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.

基于物联网（IoT）的智能电表的很大一部分被认为是一种实现能源效率，可持续发展以及提高电源质量，可靠性和效率的潜力的方法。这些结果表明了固有能力对于电力存储，销售和分配产生深远影响的重要性。现有的一些文献综述确定了在隐私，生态效率反馈和技术意识方面采用主要消费者的挑战。假设在没有理论联系的情况下调查了这些因素，这项研究通过利用扩展的技术接受和使用统一理论（UTAUT2）的变量，开发出一种代表用户意图采用智能电表的模型，从而研究了基于IoT的智能电表技术采用的障碍。数据来自马来西亚两个城市的318个智能电表用户，同时使用结构方程模型（SEM）的多分析方法验证了该模型，并将SEM的结果用作神经网络模型的输入以进行预测接受因素。结果，发现技术意识和生态效益反馈是对智能电表技术采用产生积极影响的重要决定因素，而隐私问题则带来了不利影响。总体，这些研究结果为用户，公用事业提供了有用的见解和启示；监管者和决策者。