Is the Knowledge Management the Real Future of HRM - Essay Example

You’re Name Professor Course 4-May-18 Is the Knowledge Management the real future of HRM? Discuss Introduction “Knowledge management is the set of practices aimed at discovering and harnessing an organization’s intellectual resources. It’s about finding, unlocking, sharing, and altogether capitalizing on the most precious resources of an organization: people’s expertise, skills, wisdom, and relationships. Knowledge managers find these human assets, help people collaborate and learn, help people generate new ideas, and harness those ideas into successful innovations” (Zaleznik 67–8.) Discussion The concept of knowledge management is becoming increasingly prevalent in academic and vocational' literature. The objective of this paper is to review the HRM perspective of knowledge management starting with a HRM perspective of knowledge relative to data and information. The characteristics in terms of forms, levels and categories of knowledge are discussed. (Starke 7-8) Against this background a definition of knowledge management is sought which is compared with Human Resource Management. The increasing emphasis placed on knowledge in an organisational context has given rise to a new manifestation of capital which occurs as human or structural intellectual capital. Therefore, the relation of knowledge management to corporate memory as well as the learning organisation is indicated. There are various ways in which knowledge management can be interpreted, the first being analogy; three of which occurring frequently in discussions of knowledge management (biological, economical and their convergence in bionomics) are discussed. Secondly, an indication of a paradigm shift in the approach to management is outlined and the different management styles (top-down, bottom-up and middle-up-down) are contrasted in relation to knowledge management. Finally the underlying approaches to knowledge management. (people-centred versus technologycentred) are considered. (Wilson 144) knowledge management is not a radically new concept. Horton indicated in 2002 in his evolution of resource management functions that the management of knowledge already began during the 1980s which comprised of expert systems and artificial intelligence and which recognised the economic and cultural value of knowledge (Horton 2002). The information age has resulted in major structural changes on economic and social front and global trends have resulted in the shift towards a knowledge-based economy necessitating knowledge management. Increasing amounts of information are being generated but without the corresponding increase in the ability to manage, interpret and act on relevant information resulting in an information overload. (Fahey and Prusak 265-280) It seems that future growth and prosperity depend on the ability to manage both information and knowledge. Although terminologically confusing, in this scenario knowledge management is proposed, mainly by business consultants, as the key to survival within this mass of knowledge and the only means to gain and maintain the competitive edge in the global economic marketplace. The task of defining these three concepts in a precise way, especially knowledge and its epistomology, is a task that has received attention for many years. Lately, the question of the precise nature of data and information has been raised anew, not only in philosophy, but also in information science. There are no commonly accepted definitions of these terms, but if no working definitions of these concepts are debated there will be little progress in the discussion of these concepts especially in the field of information science. Therefore, this paper will attempt to present some working definitions for discussion. According to Harris (2001:1) the lowest level of known facts is data. Data has no intrinsic meaning. It must be sorted, grouped, analysed, and interpreted. When data is processed in this manner, it becomes information. Information has a substance and a purpose. However, information does not have meaning. When information is combined with context and experience, it becomes knowledge'. (Hislop 182-202) Turban and Frenzel (2002:10-11) define these concepts from a computer science and specifically an artificial intelligence perspective as follows: data refers to numeric or alphanumeric strings that by themselves do not have meaning. These can be facts or figures to be processed. Information is data organised so that it is meaningful to the person receiving it. Knowledge has several definitions: understanding, a clear and certain perception of something, learning, all that can be perceived or grasped by the mind, practical experience or skill, cognisance, recognition, organised information applicable to problem-solving. For Taylor (2001) knowledge is formulated in the minds of individuals through experience. Knowledge is shared between groups and communities through shared experience and through the transfer of knowledge, both tacitly and explicitly. Thus the individual and community (and the organisation as a specific form of community) has a pool of knowledge. Every task or skill has specific knowledge associated with it. (Polsans 241-253) Once again knowledge is highlighted as being internal to the human being and therefore subjective whereas information and data remain mostly external and objective. Thus these concepts can be seen on a continuum where each is followed by the other and the degree of human involvement or level of added value divides the concepts. Alternatively, data, information and knowledge can be classified by their degree of abstraction and by their quantity. Knowledge is the most abstract and exists in the smallest quantity. This can be represented as in Figure 2 (Turban & Frenzel 2002:11). For the sake of