|
1. The structural complexity driving knowledge
The recognition that
knowledge is a precursor to organisational success permeates the literature:
Nonaka and Takeuchi (1995) pay heed to knowledge’s role in product innovation,
De Leo (1997) to its facilitation of operational improvements, and Baumard
(1999) to its role in reducing marketplace ambiguity. But before accepting their
premise that knowledge is important, we must understand why it is
important—that is, the concrete factors necessitating its presence in the
organisation. Although Davenport and Prusak (2000), Quintas (2002) and earlier
scholars mention drivers like heightened competition and the importance of
continuous innovation, they fail to adequately convince us of today’s knowledge
necessity. In essence, they miss a discussion of globalisation’s structural
complexity of matching supply and demand.
1.1 Causal processes
In explicating this
complexity, we first turn to the fact that the global economy entails the
acceleration of doing business across borders—as evidenced by the tripling of
global goods and services exports from 1980 to 2001 (WTO
2002).
For companies
participating in this acceleration, knowledge of different cultures and
rule-regimes becomes crucial; although transport and communication developments
have obviated the geographic barriers of conducting business in new
environments, other obstacles (cultural, legal, operational, etc.) remain.
Furthermore, as an outgrowth of
the trade acceleration, the global economy has witnessed the rise of more
demanding consumers. That is, because the acceleration implies heightened
competition and hence unlimited supply choices, consumers can place greater
demands on their purchases. For suppliers, this translates into the necessity of
knowing and meeting the needs and preferences of specific segments; it
necessitates the personalisation of products and services.
Finally, in staying abreast of
consumer and cross-border trading requirements, organisations are met with the
dynamic growth—or structural expansion—of the global economy, wherein continuous
change takes place. These changes occur in the form of new technology,
processes, product cycle times, cost structures, and so on. And as Liautaud and
Hammond (2000) note, to handle these changes, companies must be “faster, more
agile, and crucially, more intelligent” (italics original, p 4).
1.2 Alleviating the
complexity: Moving beyond technology
In attempting to
alleviate the structural complexity and fully embrace knowledge, organisations
have progressively turned to technologies that enable knowledge codification and
manipulation—from intranets/extranets to document management tools to
knowledge-based systems. But despite the proliferation of these technologies,
the management of knowledge remains a major challenge. This is well evidenced by
Strassmann’s (1999) analysis of more than 1,500 U.S. industrial firms, which
showed zero correlation between information technology expenditure and firm
profitability. Based on this study—and his subsequent estimates of firms’
knowledge assets—Strassmann (2001) concludes that knowledge has simply been used
as a means of justifying increased information technology spending, and for most
organisations, has failed to improve profits.
Picking up on this
so-called ‘productivity paradox’, Johannessen et al. (2001), Dunford (2000) and
other knowledge scholars suggest that the failure of technologies stems from
their focus on explicit and codifiable knowledge. Drawing most often on the
epistemological system of Polanyi, they argue that such a focus implies the
neglect of the second, equally significant, part of a company’s knowledge
base—namely, tacit knowledge. As will be illustrated below, the recognition of a
tacit dimension does not adequately serve to advance the management of
knowledge. While agreeing that a conceptualisation of knowledge is necessary in
order to move beyond the technology focus, we argue that a more fundamental view
is needed. The main objectives of this paper are thus to (1) discuss the
practical limitations of the tacit-explicit conceptualisation, (2) reveal the
deeper implications of this discussion and (3) describe an alternative,
functional approach to knowledge. Acknowledging the functional view’s roots in
computer science, the paper will conclude by (4) illustrating its value for
knowledge management – a value that reaches beyond the scope of pure technology.
In moving toward these goals, it is appropriate to briefly revisit the tacit
knowledge trajectory.
2. The emergence of the tacit ‘dimension’
Pushed along by
philosophers such as Gilbert Ryle, William James and Michael Polanyi, the modern
epistemological trajectory has moved from a positivistic paradigm to a more
balanced paradigm—one that recognizes the presence of the experiential and
personal facets of knowledge alongside the objective and scientific.
Definitively, this shift has expressed itself through the likes of Ryle's (1949)
‘knowing how’ and Polanyi's (1966) ‘tacit dimension.’ Polanyi's work is
exemplary in that it argues that the elimination of the personal, tacit
dimension will in essence destroy all objective knowledge, as it provides the
perception and mental models that enable us to understand the comprehensive
whole of an entity. In exemplifying, Polanyi considers how we recognize the face
of an acquaintance: We know the appearance of the face in its entirety by
‘attending from’ the tacit particulars and ‘attending to’ the explicit whole of
the face. Thus, although we can delineate the face among a crowd of people, we
are often unable to articulate precisely how we know the face. This, Polanyi
argues, is tacit knowing, and it is the foundation for all knowledge.
