DOI QR코드

DOI QR Code

The Effect of the Personalized Recommendation System of Online Shopping Platform on Consumers' Purchase Intention

온라인 쇼핑 플랫폼의 개인화 추천 시스템이 소비자의 구매의도에 미치는 영향

  • Yingying Lu (Department of Business Administration, Pusan National University) ;
  • Jongki Kim (Department of Business Administration, College of Business, Pusan National University)
  • Received : 2023.05.30
  • Accepted : 2023.08.22
  • Published : 2023.11.30

Abstract

Many online shopping sites now offer personalized recommendation systems to improve consumers' shopping experiences by lowering costs (time, cost, etc.), catering to consumers' tastes, and stimulating consumers' potential shopping needs. So far, domestic and foreign research on the personalized recommendation system has mainly focused on the field of computer science, which is advantageous for obtaining accurate personalized recommendation results for users but difficult to continuously track the users' psychological states or behavioral intentions. This study attempted to investigate the effect of the characteristics of the personalized recommendation system in the online shopping environment on consumer perception and purchase intention for consumers using the Stimulus-Organism-Response (S-O-R) model. The analysis results adopted all hypotheses on the effect of the quality of the personalized recommendation system and information quality on trust and perceived value. Through the empirical results of this study, the factors influencing consumers' use of personalized recommendation system can be identified. In order to increase more purchase, online shopping companies need to understand consumers' tastes and improve the quality of the personalized system by improving the recommendation algorithm thus to provide more information about products.

온라인 쇼핑 플랫폼은 개인화 추천 시스템을 활용하여 소비자의 개인 정보와 행동 데이터를 수집, 분석 및 마이닝을 통해 소비자에게 맞춤형 추천 서비스를 제공함으로써 소비자의 잠재적인 쇼핑 욕구를 자극한다. 본 연구는 S-O-R 모델을 기반으로 온라인 쇼핑 추천이 구매의도에 미치는 영양을 분석하기 위하여 시스템 품질인 다양성과 정확성, 정보 품질인 설득력과 완전성을 외부 자극으로 설정하고, 신뢰 및 지각된 가치에 따른 소비자의 심리상태 하 유기체로 설정하여 구매의도 간에 관계를 탐구하였다. 온라인 쇼핑 플랫폼을 이용하는 소비자를 대상으로 설문조사를 실시하였다. 분석결과는 개인화 추천 시스템의 품질과 정보 품질이 신뢰와 지각된 가치에 미치는 영향에 대한 가설이 모두 채택되었다. 신뢰가 시스템 품질, 정보 품질에 대한 구매의도와의 관계에서 매개역할을 확인하였으며 지각된 가치는 정보 품질에 대한 구매의도와의 관계에서 매개역할을 확인하였다. 추천 시스템이 제공하는 콘텐츠는 소비자 경험을 개선하고 소비자의 수용 정도를 높일 수 있는 방향으로 설계되어야 한다는 시사점을 도출하였다.

Keywords

Acknowledgement

본 연구는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.

