2009年12月8日 星期二

THE KNOWLEDGE CORE

THE KNOWLEDGE CORE :
A NEW MODEL TO CHALLENGE THE KNOWLEDGE MANAGEMENT FIELD
Griffiths, David. A.
University of Edinburgh
Edinburgh, Scotland
D.A.Griffiths@ed.ac.uk

Morse, Shona, M.; Koukpaki, Serge; Martin, Brian
University of Edinburgh
Edinburgh, Scotland
Shona.morse@ed.ac.uk; serge.koukpaki@ed.ac.uk; brian.martin@ed.ac.uk
This paper derives from a research project, which sets out to address practitioner dissatisfaction in the area of Knowledge Management. It aims to discuss common weaknesses in existing thinking about Knowledge Management and in prevailing models in particular. Modelling processes are considered and underlying assumptions that are required to be addressed in any attempt to create a Knowledge Management Model are examined. An overview is provided of the initial stages in the development of a new, synthetic and general model, The Knowledge Core , with assumptions underlying this signposted and their influences upon modelling discussed.
Introduction
This is the second in a series of papers derived from an ongoing research project, the overall aims of which are to address practitioner dissatisfaction in the field of Knowledge Management (KM). The first paper presented the outcome from an enquiry into Critical Success Factors, comparing findings from an extensive review of literature with existing models and frameworks (Griffiths & Morse, 2009).
This second paper examines the underpinning values that might influence the construction of a new model, and how this might influence the construction of a new KM assessment tool for organisations. The narrative provides an overview and visualisation of our earlier findings before developing an Action Research based approach, linking KM to Systems Thinking, Soft Systems Methodology and Logic Modelling spaces.
We conclude by identifying the next steps in pursuit of what could be seen as a general model for the field.
1. Overview
KM appears to be firmly established as a strategic management tool (Rigby & Bilodeau, 2007). However, practitioners and academics seem to be expressing dissatisfaction with its performance in organisational settings (e.g.: Rigby & Bilodeau, 2007; Fahey & Prusak; Newell & Scarborough; Storey & Barnett, cited in Smith, 2003). Theorists have been discussing dissatisfaction and potential deficiencies in the field for some time, particularly the lack of common framework to bind the process to situated settings (Rubenstein-Montano et al, 2001; Holsapple & Joshi, 2004; Metaxiotis et al., 2005; Mekhilef & Flock, 2006; Limone & Bastias, 2006). This lack of satisfaction, where KM is ranked 22 of 25 strategic management tools in a survey of 1221 global executives (Rigby & Bilodeau, 2007), coupled with an identified gap in theory, could cause a critical loss of resource value for organisations. These problems are succinctly acknowledged by Chun et al. (2008) who state that 'despite the importance of knowledge as an asset, few organisations truly understand what it means to be a knowledge-based firm and how to manage knowledge to achieve its goals' (p. 1).
These apparent deficiencies led to an extensive enquiry into the field (Griffiths & Morse, 2009). Conducting an evidence-based meta-analysis of 287 pieces of academic and practitioner KM literature and 71 KM models and frameworks, we concluded that a common framework of KM appeared plausible, existing across the disciplines of Business & Management; Engineering; Decision Science; Computer Science; Medicine & Health; and Social Science. We further concluded that this framework consisted of 16 common CSFs. In a distillation of our results we proposed four functions of KM (Capturing & Storing, Creating, Sharing and Applying) and twelve enablers (What Is Known, Extending What is Known, Reflecting, Context, Motivation, Artefacts, Space, Culture, Organisational Structure, Knowledge Structure, Catalysts, Transmission). Examining the sample of 71 models and frameworks we exposed a potential gap in current research where 0 (zero) models, and only 1% of the literature in the original meta-analysis, identified all 16 CSFs.
