The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
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Influences of Transparency and Feedback on Customer Intention to Reuse Online Recommender Systems

온라인 추천시스템에서 고객 사용의도를 위한 시스템 투명성과 피드백의 영향

  • Hebrado, Januel L. (College of Business Administration, Adamson University) ;
  • Lee, Hong Joo (Department of Business Administration, Catholic University of Korea) ;
  • Choi, Jaewon (Graduate School of Information, Yonsei University)
  • Received : 2013.03.22
  • Accepted : 2013.05.24
  • Published : 2013.05.31
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Abstract

The problem of choosing the right product that will best fit a consumer's taste and preferences extends to the field of electronic commerce. However, e-commerce has been able to create a technological proxy for the social filtering process, known as online recommender systems (RSs). RSs aid users in filtering products and decisions on matters relating to personal taste. RSs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products and services online. However, most previous research on RSs has focused on the accuracy of the algorithms, with little emphasis on user interface and perspectives. This study identified transparency and feedback as possible ways to effectively evaluate RSs from the user's perspective. Thus, this research focused on examining and identifying the roles of transparency and feedback in recommender systems and how they affect users' attitudes toward the system. Results of the study showed that both transparency and feedback positively and significantly affected perceived trust, perceived value of the process, and perceived enjoyment. Furthermore, we found that perceived trust, perceived value of the process, and perceived enjoyment positively and directly affected users' intentions to use/reuse a recommender system.

고객 취향에 가장 적합한 제품을 선택하는 것은 전자상거래에서 중요한 문제이다. 전자상거래 그러나 온라인 추천시스템으로서 알려진 소셜 필터링은 전자상거래에서 기술적 접근이 활발히 연구되어왔다. 온라인 추천시스템은 사용자의 개인적 취향과 관련하여 적절한 제품을 필터링하여 제공함으로서 사용자의 의사결정 품질을 향상시키는 것에 목적을 두고 있으며 그 결과 사용자의 제품 탐색과 선택에 대한 지원이 가능하다. 그러나 대다수 추천시스템의 선행연구들은 추천 알고리즘의 정확성을 향상시키는 것에 집중해 왔으며 사용자 기반의 인터페이스나 사용자 관점의 사용방식에 대한 연구는 매우 적은 실정이다. 추천시스템의 추천 상황에 대한 시스템 투명성과 사용자의 추천에 대한 피드백을 통한 추천방식 개선을 통하여 본 연구는 사용자 관점의 추천시스템 활용에 대한 시스템 투명성과 피드백의 영향력을 파악하고자 하였다. 실험을 통한 연구 결과에 따라, 시스템 투명성과 사용자 피드백 모두 추천시스템에 대한 사용자의 인지된 신뢰, 프로세스 가치, 인지된 즐거움에 영향을 주는 것으로 나타났다. 특히, 인지된 신뢰, 프로세스 가치, 즐거움은 사용자가 추천시스템을 지속적으로 사용하기 위한 의도를 향상시키는 것으로 나타났다.

