An Empirical Study on Factors Influencing Continuous Intention to Use of AI-Based Recommendation Services in Digital Platforms

Proceedings of the 7th International Academic Conference on Management and Economics

Year: 2023



An Empirical Study on Factors Influencing Continuous Intention to Use of AI-Based Recommendation Services in Digital Platforms

Sung Yeon Kim, Jin Min Kim




This study investigates the critical factors impacting users’ intention to continuous use of AI-based recommendation services in digital platforms. With collected data from online, this research analyzes the relationships between key variables (degrees of standardization and customization, AI information quality, AI system quality, AI recommendation quality, AI service satisfaction) and the continuous intention to use using structural equation modeling. The findings demonstrated that all the examined factors exert a positive impact on users’ continuous intention to use. Within the scope of this study, service satisfaction and recommendation quality surfaced as the most dominant elements, underlining their considerable relevance to users. In order to enhance users’ continuous intention to use, AI-based recommendation services on big data platforms, this study concludes that it’s vital to improve user satisfaction and recommendation quality. Providers should accurately discern user preferences and behavioral patterns, subsequently developing recommendation algorithms that are customized to these insights. Improving the platform’s user interface for increased ease of use, as well as continual enhancements to the quality of information and degree of customization on the platform are also necessary. Furthermore, striking a balance between standardization and customization is crucial. While standardization promotes consistency and efficiency, excessive standardization may fail to meet individual user needs. Customization caters to individual users, but over-customization may increase service complexity and related cost. Therefore, service providers should seek a balance that satisfies diverse user needs while maintaining service efficiency. In conclusion, this study enhances our understanding of users’ continuous intention to use on AI-Based recommendation services on big data platforms. It also identifies crucial factors to be considered for service improvement, facilitating the sustainable success of AI-based recommendation services.

keywords: AI Service, AI Recommendation, AI service satisfaction, E-Service, Intention to coninuous use