The potential energy of knowledge flow

Hai Zhuge, Weiyu Guo, Xiang Li
<span title="">2007</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="" style="color: black;">Concurrency and Computation</a> </i> &nbsp;
A knowledge flow is invisible but it plays an important role in ordering knowledge exchange when working in a team. It can help achieve effective team knowledge management by modeling, optimizing, monitoring, and controlling the operation of knowledge flow processes. This paper proposes the notion of knowledge energy as the driving force behind the formation of an autonomous knowledge flow network, and explores the underlying principles. Knowing these principles helps teams and the support
more &raquo; ... ms improve cooperation by monitoring the knowledge energy of nodes, by evaluating and adjusting knowledge flows, and by adopting appropriate strategies. A knowledge flow network management mechanism can help improve the efficiency of knowledge-intensive distributed teamwork. to communicate [8] [9] [10] , but seldom considered the efficiency and efficacy of knowledge sharing, especially the routing of knowledge in a geographically distributed team. The knowledge flow network is a way to formalize and optimize team management by using an approach similar to that of workflow management systems [11, 12] . In the knowledge flow process model, introduced in [12,13], knowledge flow is the passing of knowledge within a team (e.g. in the form of query-answering, broadcasting and pushing knowledge). A knowledge flow begins and ends at a knowledge node. A knowledge node is either a team member, or an agent that can generate, process, and deliver knowledge. A knowledge flow network is made up of knowledge flows and knowledge nodes. The objective of knowledge flow management is to improve the efficiency and efficacy of cooperation in knowledge team. Poor management can waste and even misdirect teamwork. Building knowledge flow networks that only pass the knowledge required for a task is a challenge, particularly for organizations that are geographically dispersed and liable to change. Although the Internet can provide a good means of communication for dispersed teams [14, 15] , use of email, file sharing, and online blackboards is often ineffective. The absence of criteria for assessing knowledge flow networks is a major obstacle. For a network to be effective, knowledge must flow to where it is needed-across time and space, and between organizations when necessary. Potential energy and hydraulic pressure cause water to flow along a river or through pipes. Voltage causes electrical energy to flow through wires. What causes knowledge to flow? What laws govern the knowledge flow? This article explores these questions. The idea of knowledge energy causing knowledge flow is used here to explore the laws governing the flow, and as criteria for judging its effectiveness. Ways to assess energy are proposed, and their validity is demonstrated by experiments with a prototype knowledge flow management support system. Finally, knowledge flow networking strategies are discussed. KNOWLEDGE ENERGY AND KNOWLEDGE SPACE Assumptions To establish a reasonable research scope, the following assumptions clarify the equality, autonomy, generosity, and simplicity in the object of our research-knowledge flow networks. Assumption 1. (Equality) Knowledge nodes in a network use similar intelligence to acquire, use, and create knowledge. We assume that people in an organization are normal in that they are able to generate, use, and spread knowledge. The possibility of extreme intelligence is ignored to simplify the analysis. Assumption 2. (Autonomy) Knowledge nodes share knowledge autonomously. We assume that sharing knowledge between nodes is free from outside influence, such as supervisory orders. Thus, we can focus on studying knowledge flow principles and their application when designing particular knowledge flow networks. Assumption 3. (Generosity) Knowledge nodes share knowledge without reserve. Knowledge Area 2 Knowledge Level 1 1 2 3 Figure 2. An example with six knowledge flow networks. Knowledge energy Knowledge energy is a parameter that expresses the degree of a node's knowledge and a person's cognitive and creative abilities in a unit field. A node's energy gives its 'rank' in a knowledge flow network. The higher a node's energy, the better it will be at learning, using, and creating knowledge. The knowledge energy in a unit field is estimated by assessing how much relevant knowledge a node contains. The knowledge energy of a node varies with time and across fields. We use a four-dimensional orthogonal space, KS (knowledge-area, knowledge-level, knowledge-energy, time), to represent the energy of a node. Any point in the space represents the knowledge energy of a node in a certain unit field at a certain time. At time t, the energy of node u in unit field UFd(i, j ) can be represented as KE (task, u, i, j , t). The curved surface in Figure 3 depicts the energy distribution of a node at a time t. KNOWLEDGE FLOW PRINCIPLES Principle 1. Between any two nodes, knowledge only flows when their energies differ in at least one unit field. Furthermore, the knowledge energy of c can be obtained by KE closed (task, c, i, j, t
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1002/cpe.1143</a> <a target="_blank" rel="external noopener" href="">fatcat:lakdeblipnbb5lghk6kgiqyjb4</a> </span>
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