IS BRAND LOYA LTY INFLUENCED BY PRICING STRATEGIES IN THE FASTFASHION INDUSTRY?
DOI:
https://doi.org/10.61173/cv9ww318Keywords:
Fast-fashion industry, Brand loyalty, Dynamic pricing, Fixed pricing, Consumer behavior, Repurchase intent, Logistic regression and Behavioral economicsAbstract
As the fast-fashion industry is estimated to be worth more than 120 billion in 2024, pricing strategies play a vital role in fostering consumer interest in the sector, where brands such as Zara, H&M, and Shein compete fiercely. The research question this investigation will explore is: Is brand loyalty influenced by pricing strategies in the fast-fashion industry? It seeks to validate the hypothesis that dynamic pricing, including flash sales based on AI and personalized discounts, increases brand loyalty by 25 points relative to fixed pricing, which is assessed through the prism of repurchase intent and net promoter scores. An online survey of 250 consumers aged 18-35 in China and the UK was conducted to gather primary data. In both simulated dynamic and fixed pricing conditions, respondents rated loyalty using a 5-point Likert scale. A binary logistic regression was adopted in this study, where loyalty (1 = loyal, score 4 or higher) was the dependent variable, dynamic pricing exposure was the central independent variable, and demographics, income, and brand familiarity were considered as controls. Model diagnostics were used to indicate goodness-of-fit (Nagelkerke R 2 = 0.28). The key findings suggest that as it was hypothesized, dynamic pricing can raise the likelihood of loyalty by 24.8 percent ( 0.223, OR = 1.25, p < 0.05), yet the results were more pronounced among price-sensitive low-income clients (OR = 1.32, p < 0.01). This also applies to the fast-fashion industry of more than $ 120 billion, which is focused on price to initiate consumer interactions amidst the high competition with other brands, such as Zara, H&M, or Shein. The research contributes to the body of knowledge about the topic of pricing-loyalty association, and poses the question of the ethical strategies towards the turbulent markets.
References
P. A. (2023). Commitment, trust, relative dependence, and customer loyalty in the B2B setting: the role of customer satisfaction. Journal of Business & Industrial Marketing, 39(5). https://doi.org/10.1108/jbim-08-2022-0375
Bafna, A. (2025). From Design to Distribution, Supply Chain Strategies in the Fast Fashion Industry: Insights from H&M, Benetton, and Zara. SSRN. https://doi. org/10.2139/ssrn.5188006
Barmes, D., Claeys, I., Dikau, S., & Luiz, S. (2024). The case for adaptive inflation targeting: monetary policy in a hot and volatile world - LSE Research Online. Lse.ac.uk. http://eprints.lse.ac.uk/129331/1/The-case-for-adaptive-inflation-targeting.pdf
Bonagas, K., & Vu Dang, T. G. (2022). Consumers’ Perspective on Loyalty Programmes and its Influence on Purchasing Decisions : A study on fast-fashion retailers’ consumers in the Swedish market. In www.diva-portal. org. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1664995
Bourdeau, B. L., Cronin, J. J., & Voorhees, C. M. (2024). Customer loyalty: A refined conceptualization, measurement, and model. Journal of Retailing and Consumer Services, 81, 104020–104020. https://doi.org/10.1016/ j.jretconser.2024.104020 Chatzoglou, P., Chatzoudes, D., Savvidou, A., Fotiadis,
T., & Delias, P. (2022). Factors affecting repurchase intentions in retail shopping: An empirical study. Heliyon, 8(9), e10619. https://doi.org/10.1016/j.heliyon.2022.e10619
Cummings, J. (2023). Means vs. Outcomes: Leveraging Psychological Insights for Media-Based Behavior Change Interventions. Springer EBooks, 137–157. https://doi. org/10.1007/978-3-031-26568-6_7
Gilbert, H. M. (2023). Digital transformation: how can it provide the most value to fashion brands while enabling sustainable change? Www.theseus.fi. https://www.theseus.fi/handle/10024/811046
