The Effects of Personalized Recommendations on Purchasing Intention in Taobao.com: A Study of Customers in Guangxi Province, China
Keywords:
Personalized Recommendations, User stickiness, Purchase IntentionAbstract
This study aimed to examine the impact of personalized recommendations on user stickiness and purchase intention among customers on Taobao.com in Guangxi Province, China. This research was quantitative research type. Data were collected from 703 consumers who had used personalized recommendations on Taobao.com to purchase goods, using non-probability and purposive sampling methods. Structural equation modeling (SEM) was employed for data analysis.
The theoretical contribution of this study lied in establishing a hypotheses model and conducting empirical testing. The data analysis results indicated that among customers on Taobao.com in Guangxi Province, China, personalized recommendations positively impact purchase intention. The findings showed that the research result of the personalized recommendations had an impact on consumer user stickiness, most of the respondents group gave the highest level of importance to the personalized recommendation accuracy (PRA) factor at ( = 3.47). The result analysis of purchase intention among customers on Taobao.com in Guangxi Province, China, found that most of the respondents group gave the highest level of importance to the Taobao.com positively affected my decision to buy from the platform (PI5) factor (
= 3.42). The result analysis of user stickiness had an impact on consumer purchase intention, found that most of the respondents group gave the highest level of importance to the continuous use (CU) factor at (
= 3.41). The structural equation model developed to assess how personalized recommendations affect purchase intention showed a good fit with the empirical data (p < 0.001, Chi-square/df = 1.163, GFI = 0.940, TLI = 0.991, CFI = 0.992, and RMSEA = 0.015). The structural equation model indicates that personalized recommendations predict user stickiness with a coefficient of 0.791 (p < 0.001), and user stickiness impacts purchase intention with a coefficient of 0.434 (p < 0.001). Notably, when user stickiness was controlled, the influence of personalized recommendations on purchase intention was significantly with a coefficient of 0.437 (p < 0.001)
For Taobao.com, providing personalized recommendations involves comprehensive and specific tracking of consumer preferences, enhancing user stickiness, strengthening consumer relationships, delivering high-quality services that make consumers feel relaxed and happy during the entire shopping process, thereby increasing purchase intention. This research offers valuable insights for e-commerce platforms, highlighting the importance of strategic personalization.
References
Alamer, A. (2022). Exploratory structural equation modeling (ESEM) and bifactor ESEM for construct validation purposes: Guidelines and applied example. Research Methods in Applied Linguistics, 1(1), 100005.
Al-Ja’afreh, A., & Al-Adaileh, R. (2020). The impact of electronic word of mouth on consumers purchasing intention. Journal of Theoretical and Applied Information Technology, 98(2), 183-193.
Bahtar, A. Z., Muthusamy, G., Yazid, Z. A., & Daud, S. (2022). The E-Servqual Effect on Mobile Stickiness Intention of E-Commerce Marketplace. at the International Academic Symposium of Social Science 2022, Kota Bharu, Malaysia.
Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., & Xia, F. (2019). Scientific paper recommendation: A survey. IEEE Access, 7, 9324-9339. DOI: 10.48550/arXiv.2008.13538
Bao, Z., & Zhu, Y. (2023). Understanding customers’ stickiness of live streaming commerce platforms: An empirical study based on modified e-commerce system success model. Asia Pacific Journal of Marketing and Logistics, 35(3), 775-793.
Bawack, R. E., Wamba, S. F., & Carillo, K. D. A. (2021). Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis. International Journal of Information Management, 58, 102309.
Behera, R. K., Gunasekaran, A., Gupta, S., Kamboj, S., & Bala, P. K. (2020). Personalized digital marketing recommender engine. Journal of Retailing and Consumer Services, 53, 101799.
Belhadi, A., Kamble, S., Benkhati, I., Gupta, S., & Mangla, S. K. (2023). Does strategic management of digital technologies influence electronic word-of-mouth (eWOM) and customer loyalty? Empirical insights from B2B platform economy. Journal of Business research, 156, 113548.
Center, C. I. N. I. China Internet Network Information Center. (2022). The 50th Statistical Report on China’s. from https://www.cnnic.com.cn/IDR/.
Chan, I. C. C., Ma, J., Law, R., Buhalis, D., & Hatter, R. (2021). Dynamics of hotel website browsing activity: the power of informatics and data analytics. Industrial management & data systems, 121(6), 1398-1416.
Chen, Y., Lu, Y., Wang, B., & Pan, Z. (2019). How do product recommendations affect impulse buying? An empirical study on WeChat social commerce. Information & Management, 56(2), 236-248.
