Knowledge networks are an organizational form with which to support knowledge sharing and creation. Few studies have been undertaken to understand the interaction among these variables and to develop a hierarchy of knowledge network variables model. The main purpose of this article is to identify a set of variables to implement a knowledge network and then to analyse and to rank them using the approach Interpretive Structural Modelling. Variables are identified through the literature review. To analyse the contextual relationships among the suggested variables, experts from academia and industry were consulted. The research shows that there exist two groups of variables, one having high driving power and low dependency requiring maximum attention and of strategic importance (such as organization environment factors, managerial processes and IT infrastructure) and the other having high dependence and low driving power and are resultant effects (such as knowledge, culture, organizational structures and communication processes).
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