IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v27y2025i3p917-934.html
   My bibliography  Save this article

Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces

Author

Listed:
  • Santiago Gallino

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Nil Karacaoglu

    (Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

  • Antonio Moreno

    (Harvard Business School, Harvard University, Boston, Massachusetts 02163)

Abstract

Problem definition : Online marketplaces have revolutionized online sales by creating platforms that connect millions of buyers and sellers. Although the presence of numerous third-party sellers attracts customers, it also results in a proliferation of listings for each product, making it difficult for customers to choose between the available options. To address this issue, online marketplaces employ algorithmic tools to curate and present different product listings to customers. Although tools that assist customers in choosing between different products , such as recommender systems and reviews, have been studied extensively, there is limited evidence regarding tools that help customers choose between different listings of the same product . This paper focuses on the buybox algorithm, an algorithmic tool that prominently presents one option as the default choice to customers. Methodology/results : We assess the influence of the buybox on marketplace dynamics by examining its staggered introduction within a major product category in a leading online marketplace. Our results show that the implementation of buybox increases the number of orders and enhances the efficiency of the customer journey. This is evidenced by an increase in conversion rates and a more pronounced buybox effect on the mobile channel, where search frictions are higher compared with the desktop channel. The introduction of buybox simplifies the process of posting new products on the marketplace, potentially reducing friction for sellers. We find supporting evidence for this hypothesis, because the number of sellers offering a product increases after the introduction of buybox. Managerial implications : Our analysis reveals that a buybox is an effective tool for reducing search frictions and stimulating competition among sellers. Customers benefit from lower prices and higher average quality levels when competition in a buybox is intense. However, the marketplace becomes more concentrated following the introduction of the buybox, representing an unintended consequence that platforms and vendors should manage. Our study contributes to the growing literature on algorithms in platforms by examining how algorithmic curation affects marketplace participants and overall marketplace dynamics.

Suggested Citation

  • Santiago Gallino & Nil Karacaoglu & Antonio Moreno, 2025. "Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces," Manufacturing & Service Operations Management, INFORMS, vol. 27(3), pages 917-934, May.
  • Handle: RePEc:inm:ormsom:v:27:y:2025:i:3:p:917-934
    DOI: 10.1287/msom.2023.0254
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/msom.2023.0254
    Download Restriction: no

    File URL: https://libkey.io/10.1287/msom.2023.0254?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:inm:ormsom:v:27:y:2025:i:3:p:917-934. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.