Table of Contents
Go Back
Towards a valuation of knowledge in systems using
qualitative system methods.
Powell JH! and Swart,
John Powell is Professor of Strategic Analysis at Bath University, UK, where he
specialises in system approaches to strategy using semi-formal methods. Prior to
taking up an academic career he held a series of board level appointments in an
international aerospace company. He holds a PhD from Cranfield University and is a
recipient of HM the Queen’s Gold Medal for academic excellence and the President’s
Medal of the OR Society. He has extensive consultancy interests, notably in
competitive strategy.
Juani Swart holds a Lectureship in Human Resource Management at Bath University
where she is a member of the Work and Employment Research Centre (WERC). She
holds degrees from universities in her native South Africa as well as a PhD from Bath
University and is a Chartered Organisational Psychologist. Juani’s present research
interests have been in the relationship between people management practices and firm
performances with growing knowledge intensive firms and, increasingly, into the
application of system methods to Knowledge Management. Juani has also worked as a
Human Resources Consultant with a number of blue-chip organisations.
" Dr JH Powell
Professor of Strategic Analysis
School of Management, Bath University
Claverton Down, BATH, UK
BA2 7AY
Email j.h.powell@bath.ac.uk
? Dr Juani Swart,
Lecturer in Human Resource Management
School of Management, Bath University
Claverton Down, BATH, UK
BA2 7AY
Email mnsjas@bath.ac.uk
Abstract:
Knowledge in a firm is a highly desirable intangible resource imbuing competitive
advantage due to its inimitability, but often that linkage between knowledge and
competitive advantage is not explicit. Moreover, it is often not explicitly valued by an
organisation so that exhortations to train, develop, disseminate and publish are often
met with resistance since no valuation on the knowledge (and particularly tacit
knowledge) in a firm is easily available. After a discussion of the types of knowledge
immanent in a firm (knowing what, knowing how, knowing why and knowing who),
we present a method of modelling the knowledge in an organisation and of relating
that knowledge specifically to its business survival. This method of modelling allows
the representation of knowledge types and the mechanisms of their contribution to the
generation of value. Using the real-life case of a professional firm we show how the
system of that firm can be modelled and used to establish the knowledge usage and
requirements of the people in that system in support of their intent for action.
Towards a valuation of knowledge in systems using
qualitative system methods.
The importance of valuation
The importance of knowledge in creating competitive advantage is well understood
(Drucker, 1993;, Lubit, 2001; Scarborough & Swan, 2001; Shadur & Snell, 2002;
Strock & Hill, 2000). The very ubiquity of this acceptance, however, tends to hinder
the efficiency of application of resources in managing that knowledge. Two specific
causes of inefficiency are: a lack of understanding of what knowledge is and an
inability to identify what contribution valuable knowledge makes to the organisation.
For instance, many organisations have sophisticated knowledge management systems
but the activities of these systems are not directed in a discretionary way towards that
knowledge which is most valuable to the business. This leads to waste of knowledge
management resource and to the ‘management of knowledge for knowledge’s sake’
without regard to its contribution to the sustainable competitive advantage of the firm.
It is inherently difficult to evaluate knowledge in a system. These difficulties exist at
two levels. Firstly, in respect of tacit knowledge, it may not be evident even where the
knowledge lies. Secondly, in respect of explicit knowledge, while the knowledge is
more visible, its réle in the value generating systems of the organisation may not be
appreciated.
In the section that follows we describe these forms of knowledge in more detail.
The ‘problem’ of knowledge
Knowledge is regarded as a slippery concept (Leonard & Strauss, 1998) that is
difficult to pin down and manage. As a result many attempts have been made to
categorise, codify and demystify knowledge in order to enable its management.
During the late 90s there was a tendency to ignore the dynamic nature of knowledge
so as to move towards a position where we can create and apply this valuable resource
(Drucker 1993). The focus then was more on the use and measurement of knowledge
rather than understanding its nature. The approach we take in this paper, however, is
that we need to understand the nature of knowledge, and indeed its value to the
organisation, before we invest in its management.
Knowledge, information and data
At the outset it is important to be clear about the differences between knowledge,
information and data. Spender ((Spender 1996), p. 65) postulates two radically
different kinds of organisational knowledge, i.e., data and meaning, each generated,
stored and applied in completely different ways, while intelligence shapes, and is
shaped by, their interaction. Data can be regarded as the cellular level of an
information system that may or may not contribute to a wider understanding (Allee
1997), p. 115) or in organisational terms as structured records of transactions
(Davenport and Prusak 1998), p. 2) an example being a spreadsheet with numerical
input.
Information can be seen as data that has been contextualised and categorised. For
example, I may obtain information about a holiday in the Caribbean in a travel agent’s
brochure. This is data that has been contextualised (my holiday being the context).
However, if I have not been on a holiday in the Caribbean and have no experience of
the heat, the culture the beaches, then I cannot say that I have knowledge of a holiday
in the Caribbean, I merely have information about what the experience may be like.
