DOI QR코드

DOI QR Code

Consumers' Usage Intentions on Online Product Recommendation Service -Focusing on the Mediating Roles of Trust-commitment-

온라인 상품추천 서비스에 대한 소비자 사용 의도 -신뢰-몰입의 매개역할을 중심으로-

  • Lee, Ha Kyung (Dept. of Business Administration, Seoul National University of Science and Technology) ;
  • Yoon, Namhee (Korea Research Institute for Fashion and Distribution Information) ;
  • Jang, Seyoon (Korea Research Institute for Fashion and Distribution Information)
  • 이하경 (서울과학기술대학교 경영학과) ;
  • 윤남희 (한국패션유통정보연구원) ;
  • 장세윤 (한국패션유통정보연구원)
  • Received : 2018.06.26
  • Accepted : 2018.08.23
  • Published : 2018.10.31

Abstract

This study tests consumer responses to online product recommendation service offered by a website. A product recommendation service refers to a filtering system that predicts and shows items that consumers would like to purchase based on their searches or pre-purchase information. The survey is conducted on 300 people in an age group between 20 and 40 years in a panel of an online survey firm. Data are analyzed using confirmatory factor analysis and structural equation modeling by AMOS 20.0. The results show that personalization quality does not have a significant effect on trust, but relationship quality and technology quality have a positive effect on trust. Three types of quality of recommendation service also have a positive effect on commitment. Trust and commitment are factors that increase service usage intentions. In addition, this study reveals the moderating effect of light users vs heavy users based on online shopping time. Light users show a negative effect of personalization quality on trust, indicating that they are likely to be uncomfortable to the service using personal information, compared to heavy users. This study also finds that trust vs commitment is an important factor increasing service usage intentions for heavy users vs light users.

