IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v35y2015i2p253-259.html
   My bibliography  Save this article

A case analysis of embryonic data mining success

Author

Listed:
  • Bole, Uroš
  • Popovič, Aleš
  • Žabkar, Jure
  • Papa, Gregor
  • Jaklič, Jurij

Abstract

Within highly competitive business environments, data mining (DM) is viewed as a significant technology to enhance decision-making processes by transforming data into valuable and actionable information to gain competitive advantage. There appears, however, to be a dearth of empirical case studies which consider in detail the initial stages in DM management to enable apt foundation for its later successful implementation. Our research applied a multi-method strategy to determine the critical success factors of embryonic DM implementation. We propose and validate, through a series of cases, a conceptual framework to guide practitioners’ adoption of DM. Our findings reveal additional issues for applied decision making in the context of DM success.

Suggested Citation

  • Bole, Uroš & Popovič, Aleš & Žabkar, Jure & Papa, Gregor & Jaklič, Jurij, 2015. "A case analysis of embryonic data mining success," International Journal of Information Management, Elsevier, vol. 35(2), pages 253-259.
    • Handle: RePEc:eee:ininma:v:35:y:2015:i:2:p:253-259
    DOI: 10.1016/j.ijinfomgt.2014.12.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401214001212
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijinfomgt.2014.12.001?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kiebler, Lorenz & Moroff, Nikolas Ulrich & Jacobsen, Jens Jakob, 2022. "Preliminary analysis on data quality for ML applications," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 207-236, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

    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:eee:ininma:v:35:y:2015:i:2:p:253-259. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    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.