Impact of Sentimental and Contextual Factors on the Acceptance of Music Recommender Systems

음악추천시스템의 수용성에 개인감정과 상황이 미치는 영향

  • 박경수 (호서대학교 벤처전문대학원 IT응용기술학과) ;
  • 문남미 (호서대학교 벤처전문대학원 IT응용기술학과)
  • Received : 2011.03.24
  • Accepted : 2011.04.27
  • Published : 2011.05.28


A recommender system is a personalized decision support tool to suggest suitable products in proper manners for the benefits of both suppliers and consumers, with the assumption of full understating of consumers' needs and preferences. However, a substantial number of studies have focused on making recommender systems more accurate and efficient. Whereas, there have been a few studies on consumers' needs and preferences under their own contexts to accept recommender systems. To this end, this study attempted to find out the impact of personal sentiments and contexts on the willingness to accept music recommender systems based on the simplified "Technology Acceptance Model" and some verified variables from the precedent studies. For the study, we conducted an empirical study using surveys and High-Order Structural Equation Model (SEM). The outcomes of the research was affirmative to the research hypothesis that the personal sentiments and contexts positively affect the acceptance of the music recommender systems.


Recommender Systems;User Acceptance;Technology Acceptance Model;Personal Sentiments;Personal Contexts


  1. H. Y. Lee, H. Ahn, and I. Han, "VCR : Virtual Community recommender using the technology acceptance model and the user's needs type," Expert Systems with Applications 33, pp.984-995, 2007.
  2. M. Balabanovic and B. Ribeiro-Neto, "Content based, collaborative recommendation," Modern Information Retrivial, Addison-Wesley, pp.325-341, 1999.
  3. G. Adomavicius, R. Sanharanarayanan, S. Sen, and A. Tuzhilin, "Incorporating Context information in recommender systems using a multi-dimensional approach," ACM Transactions on Information Systems, Vo1.23, Issue1, pp.103-145, 2005.
  4. W. Woerndl and G. Groh, "Utilizing Physical and Social Context to Improve Recommender Systems," Web Intelligence and Intelligent Agent Technology Workshops, IEEE/WIC/ACM International Conferences, pp.123-128, 2007.
  5. N. Jones and P. Pu, "User Technology Adoption Issues in Recommender Systems," In Proc. of NAEC'07, pp.379-394. 2007.
  6. R. Hu, and P. Pu, "A Comparative User Study on Rating vs. Personality Quiz based Preference Elicitation Methods," ITU'09 Proceedings of the 13th international conference on intelligent user interfaces, pp.367-372, 2009.
  7. P. Pu and L. Chen, "A user-Centric Evaluation Framework of Recommender Systems," Proceedings of the ACM RecSys 2010 Workshop, pp.366-369, 2010.
  8. T Adomavicius, G. and Tuzhilin, A. "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," Knowledge and Data Engineering, IEEE Transactions on Vol.17, Issue6, 2005.
  9. Hernandez del Olmo, F. and Gaudioso, E., "Evaluation of recommender systems: A new approach," Expert Systems with Applications, 2008.
  10. Z. Zaier, R. Godin, and L. Faucher, "Evaluating Recommender Systems," International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution, 2008.
  11. 유재현, 박철, "기술수용모델(Technology Acceptance Model) 연구에 대한 종합적 고찰," Entrue Journal of Information Technoogy, Vol.9, No2, pp.31-50, 2010(7).
  12. F. Davis, "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," MIS Quarterly, pp.319-340, 1989(9).
  13. M. Igbaria and J. Iivari, "The Effects of Self-efficacy on Computer Usage," International ournal of Management Science, Vol.23, No.6, pp.587-605, 1995.
  14. R. Agarwal and J. Prasad, "Are Individual Differences germane to the acceptance of new information technologies?," Decision Science, Vol.30, No.2, pp.361-391, 1999.
  15. E. Karahan and D. Straub, "The Psychological origins of perceived usefulness and ease of use," Information & Management, Vol35, No.4, pp.110-124, 1999.
  16. D. Straub, M. Limayem, and E. Karahanna, "Measuring system usage: implications for IS theory testing," Management Science, Vol.41, pp.1328-1342 1995.
  17. D. Gefen and D. Straub, "Gender difference in perception and adoption of e-mail: an extension to the technology acceptance model," MIS Quarterly, Vol.21, pp.389-400, 1997.
