Higher Classification of KM models

Knowledge Management is an extensive field. The concept of “the need to manage knowledge” is widely accepted today by everyone but since years the same question of “how to do it?” is still waiting for its “perfect” and “universal” answer (Model/Life cycle/Framework/School).

Here I am looking at four different types of schools and models which I came across. Of these four schools/models again I feel that they can be grouped in 2 subsets. One group is where the KMS are according to a particular point of reference. The examples of these are Earl’s 7 schools and Kakabadse, Kakabadse, Kouzmin’s Knowledge management perspectives (Earl, 2001; Kakabadse, Kakabadse, Kouzmin, 2003). The other group is the dichotomies of KM (Bots, Bruijn, 2002; Gebert, Geib, Kolbe, Brenner, 2003; Herder, Veeneman ,Buitenhuis and Schaller in 2003 ).

Group I

This group consists of 2 schools or taxonomies. The first 1 is by Earl 2001 where he thought that there is a need to give streams of knowledge management systems to the CKO. Each of his school is an ideal one and has its own orientation (Earl, 2001). But his taxonomy lacks the concept of linking the knowledge with its environment. Also this is argued by Blackman, Henderson in 2007
“This would imply some kind of pragmatic choices about knowledge management that limit what can be done to a great deal less than what can be said" for Earl’s Schools.
Also another drawback is that since an organization may fall under more than 1 school at 1 time it becomes difficult to switch between 2-3 or more schools as per the situation demands. Thus it lacks a specific plan or strategy of knowledge management.

A similar taxonomy is done by Kakabadse, Kakabadse, Kouzmin, 2003 where they have classified according to the type of knowledge and also some what on the background of the modeler. They are very true when they say “ there are as many KM models as there are practitioners and theorists alike” and I think by now we have read enough not to disagree with it.

I find that the groupings of Earl and Kakabadse and his colleagues are quite near to each other and revolve around the same attributes like “Focus”, “Primary Aim”, “Critical lever or success factors”, “role of IT”. The major difference between the two is just the attribute of “type of Knowledge” as Earl did not take it into account. He argues that he overlooked it as many Executives dint differentiate it or they were interested in both. However I argue that it is important to find out what type of knowledge the organization is seeking as then it is easier to apply the KM model.




Group II

The other subgroup is that follows the famous dichotomies of KM. The first one is Analysis Dichotomy as stated by Gebert, Geib, Kolbe, Brenner in 2003 where they explicitly state that “Almost all knowledge management models can be traced back to a basic approach to analyze knowledge” (Gebert, Geib, Kolbe, Brenner, 2003). I agree to this as for any activity we do analyze knowledge. Take for example any university where management do get all the information from regional offices and then analyze it to see from which part of the world they get more students so that they can concentrate more on that part. Here we can see that irrespective of the knowledge management model being used “analysis of Knowledge” is being done. Similar is the case with all the organizations. They classify all the models into Epistemology-oriented KM models and Ontology-oriented KM models.
Epistemological Models analyze the nature of knowledge and treat knowledge as an entity which can be defined. Gebert, Geib, Kolbe and Brenner (2003) and Moteleb and Woodman (2007) said that these models don’t look at the relationship that knowledge has with its environment. By the nature of knowledge it is meant how it can be expressed so the tacit and explicit knowledge comes here into picture. The famous SECI Model by Nonaka and Takeuchi (1995) can be classified here as the conversion of tacit knowledge to explicit takes place in this model.

Also according to Gebert and colleagues these models don’t have limited value of knowledge in business. This can be justified as such models don’t take into account the relationship of knowledge with its environment. A straight forward example is that of the university stopping one of the graduate programs. The Management not only sees the overall faring of the program but also analysis its demand and scope in the world outside. So here it is actually seeing the link of knowledge and its environment. Thus KM models of these types are not fit for making management decisions.

This is where the ontological models fit in. These models look at the relationship of knowledge with its environment. But the inherent characteristics of knowledge are unnoticed (Gebert, Geib, Kolbe and Brenner, 2003) and so is the nature of the entities that make up knowledge (Moteleb, Woodman, 2007). These models yield very good results when applied to a specific business context because they analyze knowledge in different stages like its development, modification, use etc.

The other dichotomy is process perspective. This dichotomy is developed by Bots and Bruijn in 2002 where they have further developed the cognitive and community model of knowledge management for policy making in organizations given by Sawn and his colleagues in 2000. Bots and Bruijn have classified the models according to people, processes and tools. The two paradigms to knowledge management accordingly are

Analytical perspective as it looks at subjective analysis of knowledge and its process (process, tools).
Actor Perspective as it looks at knowledge stored in organization’s employees and its interaction (People) (Bots, Bruijn, 2002). This same dichotomy is given by Herder, Veeneman, Buitenhuis and Schaller in 2003.

