IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i16p2575-d1722776.html

Retail Service, Pricing, and Channel Selection Strategies for Fashion Products in a Two-Stage Decision Model

Author

Listed:
  • Liwen Liu

    (School of Politics & Public Administration, Soochow University, Suzhou 215123, China)

  • Xuejuan Li

    (School of Politics & Public Administration, Soochow University, Suzhou 215123, China)

  • Siyu Zhu

    (College of Design and Engineering, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore 119077, Singapore)

  • Mengyao Wang

    (School of Politics & Public Administration, Soochow University, Suzhou 215123, China)

Abstract

Fashion products are typically sold through both online and offline channels during two distinct phases: the launch and markdown period. Pricing strategies present significant challenges for manufacturers, particularly as consumers increasingly adopt strategic purchasing behaviors. Key factors, including product fashion utility, purchase timing, and consumer characteristics, complicate manufacturers’ channel selection, pricing decisions, and service strategy formulation—necessitating deeper investigation. This paper establishes a two-echelon supply chain model featuring a fashion manufacturer and a retailer to determine optimal channel, pricing, and service strategies across both selling periods amid strategic consumer behavior. We examine four channel strategies: (1) the MM strategy: the manufacturer operates both channels (online and offline channels) during both periods (launch and markdown period); (2) the MR strategy: the manufacturer operates both channels during the launch stage, and the retailer sells online during the markdown period; (3) the RR strategy: the manufacturer sells offline, and the retailer operates the online channel during both stages; (4) the RM strategy: the manufacturer sells online during both stages, and the retailer sells through the offline channel. Our analysis yields critical insights: When off-season discounts are limited, the manufacturer should maintain direct control of both channels. However, when the off-season discount is significant, the manufacturer needs to set the channel strategy according to the fashion utility. If the fashion utility is small, direct sales through offline channels during the launch period, while entrusting the retailer to distribute in online channels during both periods, should be adopted. If the fashion utility is large, a dual-channel, two-stage, entirely direct sales strategy should be adopted. This study elucidates the optimal manufacturer channel and pricing strategy options and provides some theoretical contributions and practical implications.

