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Alignment of Knowledge Sharing Mechanism and Knowledge Node Positioning

Alignment of Knowledge Sharing Mechanism and Knowledge Node Positioning

Mei-Tai Chu, Rajiv Khosla
Copyright: © 2014 |Volume: 5 |Issue: 1 |Pages: 19
ISSN: 1947-3095|EISSN: 1947-3109|EISBN13: 9781466656673|DOI: 10.4018/ijsita.2014010101
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MLA

Chu, Mei-Tai, and Rajiv Khosla. "Alignment of Knowledge Sharing Mechanism and Knowledge Node Positioning." IJSITA vol.5, no.1 2014: pp.1-19. http://doi.org/10.4018/ijsita.2014010101

APA

Chu, M. & Khosla, R. (2014). Alignment of Knowledge Sharing Mechanism and Knowledge Node Positioning. International Journal of Strategic Information Technology and Applications (IJSITA), 5(1), 1-19. http://doi.org/10.4018/ijsita.2014010101

Chicago

Chu, Mei-Tai, and Rajiv Khosla. "Alignment of Knowledge Sharing Mechanism and Knowledge Node Positioning," International Journal of Strategic Information Technology and Applications (IJSITA) 5, no.1: 1-19. http://doi.org/10.4018/ijsita.2014010101

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Abstract

As the organizational memory in terms of collective knowledge evolves, how to construct an effective knowledge sharing mechanism to covert individual knowledge into collective knowledge becomes fairly demanding. CoPs approach is widely accepted as effective mechanism to facilitate knowledge sharing. Knowledge nodes in the context of knowledge flow, unlike workflow, can often transcend organizational boundaries and are distinct and different than workflow models. This paper aims to develop, implement, and analyze a CoPs Centered knowledge flow model in a multinational organization. This model is underpinned in a CoPs framework built around four expected performance including four dimensions and sixteen criteria as a comprehensive mechanism to intensify knowledge sharing effect. Next, this study clusters knowledge workers/nodes with common criteria (attitudes and beliefs) towards this model. These common attitudes and beliefs between two knowledge workers/nodes imply that knowledge sharing among them is likely to be more effective than between knowledge workers/nodes with dissimilar attitudes and beliefs. Fuzzy Multi-Criteria Decision Making MCDM) and cluster analysis techniques are adopted as research methods. A Dynamic knowledge flow activity analysis model is also defined as part of future work.

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