Limitations of network analysis for studying efficiency and effectiveness of knowledge sharing

Knowledge sharing is an important part of an employee’s tasks as it is one of the mechanisms through which they learn and innovate. Sharing of knowledge typically occurs in the informal networks in the organization by means of social interaction. Several authors have proposed to use social network analysis to study the knowledge sharing relations in organizations to identify potential barriers concerning knowledge sharing. Although social network analysis has been applied in several cases, it has not been evaluated if this approach results in reliable results in terms of findings problems related to knowledge sharing. One might for instance find an isolated person with network analysis, but given the context this might not be necessary a problem. The goal of this research is to validate the use of social network analysis to study knowledge networks. We have selected one particular technique, called Knowledge Network Analysis, to evaluate in this research. The Knowledge Network Analysis technique has been applied in a case study at an international product software developer to find potential barriers in their knowledge networks. To evaluate these results, a qualitative analysis has been executed afterwards by a different researcher. This analysis was based on interviews, document study and observations. To analyze the qualitative data we developed a new model called Knowledge Sharing Environment Model (KSEM), which identifies knowledge sharing bottlenecks in a structured manner. The results from network analysis and the qualitative analysis have been compared to validate the outcomes of the network analysis. Hence, six out of nine bottlenecks were validated. This research demonstrates that Knowledge Network Analysis is a good tool for the identification of bottlenecks but needs further validation in additional case studies. However, it was suggested to combine the Knowledge Network Analysis technique with another method such as the KSEM to validate and study the causes behind the identified bottlenecks.