How Structural Changes in Complex Networks Impact Organizational Learning Performance
The power of using knowledge against competitors is a key success factor in the information age. However, the knowledge itself is not the source of competitive advantage for an organization; rather its power lies in its use. In a learning organization, collective knowledge of the individuals is needed, in order to reach the overall goals of the organization. From an organizational perspective, the most important aspect of knowledge management is knowledge transfer. Therefore, knowledge within the organization should be available to others through social interactions. The contributions of this paper are two-fold: First, we show that the network structure that emerges from those social interactions depends on the variability in individual patterns of behavior. Second, we emphasize the importance of network structure changes for organizational learning. A consequence is that a high clustering coefficient within a network does not necessarily produce a high learning outcome. It can even result in a loss of innovation. Another consequence is that a small average shortest path length within a network of individuals positively affects organizational learning. Therefore, certain topological features of a network can help network members to have a better access to information within an organization.
|Date of creation:||Mar 2014|
|Date of revision:||Mar 2014|
|Publication status:||Published in Proceedings of the 6th International Workshop on Emergent Intelligence on Networked Agents (WEIN 2014).|
|Contact details of provider:|| Postal: |
Web page: http://temep.snu.ac.kr/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Somayeh Koohborfardhaghighi & Jorn Altmann, 2014. "How Placing Limitations on the Size of Personal Networks Changes the Structural Properties of Complex Networks," TEMEP Discussion Papers 2014110, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jan 2014.
- Justin J. P. Jansen & Frans A. J. Van Den Bosch & Henk W. Volberda, 2006.
"Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators,"
INFORMS, vol. 52(11), pages 1661-1674, November.
- Jansen, J.J.P. & van den Bosch, F.A.J. & Volberda, H.W., 2006. "Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators," ERIM Report Series Research in Management ERS-2006-038-STR, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
- Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
When requesting a correction, please mention this item's handle: RePEc:snv:dp2009:2014111. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jorn Altmann)
If references are entirely missing, you can add them using this form.