Just of late I've encountered a number of capability maturity models aimed at knowledge management. Capability maturity models have been around for a while in other disciplines, most notably in software development projects. Almost all of the models owe their origins to the collaboration between the US Department of Defense and the Software Engineering Institute of Carnegie Mellon University. The Capability Maturity Model was originally a tool to assess processes - in particular the processes of a contracted third party. In that sense its intent was to reduce risk.

Capability models now abound and even have been internationally standardised as part of ISO 15504 . ISO 15504 sets six levels of capability maturity as follows:

• Level 5 - Optimising Process,
• Level 4 - Predictable Process,
• Level 3 - Established Process,
• Level 2 - Managed Process,
• Level 1 - Performed Process, and
• Level 0 - Incomplete Process.

Notice anything here? It's all about process. Putting aside definitional issues, last time I looked knowledge management was about people, process, technology, and content. Capability maturity models are about process, so it begs the question "Does knowledge management really need a maturity model"? ...

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On the 26th of May I will be the guest lecturer on a management course at the Australian National University . I'm supposed to be talking about risk management and networks. Now part of the required reading for the students, which I thought I'd better read, was an article by Mickey Butts, Rebecca Wayland, and Lawrence Wilkinson called "Navigating the new realities of risk". The article was published by the Global Business Network and is very interesting in its own right. But the thing that really caught by attention was the STEEP framework, because I could immediately see it has utility as a knowledge management framework.

Now we're all familiar with the people, process and technology mantra, however I've always thought it lacked something. STEEP is an acronym for social, technological, economical, environmental, and political, which as a knowledge management framework I quite like. I think as a diagram it would look something like the picture below, noting it's a bit raw and unsophisticated.

Now it seems to me the STEEP framework captures all the considerations for a knowledge management system. Also the RAAAKERS™ framework , which I blogged about yesterday, fits the model reasonably well. ...

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Introduction

I'm told that a PhD has a number of stages and goes through a number of refinements - mine is no different. This link will take you to overview of my research as I thought it would develop. This page provides a very brief overview of my research as it stands at the moment.

Research Problem

The problem being addressed in my research is - ‘How can mapping knowledge and knowledge flows improve knowledge worker productivity?’

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Knowledge Management Models or Models of Knowledge? A Critical Review of the Literature . A presentation given to the actKM 2006 conference Research Forum stream.

The Knowledge Conundrum. Unraveling the Knowledge Component on Knowledge Management Models . A paper that provided background for my minor thesis and informed my PhD research proposal. The paper was researched and written in 2004.

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What is a Model?

A model is an intellectual construct in artefact form that provides an abstract, highly formalised, often visual, yet simplified representation of a phenomenon and its interactions (Coffey & Atkinson 1996; Despres & Chauvel 2000). Broadly there are three types of model: mathematical models, descriptive models, and graphical models (Satzinger, Jackson & Burd 2000). Mathematical models explain the technical aspects of a system and can be either prescriptive or predictive (Miller 2006; Ragsdale 1998). Descriptive models are narrative in form and often use symbolic or mathematic elements to aid understanding. Descriptive models are rarely predictive, but can be prescriptive. Graphic models use diagrams and symbols to illustrate simple and complex relationships. They can be predictive or prescriptive. Typically a model only includes those variables that are sufficient to represent the phenomena in question. For example if colour is irrelevant then the model will not, and should not, include it as a variable. However these simplifications may result in prediction and description errors if not considered carefully. Accordingly all models should be treated with caution. They are useful so long as the underlying assumptions are explicit, and it is recognised that they are an abstract representation of reality that may, or may not, be objective (McAdam & McCreedy 1999, p. 94). Models in the social sciences tend to be descriptive and graphical rather than mathematical, although mathematical models have their place. In the knowledge management literature almost all models are descriptive and graphical.

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A model is an intellectual construct in artefact form that provides an abstract, highly formalised, often visual, yet simplified representation of a phenomenon and its interactions.

Coffey & Atkinson

A simple model which I find useful is the “Six Knows Knowledge Model ” shown below. The model has its origins in the Rudyard Kipling poem “I Keep Six Honest Serving-Men ” , but no doubt I will be challenged on this assertion! That said it is a model that serves me well, except I would add ‘know how much’ as a node.

Now Dan Roam , quite independently of me, has enhanced the usefulness of the model by introducing a visual thinking element to it - he's also picked up on the how much dimension. Have a look at this presentation , but in particular slide seven (the preceding slides give some background and explanation). What I really like is the ability to add some discipline and consistency to my mind-mapping and rich pictures. With just six icons I can now draw the essence of a problem or a solution. Sure the devil is in the detail, but if nothing else this exercise aids clarity and helps to get to common understanding and shared meaning. ...

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For a long time now I have thought knowledge management is all about individual, group and enterprise productivity. I feel so strongly about this that I use the term knowledge productivity™ in favour of knowledge management. Why? Well knowledge management at the moment can mean whatever one wants it to mean! Indeed this was the key finding of a bunch of so-called knowledge management practitioners at one of the major knowledge management conferences of 2007 – see my Black Art Definitions post for a rant on this finding! Apparently juggling is also an essential skill for knowledge management, but I digress. In conjunction with Pat Byrne I have been writing and presenting on knowledge productivity™ since 2003. It was heartening therefore to discover Christiaan Stam's PhD thesis - 'Knowledge productivity: designing and testing a method to diagnose knowledge productivity and plan for enhancement '.

Stam says knowledge productivity™ “is the process of converting knowledge into something of value” and later defines it as “management ability to generate knowledge-based results”. Patrick Byrne and I have defined knowledge productivity™ elsewhere, and a bit more expansively, as being:

“… a trans-disciplinary approach that integrates tools, techniques, and strategies to retain, organise, share, analyse, improve, and apply business expertise. It is a disciplined, deliberate, purposeful, and conscious method to manufacture knowledge from data, information and experience. Of necessity it involves the design, implementation and review of processes to improve knowledge creation and sharing behaviours. Ultimately knowledge productivity™ relies on individual and collective discipline to follow organisational process to achieve business objectives”. ...

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A simple model which I find useful is the “Six Knows Knowledge Model ” shown below. The model has its origins in the Rudyard Kipling poem “I Keep Six Honest Serving-Men ” , but no doubt I will be challenged on this assertion! That said it is a model that serves me well, except I would add ‘know how much’ as a node. Let me describe each node.

‘Know what’ knowledge is the knowledge about how to find the relevant data to produce information. It is not about the data or information itself. This knowledge is increasing in importance simply because of the sheer volume of data that is now available to a corporation. For a knowledge manager the ‘know what’ dimension represents a real challenge, because discovering where knowledge resides, internal to and external to the enterprise, is crucial to success. ...

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