Copyright © 2006 Graham Durant-Law
2006 STEP Forum2006 STEP Forum
A Knowledge Productivity Model A Knowledge Productivity Model for the Public Sectorfor the Public Sector
byGraham Durant-Law
BSc, MHA, MKM, Grad Dip Def, Grad Dip Mngt, Grad Cert Hlth Fin, psc.
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Research MindMapResearch MindMap
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Philosophical PosturePhilosophical Posture
This research is positioned in the constructivist tradition qualitative research paradigm, based on the systems thinking school.
In this school phenomena, such as knowledge, are thought of as being complex wholes of material and immaterial things, with the component entities being hierarchical, but of themselves being able to be treated as wholes.
Social phenomena are constructed and as such must be positioned in time, space and culture, but can be decomposed to smaller components. Furthermore the properties of these phenomena are emergent depending on where the system boundary is drawn.
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Philosophical AlignmentPhilosophical Alignment
Ontology and epistemology deal with truth, however axiology is about values and ethics.
The seminal ontological question for a researcher is - ‘Is there a “real” world out there that is independent of our knowledge of it?’
The seminal epistemological question for a researcher is - ‘Can “real” or “objective” relations between social phenomena be identified, and if so how?’
The seminal axiological question for a researcher is - ‘What is the ultimate purpose of the inquiry?’
Mingers, J 2003, 'A classification of the philosophical assumptions of management science methods', Journal of the Operational Research Society, vol. 54, pp. 559-70.
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NonakaNonaka’’s SECI Spirals SECI Spiral
Nonaka, I & Konno, N 1995, The knowledge creating company: how Japanese companies create the dynamics of innovation, Oxford University Press, New York.
Nonaka, I, Takeuchi, H & Umemoto, K 1996, 'A theory of organisational knowledge creation', International Journal of Technology Management, vol. 11, no. 7/8, pp. 833-45.
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Definitional ProblemsDefinitional Problems
‘Confusion about what data, information, and knowledge are –how they differ, what the words mean – has resulted in enormous expenditures on technology initiatives that rarely deliver what the firms spending the money needed or thought they were getting’.
Davenport, TH & Prusak, L 1998, Working knowledge: how organisations manage what they know, Harvard Business School Press, Boston, p. 1.
‘Has knowledge management (KM) been done? Of course KM has been done… But whether formal interventions claiming the label KM are bona fide instances of KM practice is another matter entirely. To answer that question, we need to have clear, non-contradictory ideas about the nature of knowledge, knowledge processing and KM’.
Firestone, J 2005, 'Doing knowledge management', The Learning Organization, vol. 12, no. 2, p.189.
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Model ProblemsModel Problems
‘At the risk of oversimplification, generic knowledge models typically focus on KM from knowledge life cycle perspectives. These models are important in enriching our understandings on the essentials of KM activities; yet do not provide an integrative perspective for actual KM implementation’.
Suh, W, Sohn, J & Kwak, J 2004, 'Knowledge management as enabling R&D innovation in high tech industry: the case of SAIT', Journal of Knowledge Management, vol. 8, no. 6, p.6.
‘… practitioners do not find many applicable or useful concepts, frameworks and models. Finding a reasonably grounded and practically applicable theoretical foundation for developing, exploring, and evaluating knowledge management processes, IT applications, and KMS persists as a challenging task.’
Cecez-Kecmanovic, D 2004, 'A sensemaking model of knowledge in organisations: a way of understanding knowledge management and the role of information technologies', Knowledge Management Research and Practice, vol. 2, no. 3, p. 156.
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Knowledge DefinitionsKnowledge Definitions
‘Knowledge is a fluid mix of data, experience, practice, values, beliefs, standards, context, and expert insight that provides a conceptual arrangement for evaluating and incorporating new data, information and experiences’. Davenport, TH & Prusak, L 1998, Working knowledge: how organisations manage what they know,
Harvard Business School Press, Boston, p. 5
‘Knowledge can be conceived as being a product – that is, it is a thing produced by action. Productivity is a measure of efficiency of production, which implies a comparison of input with output. Knowledge productivity is therefore the purposeful, deliberate, and conscious action of creating, applying, organising, and measuring knowledge’.
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Research DesignResearch Design
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Methodology Rich PictureMethodology Rich Picture
Methodology+ =Philosophy
Methods(procedures, tools and techniques)
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Soft Systems Methodology Soft Systems Methodology and Grounded Theoryand Grounded Theory
Checkland, P 1999, Systems, Thinking, Systems Practice, John Wiley and Sons, Chichester, p 163.
Strauss, A & Corbin, J 1998, Basics of qualitative research. Techniques and procedures for developing grounded theory, Sage Publications, California.
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Initial Data Collection Initial Data Collection and Analysisand Analysis
Involves an organisation-wide survey to identify and target those individuals that are the central connectors, knowledge brokers, and boundary spanners of the organisation.
Social network analysis will be the principal technique used in this step.
A three part self administered survey, in the form of an anonymous questionnaire will be used to collect the data for the analysis.
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Focus Group Focus Group Data Collection and AnalysisData Collection and Analysis
The purpose of the focus-group is to capture a visual representation of root definitions and concepts and to gain someconsensus from the participants’ viewpoint.
The root definitions will be developed using Checkland’s CATWOE construct, where CATWOE is a mnemonic for:–– CCustomers – the beneficiaries of the system.–– AActors – the ‘players’ who transform the system.–– TTransformation – of input and output.–– WWeltanschauung - the specific ‘world view’ that makes the
transformation meaningful.–– OOwners – those actors who could stop or change the nature of the
transformation.–– EEnvironment – the constraints on the system that are outside of the
system boundary and its scope.
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Semi Structured Interviews Semi Structured Interviews and Data Analysisand Data Analysis
The purpose of the semi-structured interviews is to collect data and expand upon themes identified in the previous steps.
Data will be entered into NVivo® so that any patterns and themes can be identified.
Themes will be coded independently by the researcher and another coder, who will discuss the reasons for any discrepancies in keeping with good grounded theory practice.
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Research ValueResearch Value
‘knowledge management project failure is a reality that both practitioners and researchers have to reckon with’.
Chua, A & Lam, W 2005, 'Why KM projects fail: a multi-case analysis', Journal of Knowledge Management, vol. 9, no. 3, pp. 6-17.
84% of knowledge management initiatives have failed.Storey, J & Barnett, E 2000, 'Knowledge management initiatives: learning from failure', Journal of Knowledge Management, vol. 4,
no. 2, pp. 145-56.
‘A good deal of the corporate planning I have observed is like a ritual rain dance; it has no effect on the weather that follows, but those who engage in it think it does. Moreover, it seems to me that much of the advice and instruction related to corporate planning is directed at improving the dancing, not the weather’.
Mintzberg, H 1994, The rise and fall of strategic planning, Prentice Hall, London. p. 139.
The research will produce a theory and model of knowledge productivity, which includes implementation, maintenance, and sustainment components.
It will provide a basis for a public sector organisation to evaluate their existing knowledge management solutions.
Copyright © 2006 Graham Durant-Law
Questions?Questions?
‘It is better to know some of the questions than to think you know all of the answers’.
James Thurber - American Humorist.
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