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Tacit knowledge definitions tend to be extremely varied. Some argue that tacit knowledge is precisely that. Others feel that only time and effort prevent all tacit knowledge from eventually becoming articulated. For the purposes of our research “tacit knowledge”, in practice at least, encompasses a medium ground, being comprised of articulable and inarticulable subsets. Along the lines of Weber (1997), we have formalised a meaning for this “tacit knowledge” and for comparison have completed a content analysis of the literature to determine what other researchers understand “tacit knowledge” to mean.
AJIS Special Issue December 2002
C.N.G. ‘Kit’ Dampney1, Peter Busch2 and Debbie Richards2
1 School of Design, Communications and Information Technology
University of Newcastle, Callaghan,
N.S.W. Australia
2 Department of Computing,
Macquarie University, North Ryde,
N.S.W. Australia
Tacit knowledge definitions tend to be extremely varied. Some argue that tacit knowledge is precisely that. Others feel
that only time and effort prevent all tacit knowledge from eventually becoming articulated. For the purposes of our
research “tacit knowledge”, in practice at least, encompasses a medium ground, being comprised of articulable and
inarticulable subsets. Along the lines of Weber (1997), we have formalised a meaning for this “tacit knowledge” and
for comparison have completed a content analysis of the literature to determine what other researchers understand
“tacit knowledge” to mean.
Let us begin with a quote:
Data consists of raw facts … Information is a collection of facts organised in such a way
that they have additional value beyond the value of the facts themselves … Knowledge is
the body of rules, guidelines, and procedures used to select, organise and manipulate data
to make it suitable for a specific task...(Stair et. al. 1998, p.5 [italics added]).
This generalisation can be extended by noting that knowledge may be partitioned further into two categories,
namely tacit knowledge and articulate knowledge; the former, for example, being especially recognised by the
Japanese (Takeuchi, 1998). It was Polanyi (1959) (in Greeno, 1987) who was instrumental in first
… distinguish[ing] between explicit [or articulate] knowledge, “what is usually described
as knowledge, as set out in written words or maps, or mathematical formulae,” and Tacit
knowledge, “such as we have of something we are in the act of doing” (p.12).
Tacit Knowledge versus Articulate Knowledge
Tacit knowledge is thus that component of knowledge that is widely held by individuals but not able to be
readily expressed. It is expertise, skill, and ‘know how’, as opposed to codified knowledge. Alternatively:
Tacit knowledge is the personal knowledge resident within the mind, behavior and
perceptions of individuals. Tacit knowledge includes skills, experiences, insight, intuition
and judgment. It is typically shared through discussion, stories, analogies and person-to-
person interaction; therefore, it is difficult to capture or represent in explicit form. Because
individuals continually add personal knowledge, which changes behavior and perceptions,
tacit knowledge is by definition uncapped (Casonato and Harris, 1999).
Articulate knowledge is typically acquired through formal education, writings, books, rule sets, legal code to
name but a few of its ways and means. Tacit knowledge on the other hand is often acquired through a more
intimate relationship, say between a teacher and an apprentice. It is transferred more orally, more by way of
example, more by sight. More generally, and this is particularly applicable in a modern organisation, tacit
knowledge is acquired through shared experience in cooperative work.
A Working Definition of Tacit Knowledge
On the one hand, it is argued that some tacit knowledge can never actually be articulated (Leonard and Sensiper,
1998), or indeed all tacit knowledge (Burstein, 2001). On the other hand, economists arguing in reductionist
terms consider that: “only cost considerations prevent residual forms of tacit knowledge [from being] codified”
(Ancori, Bureth and Cohendet, 2000, p.281). Indeed, it is often accepted “that tacit knowledge (as distinct from
intangible investment more generally) is non-codified, disembodied know how that is acquired in the informal
take-up of learned behaviour and procedures” (Howells, 1995, p.2). Tacit knowledge also has its traces in
Gärdenfors' Conceptual Spaces (Gärdenfors, 2000). Research by Aisbett and Gibbon (2001) identifies the
“subconceptual layer” of Gärdenfors as being representable, for example, by neural nets. This suggests that if
we equate the brain's subconscious with tacit knowledge, then we have an explanation of tacit knowledge
processing as subconscious pattern matching in the human mind. Such pattern matching is not explicitly
codified of course, until a conscious effort is made to articulate such tacit knowledge, to make it conscious, and
to codify it.