This paper attempts to progress our research by suggesting a general model for the field, represented through The Knowledge Core Model. The search for new models and frameworks has been criticised by some theorists as being a contributing factor to the apparent poor performance of KM as a strategic management tool: 'The profusion of terms...flippancy as to the way the concept is used, ignorance of the classical categories of thought and the frivolous abuse of fashions...are constructing a “Tower of Babel”, provoking injustice and unease in the unnecessary formulation and accelerated substitution of propositions of new models and expressions without allowing them to mature and without making a minimal effort to contrast them to prior ones' (Bueno, translated from Spanish and cited in Limone & Bastias, 2006, p. 40).
The position of Bueno is interesting as he appears to discourage the improvement of scientific theory, specifically Popper's theory of Falsifiability, which would seem to demand a process of evolution in order to interrogate the efficiency and effectiveness of existing models and frameworks in order to determine not only when they work, but when and why they don’t work (Blackman et al., 2004). However, Bueno’s stance would also appear to inhibit the field from advancing Argyris & Schon's double loop learning theory (1982), where not only the action strategies, but also the governing variables of theories are examined.
In addition, Meadows (1982) opines that addressing issues of process change can be politically challenging as it can be easier to point away from the cause, being, in the case of KM, current action strategies within models and frameworks, than to interrogate the core of the issue, being the governing variables. Meadows further suggests that thinking such as that of Bueno can produce a state where a culture of least resistance is perpetuated and the desired performance of the field is lost to an accepted state of lower standards and poor performance. In another interpretation perhaps Bueno could be seen to be asking theorists to take more care and give greater depth of thought to analysis.
In acknowledging the concerns of Bueno, it would seem important to point towards the depth of our research which, whilst echoing the concerns of disparate language, demonstrates that current models and frameworks appeared to be deficient in demonstrating the CSFs or governing variables that could influence success within organisations. This would seem to indicate that it is in the interests of the field to consider a new model.
2. The Model
The Knowledge Core (Fig 1, 2 & 3) presents a visual representation of our findings. However, theorists have suggested that creators of knowledge should be prepared to state their assumptions or theories before mapping their view of the world (Yolles, 1996; Checkland, 2000; Knowlton & Phillips, 2009). Knowlton & Phillips suggest that a failure to do this offers the potential to pollute research and devalue the knowledge created: 'Too often...models are built without the benefit of explicitly naming the assumptions and underlying theories of change. This omission can help explain why tremendous conflict, even chaos can erupt during program development, planning and implementation, or assessment' (p. 36)
This is supported by Arbnor & Bjerke (2009) and Jackson (2001) who believe that the knowledge creator's view of the world has to be explicit if the knowledge created is to be explained, understood and validated. Jackson (2001) posits that it is not possible to determine the strengths of the methodological approach unless the theoretical view of the knowledge creator is explicitly expressed. This view can again be linked to Popper's Principle of Falsifiability and the need for scientific theory to be transparent in its underpinnings in order to enhance its testability. In this way it is possible to subject the findings to refutation and by doing so confirm its scientific status (Crease, 2001). Therefore the narrative presented in this paper will attempt to clarify the underlying assumptions of the authors in order to develop a clear perspective of the inductive process. This clarification would also seem to address another criticism suggested by Bueno, cited earlier, being the 'ignorance of the classical categories of thought'.
The knowledge creator's view of the world also provides signposts towards appropriate methodology, or blend of methodology, for enquiry and evaluation, which in turn develops the language that will mould the modelling space. By unveiling this for the examination of structures within the research paradigm the uncertainty surrounding the structural elements and their interrelationships can be reduced (Yolles, 1996; Arbnor & Bjerke, 2009). This would seem important in order to overcome issues of clarity within the KM field, which we discussed in our original research. Therefore the remainder of this paper will look to set out the assumptions employed and the journey taken in the development of The Knowledge Core as a prospective new model.







