IDEAS home Printed from https://ideas.repec.org/a/taf/reroxx/v35y2022i1p7043-7065.html
   My bibliography  Save this article

Integrating artificial intelligence for knowledge management systems – synergy among people and technology: a systematic review of the evidence

Author

Listed:
  • Rashmi Yogesh Pai
  • Ankitha Shetty
  • Adithya D. Shetty
  • Rakshith Bhandary
  • Jyothi Shetty
  • Santosh Nayak
  • Tantri Keerthi Dinesh
  • Komal Jenifer D'souza

Abstract

This paper analyses Artificial Intelligence (AI) and Knowledge Management (KM) and focuses primarily on examining to what degree AI can help companies in their efforts to handle information and manage knowledge effectively. A search was carried out for relevant electronic bibliographic databases and reference lists of relevant review articles. Articles were screened and the eligibility was based on participants, procedures, comparisons, outcomes (PICO) model, and criteria for PRISMA (Preferred Reporting Items for Systematic Reviews). The results reveal that knowledge management and AI are interrelated fields as both are intensely connected to knowledge; the difference reflects in how – while AI offers machines the ability to learn, KM offers a platform to better understand knowledge. The research findings further point out that communication, trust, information systems, incentives or rewards, and the structure of an organization; are related to knowledge sharing in organizations. This systematic literature review is the first to throw light on KM practices & the knowledge cycle and how the integration of AI aids knowledge management systems, enterprise performance & distribution of knowledge within the organization. The outcomes offer a better understanding of efficient and effective knowledge resource management for organizational advantage. Future research is necessary on smart assistant systems thus providing social benefits that strengthen competitive advantage. This study indicates that organizations must take note of definite KM leadership traits and organizational arrangements to achieve stable performance through KM.

Suggested Citation

  • Rashmi Yogesh Pai & Ankitha Shetty & Adithya D. Shetty & Rakshith Bhandary & Jyothi Shetty & Santosh Nayak & Tantri Keerthi Dinesh & Komal Jenifer D'souza, 2022. "Integrating artificial intelligence for knowledge management systems – synergy among people and technology: a systematic review of the evidence," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 7043-7065, December.
  • Handle: RePEc:taf:reroxx:v:35:y:2022:i:1:p:7043-7065
    DOI: 10.1080/1331677X.2022.2058976
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1331677X.2022.2058976
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1331677X.2022.2058976?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.

    More about this item

    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:taf:reroxx:v:35:y:2022:i:1:p:7043-7065. 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 Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rero .

    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.