Journal of the National Research University Higher School of Economics




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E-ISSN 2500-2597

Leonid Gokhberg

Ali Rezaeian 1, Rouhollah Bagheri 1
  • 1 Shahid Beheshti University, Iran, Daneshju Blvd, Evin Square, Tehran 1983963113, Iran

Modeling the Factors that Affect the Implementation of Knowledge Networks

2018. Vol. 12. No. 1. P. 56–67 [issue contents]

Knowledge is increasingly recognized by organizations as an important source of lasting competitive advantage. Knowledge networks are a mechanism used to support knowledge sharing and creation. Variables important for implementing a knowledge network are described by numerous researchers, but few studies have been undertaken to understand the interaction between these variables and to develop a model of the hierarchy of variables in the knowledge network. This paper identifies a set of variables necessary for the operation of a knowledge network and then analyzes and ranks them using interpretive structural modeling (ISM).

The study identified seven key factors affecting the implementation of knowledge networks and revealed contextual relationships among these variables. Some variables, the drivers, are little impacted by other factors, while others, the dependent variables, have limited potential to exert an influence on other factors and are furthermore determined by the drivers. The former includes such factors as an organization’s environmental factors, managerial processes, IT infrastructure, while the latter encapsulates the resultant effects such as knowledge, culture, organizational structures, and communication processes.

The use of ISM in the development of a hierarchy and the categorization of variables affecting the implementation of knowledge networks provides one with a better understanding of causal relationships between said variables, thus allowing one to identify which elements to concentrate upon and how trade-offs between variables should be managed.
Citation: Rezaeian A., Bagheri R. (2018) Modeling the Factors that Affect the Implementation of Knowledge Networks. Foresight and STI Governance, vol. 12, no 1, pp. 56–67. DOI: 10.17323/2500-2597.2018.1.56.67
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