3. Criticism of Model Building

Bueno (cited by Limone & Bastias, 2006) criticises new models for not making 'a minimal effort to contrast them to prior ones' (p. 40). However, we did demonstrate in our analysis of 71 models and frameworks that 0 (zero) identified the 16 CSFs identified in our analysis of 287 pieces of KM literature. We also suggested that existing models failed in tests of 'Comprehensiveness', 'Correctness', 'Usefulness', 'Clarity' and 'Consciousness' synthesised from the work of Rasli (2004), Bacharach (1989) and Shanks (2003). The rigour applied to our research would appear to address Bueno’s concerns and justify the development of a new model. Furthermore models are observed by Checkland (2000) as ‘intellectual devices – whose role it is to help structure an exploration of the problem situation being addressed’ (p. s26).
Model building has been criticised for attempting to be mathematical, where ‘proof and formal analysis are aesthetic crafts’ (Klein & Romero, 2007, p. 245) and Ludvall (2006) has stated that KM cannot be reduced to a set of techniques. However, Klein & Romero (2007) argue that model building brings discipline of mind and insight by applying formulaic models. They contend that proof will involve arguments that the model’s formulation is of academic interest and importance with a purpose aimed at advancing knowledge and understanding of real-world issues.
Checkland (2000) offers a non-threatening view of models in suggesting that at this stage of Soft Systems research they are not actually models of anything: ‘They are accounts of concepts of pure purposeful activity, based on declared world-views, which can be used to stimulate cogent questions in debate about the real situation and desirable changes to it’ (p. s26)
This would seem to offer some potential resolution of the political issues associated with change as intimated earlier by Meadows (1982).
Further to this the signals of dissatisfaction signposted by Rigby & Bilodeau (2007) would appear to suggest ineffectiveness at the point of practice. Knowlton & Phillips (2009) posit that models need to be designed and deployed in order to overcome key questions that can improve effectiveness; 'are you doing the right work; can you make better decisions; are you getting superior results?' (p. 13). Knowlton & Phillips further suggest that models provide a critical link between strategy and results. This coupled with our findings suggests that a new model could assist in overcoming the dissatisfaction that lies at the core of this enquiry. Finally, theorists acknowledge that models are required to provide visual literacy in stimulating the transfer of theory to practice (Handzic et al., 2008), which addresses a further issue discussed at the outset of this paper.
4.1 Action Research
Cruywagen et al. (2008) amongst others see knowledge as being socially, historically and culturally bound and, because knowledge involves people, it leads the authors to relate the knowledge process to a collection of systems that are in constant interaction with other systems. This leads theorists such as Cruywagen et al. to Social Constructivism where ‘organisations are viewed as a function of a particular set of circumstances and individuals’ (p. 105). This fundamental notion of knowledge as a socially and culturally bound construct has led this research in the direction of Action Research (AR). Carr (2006) observes AR as having its foundation in the philosophy of human action and the epistemological theories emanating from ‘the personal and contextualised nature of knowledge’ (p. 422). Schon (1983; 1987) provides a more practical description of AR as being an evolving cycle of reflection, grounded in action, where evolving research reflects upon the previous cycle to then inform the next cycle, allowing the governing variables and applied strategy to be challenged. This is the approach being pursued through this research, which will become clearer as the paper develops, where assumptions within the field are identified, reflected upon, challenged and evolved in an ongoing process of development.
The meta-analysis we conducted (Griffiths & Morse, 2009) was influenced by the AR paradigm through the manner by which we employed a co-generative approach to the data collection. This could be interpreted as being part of the Grounded Theory paradigm, where theories emerge from the collected data. However, we spoke of the need for a collaborative approach to the problems being experienced by the field and specifically identified the need to unblock the flow between academics and practitioners, as KM issues appear to be situated within practice, which is not being translated to theory. The initial research we conducted attempted to provide the foundation for a co-generative approach to the problem of KM in order to improve practitioner ‘know how’. Greenwood & Levin (2005) support this suggesting that actors within the problem need to be able to contribute to the sense making process in order to develop successful ‘know how’. Greenwood & Levin also suggest that this combination of practitioner and academic views formulates a powerful research tool situated within the AR paradigm. Carr (2006) and Checkland (2000) suggest AR to be rooted in ‘Action’ and ‘Phronesis’ and Carr (2006) proposes Phronesis as a form of reasoning where the journey of enquiry and outcome are open and subject to ethical reasoning and reflection in a search for ‘what is good’. Carr posits that the co-generative link between academic and practitioner enables the progression of knowledge into knowing, through action. He further states that this link allows: ‘practitioners who, in seeking to achieve the standard of excellence inherent in their practice, develop the capacity to make wise and prudent judgments about what, in a particular situation, would constitute an appropriate expression of the good’ (p. 426)
Whether our original research is seen as Grounded Theory or AR, it would not seem to effect the development of the model or its future testing, to be discussed later, within the AR paradigm. This assertion is supported by Teram et al. (2005) and Dick (2003) who acknowledge that a Grounded Theory approach within the AR cycle improves the recoverability of research and therefore its validity in contributing to scientific knowledge.
