원문정보
초록
영어
The importance of recommendation algorithms is underscored by the advancement of AI technology and the growing demand for personalized services. However, there is a lack of empirical studies that analyze differences in algorithmic services across markets. The objective of this study is to draw attention to discrepancy in this field and develop a dual-path model to examine the factors that influence satisfaction with recommendations. By analyzing 641 respondents’ data through Partial Least Squares (PLS), it identifies differences in user attitudes towards algorithms across domains. e-commerce firms and content providers can improve personalized recommendations through the research, which highlights the importance of understanding consumer satisfaction and trust in technology to adapt to evolving AI innovations.
목차
Ⅰ. Introduction
Ⅱ. Theoretical Background
2.1. AI-based Algorithms for RecommendationSystems
2.2. Recommendation Systems in Different Domains
2.3. Different Attitudes Toward Algorithms
Ⅲ. Research Model and Hypotheses
3.1. Research Framework
3.2. Satisfier and Dissatisfier
3.3. Influence on Satisfaction
Ⅳ. Empirical Research
4.1. Research Procedure
4.2. Instrument Item
4.3. Reliability and Validity
4.4. Hypothesis Test
Ⅴ. Conclusion
5.1. Implications
5.2. Limitations and Future Research Directions
Acknowledgements