Glassberg, J. T. (2022). Renovating the image of the Western United States on the European stage : photographs and Western displays at the Paris Exposition Universelle of 1867. Hal.science. https://theses.hal.science/tel-04416582
Guendouz, T. (2023). The emergence of Algorithmic Pricing enhanced by Hyper-Personalization in the era of Artificial Intelligence. (NAJSP), 10–32. https://najsp.com/ index.php/home/article/view/197
Hamid, N. S. (2025). Smart Strategy or Slippery Slope? Analyzing the Long-Term Effects of Sales Promotions on Brand Perception and Consumer Loyalty in the Fashion Industry. Social Science Review Archives., 3(3), 200–206. https://doi.org/10.70670/sra.v3i3.852
He, X. D., & Strub, M. S. (2022). How Endogenization of the Reference Point Affects Loss Aversion: A Study of Portfolio Selection. Operations Research, 70(6), 3035– 3053. https://doi.org/10.1287/opre.2022.2309 Jangid, M. (2022, August). Zara’s Case Study -the Strategy of the Fast Fashion Pioneer the Strategy of the Fast Fashion Pioneer. ResearchGate. https://www.researchgate.net/publication/362568585_ZARA
Ka, L. (2025). Emotion Classification, Topic Modelling, and Discourse Evaluation of Audience Responses to SNL’s Fast Fashion Sketch on Social Media : Leveraging RoBERTa, BERTopic and Discourse Analysis. DIVA. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1979224 Kopalle, P. K., Pauwels, K., Akella, L. Y., & Gangwar, Dean&Francis ISSN 2959-6130
M. (2023). Dynamic pricing: Definition, Implications for managers, and Future Research Directions. Journal of Retailing, 99(4), 580–593. https://doi.org/10.1016/j.jretai.2023.11.003
Kumar, R., Jain, V., Eastman, J. K., & Ambika, A. (2024). The components of perceived quality and their influence on online re-purchase intention. Journal of Consumer Marketing, 42(1). https://doi.org/10.1108/jcm-04-2024-
Lund, T. (2021). Research Problems and Hypotheses in Empirical Research. Scandinavian Journal of Educational Research, 66(7), 1–11. https://doi.org/10.1080/00313831. 2021.1982765 Pautassi, P. (2024, April 5). Algorithmic Pricing in the digital age. Webthesis.biblio.polito.it. https://webthesis. biblio.polito.it/31375/
Persson, E., & Olsson, L. (2024). Unlocking loyalty : A qualitative study on how loyalty programs should be designed to increase Gen Z:s loyalty in fast fashion retail. DIVA. https://www.diva-portal.org/smash/record. jsf?pid=diva2:1863208
Shane, J. (2022). Digital Market Concentration: An Institutional and Social Cost Digital Market Concentration: An Institutional and Social Cost Analysis Analysis. https:// digitalcommons.bowdoin.edu/cgi/viewcontent.cgi?article=1364&context=honorsprojects
Shuai, Q., Li, Z., & Zhang, Y. (2023). E-Commerce Channels and Platforms. 283–318. https://doi.org/10.1007/978- 981-99-0043-5_8
Siakki, M.-L. (2024). Audience engagement with Greek Instagram “Slow Fashion” accounts: A study of the impact of slow fashion factors on consumers’ intention to purchase and word-of-mouth. Acg.edu. https://sphere.acg. edu/jspui/handle/123456789/2518
Thompson, E. G., & Wilson, D. R. (2024). Dynamic Pricing Promotion Strategies on Consumer Repeat Purchase Behavior in the United States. Frontiers in Management Science, 3(3), 19–30. https://www.paradigmpress.org/fms/ article/view/1161 Venkataraman, S., & Petersen, J. A. (2022, July 15). B2B data-driven and value-based pricing strategies, price setting, and price execution. Www.elgaronline. com; Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/book/9781800376878/bookpart-9781800376878-24.xml
Wang, X., & Chen, Q. (2024). Stochastic Optimal Control for Dynamic Pricing and Production in Fashion Retailing: An Economic Sustainability Approach. Discrete Dynamics in Nature and Society, 2024(1). https://doi.org/10.1155/ ddns/4854557
Wen, H. (2025). A Case Study of Temu: Development Strategies for Emerging Cross-Border E-Commerce Platforms. Advances in Economics Management and Political Sciences, 181(1), 194–200. https://doi.org/10.54254/2754- 1169/2025.23336