Although the economist Hayek had first discussed the presence of inarticulable knowledge (Ebeling, 1999), it is
Polanyi (1958) who is considered the father and discoverer of tacit knowledge, with his reference to subsidiary
AJIS Special Issue December 2002
and focal awareness. More specifically Polanyi (1968) had actually contemplated a triad of: 1) subsidiary
particulars, 2) a focal target, and 3) the knower who links the particulars to the focal target. The linking
highlights the dependency of tacit knowledge on the context for the particular and the target, as the knower
perceives it.
By focal awareness Polanyi referred to our using systems of meaning to interpret what we see, hear or read;
whereas our subsidiary awareness arouses within us past experiences, which guide our ability to further
understand what it is we are experiencing. In short:
“… tacit knowledge is manifestly present ... not only when it exceeds the powers of
articulation, but even when it exactly coincides with them, as it does when we have
acquired it a moment before by listening to or reading a text” (Polanyi, 1968, p.92).
The last part of the quotation above relates to Polanyi’s concept of ‘indwelling’, or assimilating outside
influences within (Polanyi, 1967), so typical of the tacit acquisition process.
Tacit knowledge, depending on one’s interpretation, may actually be, we speculate, any pattern matching
process from sensory skills such as learning to ride a bicycle, through to tricks of the trade, the latter often
articulated and passed on from the senior to the apprentice. Our use of tacit knowledge in this paper refers to:
“those components of technology that are not codified into blueprints, manual patents and
the like. In other words, tacit knowledge is intangible knowledge, such as rules of thumb,
heuristics, and other “tricks of the trade'“ (Arora, 1996, p.234).
For the practical purposes of our information systems research, tacit knowledge could be said to be the implicit
articulable IT managerial knowledge that IT practitioners draw upon when conducting their ‘management of
themselves, others, and their careers’, as Wagner and Sternberg (1991a; 1991b) would say. When such tacit
knowledge is shared from mutual experience and culture it gains a dimension within an organisation. It thus
requires an added dimension to the theory to take into account the nature of learning that is particularly
applicable to knowledge evolution.
Two Approaches to Definition
According to the Macquarie Dictionary, definition is:
[Definition, n. 1. The act of defining or making definite or clear. 2. The formal statement of the
meaning or signification of a word, phrase, etc. (The Macquarie Dictionary, 1981)]
In defining tacit knowledge we have chosen to present two alternative approaches. The first approach applies
formal content analysis of the literature to define tacit knowledge, based on what other authors, the research
community, believe to be tacit knowledge. The second approach provides a formal framework or theory for
defining tacit knowledge that is based on denotation.
Following Weber (1977), definitions may be justified by both interpretation and representation: -
1) Interpretation: by agreeing amongst us that the definitions effectively describe and provide a
qualitative understanding of the reality and human value systems we are dealing with. If this is
satisfied we can assert that the definitions are relevant to our value systems and beliefs. Note that
“us” and “our” refer to the group of people entrusted with the understanding required.
2) Representation: by formalisation in a mathematical theory that has sound and valid models. If
representation is satisfied then from a strictly formal perspective, the definitions have a sound
underlying theory.
The content analysis, interpretation, and formal theory; representation, presented in the two approaches, thus
complement each other.