Polanyi's line of thinking has long since surfaced in other
theoretical disciplines, and with the help of Nelson and Winter (1982), the
realm of economics was by no means overlooked. Nelson and Winter's tacit
knowledge is defined in terms of the organisation’s automatic (and often
unconscious) skills, such as the ability to choose the right job applicant or
make the right investment. They suggest that the organisation’s tacitly driven
skills are often the basis for organisational routines, which in turn govern
smart business behaviour and hence organisational success. Following their lead,
a plethora of scholars (e.g., Nonaka & Takeuchi 1995; Spender 1996; Baumard
1999) began to dominate the economics literature in their convergence around
tacit knowledge. Unlike Polanyi and Nelson and Winter, however, these scholars
commonly offer a clear, bounded distinction between tacit knowledge and its
supposed counterpart, explicit knowledge.
As we have seen, the overreaching thread within their
conceptualisations includes the notion that explicit knowledge is that which is
explicable and transmittable; essentially it is an information stock that exists
outside the individual and/or organisational mind. Tacit knowledge definitions
are less concurrent, but on the whole their authors argue that it is highly
contextual and bound to individual experiences or firm processes, thus making it
either impossible or less conducive to codification and transfer. As such,
organisational tacit knowledge is said to be expressed in terms of employee
skills, problem solving abilities and mental models, whilst explicit knowledge
manifests itself in the form of mathematical expressions, instruction manuals,
product blueprints, and so on (e.g., Nonaka & Takeuchi 1995).
Adherence to this distinction in the accumulating wealth of
literature is grounded in the notion that it is tacit knowledge that will
determine the degree to which companies remain competitive. The rationale being
that while explicit knowledge is more easily managed, tacit knowledge has more
value, being derived from particular circumstances and therefore difficult to
imitate externally. Thus, citing the importance of tacit knowledge to
prosperity, as well as the lack of evidence for the positive impact of explicit
knowledge solutions, researchers are calling for the addition of techniques and
cultures to promote tacit knowledge transfer. Nonaka and Takeuchi (1995) suggest
a four-phase knowledge management process to facilitate the interplay of tacit
and explicit knowledge—a process ideally initiated through face-to-face employee
socialization. Subsequent authors (e.g., Johannessen et al. 2001; Dunford 2000;
Lubit 2001) likewise recommend targeted interpersonal solutions such as
apprenticeships, mentoring and narrative storytelling in order to ensure tacit
knowledge’s place beside formalized explicit knowledge.
3. Inadequacies of
the distinction
Although useful in theory
as a means of reminding organisations to manage the entirety of their knowledge
base, the tacit-explicit
distinction does not adequately serve to guide organisations through the
knowledge management process. An adequate knowledge view should, first and
foremost, help in tuning strategic goals to knowledge goals, and further, should
help in determining and realizing knowledge formalisation possibilities. The
tacit-explicit approach misses on both accounts.
3.1 Goal-dependency
issues
The simple
classification of knowledge into tacit and explicit does not directly and
concretely substantiate the relationship between goals of an organisation and
the essential role of knowledge in achieving these goals. Failing to
clearly align goals that rigidly govern the knowledge process only serves to
ensure that knowledge initiatives remain within the level of information
production and distribution – as codified knowledge is often gathered that is
irrelevant to the functional objectives of the organisation. In the end, this
equates to limited knowledge transparency and application, as the organisation
remains trapped in an information-intensive frame of reference. It also
important to note that the absence of a goal orientation hinders the awareness
that knowledge is an essential asset for optimal business performance and, as a
consequence, that knowledge management is a need-to-have activity instead of
just a nice-to-have activity.
3.2 Formalisation
issues
As noted in section 2 above, authors of the
distinction within the knowledge management literature distinguish tacit and
explicit types primarily on the basis of ease of transfer or
codification/formalisation. Spender’s (1996) account deviates slightly in its
recognition of tacit knowledge as knowledge that is ‘not yet explicated,’ thus
suggesting that it exists on a continuum and can potentially be formalized (as
Polanyi has long since told us). Attempts at operationalising the tacit-explicit
approach are complex and limited, as we see through the examination of, for
example, Schulz and Jobe (2001), Zack and Serino (2000) and Davenport and Prusak
(2000). Their somewhat vague discussions of knowledge codification converge
around the idea that the ‘richness’ or ‘abstractness’ of knowledge determines
whether it should be managed through people (tacit) or through technology
(explicit).