China Report Hall. (2022). Analysis of the proportion of e-commerce market in China. from https://m.chinabgao.com/k/dianshang/61847.html.
Chopdar, P. K., & Balakrishnan, J. (2020). Consumers response towards mobile commerce applications: SOR approach. International Journal of Information Management, 53, 102106.
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of marketing research, 28(3), 307-319.
Ekolu, S. O., & Quainoo, H. (2019). Reliability of assessments in engineering education using Cronbach’s alpha, KR and split-half methods. Global journal of engineering education, 21(1), 24-29.
Ferraro, R. (2020). The Chinese urban-rural Digital Divide and the development of E-commerce in rural China. (Master's Degree Thesis). Italy: Università Ca' Foscari Venezia
Friedrich, T., Schlauderer, S., & Overhage, S. (2021). Some things are just better rich: how social commerce feature richness affects consumers’ buying intention via social factors. Electronic markets, 31, 159-180.
Gorsuch, R. L. (1974). Factor Analysis. Philadelphia: W.B Saunders Co.
Guo, J., Hao, H., Wang, M., & Liu, Z. (2022). An empirical study on consumers' willingness to buy agricultural products online and its influencing factors. Journal of cleaner production, 336, 130403.
Gupta, U., Wu, C.-J., Wang, X., Naumov, M., Reagen, B., Brooks, D., Cottel, B., Hazelwood, K., Hempstead, M., & Jia, B. (2020). The architectural implications of facebook's dnn-based personalized recommendation. 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), San Diego, CA, USA, 2020, 488-501.
Hu, X., & Liu, J. (2021). Research on e-commerce visual marketing analysis based on internet big data. 2021 International Conference on Advances in Optics and Computational Sciences (ICAOCS) 2021. 21-23 January 2021, Ottawa, Canada
Jak, S., & Cheung, M. W.-L. (2020). Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological methods, 25(4), 430.
Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36.
Kim, M. J., Lee, C.-K., & Jung, T. (2020). Exploring consumer behavior in virtual reality tourism using an extended stimulus-organism-response model. Journal of Travel Research, 59(1), 69-89.
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
Li, M., & Wang, L. (2019). A survey on personalized news recommendation technology. IEEE Access, 7, 145861-145879.
Li, Y., Li, X., & Cai, J. (2021). How attachment affects user stickiness on live streaming platforms: A socio-technical approach perspective. Journal of Retailing and Consumer Services, 60, 102478.
Li, D. (2019). The influence of personalized recommendation in shopping platform on consumers' purchase intention. China Trade Guide (Middle) (12), 122-125.
Liu, P., Li, M., Dai, D., & Guo, L. (2021). The effects of social commerce environmental characteristics on customers’ purchase intentions: The chain mediating effect of customer-to-customer interaction and customer-perceived value. Electronic commerce research and applications, 48, 101073.
Liu, Y., & Wang, Y. (2023). Empirical study on the factors affecting user stickiness of online visual art platform from the perspective of user experience. IEEE Access, 11, 60763-60776.
Lu, H.-H., & Chen, C.-F. (2023). How do influencers’ characteristics affect followers’ stickiness and well-being in the social media context?. Journal of Services Marketing, 37(8), 1046-1058.
Martasari, G. W. (2023). Impact of Industrial Technology 4.0 In Improving Service Quality and Customer Experience on E-Commerce Platforms: Literature Review. International Journal of Social Service and Research, 3(6), 1427-1435.
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. the MIT Press.
Meng, H. (2022). Analysis of the relationship between transformational leadership and educational management in higher education based on deep learning. Computational Intelligence and Neuroscience, 2022(2). DOI: 10.1155/2022/5287922
Mertler, C. A., Vannatta, R. A., & LaVenia, K. N. (2021). Advanced and multivariate statistical methods: Practical application and interpretation. (7th Edition). Routledge.
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), 67-72.
Noman, S., Shahar, H. K., Rahman, H. A., Ismail, S., Aljaberi, M. A., & Abdulrahman, M. N. (2021). Factor structure and internal reliability of breast cancer screening Champion’s Health Belief Model Scale in Yemeni women in Malaysia: A cross-sectional study. BMC women's health, 21, 1-11.
Nunnally, J. C., & Bernstein, I. H. (1994). Validity. Psychometric Theory. McGraw-Hill, New York.
Qu, Y., Cieślik, A., Fang, S., & Qing, Y. (2023). The role of online interaction in user stickiness of social commerce: The shopping value perspective. Digital Business, 3(2), 100061.
Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, 109074.
