This research investigates the dynamics of communities of practice (CoP) seen as an essential elements of up surging knowledge societies. The concept of CoP has received much attention from researchers and practitioners in KM. In this context, CoPs are seen as voluntary associations of people who choose to improve their skills and problem solving capabilities by collectively generating and processing knowledge. These organisational settings must be, at least in part, set free to evolve and self‐organise in some way, in order to ensure an effective sharing of tacit knowledge which is the essential goal of a CoP. The problem of managing the formation and evolution of a CoP is, therefore, to investigate the conditions under which self organisation mechanisms can make CoPs improve their performance. The paper investigates this issue by adopting the approach of computational intelligence. Particularly, it uses genetic algorithms to model and simulate the formation and evolution of a CoP. It is assumed that a CoP evolves thanks to the interactions between independent agents (i.e. the members) that share knowledge for decision making. The genetic algorithms are used to investigate if there are conditions or configurations that can enable the CoP to develop and improve in terms of performance. An experimental study of a CoP’s structure optimisation is conducted, based on real data collected from a medical facility. The capability of the genetic algorithms to identify producing optimal restructuring of a set of CoP agents (in our case medical staff involved in the rehabilitation of the impaired patients) is tested. By aggregating and re‐aggregating the members in different ways, it is analysed how a performance index that measures the CoP performance can be improved. The method proves to be useful to understand the possible mechanisms by which a CoP can develop, and provides suggestions for its management

The Role of Knowledge Dynamics in Developing a Medical Community of Practice

BOLISANI, ETTORE;
2012

Abstract

This research investigates the dynamics of communities of practice (CoP) seen as an essential elements of up surging knowledge societies. The concept of CoP has received much attention from researchers and practitioners in KM. In this context, CoPs are seen as voluntary associations of people who choose to improve their skills and problem solving capabilities by collectively generating and processing knowledge. These organisational settings must be, at least in part, set free to evolve and self‐organise in some way, in order to ensure an effective sharing of tacit knowledge which is the essential goal of a CoP. The problem of managing the formation and evolution of a CoP is, therefore, to investigate the conditions under which self organisation mechanisms can make CoPs improve their performance. The paper investigates this issue by adopting the approach of computational intelligence. Particularly, it uses genetic algorithms to model and simulate the formation and evolution of a CoP. It is assumed that a CoP evolves thanks to the interactions between independent agents (i.e. the members) that share knowledge for decision making. The genetic algorithms are used to investigate if there are conditions or configurations that can enable the CoP to develop and improve in terms of performance. An experimental study of a CoP’s structure optimisation is conducted, based on real data collected from a medical facility. The capability of the genetic algorithms to identify producing optimal restructuring of a set of CoP agents (in our case medical staff involved in the rehabilitation of the impaired patients) is tested. By aggregating and re‐aggregating the members in different ways, it is analysed how a performance index that measures the CoP performance can be improved. The method proves to be useful to understand the possible mechanisms by which a CoP can develop, and provides suggestions for its management
2012
Proceedings of the 13th European Conference on Knowledge Management
9781908272645
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2528634
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