If we lend specificity to
their discussions, and enrich the tacit-explicit distinction with a formalisable/non-formalisable
dimension, we more clearly see the issues, alternatives and complications
involved in its management (Figure 1). Here we define formalisation
as the process of representing knowledge using a data structure. A data
structure can be a text, a flowchart, a decision table, a record in a database,
etc.
|
|
Formalisation impossible |
Formalisation possible |
|
Tacit
|
Knowledge management through humans
|
Are
there any chunks of knowledge worth formalising? |
|
Explicit |
Impossible
|
Render
it more
Knowledge-based? |
Figure 1.
Tacit and explicit knowledge mapped to formalisation possibility
The figure displays
three possible states: (1) tacit knowledge cannot be formalised, (2)
tacit knowledge can be formalised and (3)
knowledge is explicit. These states in turn reveal what we deem
as the key deficiencies in the tacit-explicit approach to knowledge management:
1.
It does not help to
assess whether knowledge is formalisable;
2.
It does not account for
knowledge that falls in between the dichotomous range of formalisable and non-formalisable
knowledge;
3.
When knowledge is deemed
not formalisable, it does not clarify what it is that people have when we say
they have knowledge, nor does it clarify
how
we utilize human capacity for tacit knowledge management;
4.
When knowledge is deemed
formalisable, it does not help to select and evaluate knowledge representation
formalisms such as text, flowcharts, database records, rules and formulas;
5.
When knowledge is already
explicit, it does not support the improvement of the representation, nor does it
help in deciding to move another to knowledge representation formalism;
6.
When knowledge is
explicit, it does not
help in determining the value
of rendering explicit knowledge more efficient, transparent and maintainable.
In short, the
tacit-explicit distinction is a rather superficial instrument. What is needed in
its place is a theory on the nature of knowledge that precedes and
advances knowledge management. It is to this that we turn below.
4.1 Knowledge is matching
In moving toward a more fundamental view on knowledge, it is
useful to return to Polanyi’s (1966) conceptualisation. Although often
overlooked in current discussions on tacit knowledge management, Polanyi’s
central notion of ‘attending away’ from the particulars of an entity and
‘attending to’ its joint whole lends much to our understanding of knowledge.
Polanyi explains that the relationship between the particulars and the whole are
functional, in that we rely on our awareness of the particulars in our ability
to attend to the whole in our achievement of a joint purpose. How could we
otherwise recognize the face of an acquaintance, play the piano or ride a
bicycle skilfully if we were not able to coordinate our idea of successfully
accomplishing these acts with our mental and physical performance of them
Knowledge, then, establishes a relationship between the particulars and the
whole of the entity: it provides an “understanding of the comprehensive
entity which these two terms jointly constitute” (Polanyi 1966, p 13, italics
original).
A scheme that further contributes clarity to the notion of
knowledge as a process of understanding comprehensive entities (or concepts) is
that of Ogden and Richards (1946). Ogden and Richards explain that a concept
consists of an object-type, an object and a term. The object-type refers to a
set of conditions, the object to the real-world entity that complies with these
conditions, and the term to the label that denotes the object-type. A child, for
instance, develops the object-type ‘ball’ to structure and act upon her
environment. An object that matches conditions such as ‘round form’ and ‘it
rolls when you kick it’ qualifies as a ball. The actual word ‘ball’ symbolises
or labels the object-type.
Drawing on such discussions, we define knowledge as the
competence to realize goals by matching object-types and objects (Figure 2). The
child’s ability to identify a ball by matching ‘round form’ and ‘it rolls when
you kick it’ to the real world object ‘ball’ is thus knowledge. The child’s
ability to kick the ball by matching her concept of ‘ball kicking’ to the real
world action of kicking a ball is also knowledge.
Figure 2.
Matching object-types and objects
The relation between an
object-type and its objects is that objects are referents that should comply
with the object-type. Objects are the real-world counterparts of the
object-type. As noted in the examples above, objects need not be physical
phenomena; they may also be formed by a sequence of activities. Furthermore,
because real-world object-types and objects can be highly complex, basic
abstraction mechanisms are necessary in helping us to describe them (Figure 2).