References

  1. 이윤재, "온라인 동영상 플랫폼에서의 추천품질이 추천시스템 만족과 충성도에 미치는 영향 연구", 마케팅논집, 제28권, 제4호, 2020, pp. 1-18. https://doi.org/10.22736/JMS.28.4.01
  2. Adomavicius, G. and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions", IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6, 2005, pp. 734-749. https://doi.org/10.1109/TKDE.2005.99
  3. Benhamdi, S., A. Babouri, and R. Chiky, "Personalized recommender system for e-Learning environment", Education and Information Technologies, Vol.22, 2017, pp. 1455-1477. https://doi.org/10.1007/s10639-016-9504-y
  4. Bentler, P. M. and D. G. Bonett, "Significance tests and goodness of fit in the analysis of covariance structures", Psychological Bulletin, Vol.88, No.3, 1980, pp. 588-606. https://doi.org/10.1037//0033-2909.88.3.588
  5. Berkovsky, S., R. Taib, and D. Conway, "How to recommend? User trust factors in movie recommender systems", In Proceedings of the 22nd International Conference on Intelligent User Interfaces, 2017, pp. 287-300.
  6. Burke, R., "Hybrid recommender systems: Survey and experiments", User Modeling and User-Adapted Interaction, Vol.12, 2002, pp. 331-370. https://doi.org/10.1023/A:1021240730564
  7. Cao, Y. and Y. Li, "An intelligent fuzzy-based recommendation system for consumer electronic products", Expert Systems with Applications, Vol.33, No.1, 2007, pp. 230-240. https://doi.org/10.1016/j.eswa.2006.04.012
  8. Chin, W. W., "The partial least squares approach to structural equation modeling", in G. A. Marcoulides (Ed.), Modern methods for business research, Lawrence Erlbaum Associates, Mahwah, 1998, pp. 295-336.
  9. Cohen, J., Statistical Power Analysis for the Behavioral Sciences (2nd ed.), Lawrence Erlbaum Associates, New Jersey, 1988.
  10. Cremonesi, P., F. Garzotto, and R. Turrin, "Investigating the persuasion potential of recommender systems from a quality perspective: An empirical study", ACM Transactions on Interactive Intelligent Systems (TiiS), Vol.2, No.2, 2012, pp. 1-41. https://doi.org/10.1145/2209310.2209314
  11. Davis, F. D., "User acceptance of information technology: system characteristics, user perceptions and behavioral impacts", International Journal of Man-Machine Studies, Vol.38, No.3, 1993, pp. 475-487. https://doi.org/10.1006/imms.1993.1022
  12. Filieri, R., S. Alguezaui, and F. McLeay, "Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth", Tourism Management, Vol.51, 2015, pp. 174-185. https://doi.org/10.1016/j.tourman.2015.05.007
  13. Fornell, C. and D. F. Larcker, "Evaluating structural equation models with unobservable variables and measurement error", Journal of Marketing Research, Vol.18, No.1, 1981, pp. 39-50. https://doi.org/10.1177/002224378101800104
  14. Hair, J. F., C. M. Ringle, and M. Sarstedt, "PLS-SEM: Indeed a silver bullet", Journal of Marketing Theory and Practice, Vol.19, No.2, 2011, pp. 139-151. https://doi.org/10.2753/MTP1069-6679190202
  15. Han, M. S., D. P. Hampson, Y. Wang, and H. Wang, "Consumer confidence and green purchase intention: An application of the stimulus-organism-response model", Journal of Retailing and Consumer Services, Vol.68, 2022, 103061.
  16. Harman, J. L., J. O'Donovan, T. Abdelzaher, and C. Gonzalez, "Dynamics of human trust in recommender systems", In Proceedings of the 8th ACM Conference on Recommender Systems, 2014, October, pp. 305-308.
  17. Heinrich, B., M. Hopf, D. Lohninger, A. Schiller, and M. Szubartowicz, "Data quality in recommender systems: the impact of completeness of item content data on prediction accuracy of recommender systems", Electronic Markets, Vol.31, 2021, pp. 389-409. https://doi.org/10.1007/s12525-019-00366-7
  18. Hsiao, K. L., J. C. C. Lin, X. Y. Wang, H. P. Lu, and H. Yu, "Antecedents and consequences of trust in online product recommendations: An empirical study in social shopping", Online Information Review, Vol.34, No.6, 2010, pp. 935-953. https://doi.org/10.1108/14684521011099414
  19. Hsu, C. L., K. C. Chang, and M. C. Chen, "The impact of website quality on customer satisfaction and purchase intention: Perceived playfulness and perceived flow as mediators", Information Systems and e-Business Management, Vol.10, 2012, pp. 549-570. https://doi.org/10.1007/s10257-011-0181-5
  20. Hu, L. T. and P. M. Bentler, "Fit indices in covariance structure modeling: Sensitivity to under-parameterized model misspecification", Psychological Methods, Vol.3, No.4, 1998, pp. 424-453. https://doi.org/10.1037//1082-989X.3.4.424
  21. Jannach, D. and C. Bauer, "Escaping the mcnamara fallacy: Towards more impactful recommender systems research", AI Magazine, Vol.41, No.4, 2020, pp. 79-95. https://doi.org/10.1609/aimag.v41i4.5312