Rosário, A., & Raimundo, R. (2021). Consumer marketing strategy and E-commerce in the last decade: a literature review. Journal of theoretical and applied electronic commerce research, 16(7), 3003-3024.
Sari, C., Huda, I., Pada, A., & Rahmatan, H. (2020). Construct validity of digital media literacy instrument for student teachers. The 1st Annual International Conference on Mathematics, Science and Technology Education. 14th - 15th September 2019, Kota Banda Aceh, Indonesia.
Sember, V., Meh, K., Sorić, M., Starc, G., Rocha, P., & Jurak, G. (2020). Validity and reliability of international physical activity questionnaires for adults across EU countries: systematic review and meta-analysis. Int J Environ Res Public Health, 17(19), 7161. DOI: 10.3390/ijerph17197161.
Sganzerla, G., Ravagnani, C. d. F. C., de Oliveira-Junior, S. A., & de Paula Ravagnani, F. C. (2021). Validity and reliability of the Sport Readiness Questionnaire focused on musculoskeletal injuries. Asian journal of sports medicine, 12(4), e116188
Shi, W., Li, F., & Hu, M. (2023). The influence of atmospheric cues and social presence on consumers' impulse buying behaviors in e-commerce live streaming. Electronic Commerce Research. DOI: 10.1007/s10660-023-09793-3
Taherdoost, H. (2022). Designing a questionnaire for a research paper: a comprehensive guide to design and develop an effective questionnaire. Asian Journal of Managerial Science, 11, 8-16.
Tedjakusuma, A. P., Retha, N. K. M. D., & Andajani, E. (2023). The Effect of Destination Image and Perceived Value on Tourist Satisfaction and Tourist Loyalty of Bedugul Botanical Garden, Bali. BASKARA: Journal of Business and Entrepreneurship, 6(1).
Thakkar, J. J. (2020). Structural equation modelling. Application for Research and Practice. (1st Ed). Springer
Vila, T. D., González, E. A., Vila, N. A., & Brea, J. A. F. (2021). Indicators of website features in the user experience of e-tourism search and metasearch engines. Journal of theoretical and applied electronic commerce research, 16(1), 18-36.
Wakil, K., Alyari, F., Ghasvari, M., Lesani, Z., & Rajabion, L. (2020). A new model for assessing the role of customer behavior history, product classification, and prices on the success of the recommender systems in e-commerce. Kybernetes, 49(5), 1325-1346.
Wang, H., Yang, D., & Qiu, X. (2022). Research on The Influence of Personalized Recommendation on Consumers' Purchasing Decision: The Mediating Role of Consumers' Privacy Concern. 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022),
Wang, X. (2020). Personalized recommendation framework design for online tourism: know you better than yourself. Industrial management & data systems, 120(11), 2067-2079.
Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological review, 20(2), 158.
Xiao, L., Guo, F., Yu, F., & Liu, S. (2019). The effects of online shopping context cues on consumers’ purchase intention for cross-border E-Commerce sustainability. Sustainability, 11(10), 2777.
Yamane, T. (1964). Appendix Tables for Statistics, an Introductory Analysis. Harper & Row.
Yeomans, M., Shah, A., Mullainathan, S., & Kleinberg, J. (2019). Making sense of recommendations. Journal of Behavioral Decision Making, 32(4), 403-414.
Zhang, W., Leng, X., & Liu, S. (2020). Research on mobile impulse purchase intention in the perspective of system users during COVID-19. Personal and Ubiquitous Computing, 1-9.
Zhang, Y. (2023). Study on the influence of personalized recommendation of
e-commerce platform on consumer purchase decision. Modernization of shopping malls (23), 55-57.
Zhou, B., & Zou, T. (2023). Competing for recommendations: The strategic impact of personalized product recommendations in online marketplaces. Marketing Science, 42(2), 360-376.
Zhou, K., Wang, H., Zhao, W. X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., & Wen, J.-R. (2020). S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. Proceedings of the 29th ACM international conference on information & knowledge management,
Zhu, B., Kowatthanakul, S., & Satanasavapak, P. (2020). Generation Y consumer online repurchase intention in Bangkok: Based on Stimulus-Organism-Response (SOR) model. International Journal of Retail & Distribution Management, 48(1), 53-69.
Zimmermann, R., Mora, D., Cirqueira, D., Helfert, M., Bezbradica, M., Werth, D., Weitzl, W. J., Riedl, R., & Auinger, A. (2023). Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence. Journal of Research in Interactive Marketing, 17(2), 273-298.
Zott, C., Amit, R., & Donlevy, J. (2000). Strategies for value creation in e-commerce: best practice in Europe. European Management Journal, 18(5), 463-475.
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