IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v58y2020i5p1442-1461.html
   My bibliography  Save this article

A review of operations management literature: a data-driven approach

Author

Listed:
  • Andrew Manikas
  • Lynn Boyd
  • Jian (Jeff) Guan
  • Kyle Hoskins

Abstract

Production and operations management has been a significant field of research for many years. However, other than an educated guess by researchers in the field or a perusal of textbook chapter titles, the major topics and their trends over time are not well established. This study provides a comprehensive review of production and operations management literature using a data-driven approach. We use Latent Semantic Analysis on 21,053 abstracts representing all publications in six leading operations management journals since their inception. 18 unique topic clusters were identified algorithmically. Just being aware of the history of research topics should be of great interest to all academics in the field, but to help future researchers we conducted three post hoc analyses: 1) analysis of methods used in all these studies, 2) citation rates by topic area over time, and 3) the growing prevalence of research covering multiple topics.

Suggested Citation

  • Andrew Manikas & Lynn Boyd & Jian (Jeff) Guan & Kyle Hoskins, 2020. "A review of operations management literature: a data-driven approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1442-1461, March.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:5:p:1442-1461
    DOI: 10.1080/00207543.2019.1651459
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1651459
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2019.1651459?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. Margherita Bernabei & Marco Eugeni & Paolo Gaudenzi & Francesco Costantino, 2023. "Assessment of Smart Transformation in the Manufacturing Process of Aerospace Components Through a Data-Driven Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 67-86, March.

    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:tprsxx:v:58:y:2020:i:5:p:1442-1461. 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/TPRS20 .

    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.