These include the generalization of specific object-types into a general
category (balls are a generalization of footballs); specialization of general
object-types into a specific category (footballs are a specialization of balls);
aggregation of several object-types into a new object-type (the child’s mental
and physical abilities, plus the presence of the ball are the aggregated
object-type of kicking a ball); and the instantiation of a real world
object-type (the way the child kicks the ball is an instantiation of all ball
kicking).
If object-types
determine the conditions of knowledge, then knowledge about concepts depends
upon the definition, or construction, of the object-type. From this we conclude
that in order to understand the nature of knowledge, we need to understand how
object-types are constructed. The functional view provides us with such an
understanding. Although other views on how to construct the conditions of an
object-type exist, including the classical view, the prototypical view and the
probabilistic view, we focus on the functional view (for an in-depth mutual
comparison of these views see Van Der Smagt 1985; Hendriks 1986; Lucardie 1994).
The functional view is unique in that it more clearly assigns goals as central
to knowledge, and further, it recognizes that in the real world objects may
present themselves in many different ways. This is evidenced through two basic
characteristics of the functional view: (1) the goal-oriented selection
principle and (2) functional equivalence.
4.2. The
goal-oriented selection principle
Constructing an object-type is a strikingly difficult activity.
Illustrative is the description of the object-type ‘water’ (Lucardie 1994, pp
80-91). An indefinitely large number of conditions potentially qualify for
incorporation into the object-type ‘water’. Consider the following
characteristics: at sea level water boils at 1000C; the saturation
pressure of water at 60C is 0.6 cm mercury; water is a liquid with a
refraction-index for sodium light of 1.33299 (at 200C); liquid water
has maximum density at 3.980C; the viscosity of water vapour at 200C
is 9.6 x 10-3 cP; water is a set of H2O molecules; water
is a set of T2O molecules; and water is a set of D2O
molecules. Given the innumerable possibilities, how then should we describe
water? Is it something that boils at 1000C? Should we describe water
through its isotopes T2O or D2O?
Water is by no means the only object-type that displays an
overwhelming array of conditions. In fact, all object-types are describable by a
great number of conditions. A selection principle is thus needed. The functional
approach operationalises a selection principle by assuming a goal or context of
classification. Again, for the object-type ‘water’, goals need to be introduced
such as ‘quench one’s thirst’ or ‘produce H2SO4’. Whereas
the first goal requires attributes describing the drinkability of water, the
latter goal requires the evaluation of the object attribute H2O (T2O
or D2O). Thus a change of goals or context alters the content of an
object-type. Instead of having one object-type ‘water’, we distinguish several
object-types ‘water’, each of which is true in relation to a certain goal or
context.
4.3
Functional equivalence
·
Variation limited to goal-constructed categories.
The third phenomenon contributing to functional
equivalence refers to the situation where objects may have different attribute
values, but that this variation is limited to, or falls within, goal-constructed
categories. Objects 3 and 4 in the figure below have different but functionally
similar values for ‘performance’. The variation of ‘performance’ is limited
within the goal-constructed category ≤30.
_______________________________________________________
A. Object-type ‘Client’
(Bank account ≤ 12 months)
(Performance ≤50)
Normal client
(Performance >50 and ≤75) (not wealthy) Normal client
(Performance >50 and ≤75) (wealthy) Special client
(Performance >75) Special client
(Bank account > 12 months)
(Performance ≤ 30) Normal client
(Performance > 30) Special client
B. Functionally similar
objects
Object 1: (Bank account 10 months), (performance 20) (wealthy)
Object 2: (Bank account 12 months), (performance 45) (poor)
Object 3: (Bank account 30 months), (performance 5) (poor)
Object 4: (Bank account 30 months), (performance 29) (wealthy)
___________________________________________________________________
Figure 3.
The object-type ‘client’ and functionally equivalent objects
5. The value of the functional view
As the functional view
gives insight into the basic characteristics of knowledge, it helps to clarify
the fuzziness that surfaces when organisations attempt to construct and handle
knowledge. As exemplified below,
the goal-orientation of the functional view helps organisations
more accurately define and use knowledge, while the underlying characteristic of
functional equivalence helps to guide organisations forward through the
operational processes of knowledge formalisation.
5.1 Installation of a
goal-orientation
One of the most promising
benefits of the functional view, is that it helps the organisation to start
working from a goal or system of goals. A goal-oriented approach disentangles
the confusion that often occurs when an organisation attempts to manage an
object-type (e.g., an employee, a service, a product, or a client) while not
taking into account that multiple goals are involved.
