Measuring Knowledge Management Effectiveness in Communities of Practice

Authors:
Martin Smits;Aldo de Moor
Affiliations:
-;-
Venue:
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 8 - Volume 8
Year:
2004

Citing 0
Cited 7

Knowledge management performance evaluation: a decade review from 1995 to 2004

Journal of Information Science
Comparing with your main competitor: the single most important task of knowledge management performance measurement

Journal of Information Science
Knowledge transformation for education in software engineering

International Journal of Mobile Learning and Organisation
Measuring knowledge management performance using a competitive perspective: An empirical study

Expert Systems with Applications: An International Journal
Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement

Expert Systems with Applications: An International Journal
Knowledge Management: Realizing Value through Governance

International Journal of Knowledge Management
Knowledge sharing assessment: An Ant Colony System based Data Envelopment Analysis approach

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper outlines an approach to determine key performance indicators and metrics for knowledge management (KM) in communities of practice. The approach is based on analysis of the KM literature on (i) types of knowledge, (ii) processes of knowledge development and social learning, and (iii) metrics for KM, such as from the Intellectual Capital Method. To embed communities of practice and KM processes in an organizational context, we introduce our Knowledge Governance Framework, which combines knowledge resources, KM, and organizational objectives. Our first hypothesis is that successful KM in organizations requires the linking of knowledge resources to organizational objectives. Our second hypothesis is that a precondition for successful KM is that explicit, quantitative indicators are used. We tested the framework in a small organization in the financial industry. According to our first case experience, the model can be applied in a business setting and our first hypothesis is supported: successful KM links knowledge resources to company objectives . Our second hypothesis is not supported: KM in the case is not based on explicit and quantitative indicators.