IDEAS home Printed from https://ideas.repec.org/a/ids/ijkbde/v13y2023i2-3-4p113-130.html
   My bibliography  Save this article

Research on the cultivation of innovative entrepreneurial talents for digital transformation of enterprises based on association rule algorithm

Author

Listed:
  • Jia Xu

Abstract

A talent development framework for enterprises is proposed to address the new requirements for talent development in the digital transformation stage. Through the study of the enterprise employee training framework, an employee data mining based on the improved Apriori association algorithm is proposed to realise the visual analysis of employee work performance. The experimental results show that the improved Apriori correlation algorithm takes 17s to process 7500 things, which is better than the traditional Apriori correlation algorithm. The performance score of employees is negatively correlated with the business volume of the enterprise. There is a problem of delay in the processing of complex work content by employees. And there is a positive correlation between the time and number of online learning and employee quality in talent development. The content of the study has important reference significance for the digital transformation of enterprises and the management of enterprise performance innovation.

Suggested Citation

  • Jia Xu, 2023. "Research on the cultivation of innovative entrepreneurial talents for digital transformation of enterprises based on association rule algorithm," International Journal of Knowledge-Based Development, Inderscience Enterprises Ltd, vol. 13(2/3/4), pages 113-130.
    • Handle: RePEc:ids:ijkbde:v:13:y:2023:i:2/3/4:p:113-130
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=133319
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijkbde:v:13:y:2023:i:2/3/4:p:113-130. 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=354 .

    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.