What is evident from both of these approaches is that tacit knowledge is heavily individualistic and based on
self–experience, which leads ultimately to our greater understanding for situations we will confront in the
future. The latter aspect of tacit knowledge ties in directly with Polanyi’s epistemology, while the useful nature
of tacit knowledge for improving our future understanding of situations ties in with Sternberg’s epistemology
where tacit knowledge is considered to be a management asset (Wagner and Sternberg, 1991a; 1991b).
Approach I: Interpretation - Qualitative Content Analysis of Tacit Knowledge in the
Research Literature
In previous reported research, we have used formal content analysis to find, by consensus of the research
community, a qualitative understanding of tacit knowledge. {Busch et al, 2001)
A common assertion in tacit knowledge research is that if knowledge is articulated in some way, then it is no
longer tacit. While logically this may seem true at first, it is important to note that:
…. in theory, tacit knowledge can be verbalised and taught (in which case we still refer to
it as “tacit knowledge” even though strictly speaking it is no longer tacit) (Sternberg,
AJIS Special Issue December 2002
In that research, an examination of 68 recently published documents, the following appeared the most widely
cited as explaining what tacit knowledge entails. The terms are as follows, in descending order of groundedness.
The terms given are subjectively coded ‘themes’ that have been derived from the literature, rather than direct
terms, as they exist per se. We provide only those codes that have a groundedness of greater than 2 instances in
the literature here:
Knowledge (80); Individuals (50); Organisational domain (46); Skill (35); Non-
Codification (28); Non-verbal (27); Experience (26); Context specific (24); Intuition (20);
Learned (16); Know how (15); Not formal (13); Action (12); Expertise (11); Culture (10);
Contingency based (9); Environment (9); Externalisation (9); Knowing (9); Not easily
communicated (9); Practical (9); Sub-consciousness (9); Understanding (9); Cognitive (8),
Internalisation (8); Mental models (8); Not directly taught (8); Not easily transmitted (8);
Process (8); Abilities (7); Apprenticeship (7); Low environmental support (7);
Management (7); Practice (7); Society (7); Two dimensional (7); Behaviour (6); Beliefs
(6); Conscious (6); Direct contact (6); Face to face transfer (6); Goal attainment (6);
Inferences (6); Learning by doing (6); Maxims (6); Non-awareness (6); Pattern recognition
(6); Perceptions (6); Procedural in nature (6); Routine (6); Subjectivity (6); Tasks (6);
Technology (6); Values (6); Common sense (5); Decision making (5); Embodied (5);
Implicit (5); Implied (5); Information (5); Judgement (5); No idea (5); Not easily codifiable
(5); Sharing (5); Taken for granted (5); Unconscious (5); Everyday situations (4);
Interaction (4); Job knowledge (4); Know more than we can tell (4); Not easily formalised
(4); Not formal instruction (4); Others (4); Physical control (4); Riding a bicycle (4); Rule
(4); Schema (4); Time (4); Touch sensitivity (4); Wisdom (4); Abstraction (3); Access
constraints (3); Awareness (3); Communal (3); Competitive advantage (3); Embedded (3);
Emotions (3); Experientially established cognitive structures (3); Focal awareness (3);
Groups (3); Holism (3); Ideals (3); Importance of language (3); Information retrieval (3);
Insight (3); Learning by using (3); Meaning (3); Mind (3); Motor skills (3); Observation
(3); Oneself (3); Particular uses/particular situations (3); Performance (3); Practical
intelligence (3); Procedures (3); Resistance to revelation (3); Rules of thumb (3); Selective
comparison (3); Semantics (3); Sense perception (3); Transmission (3).