As an example, we turn to a case where a computer system was used to help
determine students’ eligibility for university scholarships. The object-type
‘scholarship student’ that was incorporated into the system led to complaints
from students who were overlooked for a scholarship because the system
mistakenly failed to classify them as a ‘scholarship student’ (mismatch). It
subsequently appeared that the rather complex object-type was constructed using
the government’s goal ‘should suit budget,’ while the universities linked to the
scholarships had the implicit goal to acquire as many scholarship students as
possible. Analysis revealed that at least two distinct object-types
‘scholarships’ should have been distinguished based upon the different goals of
the actors involved. In addition to the efforts spent handling students’
complaints, the costs to reconcile both object-types in an adapted system were
substantial. The inclusion of goals and the related
distinction of several object-types (and objects) would have eliminated
irrelevant information, and increased transparency of knowledge. When
goals determine which conditions are relevant for the definition of an
object-type, knowledge becomes something in use as a function of the
organisation’s goals. This prevents knowledge from becoming obsolete, or just a
sitting stock of information; for when the goals change, knowledge changes with
it. This is true irrespective
of whether knowledge is processed through humans, systems or both.
5.3 Evaluation of
representation formalisms
Finally, the functional
view is helpful in selecting and evaluating appropriate knowledge representation
techniques for specific types of knowledge. Besides formulas and mathematical
functions for representing knowledge of a compensatory nature, other formalisms
exist for knowledge that is less homogeneous, including text, programming
languages and flow charts. By defining the characteristics of a given
representation technique, and determining these characteristics’ ability to
handle the functional equivalence of a specific knowledge area, we can determine
whether it is a suitable match. Without a framework to select and evaluate
knowledge representation formalisms, organisations often turn to the
representation of knowledge in Lotus Notes or databases while the nature of
functional equivalence requires other formalisms. As a consequence, maintenance
costs accumulate quickly.
6.
A functional blueprint
Stepping back from the examples above, we find it useful to close
with a case where the functional view served as a driving force in a
comprehensive knowledge management initiative. At the Department of Strategic
Legal Affairs within the Ministry of Traffic and Trade in the Netherlands, the
functional view helped in designing and implementing a blueprint of the
knowledge-based organisation. The blueprint described the goals of the
department, the processes necessary in achieving these goals and an assessment
of the knowledge needs related to the processes. Specifically, for each process
problems were identified through knowledge spectacles, and thus pinpointed as
either knowledge fragmentation, lack of knowledge or unbalanced knowledge
accessibility. The blueprint then measured the gap between the state of the
department as a knowledge-intensive, information-based organisation (the As-Is
situation), and as a knowledge-based organisation (the To-Be situation). The
blueprint contained descriptions of the stages that would, step by step,
transform the department into a knowledge-based organisation. Within each stage
of this transformation, the blueprint guided the department through the use of
knowledge enablers, including human resource management, organisational culture,
processes, information technology architecture (e.g., the internet) and
strategy. The choice of knowledge enabler(s) for a given knowledge area was then
functionally assessed based upon the level of homogeneity for that area.
This blueprint is now being implemented to improve the
department’s performance. For example, a new information technology architecture
was built to generate licenses consistently and quickly. This system, called
QuicKlic, prevents claims (due to the improved and consistent licenses) and
shortens the production time of a license by at least a factor of ten. QuicKlic
was put into operation a few years ago, and combined with a new working
methodology, the system has realized major improvements. Also, as a result of
the functional view blueprint, knowledge-based human resource management has
been implemented at the department. This initiative, called the Strategic
Personnel Management Project, identifies individual knowledge needs within a
five-year time frame, and tackles these needs through education and the hiring
of new types of employees who are evaluated on their knowledge sharing.
7. Conclusion: A promising view on knowledge
The complex interplay
between supply and demand forces organisations to embrace new business models
built around knowledge; it forces them to become knowledge-based. The
knowledge-based organisation is the organisation that optimises the application
of knowledge to reach operational and strategic goals. It is about finding the
most efficient, transparent and effective way of representing knowledge. It is
about decreasing information flows and increasing knowledge flows. Neither the
technology focus nor the tacit-explicit distinction suffices in helping
organisations realise a knowledge-based paradigm.
