Developing a model of smart home usage among it specialists: the role of machine learning

Models of smart home usage dominate in developed countries, while in developing countries, they are still lacking. Technology Acceptance Model (TAM) is widely used in the context of smart home, and few studies examined other technology acceptance theories. The purpose of this study is to examine the experience of using smart home by Information Technology (IT) specialists in the Gulf Cooperation Council (GCC). The study deploys existence theories and proposes that the effect of relative advantage, convenience, accessibility, and cost on the intention to use smart home is positive. In addition, it was suggested that intention to use, as well as facilitating condition, directly affects the actual use of smart home. The knowledge of machine learning was proposed as a moderator between intention to use and actual use. The data were collected from IT specialists in the GCC using purposive sampling. The analysis was conducted using the Analysis of moment structures (AMOS). The findings showed that convenience, accessibility, and relative advantage have a positive effect, while cost has a negative effect on the intention to use smart home. The intention to use and facilitating condition affected positively the actual use. Knowledge in machine learning moderated positively the effect of intention to use on actual use. Decision makers are recommended to enhance the benefits of using the Internet of Things smart home and create a customized plan to enable using smart home at all levels. The knowledge of machine learning is critical for smart home usage, and customized courses in this regard are critical to boost the usage of smart home.