IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v333y2024i2d10.1007_s10479-021-04077-1.html
   My bibliography  Save this article

Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms

Author

Listed:
  • Mujahid Mohiuddin Babu

    (Coventry University)

  • Mahfuzur Rahman

    (University of Lincoln)

  • Ashraful Alam

    (Salford University)

  • Bidit Lal Dey

    (Brunel University)

Abstract

Although innovation from analytics is surging in the manufacturing sector, the understanding of the data-driven innovation (DDI) process remains a challenge. Drawing on a systematic literature review, thematic analysis and qualitative interview findings, this study presents a seven-step process to understand DDI in the context of the UK manufacturing sector. The findings discuss the significance of critical seven-step in DDI, ranging from conceptualisation to commercialisation of innovative data products. The results reveal that the steps in DDI are sequential, but they are all interlinked. The proposed seven-step DDI process with solid evidence from the UK manufacturing and research implications based on dynamic capability theory, institutional theory and TOE framework establish the building blocks for future studies and industry practice.

Suggested Citation

  • Mujahid Mohiuddin Babu & Mahfuzur Rahman & Ashraful Alam & Bidit Lal Dey, 2024. "Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms," Annals of Operations Research, Springer, vol. 333(2), pages 689-716, February.
  • Handle: RePEc:spr:annopr:v:333:y:2024:i:2:d:10.1007_s10479-021-04077-1
    DOI: 10.1007/s10479-021-04077-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04077-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-021-04077-1?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:spr:annopr:v:333:y:2024:i:2:d:10.1007_s10479-021-04077-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.