IDEAS home Printed from https://ideas.repec.org/a/eme/ejimpp/ejim-09-2023-0753.html
   My bibliography  Save this article

Human-centricity in Industry 5.0 – revealing of hidden research topics by unsupervised topic modeling using Latent Dirichlet Allocation

Author

Listed:
  • Peter Madzik
  • Lukas Falat
  • Luay Jum’a
  • Mária Vrábliková
  • Dominik Zimon

Abstract

Purpose - The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0. Design/methodology/approach - This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0. Findings - In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections. Originality/value - Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

Suggested Citation

  • Peter Madzik & Lukas Falat & Luay Jum’a & Mária Vrábliková & Dominik Zimon, 2024. "Human-centricity in Industry 5.0 – revealing of hidden research topics by unsupervised topic modeling using Latent Dirichlet Allocation," European Journal of Innovation Management, Emerald Group Publishing Limited, vol. 28(1), pages 113-138, March.
    • Handle: RePEc:eme:ejimpp:ejim-09-2023-0753
    DOI: 10.1108/EJIM-09-2023-0753
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/EJIM-09-2023-0753/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/EJIM-09-2023-0753/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/EJIM-09-2023-0753?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.

    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:eme:ejimpp:ejim-09-2023-0753. 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: Emerald Support (email available below). General contact details of provider: .

    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.