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ORIGINAL RESEARCH article

Front. Phys.
Sec. Social Physics
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1455165
This article is part of the Research Topic Real-World Applications of Game Theory and Optimization, Volume II View all 6 articles

Research on the Recommendation Strategy of Dual-Channel Manufacturers for Hybrid E-commerce Platforms

Provisionally accepted
  • Guangzhou Railway Polytechnic, Guangzhou, China

The final, formatted version of the article will be published soon.

    In the context of hybrid e-commerce platforms with reselling mode and agency mode, this study considers the issue of channel management by manufacturers through recommendation strategies. For three dual-channel structures composed of e-commerce platforms, manufacturers, and third-party retailers, game models were constructed for manufacturer's non-recommendation, differentiated recommendation, and indiscriminate recommendation scenarios to investigate the optimal recommendation strategy for manufacturers. The results indicate that: (1) For different dual-channel structures, compared to scenarios without recommendations, it is not always profitable for manufacturers to adopt a recommendation strategy as recommended parties may not necessarily gain higher profits from recommendations. (2) The optimal recommendation strategy for manufacturers is influenced by channel structure, commission rates, and relative scale in the recommended market. Recommending direct sales channels is the preferred choice for manufacturers with a higher relative scale in the recommended market prompting them to recommend all channels to consumers. (3) Numerical simulations reveal that retail prices, total market demand, and supply chain profits are positively correlated with relative scale within the recommended market. Additionally, any recommendation strategy can increase demand for recommended parties as well as overall supply chain profit levels.

    Keywords: Recommendation strategy, Channel structure, Sales model, E-commerce platform, agency selling

    Received: 26 Jun 2024; Accepted: 11 Nov 2024.

    Copyright: © 2024 Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yang Wang, Guangzhou Railway Polytechnic, Guangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.