This is wholly consistent with the narrower cybernetic and communication theory idea
of information as a measure of the change induced between a priori and a posteriori
probabilities of states of nature before and after the arrival of a message (Wiener
1949).
Whilst information establishes itself in the sphere of common understanding,
knowledge derived from it is subjective in nature, and intimately linked to the group
of individuals generating it. For example, a folder filled with articles which have
never been read and which may be from various disciplines may be regarded as data.
Once the articles are read they become information. If the information is then
compared and contrasted, further searching strengthens particular understandings and
these understandings are then acted upon (through conversation, writing or searching)
4
it could be said that one knows something about the ‘topic’ that has been read. Data
and information are not regarded as knowledge, mainly due to the lack of interaction
and dialogue involved in communicating either.
Knowledge categories
One of the most frequently cited categorisations of knowledge is that of tacit and
explicit knowledge. This categorisation originates from work of the philosopher,
Michael Polanyi on the tacit dimension. Polanyi (Polanyi 1966), p.4) was of the
opinion that we will always know more than we can tell. That is to say that there will
always be a part of the knowledge which we have that we cannot express. The tacit
dimensions of knowledge relate mainly to embodied skills, where, for example, we
may know how to ride a horse, or be excellent at playing tennis but we cannot
translate all our skills into words for our colleague (or indeed our competitor) to learn.
It is for this reason that this form of knowledge is regarded as the key to sustainable
competitive advantage and sits at the heart of the knowledge creation process.
Nonaka and Takeuchi (Nonaka and Takeuchi 1995) built upon Polanyi’s notion of the
tacit dimension of knowledge differentiated clearly between tacit and explicit
knowledge. According to these authors, the Western perspective on knowledge is
formal and systemic, something that can be expressed in words and numbers. This is
referred to as explicit knowledge. The Japanese, however, realize that explicit
knowledge represent only a fragment of the collective knowledge. Knowledge is
therefore viewed as highly personal, difficult to formalize and communicate. This
category of knowledge is defined as tacit knowledge. “Subjective insights, intuitions,
and hunches fall into this category” (Nonaka and Takeuchi 1995), p. 8).
Tacit knowledge (TK) is often considered to be an intangible firm resource (Jacobson
1990; Barney 1991; Ambrosini and Bowman 2001) and highly desirable in creating
competitive advantage due to its inimitability (Baumard 1999). However, Nonaka
(1994), advocates that the key to understanding knowledge creation lies in the ability
to make tacit knowledge explicit. This may explain why most knowledge
management practices address the explicit qualities of knowledge and focus on
5
coding, recording and re-use of knowledge in order to build a stock of this
competitive resource.
Indeed, more recently (Cowan, David et al. 2000) we have been urged to focus on
codified and codifiable knowledge to further our understanding of the economics of
knowledge. This approach was soon criticised for its over-simplification and dilution
of the complexity of knowledge (Johnson, Lorenz et al. 2002). These critics call for
the inclusion of practice or ‘knowing’ when we want to understand knowledge in
organisations. Here the focus is on how knowledge flows through practice rather than
how it is recorded in written format, which often distracts from practice. The example
of excellent scientists is given in this context:
‘When interviewed about the background for their success as
scientists, almost all Nobel Prize winners pointed to their
interaction with other and more experienced Nobel Prize winners
as a key element in their career.’ (Johnson, Lorenz et al. 2002), p.
247)
This particular response sits comfortably with the approaches of organisation
theorists, sociologists and philosophers: valuing the dynamic nature of knowledge yet
respecting the need to manage/facilitate its management. It appears that the tension
between ‘messy’ practice-based knowledge and creating competitive advantage from
this illusive resource can be resolved in two ways. Some argue that knowledge needs
to be made manageable through the codification process (Cowan, David et al. 2000)
whilst others (Leonard and Straus 1998) prefer to work with rather than distract from
its unmanageability. The latter approach has gained considerable support for its
construct validity and ontological soundness. However, advocates of this approach
often leave questions of practicality unanswered. If knowledge remains tacit how can
it be identified within the organisation? Even more so, if knowledge is embedded in
action how can it be identified? If knowledge is so intimately personal (Polanyi 1966;
Gerard 2001; Johnson, Lorenz et al. 2002) how can we identify the owners and
influencers of knowledge in a system?
Our paper addresses the tension between theory and practice and attempts to answer
these questions by
1. Appreciating different forms of knowing that influences business success
(knowing what, knowing how, knowing why and knowing who)
2. Developing and applying a technique, which captures the dynamic and systemic
qualities of knowledge. This technique (Qualitative Politicised Influence
Diagrams or QPID) captures all four forms of knowing by examining the system
context in which knowledge is used and the roles of users and owners of that
knowledge.
3. Using the technique to establish the role of knowledge in the chains of causality
which lead to value in the system.