4.2 AR and Soft Systems Methodology (SSM)
KM has been observed as being part of a system process (Cruywagen et al., 2008; Tiwana, 2000; Alavi & Leidner, 1999). A system has various definitions, such as 'a set of components interconnected for a purpose' (Open University, cited in Hebel, 2007, p. 499) or 'an entity which maintains its existence through the mutual interaction of its parts' (Chun et al., 2008) or 'a bounded system of linked components' (Carter et al., 1986, p. 4). The view of the system involving the whole is supported by authors such as Meadows (1982) who observes a system to be a bounded whole where one is able to analyse 'where things come from and where they go' (p. 102). This leads thinking to authors, such as Arbnor & Bjerke (2009), have discussed three methodological views of the world relating to organisational research: Analytical, Systems and Actors views. The Analytical view relies predominantly on quantitative evidence and tends to look at the individual parts of the whole. The Systems view, influenced by Holism, leans more towards a qualitative approach and examines reality from the perspective of the whole. In contrast, the Actors view examines the impact of the subject and enquirer upon the environment when viewed as a social construct (Arbnor & Bjerke, 2009). A clear line of sight has also been established between knowledge and learning (Pasteur et al., 2006; Chiva & Alegre, 2005). This exposes the field to the work of Senge (1997), who identifies Systems Thinking to be at the core of learning development in his seminal work 'The Fifth Discipline'. This suggests that Systems Thinking could have a major influence on the KM field.
In reviewing the 71 models and frameworks we examined it became apparent that KM solutions are usually developed via a Systems view of the world: Of 71 models, the foundation of 11 practitioner models could not be identified and 7 were computer based hard systems models, which whilst having their place were discarded for the purpose of this study; Of the remaining 53 models, 51 (96%) employed a Systems view and 2 (4%) utilised an Analytical view. This paper does not attempt to conclude upon a correct view of the world in relation to the KM field, it only reports that the Systems view appears to be the dominant view of knowledge creators within the field. However, this view would seem to be appropriate considering the nature of KM and its grounding in knowledge, which theorists such as (Carr, 2006) observe as being embedded in the situated context of the individual and therefore difficult to quantify. In further exploring the concerns of Knowlton & Phillips (2009), with regard to a lack of theoretical underpinning within modelling practice, a further enquiry was conducted of the sample to determine the number of papers that discussed the implications of underpinning theory upon the model presented. This enquiry found that 1 model (1.8%) discussed these implications. This, coupled with the dissatisfaction suggested by other authors, would seem to validate the concerns of Knowlton & Phillips and reinforces the argument highlighted at the outset of this paper.
The theory of the bounded whole is seen as providing well structured signposts for practitioner intervention (Jackson, 2001). However, if this is the case, with 96% of our literature subset utilising the Systems view, it would seem that there is an issue with the formulation of the model as 'well structured signposts for practitioner intervention' should not lead to the level of dissatisfaction being observed by Rigby & Bilodeau (2007). We emphasise this in our original research, where of the 71 models interrogated, only an average of 10 CSFs were identified per model. We suggest that this demonstrates a lack of 'know what' in literature, which impacts the performance of models in delivering 'know how'.
