IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i24p4951-d1299925.html
   My bibliography  Save this article

Research on Optimal Group-Purchase Threshold and Pricing Strategy of Community Group Purchase

Author

Listed:
  • Shuhan Xu

    (SILC Business School, Shanghai University, Shanghai 201899, China)

  • Tianrui Chen

    (School of Qian Weichang, Shanghai University, Shanghai 200444, China)

Abstract

This study delves into the rapidly evolving community group-buying model, specifically focusing on the determination of optimal group-buying thresholds and pricing strategies for merchants. Aiming to bridge the gap in the existing literature, the methodology employs optimization models, integrating a numerical analysis to construct and evaluate a single merchant model. The findings reveal a nuanced relationship: within a specific threshold interval, a unique group-purchase threshold exists where merchants can maximize profits by balancing group and ordinary sales. The study shows that factors like ordinary selling price, group-buying publicity, and associated costs significantly influence these thresholds and pricing strategies. A critical insight is the threshold’s variability in response to market conditions, highlighting a strategic balance for maximizing profitability. The research underscores the need for merchants to adapt their strategies in response to evolving market dynamics and consumer behaviors. However, the study acknowledges its limitations due to its theoretical nature and focus on the Chinese market, suggesting the potential for future empirical studies in diverse cultural and economic contexts. Overall, this research contributes both theoretically and practically by providing a foundational framework for merchants to optimize group-purchase thresholds and pricing strategies in the dynamic realm of community group buying.

Suggested Citation

  • Shuhan Xu & Tianrui Chen, 2023. "Research on Optimal Group-Purchase Threshold and Pricing Strategy of Community Group Purchase," Mathematics, MDPI, vol. 11(24), pages 1-18, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4951-:d:1299925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/24/4951/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/24/4951/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, Shuxing & Zhang, Bin, 2022. "Operation strategies for nanostore in community group buying," Omega, Elsevier, vol. 110(C).
    2. Liang, Xiaoying & Ma, Lijun & Xie, Lei & Yan, Houmin, 2014. "The informational aspect of the group-buying mechanism," European Journal of Operational Research, Elsevier, vol. 234(1), pages 331-340.
    3. Yu, Bin & Shan, Wenxuan & Sheu, Jiuh-Biing & Diabat, Ali, 2022. "Branch-and-price for a combined order selection and distribution problem in online community group-buying of perishable products," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 341-373.
    4. Mengyao Zhang & Hasliza Hassan & Melissa Wendy Migin, 2023. "Exploring the Consumers’ Purchase Intention on Online Community Group Buying Platform during Pandemic," Sustainability, MDPI, vol. 15(3), pages 1-13, January.
    5. Zhang, Guoquan & Shang, Jennifer & Yildirim, Pinar, 2016. "Optimal pricing for group buying with network effects," Omega, Elsevier, vol. 63(C), pages 69-82.
    6. Krishnan S. Anand & Ravi Aron, 2003. "Group Buying on the Web: A Comparison of Price-Discovery Mechanisms," Management Science, INFORMS, vol. 49(11), pages 1546-1562, November.
    7. Xiaoqing Jing & Jinhong Xie, 2011. "Group Buying: A New Mechanism for Selling Through Social Interactions," Management Science, INFORMS, vol. 57(8), pages 1354-1372, August.
    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. Guanqun Ni, 2019. "A pricing model for group buying based on network effects," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-18, January.
    2. Huang, Yeu-Shiang & Ho, Jyh-Wen & Lee, Chang-Lin, 2024. "Group buying in multiple channels with consideration of heterogeneous customers," International Journal of Production Economics, Elsevier, vol. 270(C).
    3. Bi, Gongbing & Geng, Botao & Liu, Lindong, 2019. "On the fixed and flexible funding mechanisms in reward-based crowdfunding," European Journal of Operational Research, Elsevier, vol. 279(1), pages 168-183.
    4. Zihan Guan & Xiaoran Shi & Huajing Ying & Ruhui Xue & Xiaojiao Qiao, 2024. "An empirical study on traditional offline retailer’s switching intention towards community-based group buying program: A push–pull-mooring model," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-20, December.
    5. Tran, Thanh & Desiraju, Ramarao, 2017. "Group-buying and channel coordination under asymmetric information," European Journal of Operational Research, Elsevier, vol. 256(1), pages 68-75.
    6. Wang, Jie & Ma, Benedict Jun & Yang, Yanyi & Cheng, Chun-Hung & Kuo, Yong-Hong, 2025. "Group buying with consumer disappointment at failed deals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    7. Caixia Hao & Lei Yang, 2024. "Fission marketing on social media platforms with consumer sentiment considerations," Electronic Commerce Research, Springer, vol. 24(3), pages 2143-2173, September.
    8. Sun, Shuxing & Zhang, Bin, 2022. "Operation strategies for nanostore in community group buying," Omega, Elsevier, vol. 110(C).
    9. Sheng Li & Huifang Feng & Yu Huang, 2017. "Pricing for Products in Website-Dominant Static Group-buying," Business and Management Research, Business and Management Research, Sciedu Press, vol. 6(3), pages 94-101, September.
    10. Zhang, Guoquan & Shang, Jennifer & Yildirim, Pinar, 2016. "Optimal pricing for group buying with network effects," Omega, Elsevier, vol. 63(C), pages 69-82.
    11. Rui Hou & Weijian Li & Kangning Jin & Mingyue Qu & Xiaogang Lin, 2024. "Dynamic pricing for group‐buying strategy with network externality," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(2), pages 1006-1025, March.
    12. Longyuan Du & Ming Hu & Jiahua Wu, 2022. "Contingent stimulus in crowdfunding," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3543-3558, September.
    13. Chenxu Ke & Bo Yan & Ruofan Xu, 2017. "A group-buying mechanism for considering strategic consumer behavior," Electronic Commerce Research, Springer, vol. 17(4), pages 721-752, December.
    14. Oktay Karabağ & Barış Tan, 2018. "Analysis of a group purchasing organization under demand and price uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 844-883, December.
    15. Amedeo Piolatto, 2015. "Online booking and information: competition and welfare consequences of review aggregators," Working Papers 2015/11, Institut d'Economia de Barcelona (IEB).
    16. Yuecheng Yu & Alexander Pelaez & Karl R. Lang, 2016. "Designing and evaluating business process models: an experimental approach," Information Systems and e-Business Management, Springer, vol. 14(4), pages 767-789, November.
    17. Simone Marinesi & Karan Girotra & Serguei Netessine, 2018. "The Operational Advantages of Threshold Discounting Offers," Management Science, INFORMS, vol. 64(6), pages 2690-2708, June.
    18. Oksana Loginova & Andrea Mantovani, 2019. "Price competition in the presence of a web aggregator," Journal of Economics, Springer, vol. 126(1), pages 43-73, January.
    19. Tianhua Zhang & Juliang Zhang & Fu Zhao & Yihong Ru & John W. Sutherland, 2020. "Allocating resources for a restaurant that serves regular and group-buying customers," Electronic Commerce Research, Springer, vol. 20(4), pages 883-913, December.
    20. Jiahua Wu & Mengze Shi & Ming Hu, 2015. "Threshold Effects in Online Group Buying," Management Science, INFORMS, vol. 61(9), pages 2025-2040, September.

    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:11:y:2023:i:24:p:4951-:d:1299925. 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.