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

Abstract

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 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.