Artikel
Analysing drivers of knowledge leakage in collaborative agreements: A magnetic processing case firm
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 9 ; Pages: 1-17
- Classification
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Management
- Subject
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case study
collaborative agreements
driver
interpretive structural model (ISM)
knowledge leakage
MICMAC analysis
small firm
- Event
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Geistige Schöpfung
- (who)
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Foli, Samuel
Durst, Susanne
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2022
- DOI
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doi:10.3390/jrfm15090389
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Foli, Samuel
- Durst, Susanne
- MDPI
Time of origin
- 2022
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