TY  - JOUR
IS  - 1
SP  - 227
N1  - cited By 38
Y1  - 2021///
A1  - Alkawsi, G.A.
A1  - Ali, N.
A1  - Mustafa, A.S.
A1  - Baashar, Y.
A1  - Alhussian, H.
A1  - Alkahtani, A.
A1  - Tiong, S.K.
A1  - Ekanayake, J.
ID  - scholars15261
TI  - A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective
SN  - 11100168
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087868882&doi=10.1016%2fj.aej.2020.07.002&partnerID=40&md5=b986a786f363a6ef5975687942b57d40
PB  - Elsevier B.V.
KW  - Digital storage; Electric power systems; Energy efficiency; Internet of things; Smart meters
KW  -  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
KW  -  Neural networks
EP  - 240
AV  - none
JF  - Alexandria Engineering Journal
VL  - 60
N2  - A 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 usersâ?? 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 University
ER  -