How Structural Changes in Complex Networks Impact Organizational Learning Performance
AbstractThe 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.
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Bibliographic InfoPaper provided by Seoul National University; Technology Management, Economics, and Policy Program (TEMEP) in its series TEMEP Discussion Papers with number 2014111.
Length: 17 pages
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).
Complex Networks; Organizational Learning; Knowledge Management; Network Formation.;
Find related papers by JEL classification:
- C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
- L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure
- L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
- M12 - Business Administration and Business Economics; Marketing; Accounting - - Business Administration - - - Personnel Management; Executives; Executive Compensation
- O31 - Economic Development, Technological Change, and Growth - - Technological Change; Research and Development; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-04-11 (All new papers)
- NEP-CSE-2014-04-11 (Economics of Strategic Management)
- NEP-GER-2014-04-11 (German Papers)
- NEP-KNM-2014-04-11 (Knowledge Management & Knowledge Economy)
- NEP-NET-2014-04-11 (Network Economics)
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.:
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