The systems view is further explored in relation to AR by the pre-eminent theorist, Checkland (2000) who developed a differentiated approach to systems methodology, being 'Soft' or 'Hard' systems. Checkland observes ‘Hard Systems’ as evolving from a systemic view of the world where systems can be engineered, as in the case for defined technical process related problems. Whereas with ‘Soft Systems’ the creator of knowledge observes complexity in the environment, usually related to social or cultural situations, and employs a system as a process of enquiry.
Theorists such as Senge (1997), Mehta (2007), Hebel (2007) and Handzic et al. (2008) suggest that solutions to issues such as those being experienced by KM need to be developed by exploring the patterns inherent to the process as a whole in order to identify enabling patterns that produce success. This appears to support a Soft Systems approach to the field as defined by Checkland (2000). The call for the application of Systems Thinking to overcome the lack of a common framework has been supported by authors such as Rubenstein-Montano et al. (2001) and is evidently being recognised given the fact that 96% of subset models sampled in our research employ the systems view.
KM could therefore be seen as an open network of existing processes that, through their interaction, produce the whole that produced them in the first place. What is being suggested here, based on the work of authors such as Mingers (2002) and Cruywagen (2008), is that KM is made up of a network of existing processes consisting of the four functions of 'Collecting & Storing', 'Sharing', 'Creating' and 'Applying', and when they are combined they produce the output that is KM. This is furthered by theorists who suggest a coupled, autopoietic relationship between KM and the processes of the organisational macro and micro environment (Massey & Montaya-Weiss, 2003).
Criticism of current KM research suggests that practitioners and theorists are focused on the isolated functions of the KM process, such as knowledge sharing, whilst ignoring the interrelationships that contribute to the whole (Chun et al., 2008). However, this appears to be in conflict with the subset sample of models and frameworks investigated for this research, where only 4% employed an Analytical view compared to 96% that utilised a Systems view.
The systems view has been criticised by some authors for being indigenous to a ‘Western’ view of KM (Sharif, 2005). Sharif differentiates his views using the ‘Western’ and ‘Eastern’ descriptors. Sharif believes that ‘Eastern’ approaches to KM are founded upon communities of human interaction, which do not conform to a Western systems view. The human interaction view is supported by ‘Western’ authors such as Pasteur et al.,(2006) who observe knowledge as being created through situated human interaction, which would appear to negate the East/West bias suggested by Sharif. 'Viewing knowledge...as a process or practice...brings people into the picture and thus tends to be more cognitive and behaviouristic in approach. It aims to understand how people acquire and apply knowledge and under what circumstances they learn and affect change' (p. 4).
This said Pasteur et al. advocate a Systems approach to KM processes, which also appears to challenge the systems/social interaction bias suggested by Sharif. However, in the interest of balance, the models examined in this paper were predominantly Western in origin and therefore would seem to support the claim of Sharif. Sharif also suggests the Eastern view of the world to involve communities of human interaction, which is incompatible with a Western Systems view. However, the work of Checkland (2000), discussed earlier, would seem to demonstrate that these environments can be investigated through Soft Systems methodology and therefore a systems approach. This would seem to address the criticisms of authors such as Sharif.
The Systems approach has also been criticised for overcomplicating what will happen naturally (Dawn et al., 2002). They posit that the learning process within an organisation will take place naturally, acting as a stimulus for continuous change in organisational cognitive structures. This said, it could be argued that in a modern global environment, which appears to truncate product life cycles and heighten demand for quality services (Dicken, 2007), the natural process needs to be understood in order to stimulate and manipulate it for competitive advantage.
