IDEAS home Printed from https://ideas.repec.org/a/ids/ijmcph/v19y2026i2p257-270.html

Unlocking the successful business models: cluster analysis of the artificial intelligence start-ups

Author

Listed:
  • Wilson Martinez

Abstract

AI start-ups are innovation pioneers around the world and have the potential to enable BMs that were unthinkable a few years ago leading to value creation through the data-driven learning. Researchers have done extensive investigation in the AI applications, but there is a lack of research on how AI start-ups create value in AI commercial deployments. We make a selection of relevant AI start-ups worldwide in the crunch base pro database, and after a careful process of elimination we select a group of 100. Cluster analysis is done with the use of NVivo software. The results of this research not only provide valuable information to companies in their investment decisions and action to develop and deploy AI applications, but also provide a guideline to entrepreneurs in the effective developing of their value propositions. The results also make several contributions to the network effect theory and to the dynamic capabilities theory.

Suggested Citation

  • Wilson Martinez, 2026. "Unlocking the successful business models: cluster analysis of the artificial intelligence start-ups," International Journal of Management Concepts and Philosophy, Inderscience Enterprises Ltd, vol. 19(2), pages 257-270.
    • Handle: RePEc:ids:ijmcph:v:19:y:2026:i:2:p:257-270
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=153077
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:ids:ijmcph:v:19:y:2026:i:2:p:257-270. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=90 .

    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.