Mapping supply chain collaboration research: a machine learning-based literature review

Supply chain collaboration has been widely discussed in the literature. With this maturity comes a plethora of heterogeneous research that is difficult to manage and navigate. This paper, therefore, applies a novel literature review approach based on text mining analyzing 10,556 articles to provide an overview of previous research themes and future directions of the field. The applied method enables researchers to systematically analyze and structure larger samples of research publications. It allocates articles to thematic clusters using a visual hierarchical clustering approach and subsequently aggregates them into nine overarching themes to determine potential research and insights for practice. Developments regarding research interest and attention within these themes are examined and journals publishing the most impactful articles are identified. The paper thus contributes to the field of Supply Chain Collaboration research by mapping its evolvement over the last five years and by deriving a research agenda for the next decade.

• Record URL:
  • https://doi.org/10.1080/13675567.2021.2001446
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13675567
• Supplemental Notes:
  • © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Nitsche, Anna-Maria
  • Schumann, Christian-Andreas
  • Franczyk, Bogdan
  • Reuther, Kevin
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 954-982
• Serial:
  • International Journal of Logistics Research and Applications
  • Volume: 26
  • Issue Number: 8
  • Publisher: Taylor & Francis
  • ISSN: 1367-5567
  • EISSN: 1469-848X
  • Serial URL: http://www.tandfonline.com/toc/cjol20/current

Subject/Index Terms

• TRT Terms: Cooperation; Literature reviews; Supply chain management
• Subject Areas: Freight Transportation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01888660
• Record Type: Publication
• Files: TRIS, ATRI, USDOT
• Created Date: Jul 25 2023 2:13PM