First we provide a brief overview of the knowledge literature and in particular the
categorisation of knowledge. The section that follows describes the explicit system
technique we used to capture the essence of knowing in practice. We then provide the
empirical context within which we explored the notion of knowing and report on the
dominant business model within an actual firm. Our methods of data gathering
together with the analysis and results are discussed next. Finally we draw conclusions
on the use of the QPID model in understanding knowledge and knowing, and here we
report on how this advances both theory and practice of working with valuable and
intangible resources.
Organisational knowing
Several in-depth accounts of the structure and function of tacit knowing have been
presented in the literature (Polanyi 1966; Gerard 2001) and we focus here on different
types of knowing that are considered central to a firm’s success. Our framework
builds upon the work of Johnson, et al. (Johnson, Lorenz et al. 2002) and Arthur &
Parker (Arthur and Parker 2003) and classifies knowing into: knowing what, knowing
how, knowing why and knowing who. It is important for the reader to note that each
form of knowing contains both tacit and explicit dimensions and that the act of tacit
knowing is central to each.
Knowing what
Here we differ from Johnson et al (Johnson, Lorenz et al. 2002) and state that
‘knowing what’ is more than just knowing the facts (information) and we relate this in
an organisational context to ‘knowing what to do’. The awareness of appropriate
action is related to a clear picture of what the organisation is about and how future
responses/actions will benefit the organisation (Swart 2000). Importantly it is central
to organisational memory: knowing what was done in the organisation in the past. For
this to happen an individual needs to be integrated into a community in the
organisation, be that a project team, department or occupational grouping, and have
access to and memory of past organisational responses. In terms of a system-based
epistemic taxonomy ((Powell and Bradford 2000) we can equate this with knowledge
about the components of the system in focus as opposed to knowledge about major
sub-systems or about the system as a whole.
Knowing how
This form of knowing relates predominantly to embodied skill (Durrance 1998) or
know-how (Ryle 1949) and is intimately linked to professional competence and
experience. Knowing how to do something has an explicit dimension, i.e. instructions
for driving a car, and a tacit dimension, i.e. the experience of driving the car. But as
all learner drivers will know, you can only become a skilled driver with years of
experience. I would also not sign up to compete in Formula One if I am not highly
skilled, experienced and talented. The reader may note that we include the dimension
of talent here and therefore agree with models of human capital (Bontis 1998; Shadur
and Snell 2002; Swart and Kinnie 2003) as a critical organisational resource. We can
relate this form of knowledge in the systems taxonomy to what is known about the
major mechanisms of control and behaviour in a system.
Knowing why
The ability to know why something has happened, or is going to happen or indeed is
happening at the present moment points to underpinning principles and contextual
richness. Firstly, I would need to be familiar with the bigger picture. For example
understanding why a certain solution has been implemented is related to knowing
what has taken place in the organisation at large: we are being taken over by a larger
firm because of the industry conditions and our current financial situation. Secondly it
relates to meta-knowledge: not only do I know what to do but I know why it is done.
This shows that I understand the underlying systems that support my action. I
complete an expense claim form in a certain way because other financial systems are
related to that particular form and make its processing possible. Thirdly it relates to
occupational identity: as a psychologist I know why I should keep information
confidential because it relates to the ethical underpinning of my profession.
From a system perspective, this form of knowledge is related to the holistic
knowledge about how the major components of a system interact with each other to
produce a complex overall effect.
Knowing who
The notion of ‘knowing whom to ask’ has generated considerable interest in recent
accounts on knowledge sharing, knowledge management and knowledge intensive
firms. This form of knowing relates to the identification of the owners of knowledge.
In other words, knowing who knows what. We acquire this form of knowing through
our extended participation in a community and by developing and nurturing our social
networks. Although an explicit guide such as skill databases are useful in this regard,
it is mainly previous interaction and embedded relationships that guide successful
knowledge sharing across boundaries (Swart and Kinnie 2003).
The originality and power of the method on which we report here is in the explicit
representation of the ‘who’ in the system, both in terms of understanding who carries
out a system function and who owns, uses or aspires to the knowledge necessary to
carry out that role or roles.
Integrating forms of knowing
Knowing, rather than knowledge, can be considered as the key competitive advantage
of organisations in the knowledge-based economy (Drucker 1993). It is the ability of
organisations to identify and understand each of these individual forms of knowing as
well as how they interact that will provide them with an advantage in the market
place. This section therefore reviews how the forms of knowing exist as
interdependent action-based processes.
Take for example the emergence of Silicon Valley. Here experienced software
developers are experts in knowing how to write code. It is this knowing how that
makes them a respected member of their occupational community. Interestingly, their
competence would not have become ‘publicly known’ if they had not belonged to a
social network. It is in this network that ‘knowing who knows’ is the key to
connecting various experts to create a new start-up. But a few lads in a basement are
hardly enough to create the next Microsoft. This throws the light on knowing what to
do as well as knowing how to interact with venture capitalists. More importantly it
points to an understanding of the changing nature of the software industry since it
brings home the criticality of knowing why it is necessary to network, seek funding
and to locate yourself in one of those basements with one of those ‘who know who,
why and what’.