Analytical Model mainly focuses on content of knowledge in a structured way and it is a goal driven activity where codification plays a vital role. So basically this model is useful in places where continuous developments are taking place and knowledge is stored. E.g. in a software development company where new ways of coding certain modules is done and is then shared through intranet within that organization so there is no need to store it. This Model also supports the definition of KM by Davenport & Prusak 1998 “getting the right knowledge with the right people at the right time”.

Actor Model mainly is a goal seeking activity and concentrates more on emergence of new knowledge and exploration. Bots and Bruijn assert that it provides a free platform for exchange of ideas which can be linked to CoP (what we do 2Pm-5Pm every Thursdays) (Herder, Veeneman, Buitenhuis and Schaller, 2003)

Final Thoughts
Having read so many articles I feel that Earl’s school and Knowledge perspectives by Kakabadse are more like a theory that has no fruitful application in today’s organizations. Each organization needs a special model for itself which is a hybrid or may be cross product of Epistemological and ontological models and analytical and actor models. I say this because knowledge needs to be articulated, its relationship with its environments needs to be analyzed, and Process, People and tools also play a vital role to get knowledge managed. If one of them is left out there is going to be a flaw. This is the reason why each organization is developing a KM model which suits itself. E. g. customer knowledge management (CKM) model (Gebert, Geib, Kolbe and Brenner, 2003) and “new product introduction process” by Motorola (Herder, Veeneman, Buitenhuis and Schaller, 2003). Also I have observed the all the authors gave a suggestion of hybrid model which combines either Epistemological and ontological perspective or Analytical and Actor Perspective (Gebert, Geib, Kolbe and Brenner, 2003).


References

Blackman D A and Henderson S (2007) “Being and Knowing – Ontological Perspectives on Knowledge Management Systems” The Electronic Journal of Knowledge Management Volume 5 Issue 3, pp 283 - 290, available online at www.ejkm.com

Bots.P.W. and Bruijn.H (2002), “Effective Knowledge Management in Professional Organizations:
Going by the rules”, Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS-35.02), Available at: http://library.mdx.ac.uk:2414/stamp/stamp.jsp?arnumber=994051&isnumber=21442, Ieee Explore.

Davenport, T. and Prusak, L. (1998), “Working Knowledge: How organizations manage what they know”, USA: Harvard Business School Press.

Earl, M., (2001), “Knowledge Management Strategies: toward a taxonomy”, Journal of Management Information Systems, vol.18, no.1, pp. 215-233.(Source: http://www.cs.nott.ac.uk/~dap/network/previous%20workshops%20etc/understanding%20concept%20of%20knowledge%20leakage/km.pdf)

Gebert, H., Geib, M., Kolbe, L. and Brenner, W. (2003), “Knowledge-enabled customer relationship management: Integrating customer relationship management and knowledge management concepts [1]”, Journal of Knowledge Management, vol. 7 no. 5, pp. 107-123, (Source: http://www.emeraldinsight.com/Insight/viewContentItem.do;jsessionid=E14BBF634006044AE3FE45B3716B8267?contentType=Article&hdAction=lnkhtml&contentId=883838)

Herder P.M., Veeneman W.W., Buitenhuis M.D.J. and Schaller A. (2003), “Follow the rainbow: a knowledge management framework for new product introduction”, Journal of Knowledge Management vol.7 no.3, pp. 105-115.

Kakabadse, N., Kakabadse, A. and Kouzmin, A. (2003), “Reviewing the knowledge management literature: towards a taxonomy”, Journal of Knowledge Management, vol.7, no.4, pp. 75-91.(Source: http://www.emeraldinsight.com/Insight/viewContentItem.do?contentType=Article&hdAction=lnkpdf&contentId=1506523)

.Moteleb AA and Woodman M (2007), “Notions of Knowledge Management Systems: a Gap Analysis” The Electronic Journal of Knowledge Management Volume 5 Issue 1, pp 55 - 62, available online at www.ejkm.com


Swan, J., S. Newell, and M. Robertson (2000), “Limits of ITdriven Knowledge Management Initiatives for Interactive Innovation Processes: Towards a Community-Based Approach”, in: J.F. Nunamaker and R.H. Sprague (eds.), Proceedings of the 33rd Hawaii International Conference
on System Sciences, IEEE Computer Society.(Source:http://library.mdx.ac.uk:2414/stamp/stamp.jsp?arnumber=994051&isnumber=21442)