  22. Jannach, D. and G. Adomavicius, "Recommendations with a purpose", In Proceedings of the 10th ACM Conference on Recommender Systems, 2016, September, pp. 7-10.
  23. Jones, N. and P. Pu, "User technology adoption issues in recommender systems", In Proceedings of the 2007 Networking and Electronic Commerce Research Conference, 2007, pp. 379-394.
  24. Ko, H., S. Lee, S, Y. Park, and A. Choi, "A survey of recommendation systems: Recommendation models, techniques, and application fields", Electronics, Vol.11, No.1, 2022, 141.
  25. Kramer, J., S. Noronha, and J. Vergo, "A user-centered design approach to personalization", Communications of the ACM, Vol.43, No.8, 2000, pp. 44-48. https://doi.org/10.1145/345124.345139
  26. Ku, Y. C., Y. M. Tai, and C. H. Chan, "Effects of product type and recommendation approach on consumers' intention to purchase recommended products", Pacific Asia Journal of the Association for Information Systems, Vol.8, No.2, 2016, pp. 1-18. https://doi.org/10.17705/1pais.08201
  27. Ladhari, R. and M. Michaud, "eWOM effects on hotel booking intentions, attitudes, trust, and website perceptions", International Journal of Hospitality Management, Vol.46, 2015, pp. 36-45. https://doi.org/10.1016/j.ijhm.2015.01.010
  28. Liu, J., "Toward a unified model of human information behavior: An equilibrium perspective", Journal of Documentation, Vol.73, No.4, 2017, pp. 666-688. https://doi.org/10.1108/JD-06-2016-0080
  29. Logesh, R. and V. Subramaniyaswamy, "Exploring hybrid recommender systems for personalized travel applications", In Cognitive Informatics and Soft Computing, 2019, pp. 535-544.
  30. Lou, N., "Tourism destination recommendation based on association rule algorithm", Mobile Information Systems, Vol.2022, 9331178.
  31. Lu, J., D. Wu, M. Mao, W. Wang, and G. Zhang, "Recommender system application developments: A survey", Decision Support Systems, Vol.74, 2015, pp. 12-32. https://doi.org/10.1016/j.dss.2015.03.008
  32. Luo, C., X. R. Luo, L. Schatzberg, and C. L. Sia, "Impact of informational factors on online recommendation credibility: The moderating role of source credibility", Decision Support Systems, Vol.56, 2013, pp. 92-102. https://doi.org/10.1016/j.dss.2013.05.005
  33. McKnight, D. H., V. Choudhury, and C. Kacmar, "Developing and validating trust measures for e-commerce: An integrative typology", Information Systems Research, Vol.13, No.3, 2002, pp. 334-359. https://doi.org/10.1287/isre.13.3.334.81
  34. Mehrabian, A. and J. A. Russell, An Approach to Environmental Psychology, The MIT Press, 1974.
  35. Mukherjee, A. and P. Nath, "Role of electronic trust in online retailing: A re-examination of the commitment-trust theory", European Journal of Marketing, Vol.41, No.9/10, 2007, pp. 1173-1202. https://doi.org/10.1108/03090560710773390
  36. Mummalaneni, V., "An empirical investigation of web site characteristics, consumer emotional states and on-line shopping behaviors", Journal of Business Research, Vol.58, No.43, 2005, pp. 526-532. https://doi.org/10.1016/S0148-2963(03)00143-7
  37. Nunnally, J. C. and I. H. Bernstein, Psychometric Theory (3rd ed.), McGraw-Hill, New York, 1994.
  38. Overby, J. W. and E. J. Lee, "The effects of utilitarian and hedonic online shopping value on consumer preference and intentions", Journal of Business Research, Vol.59, No.10-11, 2006, pp. 1160-1166. https://doi.org/10.1016/j.jbusres.2006.03.008
  39. Panniello, U., M. Gorgoglione, and A. Tuzhilin, "Research note-In CARSs we trust: How context-aware recommendations affect customers' trust and other business performance measures of recommender systems", Information Systems Research, Vol.27, No.1, 2016, pp. 182-196. https://doi.org/10.1287/isre.2015.0610
  40. Park, J. and L. Stoel, "Effect of brand familiarity, experience and information on online apparel purchase", International Journal of Retail & Distribution Management, Vol.33, No.2, 2005, pp. 148-160. https://doi.org/10.1108/09590550510581476
  41. Pathak, B., R. Garfinkel, R. D. Gopal, R. Venkatesan, and F. Yin, "Empirical analysis of the impact of recommender systems on sales", Journal of Management Information Systems, Vol.27, No.2, 2010, pp. 159-188. https://doi.org/10.2753/MIS0742-1222270205
  42. Peng, C. and Y. G. Kim, "Application of the Stimuli-Organism-Response (SOR) framework to online shopping behavior", Journal of Internet Commerce, Vol.13, No.3-4, 2014, pp. 159-176. https://doi.org/10.1080/15332861.2014.944437
  43. Portugal, I., P. Alencar, and D. Cowan, "The use of machine learning algorithms in recommender systems: A systematic review", Expert Systems with Applications, Vol.97, 2018, pp. 205-227. https://doi.org/10.1016/j.eswa.2017.12.020