Each form of knowing plays a role in creating competitive advantage. However,
understanding their mutual interplay is far more important in beating competitors to a
new product/service offering in the market place. It is essential, therefore, that the
management of knowledge can appreciate not only the limitations of codification but
also embrace and identify each form of knowing that is located within the business
system of an organisation.
As we have intimated above, we believe that an explicit representation of the business
system in focus allows direct examination of the four categories of knowledge
described. Moreover, the ability to examine the connection between individual
knowledge users, the epistemic raw material they use and the objectives of the
organisation is a powerful one. By making explicit and visible the model of the
system under consideration we can, potentially,
e Make clear the role of specific information and knowledge to the success of the
organisation
e Understand the total knowledge and information needs of users
e Evaluate the effectiveness of proposed resource expenditure on providing specific
knowledge and information to users
And (although we do not make this extrapolation in this paper) our aim is to provide a
sound a basis for action on the part of system contributors, owners and managers.
Valuation and non-valuation of Knowledge in systems
If the management processes in an organisation are not directed at managing the
forms of knowing that are most valuable to a particular organisation, key resources
could be wasted. That is, it is critical for organisations not to find themselves in a
position where they are ‘managing knowledge for knowledge’s sake’ but to direct
their KM efforts in such a way that will maximise their competitive position within a
network. Although this statement holds some superficial validity, it poses an
incredibly difficult question to the KM system of the organisation. Evaluating which
forms of knowing are central to the organisation’s success is an inherently difficult
process for several reasons. Firstly, knowledge is often hidden. Organisations are not
always aware of the knowledge that is held within their knowledge systems. This is
the case of ‘knowing what we know’. Secondly, knowledge is socially constructed
through actors within a system and various viewpoints of valuable knowledge may
exist. This reason can be referred to as the multiplicity of value barrier. Finally, the
diffusion of the management of knowledge has traditionally been focused on a best
practice model and not on fitting knowledge management to causal organisational
systems. Here many organisations implement technology based KM systems or follow
well-known KM models that may or may not fit their business model. This final factor
is represented here as the best-practice barrier.
In the section that follows we make suggestions for a tool that can overcome the
‘hidden knowledge’, the multiplicity of value and the best-practice barriers.
What would a value-based systemic knowledge tool look like?
Pluralist
It is clear from the socially constructed nature of knowledge immanent in Polyani’s
work as described above, that any representation of system knowledge must allow of
different interpretations of reality by the owners of knowledge in a system. While it
may be convenient for System Dynamics workers to espouse a singular, positivist
view of the world where a single reality has validity for all participants, this is
unsustainable except in the most narrow confines of definition and is an unacceptably
rigid view of the realities of knowledge in any real system.
Representation of actors’ frameworks
Similarly, because the knowledge resident in a system is socially and personally
constricted, a value based systemic knowledge tool must engage the context of the
actors in the expression of their knowledge, so that there must be opportunity for the
expression and examination of the inter-relationships between actors through
examination of the local polity within which knowledge-owners operate.
Coping with the Tacit/Explicit distinction and working at the Knowledge level not the
Information level
Because of the importance of tacit knowledge to the sustainable competitive
advantage of the firm it is important to such a systemic KM tool that it should allow
of distinctions between explicit and tacit knowledge. Moreover the vital distinction
between information and knowledge must be incorporated.
Connecting Knowledge to System to Action
The intent of business is to take action in the world and whether this takes the form of
action per se or action in the sense of sensemaking, the intent of any systemic KM
tool must respect this imperative for action and, hence should be action-directed in its
product.
Representing mechanisms of effect
Any rejection of numerical approaches as a basis for knowledge representation denies
us the opportunity for numerical valuation of the usefulness of knowledge.
Consequently, we must needs replace such an aspiration by a requirement that a KM
representation must be able to show clear causal relationships between knowledge, its
owners and users and the business system in which it resides. We may not be able to
evaluate in financial terms the usefulness of actor-knowledge dyads, but if we can
show clearly the mechanisms by which the knowledge contributes to benefit, we have
a basis firstly for a structural argument for managerial attention or action and further,
for a localised financially based investigation of cost vs. benefit where possible and
appropriate.
SBKM
The quantitative, numerical approach to SD, popular and powerful as it is, has some
serious drawbacks for the study of TK and EK in organisations (Coyle 2000; Coyle
and Exelby 2000). Numerical SD requires each system component to be described by
a variable which is expressible in numerical terms. While this may be wholly
appropriate for such things as revenue, profit, reliability or fuel flow it is less easy to
see the validity of such a requirement when dealing with competence, reputation,
customer satisfaction or quality of service. One can express these variables
numerically but there is always a feeling of dissatisfaction at having to shoehorn
essentially qualitative matters into a numerical structure (Powell and Coyle 2004).
Other workers in SD have taken a deliberately non-numerical approach, using the
concept of a causal map (in essence the Influence Diagram) to capture the system
under consideration but then using topological analysis (instead of simulation) to
explore the likely dynamic behaviour of the system (Wolstenholme 1990; Powell and
Bradford 1998). Figure 1 (an extract from a full system Influence Diagram (ID)
discussed later) shows a typical structure from a qualitative system dynamics ID used
to study knowledge in a business system.
