AI Recommendation System Attributes: Impact Mechanisms on User Satisfaction and Usage Intentions in the China Market

다국어 초록 (Multilingual Abstract)

With the rapid development of artificial intelligence (AI) technology, not only existing service fields but also general studies often focus on specific contexts, but systematically studies on the core attributes of artificial intelligence recommendation systems are insufficient. Therefore, in order to supplement the limitations of previous studies and verify how the attributes of the AI recommendation system actually affect use, this study systematically studied the effects of three key attributes obtained through previous studies on user satisfaction and intention to use in a crossover service environment. Data were collected from a total of 310 survey subjects using the survey method, five key variables were measured, and the hypothesis and path relationship were verified through structural equations. As a result of the study, it was verified that the recommendation accuracy, interactivity, and personification of the AI recommendation system all significantly improved the user's satisfaction, the user's satisfaction had a greater influence on the intention to use, and the satisfaction level had an important mediating effect in the middle process. This study provides actionable insights for companies to optimize AI recommendation systems, provides theoretical developments in AI adoption research, and highlights policy considerations for ethical AI development. In other words, focusing on the role of everyday application of core attributes of AI recommendation systems in certain situations, the availability of various fields of these core attributes in fields such as service, medical care, education, and marketing was presented.