Acquiring and Sharing Tacit Knowledge
4.2 Acquiring and Sharing Knowledge
Knowledge acquisition and knowledge sharing are interrelated concepts, and there is overlap between the two, in that learning or acquiring knowledge may require the simultaneous sharing o f knowledge. Knowledge sharing is explored with particular reference to the role of social interaction in the development of transactive memory and the factors which may constrain social interaction in teams. In psychology, AI and organisation theory the traditional view of knowledge acquisition reflects the CIP paradigm, where objective knowledge can be easily transferred between people. However, constructivist views of knowledge acquisition involve personal and socially constructed knowledge, and account for the environmental and social factors involved in learning. Modem approaches in psychology, AI and organisation theory model knowledge using a constructivist approach.
4.2.1 Psychological A pproaches to K now ledge A cquisition
According to Reber (1995) cognitive psychology has concentrated on knowledge representation rather than the nature and acquisition of knowledge. Reber (1995) argues that the topic of learning has been neglected in contemporary psychology, where ‘learning and conditioning are now typically represented within a cognitive framework’ (p.3). This view is echoed by Glaser (1990) who posits that the information processing
approach was critical in the move away from learning and research into learning reflects the dominance of the information-processing paradigm, with much research focusing on the scientific experimentation and logical steps involved in learning, specifically the acquisition of knowledge.
Learning in psychology is defined as ‘a relatively permanent change in behavioural potentiality that occurs as a result of reinforced practice’ (Kimble et al. 1961, p.6) and evidence of learning is found in actual or potential changes in behaviour as a result of experience. According to Taatgen (1999) ‘task performance is an intricate interplay between learning and performance. Just focusing on performance will only give a very limited insight into what is going on.’ (p.22). It is important to look at the process of learning, even if it is difficult to quantify, since performance does not yield enough information about what has been learned. The information processing approach to learning and knowledge acquisition looks at how information is processed, resulting knowledge and perception or behaviour, but not all learning is explicit and follows this method.
4.2.2 Situated C ognition: A cquiring K now led ge is Social and C ontext D ependent Emerging from anthropology, sociology, and cognitive science, situated cognition theory represents a major shift in learning theory from traditional psychological views of learning as mechanistic and individualistic, and moves toward perspectives of learning as emergent and social (Greeno, 1998; Lave & Wenger, 1991; Salomon, 1996). Situated cognition involves taking into account interaction between the individual’s inner state and the external environment and trying to record all the influencing factors (Richards & Busch, 2003). According to Lave (1988) ‘the point is not so much that arrangements of knowledge in the head such as schemas scripts and frames, correspond in a complicated way to the social world outside the head, but that they are socially organized in such a way as to be indivisible’ (p.6). Clancey (1997) posited that ‘what is “socially shared” is not just language, tools, and expressed beliefs, but conceptual ways of choreographing action, by which descriptions and artefacts develop and are given meaning’ (p.277). This situated view of cognition has implications for learning and knowledge transfer.
4.2.3 Situated L earning
Regarded as leaders in the situated cognition movement, Lave and Wenger (1991) describe learning as an ‘integral part of generative social practice in the lived-in world’ (p. 35). Lave (1986, 1993, 1997) researched the discontinuities in performance of mathematical activity by the same persons in different settings, suggesting that the competence of the individual is situation specific. Hanks (1991) suggests that ‘[L] earning is therefore a process that takes place in a participation framework, not in an individual mind’ (p. 15). From a situational point of view, knowledge is already present in established activities and cultural norms and is imported through the contributions of new participants.
Therefore, social interaction is a critical component of situated learning because learners become involved in a ‘community of practice’ which embodies certain beliefs and behaviours to be acquired (Brown & Duguid, 1991). As the beginner or newcomer moves from the periphery of this community to its centre, they become more active and engaged within the culture and hence assume the role of expert or ‘old-timer’. Furthermore, situated learning is usually unintentional rather than deliberate. These ideas are what Lave and Wenger (1991) call the process of ‘legitimate peripheral participation.’
Situated learning is usually unintentional, is embedded in context, propagated by social interaction and involves practice. This type of learning is related to implicit learning and the acquisition of tacit knowledge.
4.2.4 A I A pproaches to A cquiring K now ledge
Artificial Intelligence research is mainly concerned with expert systems, where an expert system is ‘a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice’ (Jackson, 1990, p. 3).
Traditionally, the term knowledge acquisition in AI research has referred to gathering expertise, primarily in the form of rules from experts in order to create an expert system (Gaines 1987; Neale 1989). Indeed, the main AI research problem in the 1980s was
whether all knowledge could be reduced to explicit form. Phrases like ‘Knowledge Elicitation’ and ‘Knowledge Acquisition’ were common, illustrating the belief that knowledge could be extracted from people’s heads and contained in ‘knowledge-based systems’ (KBS), which exhibited artificial intelligence (e.g. Feigenbaum & McCorduck, 1983). This approach does not account for the findings that expertise is probably more intuitive than originally suspected and goes some way to explaining why computer programmers and cognitive psychologists have difficulty in getting the experts to articulate the rules they follow, since experts do not follow rules (Dreyfus & Dreyfus,
1986).
The problem of context was identified as one o f the main problems in capturing expert knowledge (Dreyfus & Dreyfus, 1986), since the rules typical of AI programming are context free whereas human actions are never performed without regard for context and so cannot be rule governed. In addition, the interaction between the knowledge engineer and expert is not acknowledged. In the early 1990s it became apparent that it was necessary to capture the context as well as the rules (McCarthy, 1993) and there was a movement away from the ‘expertise transfer’ view to the ‘knowledge modelling’ view. The knowledge modelling view sees knowledge as context sensitive and acknowledges that models may be inappropriate when used out of context. Researchers in AI like their counterparts in psychology and organisation theory looked to situated cognition for a solution to the problem of context. Richards and Busch (2003), posited that the situated knowledge from the expert systems perspective ‘places great emphasis on incremental techniques that allow change, capture context and which acquire knowledge without relying on a human to state or codify that knowledge’ (p. 180).
4.2.5 O rganisational A pproaches to A cq uirin g K now ledge
Organisational knowledge is a resource, and the result of organisational learning processes. According to Edmondson (2002, p. 128) an ‘organisation “learns” through actions and interactions that take place between people who are typically situated within smaller groups or teams.’
The organisational learning literature incorporates both organisational level and individual level theories. Edmondson (2002) outlines three levels of theorising about organisational learning. At the macro or organisational level, theories focus on the
stabilising effects o f routines and adaptation over time. Individual level or micro approaches look at the behaviour of individuals and their effect on organisational change. At an individual level, organisational learning is categorised as adaptive (single-loop) learning or generative (double-loop) learning (Argyris & Schon, 1978). In adaptive learning, organisational members resolve problems within the existing norms of the organisation, while generative learning involves frame-breaking disruption that transforms the routines or norms of individuals and groups. The macro and micro levels of analysis provide a foundation for a third perspective that investigates learning at the group or ‘meso’ level of analysis, where a group level approach is inherently integrative, incorporating factors from two or more levels simultaneously (Rousseau & House 1994). According to Edmondson (2002) teams, or work groups, are also important in that individual cognition and behaviour, is shaped by social influences, that is, by the attitudes and behaviours of others with whom they work closely (Salancik & Pfeffer 1978, Hackman 1992).
Organisational knowledge processes and organisational learning are interdependent, and it is impossible to study one element without studying the other (Johannessen et al. 2001, Spender & Grant, 1996). Johannessen et al. (2001) also posit that situated and contextual learning are the elements that tie tacit knowledge to organisational learning.