Keywords

References

  1. Bagozzi, R., Yi, Y., and Phillips, L., "Assessing Construct Validity in Organizational Research," Administrative Science Quarterly, Vol. 36, No. 3, pp. 421-458, 1991. https://doi.org/10.2307/2393203
  2. Baron, R. and Kenny, D., "The Moderator-Mediator Variable Distinction in Social Psychological Research : Conceptual, Strategic, and Statistical Consideration," Journal of Personality and Social Psychology, Vol. 51, No. 6, pp. 1173-1182, 1986. https://doi.org/10.1037/0022-3514.51.6.1173
  3. Belkin, N. J. and Croft, W., "Information Filtering and Information Retrieval : Two Sides of Same Coin," Communications of the ACM, Vol. 35, No. 12, pp. 29-38, 1992.
  4. Bilgic, M. and Mooney, R., "Explaining Recommendations : Satisfaction Vs. Promotion," In M. van Setten, S. McNee, and J. Konstan (eds.), Beyond Personalization 2005-A Workshop on the Next Stage of Recommender Systems Research in Conjunction with the 2005 International Conference on Intelligent User Interface (IUI'05). New York, ACM Press, 2005.
  5. Buchanan, B. and Shortcliffe, E., "Rule-Based Expert Systems : The Mycin Experiments of the Stanford Heuristic Programming Project," Addison Wesley Publishing Company, Reading, MA, 1984.
  6. Chau, P. and Lai, V., "An Empirical Investigation of The Determinants of User Acceptance of Internet Banking," Journal of Organizational Computing, Vol. 13, No. 2, pp. 123-145, 2003. https://doi.org/10.1207/S15327744JOCE1302_3
  7. Chen, L. and Pu, P., "Interaction Design Guidelines on Critiquing-based Recommender Systems," User Modeling and User-Adapted Interaction Journal (UMUAI), Springer Netherlands, Vol. 19, No. 3, pp. 167-206, 2009. https://doi.org/10.1007/s11257-008-9057-x
  8. Chin, W., "Issues and Opinion on Structural Equation Modeling," MIS Quarterly, Vol. 22, No. 1, pp. 7-16, 1998.
  9. Chin, W., "The Partial Lease Square Approach for Structural Equation Modeling in Modern Methods for Business Research," M. G. A., ed., (ed.), Lawrence Erlbaum, Mahwah, NJ, pp. 295-336, 1998.
  10. Choi, J. and Lee, H. J., "An Integrated Perspective of User Evaluating Personalized Recommender Systems : Performancedriven or User-centric," The Journal of Society for e-Business Studies, Vol. 17, No. 3, pp. 379-394, 2012.
  11. Compeau, D., Higgins, C., and Huff, S., "Social Cognitive Theory and Individual Reactions to Computing Technology : A Longitudinal Study," MIS Quarterly, Vol. 23, No. 2, pp. 145-158, 1999. https://doi.org/10.2307/249749
  12. Cramer, H., Evers, V., and Ramlal, S., "The Effects of Transparency on Trust in and Acceptance of a Content-based Art Recommender," User Modeling and User-Adapted Interaction Journal, Vol. 17, No. 6, pp. 734-749, 2008.
  13. Cosley, D., Lam, S., Albert, I., Konstan, J., and Riedl, J., "Is Seeing Believing? How Recommender Systems Influence Users' Opinions," In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Fort Lauderdale, FL, 585-592, 2003.
  14. Daft, R. L. and Lengel, R. H., "Information Richness: A New Approach to Managerial Behavior and Organizational Design," In : Cummings, L. L. and Staw, B. M. (eds.), Research in organizational behavior 6, JAI Press, Homewood, IL, pp. 191-233, 1984.
  15. Daft, R. L. and Lengel, R. H., "Organizational Information Requirements, Media Richness and Structural Design," Management Science, Vol. 32, No. 5, pp. 554-571, 1986. https://doi.org/10.1287/mnsc.32.5.554
  16. Daft, R. L., Lengel, R. H., and Trevino, L. K., "Message Equivocality, Media Selection, and Manager Performance: Implications for Information systems," MIS Quarterly, Vol. 11, No. 3, pp. 355-366, 1987. https://doi.org/10.2307/248682
  17. Davis, R., "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Technology," MIS Quarterly, Vol. 13, No. 3, pp. 319-340, 1989. https://doi.org/10.2307/249008
  18. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., "Advances in Knowledge Discovery and Data Mining," AAAIpress/MIT press, 1996.
  19. Fornell, C. and Larcker, D., "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error," Journal of Marketing Research, Vol. 18, No. 3, pp. 39-50, 1981. https://doi.org/10.2307/3151312
  20. Frawley, W., Piatetsky-Shapiro, G., and Matheus, C., "Knowledge Discovery in Databases : An Overview," AI Magazine, Vol. 13, No. 3, pp. 57-70, 1992.
  21. Flavian, C., Guinaliu, M., and Gurrea, R., "The Role Played by Perceived Usability, Satisfaction, and Consumer Trust on Website Loyalty," Information and Management, Vol. 43, No. 1, pp. 1-16, 2006. https://doi.org/10.1016/j.im.2005.01.002
  22. Gefen, D., "E-Commerce : The Role of Familiarity and Trust," Omega, Vol. 28, No. 6, pp. 725-737, 2000. https://doi.org/10.1016/S0305-0483(00)00021-9
  23. Gefen, D., Karahanna, E., and Straub, D., "Trust and TAM in Online Shopping : An Integrated Model," MIS Quarterly, Vol. 27, No. 1, pp. 51-90, 2003. https://doi.org/10.2307/30036519
  24. Gretzel, U. and Fesenmaier, D., "Persuasion in Recommender System," International Journal of Electronic Commerce, Vol. 11, No. 2, pp. 81-100, 2006. https://doi.org/10.2753/JEC1086-4415110204
