SEM); Unified theory of acceptance and use of technology; Neural networksen_US
dc.description.abstractA large part of the Internet of Things (IoT)-based smart meters is considered a method to achieve energy efficiency, sustainable development, and the potential of improving the quality, reliability, and efficiency of power supply. These outcomes indicate the importance of the inherent capacity for profound implications on storage, sale, and distribution of electrical power supply. A few of the existing literature review identified the challenges of primary consumer adoption in terms of privacy, eco-efficient feedback, and technology awareness. Provided that these factors were investigated without theoretical association, this study examined the barriers to the adoption of IoT-based smart meters technology by developing a model representing the users� intention to adopt smart meters by drawing on the variables of the extended Unified Theory of Acceptance And Use of Technology (UTAUT2). Data were collected from 318 users of smart meter from two cities in Malaysia, while the model was validated using a multi-analytic approach using Structural Equation Modelling (SEM), and the results from SEM were used as inputs for a neural network model to predict acceptance factors. As a result, it was found that technology awareness and eco-effective feedback were the important determinants with a positive impact on the adoption of smart meter technology, while privacy concerns led to an adverse impact. Overall, these study findings contribute useful insights and implications for users, utilities; regulators, and policymakers. � 2020 Faculty of Engineering, Alexandria Universityen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2020.07.002
dc.identifier.epage240
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85087868882
dc.identifier.spage227
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85087868882&doi=10.1016%2fj.aej.2020.07.002&partnerID=40&md5=b986a786f363a6ef5975687942b57d40
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26342
dc.identifier.volume60
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.titleA hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspectiveen_US
dc.typeArticleen_US
dspace.entity.typePublication
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