Figure I near here
Figure 1: A loop from a qualitative SD study
Here training investment leads to an increase in competence which leads to improved
success in winning business which funds further training. Of course the loop can also
work the other way, with falling investment in training leading to a reducing business
success. The essence of the qualitative SD approach is the identification of these loop
structures in IDs. Examination of the propensity of these loops to grow or shrink
allows both the examination of the likely behaviour of the system and also the
exploration of candidate policies and their effects on the system behaviour and by
examining the rdéle of knowledge in those system mechanisms we can establish the
valuation of the knowledge in those system mechanisms and by extension in the
system as a whole.
The loops do not, of course, stand alone. Figure 2 shows rather more of the ID we
shall be discussing later and we can see two other loops, marked B and C which show
how competence (driven by training investment) in this firm contributes to business
success. We can see that competence contributes to (among many other things)
internal efficiency in the firm which reduces job costs (Loop B). The firm in question
is an insolvency practice; a reduction in their costs will tend to improve the likelihood
of the client firm surviving its difficulties. A record of such successes will encourage
future clients to approach the insolvency practitioner since the perception of the
competence of the practice will be enhanced. The benefits accrue though increased
revenue and are applied to increasing the competence though training. Loop C shows
how the internal efficiency affects the ability to manage suppliers which in turn
increases internal efficiency (because, for example, their behaviour is more
predictable and therefore easier to manage).
Figure 2 near here
Figure 2: Wider effects of competence
A recent extension of the qualitative system dynamics method (Powell and Coyle
2002; Powell and Swart 2003) attaches actors (sometimes called agents) to the causal
arrows, indicating who has control over the strength of the connections. This is a very
powerful extension because it leads directly to the identification of actions aimed at
influencing those actors to use their position in the system in a way which suits us . A
recent study of a medical practice(Powell and Liddell 2004), for example, models the
way in which patients have to queue for medical consultations and identifies who in
the access system controls the critical causal connections, ending up with a list of
actions to be taken by the practice to improve access. Examples of actions resulting
from the analysis were the training of receptionists in triage and the establishment of
senior nursing staff to take simple procedures out of the doctors’ consulting rooms.
The variation used in the case study described below examines specifically the data,
information and knowledge and the skills and competences needed by each actor in
playing their role(s) in the system. The procedure can be simply defined as follows:-
e Establish the explicit system model (the Influence Diagram) in the standard
manner
e Using the QPID approach, attach to each causal arrow the actors who, separately
or together influence the strength of the linkage represented by that arrow
e Identify the loops in the ID and characterise them according to their strength and
speed of operation. This allows prioritisation of effort. Strong, fast loops are
analysed before slow weak ones.
¢ Loop by loop, establish, for each of the actors in each arrow in the loop what
information and knowledge is required for them to fulfil that function. Similarly
for the skill/competence set needed to carry out that actors function..
¢ Generally speaking actors will appear in more than one arrow in the diagram.
Collect together all the information/knowledge and skill/competence requirements
for each actor. These collations then constitute the information/knowledge and
skill/competence maps for each actor in that system. Moreover, each element of
these related sets can be sourced back explicitly to its origins in a model of the
overall organisation and its mechanisms of success.
We now summarise the application of this approach to a real firm of liquidation and
insolvency practitioners in the south of the UK
An example (Fanshaw Lofts)
The research context
Fanshawe Lofts Ltd (Anon 2003) is a firm based in Southampton, UK which
specialises in insolvency matters. There are three partners and around a dozen
supporting staff and managers and the firm has a high reputation in its region for
liquidation, corporate recovery and other high level professional accountancy
services. The firm is thriving and, with a growing support structure, wishes to identify
its knowledge and competence requirements for the future. A study was commenced
in Spring 2003 at Fanshawe Lofts’ request to map the firm’s knowledge and
competence sets in order that the three senior partners could identify a knowledge
strategy for the future.
Data gathering methodology
The authors carried out an initial clarification of the objectives with one of the senior
partners and as a result agreed on a programme of workshops with a senior partner
and four managers. The approach was to teach the five informants the method and
facilitate their own expression of the system model (the ID) rather than obscure their
view of the system by over-involvement. The informants took effectively 9 working
hours to produce a first ID together with the attached actor notations. This was then
tidied up, correcting some minor errors and lacunae and the final ID is shown as
Figure 3. A series of telephone conversations then resulted in the identification of the
knowledge and competences associated with actors and the final knowledge/
competence maps were presented to the Fanshawe Lofts partners for their
consideration as a management team. The subsequent discussions and actions do not
form a part of this report, which is limited to illustrating the practical use of QPID for
knowledge mapping. A full case study paper is in preparation (Powell and Swart
2003).
Model results and explanation
Figure 3 shows the ID produced by the group of informants with the actor notation
suppressed for clarity. It has about 25 variables, a fairly typical number for a diagram
representing a typical business system in this context.
