Initiating a system for visualizing and measuring dynamic knowledge

Mark E. Nissen

Technological Forecasting and Social Change, 2019, vol. 140, issue C, 169-181

Abstract: Knowledge is key to sustainable competitive advantage, but different kinds of knowledge affect competitive advantage differently, and they exhibit qualitatively different dynamic properties and behaviors. This places particular importance on understanding the dynamics of knowledge as it flows, and organization managers seek to visualize and measure such flows for efficacy and efficiency alike. Unfortunately, knowledge is inherently intangible, invisible and resistant to quantification, particularly when in dynamic motion. Moreover, managing key organization knowledge is left often to haphazard, trial and error processes, rendering pursuits of sustainable competitive advantage daunting at best and infeasible in many cases. Even when guided by well-accepted models in extant theory, managers may not be selecting the best knowledge flow processes for their purposes. The research described in this article builds upon Knowledge Flow Theory and application to initiate a system for visualizing and measuring dynamic knowledge. We leverage a multidimensional model to delineate and analyze a diversity of knowledge as it flows through the organization, and we draw analogically to develop a system of dynamic knowledge equations that enable measurement. We then illustrate its practical use and utility through a representative organization example, which we supplement with decision guidance pertaining to some fundamental knowledge flow tradeoffs facing decision makers. This article closes with a summary of key results and implications, which give us cause to rethink some concepts, assumptions and implications in the literature.

Keywords: Knowledge; Knowledge flow; Knowledge management; Knowledge flow theory; Dynamics; Measurement (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:140:y:2019:i:c:p:169-181

DOI: 10.1016/j.techfore.2018.04.008

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