thoroughness the objective nature of knowledge is also mentioned in this 'paper. Karl Popper proposed the three world model where world 1 is the physical world, world 2 the world of conscious experiences and thoughts and world 3 the results from world 2, in other words the contents of books, libraries and computer memories. Thus the knowledge in world 3 is the ideas from world 2 that has been objectified. Furthermore, Popper maintains that world 3 is autonomous and has an existence separate from human minds and therefore that this knowledge is not objectified but also objective (Neill 2002:7-11). This theory has largely been refuted although not falsified. (Nonaka 18-31) In 1966 the Hungarian philosopher Michael Polanyi in The tacit dimension distinguished between two forms of knowledge that can be found in an organisation, namely explicit and tacit knowledge. Explicit knowledge can be articulated in formal language and transmitted among individuals, whereas tacit knowledge is personal knowledge embedded in individual experience and involving such intangible factors as personal belief, perspective and values. Levels of knowledge Another distinction that can be made is the level of the knowledge: shallow or deep. Shallow knowledge refers to surface level information that can be used to deal with very specific situations for example `if You've got a cough, try cough mixture'. It is usually a rule-of-thumb without explanation or heuristic rule. Deep knowledge on the other hand refers to the internal and causal structure of a system and considers the interactions among the system's components. Deep knowledge can be applied to different tasks and different situations. It is based on a complete, integrated body of human consciousness and includes emotions, common sense and intuition (Turban & Frenzel 2002:120-123). This type of knowledge is extremely difficult to document, that is, make explicit. An example of deep knowledge is 'I gave the patient these pills because he had symptoms which indicate a certain condition that the pills are effective against' and even this example fails to make explicit the deep knowledge that underlies it. (Scarborough 501-516) Furthermore, abstraction implies that, knowledge does not rely on access to the original information. A symbol can be created to represent the original information and as a result knowledge can be transferred from one person to another without having to transfer all of the information. Turban and Frenzel (2002:123) on the other hand, approach knowledge from a more subjective view. Declaralive knowledge answers the `what' question and is shallow-explicit knowledge, or surface-level knowledge. Procedural knowledge answers the `how' question. It elucidates the procedure or method that must be followed in a certain situation through step-by-step instruction. Generally it indicates how to use declarative knowledge. Semantic knowledge, on the other hand, reflects the cognitive structure of the subject and involves the use of long-term memory. Episodic knowledge is autobiographical and experimental (thus empirical) information organised by case: or episode, classified by date and place and resides in longterm memory, Metaknowledge is knowledge about how to reason, how to apply knowledge and how to learn. The knowledge mostly captured in today's knowledge-based systems is declarative and procedural knowledge and not semantic, episodic and metaknowledge, which is really the knowledge that organisations and managers aspire to capture (i.e. make explicit) in knowledge management systems. Thus the difference between a knowledge-based system and a knowledge management system would be the categories of knowledge it contains. Although a knowledge management system could contain the `knowledge of the knowledge-based system the reverse would not be true as the knowledge-based system is very much the expert system of rules and cases. Many of the principles of knowledge management have historical roots in a variety of disciplines and thus similar ideas with different names have evolved in all these disciplines that are contributing to knowledge management. This results in a confusion of terminology from these different contributing disciplines. Furthermore, interpretations and use of the terminology may differ according to duties and functions: managers, practitioners and technologists may have their own :ideas and perspectives on what knowledge management actually is. This makes finding a clearcut definition of knowledge management a difficult task. The most obvious definition would be application of traditional management processes to knowledge within an organisation where these Human Resource Management processes include planning, organisation, co-ordination and control. Seeing that the nature of knowledge defies control, this definition is trivial and almost inconsequential.' Thus it seems that knowledge management is concerned with (a) the identification of knowledge needs and assets, knowledge problems and opportunities and (b) the design, development and implementation of knowledge management strategies and solutions. But what are these knowledge assets? Knowledge assets are mostly referred to as intellectual capital (less frequently to as corporate memory and sometimes intangible assets). According to Huang (2000) from IBM intellectual capital consists of `information, knowledge, assets, experience, wisdom, and/or ideas that are structured to enable sharing for reuse and to deliver value to customers and shareholders'. There are two kinds of intellectual capital: human and structural. Human intellectual capital is important as the source of innovation and renewal, but is useless if it cannot be exploited. Exploiting it requires structural intellectual capital, such as software applications, manuals and already captured know-how - to turn individual