Having established the rationale for developing research under the AR paradigm, the link between KM and Systems and the subsequent link between systems and Checkland’s Soft Systems, it would seem appropriate to expand upon Soft Systems Methodology (SSM). Kemmis & McTaggart (2005) suggest that Soft Systems methodology provides a suitable framework for hypothesis testing. The authors state that the initial stages should comprise ‘a process of problem identification’; this has been contextualised through our research, which is followed by a ‘modelling phase’ where a potential solution to the problem is developed and used to question the situation. This approach is supported by Checkland (2000) who provides a more detailed seven-step process for SSM. The steps relevant to this research include: ‘The problem unstructured’, provided by authors such as Rigby & Bilodeau (2007); ‘The problem expressed’, expressed in our first paper (Griffiths & Morse, 2009); ‘Root definition of the relevant system’, again, expressed in our first paper; and finally, ‘Development of conceptual models’, which we generate through this paper.
The model building process will then be validated through Checkland’s (2000) sixth step, being the examination of possible changes within the situated environment, and the seventh step, being the action taken to address the problem situation. This is further discussed in our conclusion.
4.3 The Logic Modelling Space
Checkland (2000), states that SSM involves a 'logic-based stream of analysis' (p. s21). This would appear to lead this research towards a Logic Modelling method as a tool to develop visual literacy as an expression of our research.
The Logic Modelling space offers a visual representation of the world in order to 'offer a way to describe and share an understanding of relationships among elements necessary to operate a program or change effort' (Knowlton & Phillips, 2009, p. 5). Knowlton & Phillips offer two distinct pallets in the Logic Modelling space, 'Theory of change' and 'Program'. The fundamental differences between the two pallets is that Theory of Change Models provide a high magnification, giving a simple view of the world, which is seen as a version of the truth that guides knowledge development. Whereas a Program Model provides a lower level of magnification that provides precise situated detail (Knowlton & Phillips, 2009).
There appears to be an issue of Logic Modelling definition when applied against the needs of this research. Theory of Change Models are seen as delivering plausible 'big picture' overviews which are designed to demonstrate the deliverables achievable through structured intervention (Knowlton & Phillips, 2009). However, we criticised existing models for a lack of demonstrable 'know how' in their construction. It would appear that a high level of magnification would not satisfy the current needs of the field. Program Logic Models 'help with more precise decisions about which activities in a given strategy are most effective' (Knowlton & Phillips, 2009, p. 14), but the authors state that these models are situated in their focus and are firmly grounded in validated knowledge of what is known. This is not the case at this stage of this enquiry. Theory of Change Models are observed by Knowlton & Phillips as 'drafts' that are subject to change as the model evolves, which would appear to support the Soft Systems methodology proposed by Checkland (2000) and the position of this research as an evolving reflective process. The core of the problem would seem to be that the Theory of Change modelling space does not provide for the details that affect planning, implementation and evaluation, all of which would appear necessary to overcome issues of 'know-how' identified in our first paper (Griffiths & Morse, 2009)
Therefore this research utilises a blended modelling space, landscaping the KM field through a Theory of Change Model, whilst using elements of lower magnification provided by Program Logic Models. Knowlton & Phillips (2009) offer three key characteristics that need to be present in a Theory of Change Model: 'Co-created with shared meaning; evidence based; appropriate scale' (p. 61). Our research applied a co-generative approach to their meta-analysis, which combined with the scope and scale of their research would seem to satisfy the first two requirements of Knowlton & Phillips. The third point will directly inform the design of The Knowledge Core.
Checkland (2000) offers a potential explanation for the plethora of models identified by Bueno and supported by our findings. Checkland suggests that problems can be subject to contextualised versions of the truth, which would seem to be the case in the KM field where a lack of a generalised framework would seem to increase demand for situated models that appear to be difficult to translate across disciplines. Checkland suggests that this is because 'interpretations of purpose will always be many and various, there would always be a number of models in play, never simply one model purporting to describe “what is the case”' (p. s15). The field has been demonstrated to be systems based and linked to logic analysis, which can employ models that display one of many versions of the truth. This would appear to be a possible contributor to the dissatisfaction being experienced in the field. This paper therefore proposes The Knowledge Core to be a general model for KM, which can then be applied according to situated need.

