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Time for Reflection: Going Back to Autopoiesis to Understand Knowledge Management
| Content Provider | Semantic Scholar |
|---|---|
| Author | Parboteeah, Paul |
| Copyright Year | 2020 |
| Abstract | The field of Knowledge Management has lots of ideas and models, but the problem lies in that the discipline has no solid foundations on which to build new ideas and developments. A lot of the theory in knowledge management is scientifically unfounded and unproven, possibly a result of the difficulty in testing ideas resulting in numerous debates and leaving little time for new developments in the field. The paper introduces the concept of applying Autopoiesis to the Knowledge Management field in order to provide the discipline with a foundation from which to build. 1. KNOWLEDGE MANAGEMENT 1.1 What can we learn from revisiting the Building Blocks of Knowledge Management? Knowledge management is a relatively young discipline, and is rapidly evolving with new ideas. Whilst knowledge management can be defined as using knowledge as the key asset to drive organisational survival and success (Jashapara, 2004), numerous methods and perspectives exist for implementing knowledge management systems. There is general agreement among the academic community that definitions of knowledge have their foundations in the work carried out by Ryle and Polanyi (Ryle, 1949; Polanyi, 1967), providing a logical behaviourist perspective. Polanyi suggests that knowledge exists on a continuum between tacit knowledge and explicit knowledge. Tacit knowledge is explained by Ryle as ‘knowing how’ whilst explicit knowledge is described as ‘knowing that’. Ryle provided the example of a person riding a bike. The person has tacit knowledge in that they know how to stay upright, but often they can not explain what keeps them upright. The main idea behind tacit and explicit knowledge appears to be that ‘we can know more than we can tell’ (Jashapara, 2004). Davenport and Prusak (1998) extended the work of Ryle and Polanyi to create a continuum with experience (tacit knowledge) and information (explicit knowledge) at each end. ‘Insight’, ‘values’ and ‘data’ were also added as recognition that ‘there is no knowledge which is totally tacit and none without at least some tacit aspect’ (Eraut et al., 1998). This approach recognises that whilst a person may not have experience of something, they can still have an insight or information about an experience. Nonaka (1994), whose work was based on that of Ryle and Polanyi, attempts to show that knowledge can be converted between tacit and explicit form, and vice versa, and be transferred between different people. Whilst recognising this takes place, Nonaka does not provide any framework as to how this might happen or what processes are involved. Nonaka’s work is almost holistic in its approach. Whilst most authors have different views on what knowledge is, an agreement that the ideas are based on the work of Ryle and Polanyi means that regardless of what the finer points of the definition are, there is a common understanding that knowledge can exist on a continuum between tacit and explicit knowledge. An understanding of what knowledge is, allows an analysis of what knowledge management is and how knowledge management has developed. 1.2 The History of Knowledge Management Metaxiotis et al. (2005) split the history of knowledge management into three generations. The first generation was concerned with defining knowledge management, investigating possible systems and looking at the benefits of such systems. Advances in artificial intelligence also prompted study into how knowledge could be represented and stored. The second generation recognised the influence knowledge management could have in management information systems, for example creating frameworks and instigating organisational change. The third, and current, generation is based on new insights and practices developed from the second generation. According to Wiig (2002), the third generation is more ‘integrated with an enterprise’s philosophy, strategy, goals, practices, systems and procedures’. This is in recognition that knowledge management has links wider than information management. The third generation reflects the work of Ryle and Polanyi by emphasising the link between knowing and action (Paraponaris, 2003). The three generations of knowledge management have given rise to numerous definitions, although two authors have tried to create a definition that encompasses current views. Jashapara (2004) defines knowledge management as: ‘the effective learning processes associated with exploration, exploitation and sharing of human knowledge (tacit and explicit) that use appropriate technology and cultural environments to enhance an organisation’s intellectual capital and performance’ and Davenport and Prusak (1998) define knowledge management as: ‘concerned with the exploitation and development of the knowledge assets of an organisation with a view to furthering the organisations objectives. The knowledge to be managed includes both explicit, documented knowledge, and tacit, subjective knowledge’ Both of these definitions consider exploiting knowledge, but then deviate to focus on separate things. Jashapara (2004) is more concerned with sharing knowledge and different methods for sharing, whilst Davenport and Prusak (1998) are more concerned with developing and managing knowledge. Whilst both definitions are different, they are complementary and necessary, since without the ability to develop and manage an organisation’s knowledge, it is impossible to exploit and share it. As this paper has shown, knowledge management theory has lots of ideas and different routes for research. However, the problem is that the research is very conceptual, with high level ideas, and needs to be grounded in science to become sufficient for new and necessary improvements in knowledge management. 1.3 People Focused KM As knowledge management is concerned with people, substantial work was done to develop the idea of knowledge networks, as introduced by Seufert et al. (1999). Based on the idea of networks and social interactions, knowledge networks were defined as ‘a number of people, resources and relationships among them, who are assembled in order to accumulate and use knowledge primarily by means of knowledge creation’. This definition implies that people are working together to share knowledge with the common aim of knowledge creation. Seufert et al. (1999) suggest a framework for knowledge networks, but they neglect to suggest a mode 5 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.irma-international.org/viewtitle/33019/?isxn=9781599049298 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
