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Online retailer recommender systems: a competitive analysis

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  • Dong-Hui Yang
  • Xing Gao

Abstract

As thriving and fast-moving technologies, recommender systems have been widely adopted by online retailers to increase their sales recently. This has significant impacts on the stakeholders in the online supply chain. How an online retailer uses recommender systems to maximise its profit through choosing different recommendation strategies for two upstream competing manufacturers is explored in this paper. In particular, a game between one online retailer and two competitive manufacturers is constructed in which these manufacturers can be selectively and strategically recommended by the retailer. The analytical results show that as the recommendation strength of recommender systems increases, neither manufacturers nor the retailer can always enjoy higher profits, which is counterintuitive. Furthermore, this study reveals that (i) a recommended manufacturer may enjoy a higher profit through sharing the recommendation market with its rival than through monopolising this market; (ii) recommending two manufacturers in both is the most feasible way for the online retailer to benefit from controlling the supply chain. Finally, it is interesting that recommender systems are found to be good mechanisms to help to coordinate the online supply chain with one retailer and two manufacturers because the recommendation market generated by recommender systems alleviates channel conflict.

Suggested Citation

  • Dong-Hui Yang & Xing Gao, 2017. "Online retailer recommender systems: a competitive analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 55(14), pages 4089-4109, July.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:14:p:4089-4109
    DOI: 10.1080/00207543.2016.1253888
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    Cited by:

    1. Donghui Yang & Yan Wang & Shue Mei, 2021. "How to balance online healthcare platforms and offline systems? A supply chain management perspective," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(2), pages 502-515, March.
    2. Feiyan Han & Herui Wang & Hongyu Lv & Bo Li, 2022. "Research on Manufacturers’ Referral Strategy Considering Store Brand Retailers and Traditional Retailers," Mathematics, MDPI, vol. 10(18), pages 1-23, September.
    3. Bo Zhou & Tianxin Zou, 2023. "Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces," Marketing Science, INFORMS, vol. 42(2), pages 360-376, March.
    4. Ya-Ling Chiu & Yuan-Teng Hsu & Xiaoyu Mao & Jying-Nan Wang, 2021. "An Empirical Study of Holiday Season Discounts: A Comparison Between Third-Party Marketplace Sellers and Fulfilled by Walmart Sellers," SAGE Open, , vol. 11(2), pages 21582440211, June.
    5. Yan Wang & Shue Mei & Weijun Zhong, 2022. "Advertising or recommender systems? A game‐theoretic analysis of online retailer platforms' decision‐making," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2119-2132, September.

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