Wood, S., Watson, I., & Teller, C. (2021). Pricing in online fashion retailing: implications for research and practice. Journal of Marketing Management, 37(11-12), 1–24. https://doi.org/10.1080/0267257x.2021.1900334 Appendixces Survey Questionnaire (Full instrument; consent, scales). Consent Statement (Must Accept to Proceed) You are invited to participate in a study on how pricing affects loyalty to fast-fashion brands (e.g., Zara, H&M, Shein). Participation is voluntary, and you may withdraw at any time without penalty. Responses are confidential, anonymized, and stored securely on university servers compliant with GDPR/PIPL. No personal data will be shared; aggregated results may be published in an academic report. If you agree, tick below and continue. [ ] I consent to participate. [ ] I do not consent (survey ends). By proceeding, you confirm you are aged 18-35 and have purchased fast-fashion items in the past 6 months. Section A: Background and Demographics (For Sample Description Only) A1. Which country are you currently residing in? · China · United Kingdom · Other (please specify): ___________ A2. Age: ___________ (years; 18-35) A3. Gender: · Male · Female · Non-binary/Other · Prefer not to say A4. Annual household income (approximate; select band): · Low (<£20,000 / ¥150,000 equivalent) · Medium (£20,000-£40,000 / ¥150,000-¥300,000 equivalent) · High (>£40,000 / ¥300,000 equivalent) · Prefer not to say A5. Which fast-fashion brands have you purchased from in the past 6 months? (Select up to 3) · Zara · H&M · Shein · Other (please specify): ___________ A6. On a scale of 1-5, how familiar are you with your most-purchased brand? (1 = Not at all familiar, 5 = Very familiar) 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] Section B: Pricing Exposure and Scenarios Instructions: Think about your experiences with fast-fashion brands. For each scenario below, rate how likely you would be to repurchase from that brand in the next month (1 = Very unlikely, 5 = Very likely). Scenarios are ran- Dean&Francis Tianduo Lyu domized. B1. Scenario 1: Fixed Pricing (e.g., a top consistently priced at £12 / ¥90 across seasons). Repurchase intent: 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] B2. Scenario 2: Dynamic Pricing (e.g., a top usually £15 / ¥110, but drops to £8 / ¥60 via a personalized app flash sale). Repurchase intent: 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] B3. Overall, how often do you encounter dynamic pricing (personalized deals/flash sales) when shopping fast fashion? · Rarely (<25% of purchases) · Sometimes (25-50%) · Often (51-75%) · Very often (>75%) B4. On a scale of 1-5, how fair do you perceive dynamic pricing to be? (1 = Very unfair, 5 = Very fair) 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] Section C: Brand Loyalty Measures Instructions: For your most-purchased fast-fashion brand, rate the following statements on a 5-point scale (1 = Strongly disagree, 5 = Strongly agree). These assess loyalty aspects. C1. I intend to continue buying from this brand in the future. 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] C2. I would recommend this brand to friends/family. 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] C3. This brand is my first choice for fast-fashion purchases. 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] C4. I feel emotionally attached to this brand. 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] C5. If prices increased by 10%, I would still shop here over competitors. 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] Composite Repurchase Intent Score: Average of C1-C3 (calculated post-survey for analysis). Section D: Open-Ended Insights (Optional) D1. In your own words, how does pricing (fixed or dynamic) affect your loyalty to fast-fashion brands? (Max 150 words) D2. What one change to pricing strategies would make you more loyal to your favorite brand? (Max 100 words) End of Survey
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
