Ключевые слова
готовность к использованию
онлайновая система персонализированных рекомендаций
пользовательский выбор
взаимодействие с рекомендательными системами
Как цитировать
Аннотация
Вопросы привлечения клиентов и увеличения продаж посредством совершенствования систем персонализированных рекомендаций вызывают значительный интерес. Исследования в данной области направлены в основном на повышение точности и эффективности рекомендательных алгоритмов, а также на минимизацию рисков. Однако недостаточное внимание уделяется специфике взаимодействия клиентов с подобными системами. Для восполнения этого пробела в статье анализируются факторы, определяющие принятие покупателями рекомендаций, предлагаемых системой. Полученные эмпирические результаты на примере китайских студентов свидетельствуют, что готовность пользоваться рекомендательными системами напрямую зависит от восприятия взаимодействия с другими пользователями. Опосредованную роль при этом играют субъективные оценки простоты применения и функциональности систем. Итогом исследования стали предложения по повышению эффективности работы с системами персонализированных рекомендаций.
Литература
Anderson J.C., Gerbing D.W. (1988) Structural equation modeling in practice: A review and recommended two-step approach // Psychological Bulletin. Vol. 103. № 3. P. 411-423.
Bagozzi R.P., Phillips L.W. (1982) Representing and testing organizational theories: A holistic construal // Administrative Science Quarterly. Vol. 27. № 3. P. 459-489.
Bagozzi R.P., Yi Y. (1989) On the use of structural equation models in experimental designs // Journal of Marketing Research. Vol. 26. № 3. P. 271-284.
Bechwati N.N., Xia L. (2003) Do computers sweat? The impact of perceived effort of online decision aids on consumers' satisfaction with the decision process // Journal of Consumer Psychology. Vol. 13. № 1/2. P. 139-148.
Bhattacherjee A., Premkumar G. (2004) Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test // MIS Quarterly. Vol. 28. № 2. P. 229-254.
Bo X., Benbasat I. (2007) E-commerce product recommendation agents: Use, characteristics, and impact // MIS Quarterly. Vol. 31. № 1. P. 137-209.
CNNIC (2019) The 43rd China Statistics Report on Internet Development. Beijing: China Internet Network Information Center.
Carlson J., O'Cass A., Ahrholdt D. (2015) Assessing customers' perceived value of the online channel of multichannel retailers: A two country examination // Journal of Retailing & Consumer Services. Vol. 27. № 6. P. 90-102.
Dabholkar P.A., Sheng X. (2012) Consumer participation in using online recommendation agents: Effects on satisfaction, trust, and purchase intentions // Service Industries Journal. Vol. 32. № 9. P. 1433-1449.
Davis F.D. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology // MIS Quarterly. Vol. 13. № 3. P. 319-340.
Dodds W.B. (1991) Effects of price, brand, and store information on buyers' product evaluations // Journal of Marketing Research. Vol. 28. № 3. P. 307-319.
Dong H.Z., Chang Y.P., Jian J.L., Xin L. (2014) Understanding the adoption of location-based recommendation agents among active users of social networking sites // Information Processing & Management - An International Journal. Vol. 50. № 5. P. 675-682.
Fernandez-Perez V., Montes-Merino A., Rodriguez-Ariza L., Galicia P.E.A. (2019) Emotional competencies and cognitive antecedents in shaping student's entrepreneurial intention: The moderating role of entrepreneurship education // International Entrepreneurship and Management Journal. Vol. 15. № 1. P. 281-305. DOI: https://doi.org/10.1007/s11365-017-0438-7
Fornell C., Larcker D.F. (1981) Evaluating structural equation models with unobservable variables and measurement error // Journal of Marketing Research. Vol. 18. № 1. P. 39-50.
Gou L., You F., Guo J., Wu L. (2011) Sfviz: Interest-based friends exploration and recommendation in social networks. ACM International Conference Proceeding Series, 2011. Режим доступа: https://www.researchgate.net/publication/254003084_SFViz_Interest-based_friends_exploration_and_recommendation_in_social_networks, дата обращения 23.10.2019. DOI: https://doi.org/10.1145/2016656.2016671
Hair J.F., Black B., Babin B., Anderson R.E., Tatham R.L. (2010) Multivariate data analysis (7th ed.). London: Pearson Prentice Hall.
He Y., Chen Q., Kitkuakul S., Wright L.T. (2018) Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy // Cogent Business & Management. Vol. 5. № 1. P. 1-22. Режим доступа: https://www.cogentoa.com/article/.pdf, дата обращения 25.10.2019. DOI: https://doi.org/10.1080/23311975.2018.1459006
Herlocker J.L., Konstan J.A., Terveen L.G., Riedl J.T. (2004) Evaluating collaborative filtering recommender systems // ACM Transactions on Information Systems. Vol. 22. № 1. P. 5-53.
