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The effect of transactive memory systems on supply chain network collaboration

Kevin P. Scheibe (Department of Information Systems and Business Analytics, Iowa State University, Ames, Iowa, USA)
Prabhjot S. Mukandwal (Mike Ilitch School of Business, Wayne State University, Detroit, Michigan, USA)
Scott J. Grawe (Department of Supply Chain Management, Iowa State University, Ames, Iowa, USA)

International Journal of Physical Distribution & Logistics Management

ISSN: 0960-0035

Article publication date: 5 December 2022

Issue publication date: 9 December 2022




This research is aimed at understanding how inter-organizational team members' ability to encode, interpret, retain and recall knowledge can lead to effective supply chain collaboration, resulting in improved firm performance. Using the lens of transactive memory systems (TMS), this research demonstrates the value of knowing who knows what (specialization), is it trustworthy (credibility) and how to retrieve it (coordination) on supply chain firm performance through network collaboration.


The authors used a multi-method approach that includes quantitative survey methodology and a qualitative methodology using semi-structured interviews. In total, 207 survey responses and six semi-structured interviews provided valuable insights into the use of TMS in supply chain relationships.


This study shows that TMS can enable firms to exploit potential benefits of collaboration on network optimization, thus improving the overall efficiency and process innovations.

Practical implications

To maintain the efficient use of a firm's assets while suppliers get added or removed from the network, this study’s findings suggest that managers should be more knowledgeable of supply chain partners carrying codified knowledge, which can contribute to superior firm performance. Recognizing that when two or more firms collaborate, there are multiple supply chains affected by each decision, it is important that managers carefully assign the specific role of each firm within the supply chain.


This research takes a new approach to network optimization by specifically considering how firms work together to share information about their changing networks to allow firms throughout the supply chain to gain greater levels of asset efficiency and process improvement.



Scheibe, K.P., Mukandwal, P.S. and Grawe, S.J. (2022), "The effect of transactive memory systems on supply chain network collaboration", International Journal of Physical Distribution & Logistics Management, Vol. 52 No. 9/10, pp. 791-812.



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