Study: Reconstituting Knowledge Management

Study: Reconstituting Knowledge Management


Complexity theory, Knowledge management


Jean-Baptiste Faucher, AndreΒ΄ M. Everett and Rob Lawson





Purpose – The purpose of the paper is to improve traditional knowledge management models in light of complexity theory, emphasizing the importance of moving away from hierarchical relationships among data, information, knowledge, and wisdom. Design/methodology/approach – Traditional definitions and models are critically reviewed and their weaknesses highlighted. A transformational perspective of the traditional hierarchies is proposed to highlight the need to develop better perspectives. The paper demonstrates the holistic nature of data, information, knowledge, and wisdom, and how they are all based on an interpretation of existence. Findings – Existing models are logically extended, by adopting a complexity-based perspective, to propose a new model – the E2E model – which highlights the non-linear relationships among existence, data, information, knowledge, wisdom, and enlightenment, as well as the nature of understanding as the process that defines the differences among these constructs. The meaning of metas (such as meta-data, meta-information, and meta-knowledge) is discussed, and a reconstitution of knowledge management is proposed. Practical implications – The importance of understanding as a concept to create useful metaphors for knowledge management practitioners is emphasized, and the crucial importance of the metas for knowledge management is shown. Originality/value – A new model of the cognitive system of knowledge is proposed, based on application of complexity theory to knowledge management. Understanding is identified as the basis of the conversion process among an extended range of knowledge constructs, and the scope of knowledge management is redefined



The Link Between Complexity Theory And Emergent Knowledge

Complexity theory emphasizes the importance of non-linear relationships within a system. Therefore, it is not so much knowledge of the elements of a system that is important but more are all based at the University of Otago School of Business, Dunedin, New Zealand. comprehension of how they interact to form feedback systems. Complexity theory suggests that innovation and creativity occur when systems operate at the β€˜β€˜edge of chaos,’’ where they show emergent behaviors that enhance their ability to adapt to a particular situation of their environment (Bak, 1996; Capra, 1996; Stacey, 1996). Hence, complexity theory provides a framework to understand how knowledge forms at the level of individuals and then influences knowledge processing at the collective level of the organization (McElroy, 2000). A key presumption of this paper is that the concept of knowledge is scale free; it should apply in the same manner to individuals and organizations. Consequently, this paper proposes a reconstitution of knowledge management through the use of concepts borrowed from complexity theory.


This paper highlighted the lack of consensus on the definition of knowledge in the literature. However, it was possible to illustrate how all definitions agree on a common basis to define data, information, knowledge, and wisdom. Revisited definitions were provided. It was shown that data, information, knowledge, and wisdom could all be tacit or explicit, and that understanding is the basis of the conversion processes among them. The classical knowledge hierarchy was then discussed, and it was determined that it needed to be extended. Indeed, in order to attain the full scope of the knowledge hierarchy, it was necessary to add two concepts: existence and enlightenment. Consequently, a revised hierarchy was proposed (Figure 3). However, this paper has established how this type of thinking, even if it could be improved, is limited. What is needed is a holistic approach. Therefore, the paper provides evidence that data, information, knowledge, and wisdom are all constructs based on the same process: abstraction of existence. What really differentiates these constructs is the level of understanding they require. The paper also describes the cognitive system of knowledge and how existence, data, information, knowledge, wisdom, and enlightenment relate to each other by introducing the E2E model. This model is based on insights from complexity theory and emphasizes the non-linear and systemic basis of the cognitive system of knowledge. Complexity theory facilitates understanding of the meaning of knowledge management and the concept of knowledge. Finally, knowledge management has been reconstituted around the metas. The metas are the understanding of the conversion processes among data, information, knowledge, and wisdom. They provide a powerful new understanding of the concept of knowledge management.