5. Influences On Design

Carter et al. (1986) describe key principles in the construction of system boundaries; only include those elements or relationships that cause an impact upon the process; include elements that are inherently controlled by the system or its user, but similarly it is important to remove those elements that cannot be controlled by the system or user. Yolles (1996) suggests that this approach dissolves uncertainty, where system boundaries should avoid cutting across processes by either including or excluding them from the system's whole. Carter et al. (1986) develop this position stating that this approach removes uncertainty when examining the effect of elements upon the system. Carter et al. also suggest that a useful description is needed, in which the open or closed, or partial open/closed processes are clear to the user (an open process being one that interacts with the environment and a closed process being one that is insulated from the environment). This would seem to be supported by Senge (1995) who discusses the need for systems that are generative in nature. He suggests that these convey 'what causes the patterns of behaviour' (p. 53), which in turn allows the user to understand how changes to these patterns can produce different behaviours within the system. Senge promotes this approach over the 'responsive processes' (those which examine patterns of behaviour), or 'reactive processes' (those which examine events). Therefore the model sets out to demonstrate the 16 Critical Success Factors broken down into 4 functions and 12 enablers that we discussed in our first paper (Griffiths & Morse, 2009), along with an element of environmental interaction, which will be discussed later.
Meadows (1982) suggests that Systems Thinking determines a weighting towards the whole and not towards myths or perceived major factors – which could inhibit success through a failure to identify a limiting factor, having true influence over the process. With this being the case the model does not take into account the frequency of findings discussed in our meta-analysis, as limiting factors would seem to be situationally embedded and cannot be represented within the blended Theory of Change Model being applied in this paper. This has therefore informed the appropriate scale of the model as suggested earlier by Knowlton & Phillips (2009)
KM has been suggested in this paper to be a system of processes that interacts with the environment to produce its whole. This interaction would seem to suggest that it informs and is informed by the situated environment and would appear to require representation within the flows of the model process. Leonard (1999) posits that knowledge needs to be maintained in order to be of value and Markus (2001) suggests that knowledge reuse is of importance to the viability of knowledge as a value creating resource. This suggests the need for a KM tool that is designed to create a loop as opposed to a linear chain. McElroy (2000) reinforces this, stating that KM is a complex open system, influenced by complexity and System theory, which constantly interacts with its environment. Chowdhury (2006) links Bandura’s Social Learning Theory to demonstrate that human behaviour develops in a ‘continuous reciprocal interaction between cognitive, behavioural and environmental determinants (p. 5). This seems to underpin the need for a loop, where the system both influences and is influenced by the environment through its actions, and is demonstrated in diagram 1 (p. 3) as the flow through and around the model in a cyclic relationship.
Handzic et al. (2008) conducted narrative research into current KM models and suggested that many to be deficient in their use of double-loop feedback. Handzic et al. Support the link between knowledge and learning, discussed earlier, and consequently observe this omission as a critical flaw in the field. The need for a feedback loop is also discussed by Meadows (1982) who suggest that where systems experience situated failure it can often be directly attributed to structural behavioural issues. Meadows suggests that a feedback loop is required in order for the model to flex and overcome issues of situated failure. We also identified this, where we observe reflection or testing as one of our 16 CSFs (Griffiths & Morse, 2009). This also satisfies the need for a double loop approach to modelling, as suggested by Argyris & Schon (1982), where the governing variables and applied strategy are constantly challenged.
Feedback loops have been criticised for not providing an ongoing testing process, where proposed solutions are fed back into the process and continuously tested to determine effectiveness against other alternative solutions (Blackman et al. 2004). Blackman et al. link their theory back to the work of Popper to suggest that double-loop thinking fails the falsifiability test, in that is identifies when a system works, but fails to identify when it doesn't. However the Theory of Variety Attenuation suggests that variety overload can break down the system (Schwaninger, 2009). It could also be said that solutions are effective until a flaw is identified through application, at which time an optimised solution should be implemented. This could be linked to value and context, this was discussed in Griffiths & Morse (2009), where we cite the work of Hori et al. (2004) in overcoming issues such as variety overload through the following formula: Representational Context [Artefacts] + Conceptual Context [Existing in the mind] + Real world context [Situated Application] = Value.
