Analysing Drivers of Knowledge Leakage in Collaborative Agreements: A Magnetic Processing Case Firm

Samuel Foli and Susanne Durst ()
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Samuel Foli: Department of Business Administration, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Susanne Durst: Department of Business Administration, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia

JRFM, 2022, vol. 15, issue 9, 1-17

Abstract: Due to the embeddedness of organisations in networks, collaborations, and business relationships, knowledge leakage has become a common concern. In this regard, this paper aims to investigate drivers of knowledge leakage in collaborative agreements using an integrated ISM-MICMAC model. Based on insights from employees including the CEO of a magnetic processing firm, we validate the proposed model. The findings of our study reveal nine key drivers that influence knowledge leakage in collaborative agreements. In terms of level of influence, incomplete contract is the most influential driver, followed by sub-contracting activities. Last, the nine drivers are classified into two main clusters: independency cluster—weak dependence power with high driving power—and linkage cluster—strong dependence and driving power.

Keywords: knowledge leakage; interpretive structural model (ISM); MICMAC analysis; collaborative agreements; driver; case study; small firm (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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