A Study on the Intention to Use of Smart Home Service Based on AI - Focusing on Fine Dust Detection Solution

This study is verified by empirical analysis to find out the intention to use fine dust detection solutions for AI-based smart home services. Variables were selected by dividing them into system characteristics and product characteristics as independent variables of fine dust detection solutions. Implicity, interoperability, and reliability were used as factors of system characteristics, and safety and measurement accuracy were selected as factors of product characteristics. Using the technology acceptance model and expectation satisfaction theory, perceived ease of use and expectation satisfaction were used as parameters, and intention to use was selected as a dependent variable. A total of 192 people were utilized by distributing a questionnaire to those currently using the fine dust detection solution. As a result of the analysis, it was found that interoperability, reliability, safety, and measurement accuracy had a positive effect on perceived ease of use, and only Implicity, reliability and safety variables had a positive effect on satisfaction of expectations. Finally, it was found that ease of use and satisfaction of expectations had a significant effect on intention.