(insert Fig 3 full page near here)
Interpreting these diagrams is best done by tracing the loops. We have already
discussed loops A, B and C (Figure 2) . Loop A describes the beneficial effect of
training on competence and hence business winning. Loop B indicates a specific
mechanism of business winning through the medium of internal cost reduction and
loop C describes the way in which internal efficiency and the ability to manage
external parties work together.
Figure 4 shows another loop, D, to be found near the centre of Figure 3.
Figure 4 near here
Figure 4: Loop D - Competence leads to improved risk management and
improved service
Loop D describes another specific mechanism for success which lies at the heart of
Fanshawe Lofts’ survivability. As we have seen, the recovery rate for clients supports
their reputation and an integral part of recovery rate improvement is their ability to
manage the risk of an opportunity. It would not be in Fanshawe Lofts’ interest to take
on potentially lucrative business if it carried with it significant risk of failure, since
their reputation would then suffer.
As final extracts from the full ID, Figure 5 shows loops E and F.
Figure 5 near here
Figure 5: Loops E and F
Here we see another reality of Fanshawe Lofts’ business context. Loop E shows,
unsurprisingly, that increased competence will lead to an excess of work over
capacity which induces recruitment, increasing the number of staff so that the
repository of both EK and TK increases. Loop F illustrates that the capacity itself
brings in business. Size in and of itself is an advantage in the insolvency business, it
would appear.
Examination of Figure 3 will show many other loops > some of them concerned
explicitly with competences or knowledge and others where the knowledge is
implicit within the mechanism captured by the loops. We discuss the extent to which
the informants are aware of the role of their knowledge sets in the expressed business
model later in this paper.
> The diagram can be covered, without duplication of paths, with about a dozen loops
Analysis and observations
Having established common agreement among informants on the business model to
which Fanshawe Lofts works, the next step in the QPID procedure is to attach
symbols to the arrows in each loop to indicate which persons or groups (both inside
and outside the firm) control the strength of connection of the arrows in the loops. If
we can influence the strength of these connections we have the chance to push the
system behaviour in a direction we favour. We illustrate this by attaching actor
symbols to loop D, since this loop has a wide spread of actors both inside and outside
the firm. The process of analysis for other loops is similar.
Figure 6 near here
Figure 6: Loop D with actors attached to loops
We see, then, that the informants’ view was that the connection between collective
competence and ability in managing and identifying risk was controlled primarily by
partners (P) and managers (M), being the constituency which exercised the primary
professional skills to make that business judgement. What knowledge and skills might
be used to mobilise overall competence in the exercise of the risk assessment? The
partners may well need to be aware of specific knowledge held elsewhere in the firm
about particular clients. Southampton, although a large city, has only a finite number
of firms and business people, and it may well be that a relatively junior member of
the firm may have personal knowledge of a potential client. At a more aggregated
level, partners or managers may need technical skills to assess the business risk,
drawing on historical experience as well as more technical accounting skills. By such
argument the specific skills and knowledge of the actors for each arrow are built up
and recorded on a spread sheet to be collated later so as to build up a compete list of
knowledge and skills needed for each actor to play their part in the system described.
Not all the actors are to be found inside the firm, of course. The arrow perception of
competence > business winning in Figure 6 is controlled by the partners of Fanshawe
Lofts but also by a@, the advertising agency which they employ and by OQ, other
professionals. Clearly the advertising agency have some control over the extent to
which Fanshawe Lofts converts a reputation into won business through its image
making and marketing in the national and local media, but it is less easy to see why
18
other professionals play here as well. The informants were of the clear view that
business is brought to Fanshawe Lofts not only through the free will of clients but also
through the agency of professional advisers such as lawyers and perhaps existing
accountants. Thus these outside professionals control part of the business system that
is Fanshawe Lofts. This observation was a significant one for the firm. Until this point
in the analysis it had not occurred to them that part of their management of knowledge
should include people outside their firm and that it might well be worthwhile
investing in improving the skill sets and knowledge sets of these people in order that
(in this specific case) they should see more clearly the benefits that the firm could
bring to their troubled clients.
Production of the skill set and knowledge sets then follow naturally from a systematic
examination of the requirements for each actor in each arrow of the loops of the
diagram.
Observations: how the system diagram structures the knowledge
The reactions of the informants to the process were complex. Initially the objectives
of the study were (from their point of view) to help them identify explicitly and
specifically what they and others needed to do to carry out their functions and further
the interests of their firm. We could see this colouring their construction of the model
of Figure 3. Variables like interpersonal competence, training and (quite specifically)
risk management are examples of system variables put early into the diagram when
the QPID process was seen as a recording medium, a way of identifying just the
knowing what component of our earlier taxonomy. Soon, however, with guidance
from the facilitators their concentration shifted towards using QPID to understand the
business system in which they worked. It was not clear to them at that point how the
necessary knowledge would emerge.
