IDEAS home Printed from https://ideas.repec.org/a/taf/tkmrxx/v18y2020i2p199-213.html
   My bibliography  Save this article

A conceptual model of knowledge dynamics in the industry 4.0 smart grid scenario

Author

Listed:
  • Nikolina Dragicevic
  • André Ullrich
  • Eric Tsui
  • Norbert Gronau

Abstract

Technological advancements are giving rise to the fourth industrial revolution – Industry 4.0 –characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrial product-service systems. The purpose of this paper is to propose a theory-based knowledge dynamics model in the smart grid scenario that would provide a holistic view on the knowledge-based interactions among smart objects, humans, and other actors as an underlying mechanism of value co-creation in Industry 4.0. A multi-loop and three-layer – physical, virtual, and interface – model of knowledge dynamics is developed by building on the concept of ba – an enabling space for interactions and thee mergence of knowledge. The model depicts how big data analytics are just one component in unlocking the value of big data, whereas the tacit engagement of humans-in-the-loop – their sense-making and decision-making – is needed for insights to be evoked from analytics reports and customer needs to be met.

Suggested Citation

  • Nikolina Dragicevic & André Ullrich & Eric Tsui & Norbert Gronau, 2020. "A conceptual model of knowledge dynamics in the industry 4.0 smart grid scenario," Knowledge Management Research & Practice, Taylor & Francis Journals, vol. 18(2), pages 199-213, April.
    • Handle: RePEc:taf:tkmrxx:v:18:y:2020:i:2:p:199-213
    DOI: 10.1080/14778238.2019.1633893
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14778238.2019.1633893
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14778238.2019.1633893?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. Cruz, Yarens J. & Villalonga, Alberto & Castaño, Fernando & Rivas, Marcelino & Haber, Rodolfo E., 2024. "Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises," Operations Research Perspectives, Elsevier, vol. 12(C).

    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:tkmrxx:v:18:y:2020:i:2:p:199-213. 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/tkmr .

    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.