know-how into the property of the organisation (Stevenson 2003). According to Edvinsson (in Stevenson 2003), for managers and shareholders the structural intellectual capital is more important and it remains the property of the organisation and `it puts new ideas to work, it amplifies the value of the human capital, and it can be used again and again to create value' (Stewart in Stevenson 2003). According to Willard's (2000: 32) categorisation of knowledge, personal knowledge residing in people can be classified as human intellectual capital and embedded and recorded knowledge as structural intellectual capital. In the learning organisation context knowledge management can be seen as the management of what has been learnt through organisational,learning. This manifestation of knowledge through learning in an organisation is also referred to as intellectual capital. Organisational learning is the process of acquiring or collecting these intellectual assets whereas the structure wherein the learning takes place is called the learning organisation. According to Remeikis (2001:5) knowledge management can be `a "grass roots" sort of effort' without the involvement of senior management or their support (although she does add that top-level support is the ideal). This contrasts with Davenport's (2000a) view that assuming that knowledge management can thrive without support from senior executives is a pitfall. According to him, one might be able to build a little knowledge repository without top management support but this is unlikely to bring about any real transformations in the organisation. Grayson (2003) puts it very plainly: if knowledge management is not central to the strategy of an organisation, it is not likely `to go anywhere'. It seems that it is necessary to find the a golden midway where the best of each of these approaches are combined. Is the synthesis of these two opposing management styles to be found in the middle-up-down management process of Nonaka and Takeuchi (2003:127)? In middle-up-down management, top management gives the HRM perspective framework and support (Nonaka 2003) and `encourage, recognize, and reward openness, systemic thinking, creativity' (Malhorta 2001:3). Middle-up-down management is based on teamwork where teams are largely autonomous in interpreting the strategy of top management with top management acting as a catalyst more than a leader: `A more equivocal vision gives employees and work groups the freedom and autonomy to set their own goals. This is important because while the ideals of senior management are important, on their own they are not enough. The best that top managers can do is to clear away any obstacles and prepare the ground for self-organising groups and teams. Then it is up to the team to figure out what the ideals of the top mean in reality' (Nonaka 2003:104). A comparison of these three approaches and their key characteristics can be seen in Table I which is adapted from Nonaka and Takeuchi (2003:130). According to Murray there are two typical strategies in which these Human Resource Management approaches manifest themselves. Business managers (and knowledge management consultants who sell services to them) tend to adopt a top-down viewpoint of knowledge management. They are concerned with what managers have to know and do in order to push their organisations toward leveraging corporate intellectual assets. By contrast, technologists tend to focus on knowledge sharing and re-use, on enabling technology that can be used by all members of an organisation. They believe the right tools will make good things happen and that explicit knowledge, properly modelled, should be our primary concern. The benefits will be spread through the organisation by means of the ripple effect, often unpredictably and even chaotically, Knowledge can not be directly managed but only indirectly through the carriers of the knowledge - the people (through human resource management) and the `technology (through Human Resource Management and information technology management) and the interaction between them (information flows and communication). Of the two branches in the management of knowledge (strongly coupled to the subjective versus objective nature of knowledge) the authors would side with the management of people, since people are the primary source of knowledge and technology the secondary (as storage medium of knowledge transferred from people). Placing technology first is' almost certain to invite failure as (most) people resent being made to feel inferior to a machine. But although people possess the knowledge, technology is necessary for the orderly storage, retrieval, and sharing of knowledge. A balance of these two approaches is the ideal and each organisation must determine where this balance lies in their situation. Conclusion In 1988, Tom Peters (Orna 2002:161) identified the turbulence, unpredictability and instability of the economic climate of the 1980s and predicted that the successful organisation of the 1990s and beyond will have certain characteristics, namely fewer organisational layers, more local autonomy, oriented towards differentiation, high value-added goods and services and geared towards niche markets, quality and service conscious, responsiveness, fast at innovation, utilising highly trained, flexible people as the principal means of adding value (Orna 2002'161). As has been illustrated, in principle, knowledge management promises to deliver most, if not all, of these characteristics to the organisation that successfully implements it. In the global market economy of today, an organisation will either have to join its competitors or eventually die (Stewart, 2003:58) and seeing that some organisations, mostly Japanese, have taken this route others will have to follow. 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