Checkland (2000) suggest that defined arrows and boxes demonstrate a certainty in the process, which Soft Systems research at the stage of this paper is not able to offer. Checkland believes that visual representations of the proposed solution should reflect the volatility of the Action Research Process. However, this research is attempting to move towards a paradigm that can be viewed as 'what really exists' in an attempt to overcome uncertainty in the field. With this being the case the model is represented at the point of research conducted to date. This divergence from Checkland's approach to SSM would appear to be supported in the Logic Modelling space, where Theory of Change Models are represented with defined flows that reflect the certainty of the creator at that time (Knowlton & Phillips, 2009).
The Knowledge Core has been designed to demonstrate the interaction between the system and the environment. It has also been structured to demonstrate the interrelated support of the four main functions, which provide the parameters of the bounded whole. The enablers are demonstrated to be interlinked, but volatile, in that they are not stationery and will move according to the need of the function and the demand of the situated environment.
It is proposed that in order for an organisation is to create value it must look at the whole, being the bounded functions of ‘Capturing & Storing’, ‘Sharing’, ‘Creating’ and ‘Applying’. From this position it would seem possible to enquire in to the efficiency and effectiveness of the function through the engagement of the enablers.
Conclusion
The Knowledge Core Model appears to provide an evidence based representation of ‘know what’, but for it to be an effective management tool it will need to transmit ‘know how’. This is supported by authors such as Meadows (1982) who suggests that in order to heighten standards it is necessary to identify leverage points, which in the case of this research has been identified by the functions and enablers. However the 'know how' required to manipulate them would still appear to be ambiguous, which does not satisfy the current needs of the field. This is accented by Handzic et al. (2007) who find that many model processes detail the 'what' but appear to fail in transmitting the 'how', rendering them incomplete. This lack of identification of leverage points and 'know-how' is evident beyond the KM field, with social scientists being criticised for not extrapolating clearly signposted means of intervention to effect change at a practitioner level (Jackson, 2001).
It would therefore appear necessary to develop an assessment tool for organisations to evaluate their processes and their effectiveness as part of the KM system. However, it would first seem appropriate to gather a second data set to compare and contrast our findings against the situated views of practitioners. This approach can improve the quality of the research whilst providing the enquiry with prompts for the AR cycle where the data sets converge and diverge (Dick, 2003). A practitioner survey will therefore be deployed as part of a dual stream of research investigating Checkland’s next step in Soft Systems Methodology, which involves the comparison of the proposed model against real world situations (Checkland, 2000). The second step in this enquiry will involve the development of a participatory assessment tool for use as part of an organisational enquiry. This process will use document analysis, interviews and surveys as a blended approach to identify gaps in existing practice. These gaps will then be addressed as part of an Action Learning activity within the organisation, the outcomes of which will then be used to inform strategic and operational plans for KM development. This will allow for the validation of the model through Checkland’s (2000) sixth step in SSM, being the examination of possible changes within the situated environment and the seventh step, being the action taken to address the problem situation. This would also allow for the contextualisation of the situated issues through the development of a Programme Logic Model as suggest earlier in this paper.
This process may therefore have the potential to develop a model that has utility across sectors and cultures, with the model being designed for situated application and a participatory framework for implementation as part of the AR paradigm in a co-generative approach to situated problem identification (Teram et al. 2005), which we have suggested as being essential if KM is to shrug off its shroud of dissatisfaction.
This paper set out to present a new general model for the KM field. Expanding upon the findings from our earlier research, this paper has examined underlying assumptions to contextualise the presentation of a new KM model, The Knowledge Core. We have outlined these assumptions and signposted their influence upon the development of the model for testing. Criticism of the modelling process within the KM field has been discussed and addressed. Finally a pathway for the validation of The Knowledge Core has been developed as a progression in accordance with the AR paradigm and Soft Systems Methodology. This will be further addressed in our next paper, which will explore the situated views of KM and their potential contribution to a general model for the field.
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