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Exploration, Exploitation, and Knowledge Management Strategies in Multi-Tier Hierarchical Organizations Experiencing Environmental Turbulence
North American Assoc. for Computational Social and Organizational Science (NAACSOS) Conference (2006)
  • David A. Bray, National Defense University
Abstract

James G. March conceived organizational learning as a balance between the exploration of new alternatives and the exploitation of existing competencies in an organization. This study extends March's model to consider exploration and exploitation in a hierarchical organization. First, the effect of additional tiers in a hierarchical organization is analyzed and related to March's original constructs of exploration, exploitation, personnel turnover, and environmental turbulence. Second, the study evaluates additional effects of a knowledge management system that collects and shares knowledge from expert individuals in an organization. This study finds that in the absence of personnel turnover, a knowledge strategy of high exploitation and low exploration for a multi-tiered hierarchical organization reduces the veracity of average individual knowledge levels when compared to alternative strategies. The magnitude of this reduction in veracity increases as the number of tiers in a hierarchical organization increase; flat organizations will see less of a reduction compared to multi-tiered organizations. A weighted least-squares regression performed on a second set of data corroborates this central observation. Cumulative findings have strategic relevance for both organizational theory and the application of knowledge management systems.

Keywords
  • organizational learning,
  • exploration,
  • exploitation,
  • personnel turnover,
  • environmental turbulence,
  • hierarchical organizations,
  • knowledge management
Publication Date
June, 2006
Citation Information
David A. Bray. "Exploration, Exploitation, and Knowledge Management Strategies in Multi-Tier Hierarchical Organizations Experiencing Environmental Turbulence" North American Assoc. for Computational Social and Organizational Science (NAACSOS) Conference (2006)
Available at: http://works.bepress.com/dbray/4/