IDEAS home Printed from https://ideas.repec.org/a/taf/tkmrxx/v21y2023i4p892-907.html
   My bibliography  Save this article

Knowledge generation from Big Data for new product development: a structured literature review

Author

Listed:
  • Pasquale Del Vecchio
  • Gioconda Mele
  • Giuseppina Passiante
  • Donata Serra

Abstract

This study aims to provide a contribution to the systematisation of the state-of-the art of the literature on Big Data for the process of New Product Development (NPD). Based on the evidence of a structured literature review (SLR) on articles published from 2015 to 2021, the paper aims to identify new areas for future research, by highlighting the contribution of Big Data in the perspective of knowledge management for the improvement of the new product development process. Findings demonstrate a lack of research in this field and the fragmentation of the publications that require more in-depth investigation. The analysis allows the identification of quantitative and qualitative evidence of the research trends emerging at the intersection of the two well-known areas and to derive several implications both for research and practice.

Suggested Citation

  • Pasquale Del Vecchio & Gioconda Mele & Giuseppina Passiante & Donata Serra, 2023. "Knowledge generation from Big Data for new product development: a structured literature review," Knowledge Management Research & Practice, Taylor & Francis Journals, vol. 21(4), pages 892-907, July.
    • Handle: RePEc:taf:tkmrxx:v:21:y:2023:i:4:p:892-907
    DOI: 10.1080/14778238.2022.2094292
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14778238.2022.2094292
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14778238.2022.2094292?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:tkmrxx:v:21:y:2023:i:4:p:892-907. 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.