IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i1p31-d1025008.html
   My bibliography  Save this article

Artificial Intelligence-Driven Talent Management System: Exploring the Risks and Options for Constructing a Theoretical Foundation

Author

Listed:
  • Ali Faqihi

    (Newcastle Business School, The University of Newcastle, Newcastle, NSW 2300, Australia
    College of Business Administration, Jazan University, Jazan 82817, Saudi Arabia)

  • Shah Jahan Miah

    (Newcastle Business School, The University of Newcastle, Newcastle, NSW 2300, Australia)

Abstract

AI (Artificial intelligence) has the potential to improve strategies to talent management by implementing advanced automated systems for workforce management. AI can make this improvement a reality. The objective of this study is to discover the new requirements for generating a new AI-oriented artefact so that the issues pertaining to talent management are effectively addressed. The design artefact is an intelligent Human Resource Management (HRM) automation solution for talent career management primarily based on a talent intelligent module. Improving connections between professional assessment and planning features is the key goal of this initiative. Utilising a design science methodology we investigate the use of organised machine learning approaches. This technique is the key component of a complete AI solution framework that would be further informed through a suggested moderation of technology-organisation-environment (TOE) theory with the theory of diffusion of innovation (DOI). This framework was devised in order solve AI-related problems. Aside from the automated components available in talent management solutions, this study will make recommendations for practical approaches researchers may follow to fulfil a company’s specific requirements for talent growth.

Suggested Citation

  • Ali Faqihi & Shah Jahan Miah, 2023. "Artificial Intelligence-Driven Talent Management System: Exploring the Risks and Options for Constructing a Theoretical Foundation," JRFM, MDPI, vol. 16(1), pages 1-18, January.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:1:p:31-:d:1025008
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/1/31/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/16/1/31/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Zavodna Lucie Sara & Überwimmer Margarethe & Frankus Elisabeth, 2024. "Barriers to the implementation of artificial intelligence in small and medium-sized enterprises: Pilot study," Journal of Economics and Management, Sciendo, vol. 46(1), pages 331-352.
    2. Chen, Pengyu & Chu, Zhongzhu & Zhao, Miao, 2024. "The Road to corporate sustainability: The importance of artificial intelligence," Technology in Society, Elsevier, vol. 76(C).
    3. Damaris Ndungwa Peter, 2024. "Influence of Augmented Artificial Intelligence Platforms on Talent Management in Energy Parastatals in Nairobi, Kenya," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(4), pages 754-766, April.

    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:gam:jjrfmx:v:16:y:2023:i:1:p:31-:d:1025008. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.