The Influence of AI-Driven Factors on Customer Satisfaction and Loyalty in E-commerce: A Case Study of Taobao Users in China
DOI:
https://doi.org/10.65205/jmsr.2026.e286305คำสำคัญ:
AI Personalization, Customer Satisfaction, Customer Loyalty, Trust in AI, E-Commerceบทคัดย่อ
This research, distant from the ubiquitous siloed AI scenario, investigates how customer satisfaction and loyalty are jointly shaped by different AI-driven factors in B2C e-commerce. We conducted a cross-sectional online survey through the Wenjuanxing platform, collecting data from 405 active Taobao users in China. A 5-point Likert scale was employed to measure the instrument; content validity was assessed using IOC values (0.67–1.00); and reliability for each construct was confirmed with Cronbach's alpha > 0.83. Proposed hypotheses were tested using Pearson correlation and multiple linear regression. The results demonstrated that perceived synergistic AI personalization (PSAP), perceived warmth (PW), perceived procedural value (PPV), AI competence (AIC), and information quality (IQ) all had significant positive effects on CS; and PSAP and TAI significantly predicted CL. PSAP (β = 0.170, p < 0.001) was the dominant predictor of customer satisfaction, with PW (β = 0.147, p < 0.01), IQ (β = 0.145, p < 0.01), PPV (β = 0.144, p < 0.01), and AIC (β = 0.119, p < 0.04); for exploratory behavior, PSAP (β = 0.263, p < 0.001) and TAI (β = 0.240, p < 0.001). From an applied perspective, the results suggest that Taobao and other AI-backed commerce platforms should pursue closer integration of chatbots and recommendation systems, plan for more personable and transparent AI interactions, and elevate trust-building features including clearer policies on data usage and human escalation as first-order drivers of long-term loyalty.
เอกสารอ้างอิง
Alibaba Group. (2024). Annual report 2024. Alibaba Group Holding Limited. https://www.alibabagroup.com/
iResearch. (2025). China online shopping market data report 2025. iResearch Consulting Group. https://www.iresearchchina.com/
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445. https://doi.org/10.1007/s12525-020-00414-7
Adomavicius, G., & Tuzhilin, A. (2005). 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, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99
DeLone, W. H., & McLean, E. R. (2016). Information systems success measurement. Foundations and Trends in Information Systems, 2(1), 1–116. https://doi.org/10.1561/2900000005
Edelman, D. C., & Abraham, M. (2022). Customer experience in the age of AI. Harvard Business Review, 100(3–4), 116–125.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Fiske, S. T., Cuddy, A. J. C., & Glick, P. (2007). Universal dimensions of social cognition: Warmth and competence. Trends in Cognitive Sciences, 11(2), 77–83. https://doi.org/10.1016/j.tics.2006.11.005
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hollebeek, L. D., Sprott, D. E., & Brady, M. K. (2021). Rise of the machines? Customer engagement in automated service interactions. Journal of Service Research, 24(1), 3–8. https://doi.org/10.1177/1094670520975110
Huang, M. H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2022). My teacher is a machine: Understanding students' perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 38(15), 1432–1444. https://doi.org/10.1080/10447318.2021.2018339
Lankton, N. K., McKnight, D. H., & Tripp, J. (2015). Technology, humanness, and trust: Rethinking trust in technology. Journal of the Association for Information Systems, 16(10), 880–918. https://doi.org/10.17705/1jais.00411
Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), 1–16. https://doi.org/10.1177/2053951718756684
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(Special Issue), 33–44. https://doi.org/10.1177/00222429990634s105
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156
Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207. https://doi.org/10.1016/j.jretconser.2020.102207
Riemer, K., & Peter, S. (2021). Algorithmic audiencing: Why we need to rethink free speech on social media. Journal of Information Technology, 36(4), 409–426. https://doi.org/10.1177/02683962211013358
Roy, R., & Naidoo, V. (2021). Enhancing chatbot effectiveness: The value of warm communication. Journal of Business Research, 126, 23–34. https://doi.org/10.1016/j.jbusres.2020.12.051
Rovinelli, R. J., & Hambleton, R. K. (1977). On the use of content specialists in the assessment of criterion-referenced test item validity. Dutch Journal of Educational Research, 2, 49–60.
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human–Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Statista. (2024). Retail e-commerce sales worldwide from 2014 to 2027. Statista. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
eMarketer. (2024). Worldwide retail e-commerce forecast 2024. Insider Intelligence. https://www.emarketer.com/
Vargo, S. L., & Lusch, R. F. (2016). Institutions and axioms: An extension and update of service-dominant logic. Journal of the Academy of Marketing Science, 44(1), 5–23. https://doi.org/10.1007/s11747-015-0456-3
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
Wang, L., & Prompanyo, M. (2020). Modeling the relationship between perceived values, e-satisfaction, and e-loyalty. Management Science Letters, 10(11), 2609–2616. https://doi.org/10.5267/j.msl.2020.3.039
Yamane, T. (1973). Statistics: An Introductory Analysis. 3rd Edition, Harper and Row, New York.