  18. J. Moon and Y. Kim, "Extending the TAM for a World-Wide context," Information & Management, Vol.38, No.4, pp.217-230, 2001.
  19. Y. Lee, K. Koazr, and K. Larsen, "The Technology Acceptance Model: Past, Present, and Future," Communications of the Association for Information Systems, Vol.12, pp.752-780, 2003.
  20. R. Hu and P. Pu, "Acceptance issues of personality based recommender systems," '09 Proceedings of he third ACM conference on Recommender systems, pp.221-224, 2009.
  21. R. Hu and P. Pu, "A Study on User Perception of Personality-Based Recommender Systems," User Modeling, Adaptation, and Personalization 18th International Conference, UMAP2010, pp.292-302, 2010.
  22. L. Chen and P. Pu, "User Evaluation Framework of Recommender Systems," Workshop on Social Recommender Systems, 2010.
  23. C. S. Hwang, Y. C. Su, and K. C. Tseng, "E-Commerce Recommender Application in Taiwan," New Trends in Information, pp.565-568, 2010(5).
  24. P. J. Rentfrow and S. D. Gosling, "The do re mi's of everyday life : The Structure and Personality Correlates of Music Preferences," Journal of Personality and Social Psychology Vol.84, pp.1236-1256, 2003.
  25. D. Compeau and C. Higgins, "Computer Self-Efficacy: Development of a Measure and Initial test," MIS Quarterly, Vol.23, No.2, pp.145-158, 1995.
  26. R. Agrwal and E. Karahanna, "Time Files When You're Having Fun: Cognitive Abosption and Beliefs about Information Technology," MIS Quaterly, Vol.24, No.4, pp.665-694, 2000.
  27. V. Venkatesch, "Determinants of Perceived Ease of Use: Integrtating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, Vol.11, No.4, pp.342-365, 2000.
  28. Y. S. Wang, H. Y. Wang, and D. Y. Shee, "Measuring e-learning systems success in an organizational context: Scale development and validation," Computer in Human Behavior, IN Press, Corrected Proof, Available on line 5, 2000.
  29. Hsu and H. P. Lu, "Why do people play on-line games? An extended TAM with social influences and flow experience," Information & Management 41, pp.853-868, 2004.
  30. F. D. Davis, R. P. Bagozzi, and R. R. Warshaw, "Extrinsic and Intrinsic Motivation to use Computers in the Workplace," Journal of Applied Social Psychology, Vol22, No.14, pp.1111-1132, 1992.
  31. P. Y. K. Chau, "An Empirical Investigation on Factors Affecting the Acceptance of CASE by Systems Developers," Infromation and Management, Vol.30, No.6, pp.269-280, 1996.
  32. J. H. Wu, Y. C. Chen, and L. M. Lin, "Empirical evaluation of the revised end user computing acceptance model," Computers in Human Behavior Vol.23, pp.162-174, 2007.
  33. Y. Malhotra and D. F. Galletta, "Extending the Technology Acceptance Model to Account for Social Influence Theoretical Bases and Empirical Validation," Proceedings of the 32nd Hawaii International Conference on System Science, 1999.
  34. P. Y. K. Chau and P. J. H. Hu, "Information Technology Acceptance by Individual Professionals: A Exploratory Study," Journal of Management Infromation Systems, Vol.18, No.4, pp.191-229, 2002.
  35. V. Venkatesh, M. G. Morris, G. D. Davis, and F. Davis, "User acceptance of information technology: Toward a unified view," MIS Quarterly, Vol.27, No.3, pp.425-478, 2003.
  36. E. Smirni, and G. Ciardo, "Workload-Aware Load Balancing for Cluster Web Servers," IEEE Trans. on Parallel and Distributed Systems, Vol.16, No.3, pp.219-232, 2005(3).
  37. J. Hair, W. C. Black, B. L. Babin, R. E. Anderson, and R. L. Tatham, Multivariate Data Analysis, 6th ed. Pearson-Prentice Hall, 2006.
  38. C. B. Javi, S. B. MacKenzie, and P. M. Podsakoff, "A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, pp.199-218, 2003.
  39. 장정무, 김종욱, 김태웅, "무선인터넷서비스 수용의 영향요인 분석: 플로우이론을 가민한 기술수용모델의 확장", 경영정보학연구, 제14권, 제3호, pp.93-120, 2004.
  40. E. Carmines and J. Mclver, "Analysing Models with Unobserved Variables: Analysis of Covariance Structures, In G. Bohrnstedt and E. Borgatta(eds)," Social Management: Current Issues, Beverly Hills, Calif.: Sege, 1981.
  41. 이학식, 임지훈, 구조방정식모형분석과 AMOS 16.0, 법문사, pp.81-85, 2011.
  42. 김석우, 사회과학을 위한 SPSS Win 12.0활용의 실제, 교육과학사, p.243, 2007.
  43. 이학식, 마케팅조사, 제2판, 법문사, p.188, 2005.