Hoffman D.L., Novak T.P. (1996) Marketing in hypermedia computer-mediated environments: Conceptual foundations // Journal of Marketing. Vol. 60. № 3. P. 50-68.
Hsu M.H., Chang C.M., Chu K.K., Lee Y.J. (2014) Determinants of repurchase intention in online group-buying // Computers in Human Behavior. Vol. 36. Issue C. P. 234-245.
IResearch (2018) China university students' consumption insight report in 2018. Beijing: IResearch.
Jannach D., Zanker M., Felfernig A., Friedrich G. (2010) Recommender systems: An introduction. Cambridge: Cambridge University Press.
Jeng R., Tseng S.M. (2018) The relative importance of computer self-efficacy, perceived ease-of-use and reducing search cost in determining consumers' online group-buying intention // International Journal of Human and Technology Interaction. Vol. 2. № 1. P. 1-12.
Kolar T., Zabkar V. (2010) A consumer-based model of authenticity: An oxymoron or the foundation of cultural heritage marketing? // Tourism Management. Vol. 31. № 5. P. 652-664. DOI: https://doi.org/10.1016/j.tourman.2009.07.010
Narver J.C., Slater S.F. (1990) The effect of a market orientation on business profitability // Journal of Marketing. Vol. 54. № 4. P. 20-35.
Pereira R.E. (2001) Influence of query-based decision aids on consumer decision making in electronic commerce // Information Resources Management Journal. Vol. 14. № 1. P. 31-48.
Preacher K.J., Hayes A.F. (2008) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models // Behavior Research Methods. Vol. 40. № 3. P. 879-891.
Qiang Y., Lin Z., Li Y., Shuang W., Sun T., Wang L., Chen H. (2016) Effects of product portfolios and recommendation timing in the efficiency of personalized recommendation: Effects of recommendation portfolios & timing // Journal of Consumer Behaviour. Vol. 15. № 6. P. 516-526.
Resnick P., Varian H.R. (1997) Recommender systems // Communications of the ACM. Vol. 40. № 3. P. 56-58.
Ricci F., Rokach L., Shapira B., Kantor P.B. (2011) Recommender Systems Handbook. Heidelberg, New York, Dordrecht, London: Springer.
Roca J.C., Gagne M. (2008) Understanding e-learning continuance intention in the workplace: A self-determination theory perspective // Computers in Human Behavior. Vol. 24. P. 1585-1604.
Rodrigues L.F., Oliveira A., Costa C.J. (2016) Playing seriously - how gamification and social cues influence bank customers to use gamified e-business applications // Computers in Human Behavior. Vol. 63. № 9. P. 392-407.
Salton G., McGill M. (1986) An introduction to modern information retrieval. New York: McGraw-Hill.
Smith A.D. (2013) Information exchanges associated with internet travel marketplaces // Online Information Review. Vol. 28. № 4. P. 292-300.
Taylor A.B., Mackinnon D.P., Tein J.Y. (2008) Tests of the three-path mediated effect // Organizational Research Methods. Vol. 11. № 2. P. 241-269.
Tsai M.-T., Cheng N.-C., Chen K.-S. (2011) Understanding online group buying intention: The roles of sense of virtual community and technology acceptance factors // Total Quality Management & Business Excellence. Vol. 22. № 10. P. 1091-1104. DOI: https://doi.org/10.1080/14783363.2011.614870
Villegas N.M., Sanchez C., Diaz-Cely J., Tamura G. (2018) Characterizing context-aware recommender systems: A systematic literature review // Knowledge-Based Systems. Vol. 140. № 15. P. 173-200.
Yi H., Chen Q., Kitkuakul S. (2018) Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy // Cogent Business & Management. Vol. 5. P. 1-22.
Yuan S., Jeyaraj A. (2013) Information technology adoption and continuance: A longitudinal study of individuals' behavioral intentions // Information & Management. Vol. 50. № 7. P. 457-465.
Zampetakis L.A., Lerakis M., Kafetsios K., Moustakis V.S. (2015) The moderating role of anticipated affective ambivalence in the formation of entrepreneurial intentions // International Entrepreneurship and Management Journal. Vol. 12. № 3. P. 815-838. DOI: https://doi.org/10.1007/s11365-015-0367-2
Zhao S., Zhou M.X., Quan Y., Zhang X., Zheng W., Fu R. (2010) Who is talking about what: Social map-based recommendation for content-centric social websites // Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, September 26-30, 2010 / Eds. X. Amatriain, M. Torrens, P.J. Resnick, M. Zanker. New York: Association for Computing Machinery. P. 143-150. DOI: https://doi.org/10.1145/1864708.1864737
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.