Application of social network analysis in knowledge processes
|Application of social network analysis in knowledge processes|
|Author(s)||Mueller C., Gronau N., Lembcke R.|
|Published in||16th European Conference on Information Systems, ECIS 2008|
|Keyword(s)||Knowledge processes, Modeling method, Social network analysis, Wiki (Extra: Business process modeling, Description languages, Knowledge exchange, Knowledge modeling, Knowledge process, Knowledge-intensive business, Object of interests, Practical implementation, Social Network Analysis, Specific knowledge, Traditional techniques, Wiki, Work in progress, Industrial engineering, Information systems, Social networking (online), Systems engineering, Knowledge management)|
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Application of social network analysis in knowledge processes is a 2008 conference paper written in English by Mueller C., Gronau N., Lembcke R. and published in 16th European Conference on Information Systems, ECIS 2008.
Social network analysis (SNA) is increasingly applied to analyze existing knowledge exchange processes in companies. In the past, these processes were mainly analyzed by traditional business process modeling methods. Same of these methods are working with a specific knowledge perspective, e.g. KMDL (Knowledge Modeling and Description Language). The knowledge needs and demands are modeled along knowledge-intensive business processes. The SNA however has no direct reference to processes; here persons with specific relationships (e.g. knowledge exchange) are mainly the object of interest. Therefore a combination of both views - the SNA and knowledge-intensive business process modeling - is valuable. In the following contribution two approaches are introduced to combine social network analysis with the modeling of knowledge intensive business processes based on KMDL. In addition to a theoretical introduction to SNA and KMDL there are two examples introduced, which illustrate the practical implementation of these approaches. In this work in progress traditional techniques like interviews are combined with electronically gathered data. Also by using different perspectives a better understanding of all events in knowledge processes can be extracted.
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