# Knowledge Flow

DOI: 10.4018/978-1-4666-4727-5.ch003
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## Abstract

This chapter focuses phenomenologically on the dynamics of knowledge flows. The authors look at the organizational processes responsible for knowledge flows and then discuss knowledge flow patterns. The discussion turns subsequently to examine interactions between knowledge flows and workflows, in addition to timing and obstacles of dynamic knowledge. The chapter concludes with five knowledge flow principles and includes exercises to stimulate critical thought, learning, and discussion.
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## Knowledge Flow Processes

Recall from your high school physics the concept inertia: objects at rest tend to stay at rest; and objects in motion tend to stay in motion. Then of course you have Newton’s famous second law: F = ma; that is, force (a vector) equals mass times acceleration (a vector). Combine the concept with the law and note acceleration (i.e., the change in the velocity vector) is related directly to the force applied to an object and inversely to the object’s mass. Even such basic principles from physics have been used successfully for centuries to describe, explain and predict the dynamics of objects in the world. They enable a person to analyze nearly any physical object (e.g., within the Newtonian realm), given a set of initial conditions (e.g., starting position, velocity, acceleration), and to predict precisely in which direction, how fast, how far and how long it will move (if at all). Further, for more complex systems of objects and forces, such principles enable the understanding and prediction of intuitive (e.g., ballistic) as well as seemingly random (e.g., chaotic) dynamic patterns. Other factors such as friction, elasticity, energy losses and the like pertain as well, but we can ignore these for now in our present context.

The science addressing physical flows is very advanced. The corresponding principled knowledge enables sophisticated, precise analysis and prediction of dynamic patterns. Unfortunately, the science addressing knowledge flows has not caught up to physics in terms of understanding dynamics, but several knowledge flow principles and discernible patterns are emerging from this science, principles and patterns that enable some degree of description, explanation, prediction and understanding. We begin this discussion by borrowing some concepts from physics for insight through flow metaphors1. We delve then into the phenomenology of knowledge flows.

Several principles from physics can be used to conceptualize knowledge flows metaphorically. For instance, knowledge at rest tends to stay at rest. Say some particular knowledge (e.g., a chunk, a fact, a procedure, an association, an inference) is possessed by a single individual, in a particular organization, at a specific location, at a unique point in time. Unless something is done to move such knowledge, it will likely remain confined to that single coordinate (i.e., person, organization, location, time). Looking around most organizations today, such confined, single-coordinate knowledge clearly represents a common case. Hence some kind of metaphorical force is required for knowledge at rest to “move” (we use quotation marks here to indicate knowledge does not represent some tangible physical object that can be rolled around like a ball on the floor).

Further, some aspects of such metaphorical force and the knowledge itself (e.g., organizational analogs to mass, friction, energy) may affect how fast and how far it will move, if at all. Borrowing from Newton, if the “force” is strong and the “mass” is light, then the associated knowledge should flow swiftly and broadly. Moving from metaphor to example, a gifted teacher, for instance, may represent the organizational analog of a strong force. A conceptually simple chunk of knowledge, as a related instance, may represent the analog of light mass. Together the gifted teacher and simple concept may result in rapid and broad knowledge flows. A less skilled teacher and more complex knowledge, as a counter instance, may result in comparatively slow and confined knowledge flows, or even no flows at all. Hence we can argue that knowledge at rest tends to stay at rest and that organizational analogs to forces and masses can affect if, how fast and how far any particular chunk of knowledge may flow. Here the inertia principle and associated flow metaphors appear to apply relatively well in our organizational context.

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