Abstract: Web 2.0 applications aim at improving the interaction between users. Web 2.0 principles overlap with characteristics of knowledge management (KM) or could be applied to reshape KM practices. Applying Web 2.0 applications to KM has the potential to improve the sharing and creation of knowledge. However, little research has been conducted in this area. This research aims at identifying Web 2.0 applications for bolstering up organizations’ KM practices. An additional aspect addressed is how Web 2.0 applications for KM can be categorized and how they match different aspects of the KM strategy of an organization. The research examines the suitability of Web 2.0 applications in KM by conducting exploratory case studies in two student‑run organizations, which are an interesting research subject because their members are considered most open towards new technologies. The case studies aim at exploring which Web 2.0 applications are in place. Based on the findings we propose a framework for categorizing Web 2.0 applications for KM. The findings indicate that Web 2.0 applications may enhance KM and may even initiate a new era of KM. Moreover, the article provides a discussion of a number of Web 2.0 applications and proposes a way of categorizing these applications. The proposed framework allows assessing the use of Web 2.0 applications for KM and can be used as an orientation for the introduction of Web 2.0 applications in organizational KM. The research contributes to the general understanding of how Web 2.0 applications can be used in KM. The proposed framework for categorizing Web 2.0 applications provides an orientation for organizations that want to use these applications for bolstering up their KM practices.
Keywords: Web 2.0, collective intelligence, user-generated content, social computing, knowledge management, KM 2.0
Limitations of Network Analysis for Studying Efficiency and Effectiveness of Knowledge Sharing pp53-68
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.
Keywords: knowledge sharing, communities of practice, learning network, knowledge network analysis, social network analysis