  25. Hebrado, J., Lee, H. J., and Choi, J., "The Role of Transparency and Feedback on the Behavioral Intention to Reuse a Recommender System," In Proceeding of CONF-IRM 2011, Paper 8, 2011.
  26. Herlocker, J., Konstan, J., and Riedl, J., "Explaining Collaborative Filtering Recommendations," In Proceeding at the ACM 2000 Conference on Computer Supported Cooperative Work, pp. 241-250, 2000.
  27. Herlocker, J., Konstan, J., Terveen, L., and Riedl, J., "Evaluating Collaborative Filtering Recommender Systems," ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 5-53, 2004. https://doi.org/10.1145/963770.963772
  28. Jones, N. and Pu, P., "User Technology Adoption Issues in Recommender Systems," In Proceedings of Networking and Electronic Commerce Research Conference (NAEC2007), pp. 379-394, 2007.
  29. Lee, H., Kim, J., and Park, S., "Understanding Collaborative Filtering Parameters for Personalized Recommendations in e-Commerce," Electronic Commerce Research, Vol. 7, No. 3-4, pp. 293-314, 2007. https://doi.org/10.1007/s10660-007-9004-7
  30. Lee, H., Ahn, H., and Han, I., "VCR : Virtual Community Recommender Using the Technology Acceptance Model and the User's Needs Type," Expert Systems with Applications, Vol. 33, No. 4, pp. 984-995, 2007. https://doi.org/10.1016/j.eswa.2006.07.012
  31. Lee, J. and See, K., "Trust in Automation: Designing for Appropriate Reliance," Human Factors : The Journal of the Human Factors and Ergonomics Society, Vol. 42, No. 1, pp. 50-80, 2004.
  32. McNee, S., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S., Rashid, A., Konstan, J., and Riedl, J., "On the Recommending of Citations for Research Papers," In Proceeding Of ACM CSCW 2002, ACM Press, pp. 116-125, 2002.
  33. McNee, S., Lam, S., Konstan, J., and Riedl, J., "Interfaces for Eliciting New User Preferences in Recommender Systems," In Proceedings of the 9th International Conference on User Modeling, pp. 178-187, 2003.
  34. Medhi, D. and Dakua, J., "MovieReco: A Recommendation System," In Proceeding of World Academy of Science, Engineering and Technology, Vol. 4, pp. 70-75, 2005.
  35. Montaner, M., Lopez, B., and Dela Rosa, J., "A Taxonomy of Recommender Agents on the Internet," Artificial Intelligence Review, Vol. 19, No. 4, pp. 285-330, 2003. https://doi.org/10.1023/A:1022850703159
  36. Ozok, A., Fan, Q., and Norcio, A., "Design Guidelines for Effective Recommender System Interfaces Based on a Usability Criteria Conceptual Model : Results from a College Student Population," Behaviour and Information Technology, Vol. 29, No. 1, pp. 57-83, 2010. https://doi.org/10.1080/01449290903004012
  37. Pereira, R., "Optimizing Human-Computer Interaction for the Electronic Commerce Environment," Journal of Electronic Commerce Research, Vol. 1, No. 1, pp. 23-44, 2000.
  38. Pu, P. and Chen, L., "A User-Centric Evaluation Framework of Recommender Systems," In proceedings of the ACM RecSys 2010 Workshop on User-Centric Evaluation of Recommender Systems and their Interfaces (UCERSTI), 2010.
  39. Resnick, P. and Varian, H., "Recommender Systems," Communications of the ACM, Vol. 40, No. 3, pp. 56-58, 1997.
  40. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Application of Dimensionality Reduction in Recommender Systems-A Case Study," In Proceeding of ACM WebKDD Workshop, 2000.
  41. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Item-based collaborative filtering recommendation algorithms," In Proceeding of WWW10, pp. 285-295, 2001.
  42. Schwab, D., "Construct Validity in Organizational Behavior," In L. L. Cummings and B. Staw (eds.), Research in Organizational Behavior, 12, JAI Press, Greenwich, CT, pp. 3-43, 1980.
  43. Sinha, R. and Swearingen, K., "The Role of Transparency in Recommender Systems," In Proceeding of CHI'02, pp. 830-831, 2002.
  44. Swearingen, K. and Sinha, R., "Interaction Design for Recommender Systems," In Proceeding of Interactive Systems (DIS2002), 2002.
  45. Tintarev, N. and Masthoff, J., "Survey of Explanations in Recommender Systems," In Proceeding for ICDE Workshops, pp. 801-810, 2007.
  46. Wang, W. and Benbasat, I., "Interactive Decision Aids for Consumer Decision Making in e-Commerce : The Influence of Perceived Strategy Restrictiveness," MIS Quarterly, Vol. 33, No. 2, pp. 293-320, 2009. https://doi.org/10.2307/20650293
  47. Xiao, B. and Benbasat, I., "E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact," MIS Quarterly, Vol. 31, No. 1, pp. 137-209, 2007. https://doi.org/10.2307/25148784
  48. Xiao, B. and Benbasat, I., "Product-Related Deception in E-Commerce : A Theoretical Perspective," MIS Quarterly, Vol. 35, No. 1, pp. 169-195, 2011. https://doi.org/10.2307/23043494
  49. Zhang, Y., "Literature Review for Online Communities and Recommender Systems: State of the Art Paper," Unpublished doctoral dissertation, University of Minnesota, Minneapolis, 2002.
  50. Ziegler, C., McNee, S., Konstan, J., and Lausen, G., "Improving Recommendation Lists through Topic Diversification," In Proceeding of International World Wide Web Conference Committee (IW3C2), 2005.

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