After the fact, of course, we can see quite clearly how modelling the business system
in this way does help to identify knowledge sets. In particular examination of Figure 3
(as a typical example of a business system model) shows how QPID addresses the
identification of all the types of knowledge discussed in the first section, namely
knowing what, who, and why, together with the integrated form of knowing.
Figure 3 shows clearly that certain knowledge is not only known to exist by the
informants but is also known to have specific attributes. For example, risk
management, as already discussed, is commonly understood to be an important
knowledge/skill set and appears explicitly in the diagram. Similarly inter-personal
communication is seen as being so important that it appears explicitly. It is readily
expressed by the informants without explicit contextualisation. Other information,
equally important in the success of the business system, appears only after
consideration of the role of the actors in the context of the business system. An
example of this is the integrating data gathering skill required of the partners and
managers in exercising effective risk management, a different skill from the risk
management itself. This system contextualisation of the actors (who are, after all, the
executors and repositories of the knowledge and skills) appears to be the key added
value of the QPID approach.
In applying qualitative system dynamics to the management of competitive
intelligence Powell and Bradford (Powell and Bradford 2000) link the epistemic level
to the source of the knowledge (here intelligence information) so that intelligence
which derives from appreciation of the system considered as a whole is likely to be
more valued than intelligence deriving from knowledge of a variable in isolation.
Thus knowledge of the competitor’s policy response to the business context is more
valuable than information about, say, a technology advance or a price. Knowledge, in
a sense, is more valuable than data and derives from a systemic understanding.
We can now see a clear connection between our earlier taxonomy (knowing what etc)
and the concept of higher epistemic objects (knowledge vis-d-vis data) deriving from
wider systemic consideration Here, the identification of knowing what falls naturally
from the systematic examination of the specific knowledge required by actor in the
individual contributions they make to the system (the arrows). Knowing who emerges
from the attribution of actors to the components of the business system and the
knowing why from a knowledge of how information and knowledge acts within the
system to produce the desired effect, in this case the further success of our insolvency
company.
20
Not only does the QPID method drive out more knowledge than the informants could
(and indeed did) express prima facie, but it does so in accord with the natural split of
that knowledge into the various types of knowing. Additionally, and specifically with
reference to our five requirements of a systemic KM tool
e it exhibits plurality of knowledge definition (since the expresio of knwoedge
comes form informants directly)
e it respect the context of actors’ action, again since the definition and declaration of
knowledge is done by actors with regard to their relationship context
e it distinguishes clearly between information and knowledge
e it is demonstrably aimed at an action product
e by virtue of the loop analysis it relates the valuation of knowledge to system
mechanisms.
Lastly, and significantly, it is a clearly effective and practical way of capturing the
most difficult part of that taxonomy, namely the integrated knowledge required by the
organisation for success and growth.
Conclusions
Our observation of the practical use of QPID in knowledge and competence mapping
is that it provides a natural and accessible method of relating knowledge within a firm
to that firm’s situation and objectives. Generally speaking, informants find the method
easy to learn and a natural way to explore the organisational system and context in
which they operate. Fanshawe Lofts, being a professional services firm, was
populated with very well-educated and confident informants. Here we found that with
only about two hours of close guidance they were sufficiently fluent in the
diagramming method that the researchers could stand back into a facilitation and
observation role. With other groups of informants it is necessary to take more of an
active role, constructing the system model for them on the basis of the (structured)
conversation of the informants.
With all types of informants, however, there is immediate ‘buy-in’ to the process
because the output is clearly connected to activity and action. Fanshawe Lofts’
people, for example, immediately saw how their prima facie information sets made
21
sense in the context of how their business worked, so that they received the immediate
reinforcement of their judgement that what they thought initially was important to
know made sense in the declared business system as it emerged.
Significantly, at the end of the modelling and analysis period with the Fanshawe Lofts
people their reaction to what they had discovered was enthusiastic, It mirrored, in a
very direct and satisfying way, the knowing what, how, why and who structure in that
the informants declared a clear sense of context for information and knowledge which
they had never before been able to see clearly. Comments such as “That’s interesting;
I never saw why we needed to know that before” were ubiquitous and indicated a
clear contextualisation of knowledge on which basis action can be taken to achieve
the aims of the firm.
Further work
While this work has concentrated on the knowledge aspects of EK and TK the
associated competence issues have been entrained. The issue of competence sets and
in particular the rich connections between these and business strategy in the form of
core competence theory are of immense importance to firms. The application of QPID
particularly to the core competence identification problem is an immediate and
obvious next step, as is the further development of software support to make the
collation of actors’ knowledge needs into overall knowledge sets easier.
This application of system dynamics in the form of QPID represents an exciting and
fertile route for the operationalisation of much of the important work on EK and TK
which has addressed within-firm issues, concentrating on resource application and the
appropriateness of knowledge management activities in the firm. Connections may
well be made in due course with what is known as ‘alignment’ in strategic IS
specification, the process of ensuring that the Strategic IS is configured to match any
proposed strategy.