  44. Preacher, K. J. and A. F. Hayes, "Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models", Behavior Research Methods, Vol.40, 2008, pp. 879-891. https://doi.org/10.3758/BRM.40.3.879
  45. Pu, P. and L. Chen, "A user-centric evaluation framework of recommender systems", In Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces (UCERSTI'10), ACM Conferenceon Recommender Systems (RecSys'10), 2010, pp. 14-21.
  46. Pu, P. and L. Chen, "Trust building with explanation interfaces", In Proceedings of the 11th International Conference on Intelligent User Interfaces, 2006, pp. 93-100.
  47. Pu, P., L. Chen, and R. Hu, "A user-centric evaluation framework for recommender systems", In Proceedings of the fifth ACM Conference on Recommender Systems, 2011, October, pp. 157-164.
  48. Pursel, B., C. Liang, S. Wang, Z. Wu, K. Williams, B. Brautigam, S. Saul, H. Williams, K. Bowen, and C. L. Giles, (2016, April), "Bbookx: Design of an automated web-based recommender system for the creation of open learning content", In Proceedings of the 25th International Conference Companion on World Wide Web, 2016, April, pp. 929-933.
  49. Ricci, F. and H. Werthner, "Introduction to the special issue: Recommender systems", International Journal of Electronic Commerce, Vol.11, No.2, 2006, pp. 5-9. https://doi.org/10.2753/JEC1086-4415110200
  50. Ricci, F., L. Rokach, and B. Shapira, "Introduction to recommender systems handbook", In Recommender Systems Handbook, MA: springer US, Boston, 2010, pp. 1-35
  51. Roudposhti, V. M., M. Nilashi, A. Mardani, D. Streimikiene, S. Samad, and O. Ibrahim, "A new model for customer purchase intention in e-commerce recommendation agents", Journal of International Studies, Vol.11, No.4, 2018, pp. 237-253. https://doi.org/10.14254/2071-8330.2018/11-4/17
  52. Schafer, J. B., J. A. Konstan, and J. Riedl, "E-commerce recommendation applications", Data Mining and Knowledge Discovery, Vol.5, 2001, pp. 115-153. https://doi.org/10.1007/978-1-4615-1627-9_6
  53. Shankar, V., M. Kleijnen, S. Ramanathan, R. Rizley, S. Holland, and S. Morrissey, "Mobile shopper marketing: Key issues, current insights, and future research avenues", Journal of Interactive Marketing, Vol.34, No.1, 2016, pp. 37-48. https://doi.org/10.1016/j.intmar.2016.03.002
  54. Strom, R., M. Vendel, and J. Bredican, "Mobile marketing: A literature review on its value for consumers and retailers", Journal of Retailing and Consumer Services, Vol.21, No.6, 2014, pp. 1001-1012. https://doi.org/10.1016/j.jretconser.2013.12.003
  55. Tenenhaus, M., V. E. Vinzi, Y. M. Chatelin, and C. Lauro, "PLS path modeling", Computational Statistics & Data Analysis, Vol.48, No.1, 2005, pp. 159-205. https://doi.org/10.1016/j.csda.2004.03.005
  56. Tintarev, N. and J. Masthoff, "Explaining recommendations: Design and evaluation", in F. Ricci, L. Rokach, and B. Shapira (eds,), Recommender Systems Handbook, Springer US, Boston, MA, 2015, pp. 353-382.
  57. Wang, Y., J. Xu, A. Wu, M. Li, Y. He, J. Hu, and W. Yan, "Telepath: Understanding users from a human vision perspective in large-scale recommender systems", In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.32, No.1, 2018.
  58. Yang, J., M. Y. P. Peng, S. Wong, and W. Chong, "How E-learning environmental stimuli influence determinates of learning engagement in the context of COVID-19? SOR model perspective", Frontiers in Psychology, Vol.12, 2021, 584976.
  59. Yang, X., "Determinants of consumers' continuance intention to use social recommender systems: A self-regulation perspective", Technology in Society, Vol.64, 2021, 101464.
  60. Yang, X., "Influence of informational factors on purchase intention in social recommender systems", Online Information Review, Vol.44, No.2, 2020, pp. 417-431. https://doi.org/10.1108/OIR-12-2016-0360
  61. Yin, J. and X. Qiu, "AI technology and online purchase intention: Structural equation model based on perceived value", Sustainability, Vol.13, No.10, 2021, 5671.
  62. Yu, T., J. Guo, W. Li, H. J. Wang, and L. Fan, "Recommendation with diversity: An adaptive trust-aware model", Decision Support Systems, Vol.123, 2019, p. 113073.
  63. Zeithaml, V. A., "Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence", Journal of Marketing, Vol.524, No.3, 1988, pp. 2-22. https://doi.org/10.1177/002224298805200302
  64. Zhang, W. and F. Ling, (2021), "Research on news recommendation system based on deep network and personalized needs", Wireless Communications and Mobile Computing, Vol.2021, 2021, 7072849.