By providing a framework
in which organisations can align goals, assess knowledge and select appropriate
knowledge solutions and representation formalisms, the functional view offers a
promising alternative. And one that can be operationalised. During the last ten
years, the functional approach has been successfully applied in various economic
sectors—the cases mentioned above are just a few examples. The next step is to
clarify the intricacies of the view in scientific publications, which in turn
will help initiate its acceptance as a serious approach to handling
organisational knowledge. Maybe then organisations can begin to move past their
technology focus and toward being truly knowledge-based, which in turn will
equate to better performance.
References
Baumard, P. (1999)
Tacit Knowledge in Organisations, Sage, London.
Davenport, T.H. and
Prusak, L. (2000) Working Knowledge: How Organisations Manage What They Know,
Harvard Business School Press, Boston.
De Leo, F. (1997)
“The transfer of tacit knowledge within firm boundaries – An exploratory study,”
in Corporate Transformation, Alessandro Sinatra (Ed), Kluwer Academic
Publishers, Netherlands, pp 448-59.
Dunford, R. (2000)
“Key challenges in the search for the effective management of knowledge in
management consulting firms,” Journal of Knowledge Management, Vol 4, No.
4, pp 295-302.
Hendriks, P. H. J. (1986) De Relationele Definitie van
Begrippen. Een Relationeel Realistische Visie op het Operationaliseren en
Representeren van Begrippen, Thesis (PhD), University of Nijmegen.
Johannessen, J-A.,
Olaisen, J. and Olsen, B. (2001) “Mismanagement of tacit knowledge: The
importance of tacit knowledge, the danger of information technology, and what to
do about it,” International Journal of Information Management, Vol 21,
No. 1, pp 3-20.
Liautaud, B. and Hammond,
M. (2000) E-Business Intelligence: Turning Information into Knowledge into
Profit, McGraw-Hill, New York.
Lubit, R. (2001) “The
keys to sustainable competitive advantage: Tacit knowledge and knowledge
management,” Organisational Dynamics, Vol 29, No. 3, pp 164-78.
Lucardie, G. L. (1994)
Functional Object-types as a Foundation of Complex Knowledge-Based Systems,
Thesis (PhD), Technical University Eindhoven.
Nelson, R.R. and Winter,
S.G. (1982) An Evolutionary Theory of Economic Change, Harvard University
Press, London.
Nonaka, I. and Takeuchi,
H. (1995) The Knowledge Creating Company, Oxford University Press,
Oxford.
Ogden, C.K. and Richards,
I.A. (1946) The Meaning of Meaning, Harcourt Brace Javanovitch, New York.
Polanyi, M. (1966) The
Tacit Dimension, Routledge & Kegan Paul Ltd., London.
Quintas, P. (2002)
“Managing Knowledge in a New Century,” in Managing Knowledge: An Essential
Reader, Stephen Little, Paul Quintas and Tim Ray (Eds.), Sage, London, pp
1-14.
Ryle, G (1949) The
Concept of the Mind, Penguin Books, New York [1990].
Schulz, M., Jobe, L.A.
(2001) “Codification and tacitness as knowledge management strategies: An
empirical exploration,” Journal of High Technology Management Research,
Vol 12, No. 1, pp 139-65.
Spender, J.-C. (1996)
“Competitive advantage from tacit knowledge? Unpacking the concept and its
strategic implications,” in Organisational Learning and Competitive Advantage,
Bertrand Moingeon and Amy Edmonson (Eds), Sage, London, pp 56-71.
Strassmann, P.A. (1999)
Measuring and Evaluating the Productivity of U.S. Corporate Information
Management [Online], Available: http://www.strassmann.com/pubs/frb-041599.pdf
(2002, July 25).
Strassmann, P.A. (2001)
“R&D and Knowledge,” Knowledge Management Magazine [Online], Available:
http://www.strassmann.com/pubs/km/2001-6.php (2002, July 25).
Van Der Smagt, A.G. M.
(1985) Definiëren en Relateren in Sociaal-Wetenschappelijk Onderzoek,
Thesis (PhD), University of Nijmegen.
World Trade Organisation
(2002), Historical Series: Merchandise, Commercial Services Trade by Region
and Selected Economies [Online], Available:
http://www.wto.org/english/res_e/statis_e/statis_e.htm (2002, July
17).
Zack, M. and Serino, M.
(2000) “Knowledge management and collaboration technologies," in Knowledge,
Groupware and the Internet, David E. Smith (Ed), Butterworth-Heinemann,
Boston, pp 303-16.
|
