IDEAS home Printed from https://ideas.repec.org/a/spr/intemj/v20y2024i2d10.1007_s11365-023-00922-w.html
   My bibliography  Save this article

Value creation and appropriation from the use of machine learning: a study of start-ups using fuzzy-set qualitative comparative analysis

Author

Listed:
  • Ricardo Costa-Climent

    (Uppsala University
    University of Economics and Human Sciences)

  • Samuel Ribeiro Navarrete

    (University of Economics and Human Sciences
    Esic University Madrid)

  • Darek M. Haftor

    (Uppsala University)

  • Marcin W. Staniewski

    (University of Economics and Human Sciences)

Abstract

This study focuses on how start-ups use machine learning technology to create and appropriate value. A firm’s use of machine learning can activate data network effects. These data network effects can then create perceived value for users. This study examines the interaction between the activation of data network effects by start-ups and the value that they are able to create and appropriate based on their business model. A neo-configurational approach built on fuzzy-set qualitative comparative analysis (fsQCA) explores how the design of a firm’s business model interacts with various aspects to explain value creation and appropriation using machine learning. The study uses a sample of 122 European start-ups created between 2019 and 2022. It explores the system of interactions between business model value drivers and value creation factors under the theory of data network effects. The findings show that start-ups primarily activate the efficiency and novelty elements of value creation and value capture.

Suggested Citation

  • Ricardo Costa-Climent & Samuel Ribeiro Navarrete & Darek M. Haftor & Marcin W. Staniewski, 2024. "Value creation and appropriation from the use of machine learning: a study of start-ups using fuzzy-set qualitative comparative analysis," International Entrepreneurship and Management Journal, Springer, vol. 20(2), pages 935-967, June.
    • Handle: RePEc:spr:intemj:v:20:y:2024:i:2:d:10.1007_s11365-023-00922-w
    DOI: 10.1007/s11365-023-00922-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11365-023-00922-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11365-023-00922-w?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:spr:intemj:v:20:y:2024:i:2:d:10.1007_s11365-023-00922-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.