Working Toward a System for Measuring Dynamic Knowledge

Working Toward a System for Measuring Dynamic Knowledge

Mark E. Nissen (Information Science and Management Department, US Naval Postgraduate School, Monterey, CA, USA)
Copyright: © 2017 |Pages: 19
DOI: 10.4018/IJKM.2017070101

Abstract

Knowledge is inherently intangible, invisible and resistant to quantification, particularly when in dynamic motion. This makes it a considerable and persistent challenge to understand, visualize and measure. The research described in this article builds upon emerging knowledge measurement techniques and well-established knowledge flow theory to develop a system for measuring dynamic knowledge in the organization. This measurement capability is developed judiciously and analogically from the author's understanding of dynamic physical systems. As a background, the key literature on knowledge measurement and knowledge flow theory is reviewed. Then this system for measuring dynamic knowledge is conceptualized, and its use, utility and theoretical coherence are illustrated through practical application. This research makes a theoretical contribution by advancing a coherent approach to dynamic knowledge measurement, and it makes a practical contribution through illustration in the organization context. A related goal is to stimulate considerable thinking, discussion, debate and continued research.
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Background

In searching for relevant literatures to review, a relatively wide metaphoric net is cast, looking for background, insight and inspiration well beyond the knowledge management field. Indeed, much of the research to measure knowledge has been conducted by economists (e.g., Hayek, 1937; Machlup, 1962; Glazer, 1998; Edvinsson, 2002), who have understood the power and prominence of knowledge as an economic resource for a long time. Shifting to Strategy, the techniques that center on the Balanced Scorecard (e.g., Bose & Thomas, 2007; Wake, 2015) are found, along with those enabling the knowledge based firm that competes on knowledge (Kogut & Zander, 1992; 1993).

Shifting again, a number of researchers look to the education context for measurement techniques, including academic tacit knowledge (Insch, McIntyre & Dawley, 2008), student knowledge retention (Bacon & Stewart, 2006), self-confidence in one’s answers (Hunt, 2003), and outcomes of student learning, among other measures of the global knowledge economy (Marginson, 2009).

Additionally, numerous works are found that use patent citation as a proxy for knowledge flow (Ye, Zhang, Liu & Su, 2015; Chavez & Viquez, 2015; Duguet & MacGarvie, 2005), even though such method is notably circumspect (Roach & Cohen, 2013), and a number of researchers offer the familiar learning curve as a measurement tool (Epple, Argote, & Devadas, 1991; Ingram & Simons, 2002), augmented by the employment of vectors in knowledge flow analysis (Sultanow, Cox, Brockmann & Gronau, 2014).

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