THE COGNITIVE MECHANISM OF CONSUMER RETURN BEHAVIOR IN CHINESE FASHION E-COMMERCE: EVIDENCE FROM EXPECTATION CONFIRMATION THEORY

Yuhai Sun; Ammara Dokmai

ผู้แต่ง

Yuhai Sun Faculty of Management Science, Ayutthaya Rajabhat University
Ammara Dokmai Faculty of Management Science, Ayutthaya Rajabhat University

คำสำคัญ:

Chinese Fashion E-Commerce, Consumer Return Behavior, Expectation Confirmation, Satisfaction, Customer Satisfaction

บทคัดย่อ

This research article adopts sequential mixed-methods grounded in Expectation Confirmation Theory to explore consumers’ cognitive return mechanism within China’s booming fashion e-commerce industry plagued by high return rates. Three core research objectives guide the whole investigation: verifying antecedent effects on expectation confirmation, testing the chained path from confirmation via satisfaction to return, and interpreting return psychology through multi-stakeholder qualitative data. Quantitatively, 603 valid stratified-sampling questionnaires are analyzed via CFA and SEM. Qualitatively, nine interviewees covering consumers, sellers and industry regulators complete semi-structured interviews with three-stage thematic coding. Empirically, expectation and perceived performance significantly improve confirmation; confirmation lifts satisfaction and further curbs return behavior, with serial mediations statistically validated. Qualitative outcomes reveal mismatches of size/fabric/picture and convenient logistics plus biased online comments are core return drivers. This study enriches localized ECT application and proposes targeted information disclosure and differentiated return policies for domestic fashion platforms.

เอกสารอ้างอิง

Al Amin, M. et al. (2021). Using mobile food delivery applications during the COVID-19 pandemic: An extended model of planned behavior. Journal of Food Products Marketing, 27(2), 105–126.

Ambilkar, P. et al. (2022). Product returns management: A comprehensive review and future research agenda. International Journal of Production Research, 60(12), 3920–3944.

Anderson, J. C. & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.

Bolton, R. N. & Drew, J. H. (1991). A multistage model of customers' assessments of service quality and value. Journal of Consumer Research, 17(4), 375–384.

Browne, M. W. & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258.

Burnham, T. A. et al. (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109–126.

Cahigas, M. M. L. et al. (2023). Determinants of continuous visiting behavior to Palawan, Philippines: Integrating Uncertainty Reduction Theory and Expectation Confirmation Theory. PLOS ONE, 18(10), e0291694.

Cantini, R. et al. (2021). A visual tool for reducing returns in e-commerce platforms. In 2021 IEEE International Forum on Research and Technology for Society and Industry (RTSI) (pp. 78–85). IEEE. https://doi.org/10.1109/RTSI50628.2021.9597230.

Carter, C. R. & Ellram, L. M. (1998). Reverse logistics: A review of the literature and framework for future investigation. Journal of Business Logistics, 19(1), 85–102.

Changchit, C. & Klaus, T. (2020). Determinants and impact of online reviews on product satisfaction. Journal of Internet Commerce, 19(1), 82–102.

Chen, W. K. et al. (2023). Understanding the influence of impulse buying toward consumers’ post-purchase dissonance and return intention: An empirical investigation of apparel websites. Journal of Ambient Intelligence and Humanized Computing, 14(12), 14453–14466.

Chen, Y. Y. et al. (2010). Confirmation of expectations and satisfaction with internet shopping: The role of internet self-efficacy. Computer and Information Science, 3(3), 14–22.

Chou, H. K. et al. (2012). Engagement in e-learning opportunities: An empirical study on patient education using expectation confirmation theory. Journal of Medical Systems, 36(3), 1697–1706.

Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386–405.

Creswell, J. W. & Clark, V. L. P. (2017). Designing and conducting mixed methods research (3rd ed.). Thousand Oaks, CA: SAGE Publications.

Das, L. & Kunja, S. R. (2024). Why do consumers return products? A qualitative exploration of online product return behaviour of young consumers. Journal of Retailing and Consumer Services, 78, 103770.

E-commerce Research Center. (2024). 2024 China e-commerce user experience and complaint data report [Report].

Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: Evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294–311.

Ertekin, N. et al. (2020). Assessing impacts of store and salesperson dimensions of retail service quality on consumer returns. Production and Operations Management, 29(5), 1232–1255.

Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Hair, J. F. et al. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

_____. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Education.

Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420.

Hipólito, F. et al. (2025). Influence of consumer trust, return policy, and risk perception on satisfaction with the online shopping experience. Systems, 13(3), 158.

Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

Ikonen, I., Eelen, J., & Sotgiu, F. (with Zhang, Y.). (2025). One size does not fit all: Optimizing size-inclusive model photography mitigates fit risk in online fashion retailing. Journal of the Academy of Marketing Science, 53(2), 643–672.

Ivankova, N. V. et al. (2006). Using mixed methods sequential explanatory design: From theory to practice. Field Methods, 18(1), 3–20.

Kapoor, A. P. & Vij, M. (2018). Technology at the dinner table: Ordering food online through mobile apps. Journal of Retailing and Consumer Services, 43, 342–351.

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.

Latané, B. (1981). The psychology of social impact. American Psychologist, 36(4), 343–356.

Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1–55.

Lv, J. & Liu, X. (2022). The impact of information overload of e-commerce platform on consumer return intention: Considering the moderating role of perceived environmental effectiveness. International Journal of Environmental Research and Public Health, 19(13), 8060.

Maity, D. & Arnold, T. J. (2013). Search: An expense or an experience? Exploring the influence of search on product return intentions. Psychology & Marketing, 30(7), 576–587.

Mollenkopf, D. A., Rabinovich, E., Laseter, T. M., & Boyer, K. K. (2007). Managing internet product returns: A focus on effective service operations. Decision Sciences, 38(2), 215–250.

National Retail Federation. (2023). 2023 Consumer returns in the retail industry [Report].

Oliver, R. L. (1977). Effect of expectation and disconfirmation on postexposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 62(4), 480–486.

_____. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469.

Preacher, K. J. & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

Robichaud, Z. et al. (2024). Bridging the information asymmetry in e-commerce: An intercultural perspective on sustainable clothing. International Journal of Retail & Distribution Management, 52(10/11), 1004–1019.

Shang, H. J. & Wu, C. W. (2017). The impact of product returns on customer satisfaction and repurchase intention. Journal of Business Research, 75, 108–117.

Treiblmaier, H. & Garaus, M. (2023). Using blockchain to signal quality in the food supply chain: The impact on consumer purchase intention and the moderating role of brand familiarity. International Journal of Hospitality Management, 68, 1–12.

Wang, Y. et al. (2023). Factors affecting customer intention to return in online shopping: The roles of expectation disconfirmation and post-purchase dissonance. Electronic Commerce Research. Advance online publication. Electron. Commer. Res. 25(4), https://doi.org/10.1007/s10660-023-09769-3

Westbrook, R. A. (1980). Intrapersonal affective influences on consumer satisfaction with products. Journal of Consumer Research, 7(1), 49–54.

Wijaya, A. et al. (2019). The effect of expectation confirmation and perceived value on customer satisfaction and loyalty. International Journal of Scientific & Technology Research, 8(11), 112–117.

Williamson, O. E. (1981). The economics of organization: The transaction cost approach. American Journal of Sociology, 87(3), 548–577.

Zhao, X. et al. (2019). Who should pay for return freight in the online retailing? Retailers or consumers. Electronic Commerce Research, 20, 427–452.