Suggested Citation

  • Liwen Liu & Xuejuan Li & Siyu Zhu & Mengyao Wang, 2025. "Retail Service, Pricing, and Channel Selection Strategies for Fashion Products in a Two-Stage Decision Model," Mathematics, MDPI, vol. 13(16), pages 1-25, August.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:16:p:2575-:d:1722776
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/16/2575/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/16/2575/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Namin, Aidin & Soysal, Gonca P. & Ratchford, Brian T., 2022. "Alleviating demand uncertainty for seasonal goods: An analysis of attribute-based markdown policy for fashion retailers," Journal of Business Research, Elsevier, vol. 145(C), pages 671-681.
    2. Xuanming Su, 2007. "Intertemporal Pricing with Strategic Customer Behavior," Management Science, INFORMS, vol. 53(5), pages 726-741, May.
    3. Pangpang Liu & Zhuoran Yang & Zhaoran Wang & Will Wei Sun, 2025. "Contextual Dynamic Pricing with Strategic Buyers," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 120(550), pages 896-908, April.
    4. Yangyang Qin, 2025. "The Mutual Impact of Suppliers’ Online Sales Channel Choices and Platform Credit Decisions for Offline Channels," Mathematics, MDPI, vol. 13(6), pages 1-29, March.
    5. Peng Wang & Jing Shao & Liping Liang & Yu Tang, 2025. "Multi-channel retailing and consumers’ environmental consciousness," Annals of Operations Research, Springer, vol. 345(1), pages 467-515, February.
    6. Lin, Jing & Ma, Xin & Talluri, Srinivas & Yang, Cheng-Hu, 2021. "Retail channel management decisions under collusion," European Journal of Operational Research, Elsevier, vol. 294(2), pages 700-710.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vincent Mak & Amnon Rapoport & Eyran J. Gisches & Jiaojie Han, 2014. "Purchasing Scarce Products Under Dynamic Pricing: An Experimental Investigation," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 425-438, July.
    2. Ying‐Ju Chen & Leon Yang Chu, 2020. "Synchronizing pricing and replenishment to serve forward‐looking customers," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 321-333, August.
    3. Tong Wang & Xiaofang Wang, 2017. "Intertemporal pricing strategies for fashion tech products with consumption externalities," Frontiers of Business Research in China, Springer, vol. 11(1), pages 1-14, December.
    4. Vincent Mak & Amnon Rapoport & Eyran J. Gisches, 2018. "Dynamic Pricing Decisions and Seller-Buyer Interactions under Capacity Constraints," Games, MDPI, vol. 9(1), pages 1-23, February.
    5. Ibrahim Mohammed & Basak Denizci Guillet & Rob Law & Wassiuw Abdul Rahaman, 2021. "Predicting the direction of dynamic price adjustment in the Hong Kong hotel industry," Tourism Economics, , vol. 27(2), pages 346-364, March.
    6. Namin, Aidin & Soysal, Gonca P. & Ratchford, Brian T., 2022. "Alleviating demand uncertainty for seasonal goods: An analysis of attribute-based markdown policy for fashion retailers," Journal of Business Research, Elsevier, vol. 145(C), pages 671-681.
    7. Correia-da-Silva, João, 2021. "Optimal priority pricing by a durable goods monopolist," Games and Economic Behavior, Elsevier, vol. 129(C), pages 310-328.
    8. Hua, Jiawen & Lin, Jun & Wang, Kai & Qian, Yanjun, 2025. "Levying carbon tariffs considering foreign competition and technology choice," Omega, Elsevier, vol. 135(C).
    9. Javad Nasiry & Ioana Popescu, 2011. "Dynamic Pricing with Loss-Averse Consumers and Peak-End Anchoring," Operations Research, INFORMS, vol. 59(6), pages 1361-1368, December.
    10. Liu, Changyu & Song, Yadong & Wang, Wei & Shi, Xunpeng, 2023. "The governance of manufacturers’ greenwashing behaviors: A tripartite evolutionary game analysis of electric vehicles," Applied Energy, Elsevier, vol. 333(C).
    11. Xuanming Su, 2010. "Optimal Pricing with Speculators and Strategic Consumers," Management Science, INFORMS, vol. 56(1), pages 25-40, January.
    12. Mushegh Harutyunyan & Chakravarthi Narasimhan, 2024. "Don’t Hurry, Be Happy! The Bright Side of Late Product Release," Marketing Science, INFORMS, vol. 43(6), pages 1188-1203, November.
    13. Huang, Yeu-Shiang & Ho, Jyh-Wen & Wu, Guan-Jin, 2022. "A study on promotion with strategic two-stage customized bundling," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    14. Yingxiao Li & Jianheng Zhou, 2023. "Modeling the relationship between fairness concern and customer loyalty in dual distribution channel," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-25, January.
    15. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
    16. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.
    17. Liu, Kemeng & Li, Gang, 2026. "Trendsetting in conspicuous consumption: The impact of a resale market," Omega, Elsevier, vol. 138(C).
    18. Selcuk, Cemil & Gokpinar, Bilal, 2017. "Fixed vs. Flexible Pricing in a Competitive Market," Cardiff Economics Working Papers E2017/9, Cardiff University, Cardiff Business School, Economics Section.
    19. Mierendorff, Konrad, 2016. "Optimal dynamic mechanism design with deadlines," Journal of Economic Theory, Elsevier, vol. 161(C), pages 190-222.
    20. Du, Shaofu & Sun, Xiahui & Hu, Li & Choi, Tsan-Ming, 2025. "Does the quantity discount mechanism offer a loophole for retailer collusion? Impacts and responses," European Journal of Operational Research, Elsevier, vol. 323(3), pages 999-1012.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:16:p:2575-:d:1722776. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.