Earticle

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

ABSTRACT
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

키워드

저자

• Won-jun Lee [ Professor, Department of Business Administration, Cheongju University, Korea ]
• Seongtae Hong [ Professor, College of Business and Economics, Sangmyung University, Korea ]
• Il Im [ Professor, School of Business, Yonsei University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658