In order to cope with the developments in the knowledge economy, many organisations are looking for ways to optimize knowledge exchange and cooperation within and outside the organization. One of the methods often used to stimulate these forms of social interaction is the Community of Practice (CoP). A community of practice is a social network where people in an organisational context come together around a common topic, passion or interest and regularly interact on- and offline with a focus on knowledge management, innovation, learning and social networking. For decades, many academics have studied the CoP forms, developments, their effects, and factors that influence them.
This dissertation addresses in an exploratory manner, the developments of communities based on a data-driven approach. A new more reliable combination of methods and techniques is applied to data sources that describe and explain social behaviour in a CoP. Developments are described by distilling complex network patterns and visualizations from web-based social interaction data, while explaining these developments has been done by conducting a survey in four Dutch cases. These two different data sources were then merged to paint a reliable and valid picture of developments within the CoPs.
The dissertation contributes to our expertise in building and maintaining CoPs and our understanding of the how and why of developments in these CoPs. In addition, first steps have been set towards the development of a Community Monitoring and Evaluation Methodology (CMEM) that is developed based on the combination of methods and techniques used in this dissertation to cope with the increasingly large datasets available in CoPs and online social environments in general. Based on these contributions, we go beyond the hype about CoPs in organisations and take a new step towards a more targeted development of CoPs.
Link to my dissertation: https://doi.org/10.3990/1.9789036548205