Keywords

References

  1. Ali, A., Krapfel Jr., R., & LaBahn, D. (1995). Product innovativeness and entry strategy: Impact on cycle time and break-even time. Journal of Product Innovation Management, 12(1), 54-69. doi:10.1016/0737-6782(94)00027-D
  2. Bae, S. J., & Cho, B. J. (2008). An investigation of the relationship marketing factors, relationship quality, and relationship marketing performance focusing on car sales business. Journal of Consumption Culture, 11(3), 1-22. doi:10.17053/jcc.2008.11.3.001
  3. Cho, M. H. (2011). The role of prior knowledge in information search and factors to influence search behavior through the internet website of tourism destination. Korean Journal of Tourism Research, 26(4), 567-589.
  4. Choi, I. Y. (2012). 브랜드, 소비자, 광고모델 개성이 브랜드 태도, 구매 의도에 미치는 영향 - 제품 관여도의 조절효과를 중심으로 - [The effects of personality of brand, consumer and celebrity endorsor on brand attitude and purchase intention - Focusing on the moderating effect of product involvement -]. Unpublished doctoral dissertation, Kyung Hee University, Seoul.
  5. Choi, J., & Lee, H. J. (2012). An integrated perspective of user evaluating personalized recommender systems: Performance-driven or user-centric. The Journal of Society for e-Business Studies, 17(3), 85-103. doi:10.7838/jsebs.2012.17.3.085
  6. Dwyer, F. R., Schurr, P. H., & Oh, S. (1987). Developing buyer-seller relationships. Journal of Marketing, 51(2), 11-27. doi:10.2307/1251126
  7. Engel, J. F., Blackwell, R. D., & Miniard, P. W. (1990). Consumer behavior (6th ed.). Chicago, IL: Dryden Press.
  8. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388. doi:10.2307/3150980
  9. Hampton, G. M., & Hampton, D. L. (2004). Relationship of professionalism, rewards, market orientation and job satisfaction among medical professionals: The case of Certified Nurse-Midwives. Journal of Business Research, 57 (9), 1042-1053. doi:10.1016/S0148-2963(02)00356-9
  10. Hyun, Y. H., Kim, H. C., & Kim, Y. G. (2014). A verification of the structural relationships between system quality, information quality, service quality, perceived usefulness and reuse intention to augmented reality by applying transformed TAM model: A focus on the moderating role of telepresence and the mediating role of perceived usefulness. Korean Management Review, 43(5), 1465-1492.
  11. Joreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409-426. doi:10.1007/BF02291366
  12. Kim, J. (2017). An exploratory study of factors affecting the collaborative filtering on Twitter using social network mining. The e-Business Studies, 18(1), 145-159. doi:10.15719/geba.18.1.201702.145
  13. Kim, J., Park, S. C., & Lee, W. J. (2005). The moderating effect of product involvement on the online consumer's purchasing behavior. Korean Management Science Review, 22 (2), 51-76.
  14. Kim, N. (2018). The impact of social ties, trust, and adoption on intention in sharing travel information on social network sites. Journal of Tourism Sciences, 42(5), 117-136. doi:10.17086/JTS.2018.42.5.117.136
  15. Kim, Y., Kim, M. S., Kim, Y. B., & Park, J. H. (2009). A study on personalized recommendation method based on contents using activity and location information. Journal of the Korean Society for Information Management, 26(1), 81-105. doi:10.3743/KOSIM.2009.26.1.081
  16. Ko, H., Kim, S., & Kang, N. (2017). Design and implementation of smart-mirror supporting recommendation service based on personal usage data. KIISE Transactions on Computing Practices, 23(1), 65-73. doi:10.5626/KTCP.2017.23.1.65
  17. Kwon, E., & Ha, H. Y. (2015). Effects of web site service quality and customer satisfaction on trust in the context of beauty service products: Moderating roles of perceived risk and types of web site. The e-Business Studies, 16(6), 305-334. doi:10.15719/geba.16.6.201512.305
  18. Lee, D., Kim, U., & Yeom, K. (2017). Content recommendation system using user context-aware based knowledge filtering in smart environments. The Journal of Korean Institute of Next Generation Computing, 13(2), 35-48.
  19. Maeng, J. H. (2018). User cognitive experiences depending on contents characteristics of personalized recommendation service on mobile commerce: Focusing on mediating effect of choice overload and moderating role of self-construal. Unpublished master's thesis, Sogang University, Seoul.
  20. Mock, Y. S., & Yi, C. Y. (2015). Moderating effects of online brand community perception and influence of self-derermination on relationship quality between community and its registered members. A Treatise on The Plastic Media, 18(1), 51-60.
  21. Moorman, C., Zaltman, G., & Deshpande, R. (1992). Relationships between providers and users of market research: The dynamics of trust within and between organizations. Journal of Marketing Research, 29(3), 314-328. doi:10.2307/3172742
  22. Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38. doi:10.2307/1252308
  23. Mukherjee, A., & Nath, P. (2007). Role of electronic trust in online retailing: A re-examination of the commitment-trust theory. European Journal of Marketing, 41(9/10), 1173-1202. doi:10.1108/03090560710773390
  24. Na, K. T., & Lee, J. H. (2017). Collaborative filtering using co-occurrence and similarity information. Journal of Internet Computing and Services, 18(3), 19-28. doi:10.7472/jksii.2017.18.3.19
  25. Nkwocha, I., Bao, Y., Johnson, W. C., & Brotspies, H. V. (2005). Product fit and consumer attitude toward brand extensions: the moderating role of product involvement. Journal of Marketing Theory and Practice, 13(3), 49-61. doi:10.1080/10696679.2005.11658549
  26. Oh, D. H. (2018, February 9). 네이버, AI기반 개인 상품 추천 시스템 '에이아이템즈' 고도화 [NAVER, Advancement of personalization product recommendation system based on AI 'AiTEMS']. NEWSIS. Retrieved July 21, 2018, from http://www.newsis.com/view/?id=NISX20180209_0000226324&cID=13001&pID=13000
  27. Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67(4), 420-450.
  28. Park, J. W., & Choi, E. I. (2017). Personalized recommendation service framework using the beacon. Journal of Knowledge Information Technology and Systems, 12(2), 227-233. https://doi.org/10.34163/jkits.2017.12.2.002
  29. Park, S. C., & Koh, J. (2013). Determinants of continuance intention to use social commerce for group purchasing: The moderating role of product involvement. Entrue Journal of Information Technology, 12(2), 139-154.
  30. Park, Y. J. (2016). An analysis of customer preferences of recommendation techniques and influencing factors: A comparative study of electronic goods and apparel products. Information Systems Review, 18(2), 59-77. doi:10.14329/isr.2016.18.2.059
  31. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. doi:10.1080/10864415.2003.11044275
  32. Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123-205. doi:10.1016/S0065-2601(08)60214-2
  33. Schurr, P. H., & Ozanne, J. L. (1985). Influences on exchange processes: Buyers' preconceptions of a seller's trustworthiness and bargaining toughness. Journal of Consumer Research, 11(4), 939-953. doi:10.1086/209028
  34. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290-312. doi:10.2307/270723
  35. Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS Quarterly, 30(4), 865-890. doi: 10.2307/25148757
  36. Yoon, S. J. (2002). The antecedents and consequences of trust in online-purchase decisions. Journal of Interactive Marketing, 16(2), 47-63. doi:10.1002/dir.10008