As we have already said, the emphasis in this paper is on system sensemaking and
knowledge identification as distinct from the resulting managerial action aimed at
nurturing and growing the knowledge necessary for success. This, together with the
dual problems of knowledge denial to competitors and knowledge sharing with
partners forms a third major thread of further development.
22)
On this latter point, the ease with which QPID identifies knowledge needs both within
and outwith the firm allows its application to competitive intelligence where, in
contrast with the nurturing management of knowledge within the firm, knowledge can
be denied to or protected from the sight of competitors on a more rational basis,
allowing more effective application of what can be costly and rare security and data
gathering assets.
References
Allee, V. (1997). The knowledge evolution: expanding organizational intelligence.
Boston, Butterworth-Heinemann.
Ambrosini, V. and C. Bowman (2001). “Tacit knowledge: some suggestions for
operationalization.” J of Management Studies 38(6): 811-829.
Anon (2003). http://www.fanshawe-lofts.co.uk/, Fanshawe Lofts Ltd. 2003.
Arthur, M. and P. Parker (2003). “Technology, community and the practice of
HRM.” Human Resource Planning (in preparation).
Barney, J. (1991). “Firm resources and sustained competitive advantage.” J of
Management 17(1): 99-120.
Baumard, P. (1999). Tacit knowledge in organisations. London, Sage.
Bontis, N. (1998). “Intellectual capital: an exploratory study that develops
measures and models.” Management Decision 36(2): 63-76.
Cowan, R., P. David, et al. (2000). “The explicit economics of knowledge
codification and tacitness.” Industrial and Corporate Change 9(2): 211-253.
Coyle, R. G. (2000). “Qualitative and quantitative modelling in system dynamics:
some research questions.” System Dynamics Review 16(3): 225-244.
Coyle, R. G. and D. Exelby (2000). “The validation of commercial system
dynamics models.” System Dynamics Review 16 (Spring 2000((1): 27-41.
23
Davenport, T. and L. Prusak (1998). Working knowledge: how orgnizations
manage what they know. Boston, MA, Harvard Business School Press.
Drucker, P. (1993). Post-capitalist society. Oxford, Butterworth-Heinemann.
Durrance, B. (1998). “Some explicit thoughts on tacit learning.” Training and
Developent 52(12): 24-30.
Gerard, J. (2001). The tacit knowing framework: a look at sustained competiive
advantage under a unified tacit and explicit knowledge. 4th Int;] conference on
Organizational Learning and Knowledge Management, Ontario, Canada.
Jacobson, R. (1990). “Unobservable effects and business performance.”
Marketing Science 9(1): 74-85.
Johnson, B., E. Lorenz, et al. (2002). “Why all this fuss about codified and tacit
knowledge?” Industrial and Corporate Change 11(2): 245-262.
Leonard, D. and S. Straus (1998). Putting your company's whole brain to work:
Harvard Business_Review_on_ Knowledge Management. Boston, Harvard
Business School Press.
Nonaka, I. and H. Takeuchi (1995). The Knowledge Creating Company. Oxford,
OUP.
Polanyi, M. (1966). The logic of tacit inference. Knowing and Being, essays by
Michael Polanyi. M. Grene. London, Routledge and Kegan Paul.
Powell, J. and J. Bradford (1998). “The security-strategy interface: using
qualitative process models to relate the security function to business dynamics.”
Security Journal 10: 151-160.
Powell, J. and J. Bradford (2000). “Targeting intelligence gathering a dynamic
competitive environment.” Int'l J of Information Management April 2000.
Powell, J. and R. Coyle (2002). The ordering of strategic decision making: The
appropriate use of qualitative methods in highly politicised strategic decisions.
International Conference of the System Dynamics Society, Palermo.
24
Powell, J. and R. Coyle (2004). “Identifying strategic action in highly politicised
contexts using agent-based qualitative system dynamics.” J of Operational
Research Society (in press).
Powell, J. and W. Liddell (2004). “Reconciling patient access and GP
effectiveness using system modelling.” System Dynamics Review (forthcoming)
(In press).
Powell, J. and J. Swart (2003). “Knowledge and competence mapping using
QPID: Fanshawe Lofts Ltd.” Omega (In preparation).
Ryle, G. (1949). The concept of mind. Chicago, University of Chicago Press.
Shadur, M. and S. Snell (2002). Knowledge management strategies, human
resources and firm innovation: An empirical study of Australian firms in
knowledge intensive industries. Conf on HRM and Performance, Bath, UK.
Spender, J.-C. (1996). “Organizational kowledge, learning and memory: three
concepts in search of a theory.” J_ of Organizational Change Management 9(1):
63-78.
Back to the T:
Swart, J. (2000). Collective tacit knowledge and self-awareness (PhD Thesis).
School of Management. Bath, University of Bath.
Swart, J. and J. Kinnie (2003). “Sharing knowledge in knowledge intensive
firms.” Human Resource Management J 13(2).
Wiener, N. (1949). Cybernetics. | Washington, Corporation.
Wolstenholme, E. F. (1990). System Enquiry. Chichester, John Wiley.
25
Back to the Top
CC BY-NC-SA 4.0