This list is not complete, and a significant number of codes remain that contain a groundedness of 1 and 2
instances in the literature (code total 1,310), which were considered too trivial for inclusion here. Note can
nevertheless be made from the codes above that tacit knowledge is typically individualistic (50) (beliefs (6);
oneself (3)), heavily organisationally based (46), it is directly related at least to skill (35) and it is context
specific (24). Furthermore it tends to be practically (9) rather than theoretically oriented in nature (practice (7);
learning by doing (6); learning by using (3); practical intelligence (3)), and given the nature of human
competition, it is acquired in conditions of low environmental support (7), which leads to it’s being used for
competitive advantage (3). One other very important issue, often not realised with tacit knowledge is the need
for understanding (9) (internalisation (8); others (4); awareness (3); meaning (3); oneself (3)) on the part of the
Sveiby (1997) for example, states that “knowledge cannot be described in words because it is mainly tacit … it
is also dynamic and static”, and furthermore, “information and knowledge should be seen as distinctly different.
Information is entropic (chaotic); knowledge is nonentropic. The receiver of the information – not the sender –
gives it meaning. Information as such is meaningless” (pp.38, 49). In other words, tacit knowledge is not
knowledge if the receiver does not understand it. This may help explain why tacit knowledge is so culturally
loaded (10) (environment (9); society (7); beliefs (6); values (6); ideals (3); importance of language (3)), and
why others of, for example, NESB1 people may not understand immediately what is taking place, even if they
do happen to understand the syntax and semantics of English. Over time the tacit knowledge component, in
addition to the already acquired syntax and semantics, aids in improved communication amongst people.
The content analysis has provided a means by which a balance or ‘reality check’ was able to be obtained, in
addition to formalising what could be said to comprise tacit knowledge. The definitions provide a view of what
many other authors have considered comprises tacit knowledge. The importance in particular of the
individualistic nature of tacit knowledge serves, if nothing more, to establish the contextual nature of this
knowledge and its reliance upon an individual’s Weltanschauung2. The disadvantage of attempting any such
form of content analysis is that authors’ definitions often tend to vary wildly and so finding any one ‘true’
definition can be difficult, if not impossible. Truth, like the contextual nature of tacit knowledge is finally a
subjective assessment.
1 Non-English Speaking Background.
2 Philosophy of life or world outlook.
AJIS Special Issue December 2002
We therefore state, that there exist articulable tacit knowledge properties, and inarticulable tacit knowledge
properties. Consider the articulable properties that we have selected to exemplify tacit knowledge:
{a, … ,n} aTK
where {a … n} is the set of the following articulable tacit knowledge constructs:
{Abstract high level plans, Abstraction, Access constraints, All purpose algorithms,
Analogies, Aphorisms, Artistic vision, Assumptions, Behaviour, Beliefs, Business
knowledge, Common sense, Competitive advantage, Complex multi-conditional rules,
Concepts, Constructs, Content, Contradiction, Convincing people, Crafts, Culture,
Customer's attitudes, Customs, Data, Decision making, Descriptors, Discussion, Everyday
situations, Examples can be articulated, Expectations, Externalisation, Face to face
transfer, Goal attainment, Grammatical rules, Gut feel, Habits, Heuristics, Hunches, Ideals,
Imitation, Impressions, Information, Information placed in meaningful context - eg.
Message, Innovation, Interaction, Job knowledge, Judgement, Justified true belief, Know
how, Knowledge base that enables us to face the everyday world, Knowledge of designs,
Logical rules, Maxims, Meaning, Methods, Negotiation, Observation, Perceptions,
Performance, Perspectives, Political correctness, Practical know how, Practice,
Prescriptive knowledge, Principles, Private knowledge, Procedural in nature, Procedures,
Process, Proverbs, Reproduction, Riding a bicycle, Ritual, Routine, Rule, Rules of thumb,
Schema, Script/Scripted, Semantics, Shop lore, Stories, Subjectivity, Swimming, Task
management, Tasks, Team coordination, Technique, Technology, Theories, Tradition,
Trial and error, Tricks, Understanding, Understanding of categories, Values, Way things
are done, Wisdom} aTK
In other words, the above subset forms tacit knowledge examples that are actually considered articulable,
necessarily from a subjective point of view. Note once again that we have taken these terms in qualitative
fashion from those researchers who have sought to define tacit knowledge to date.
Furthermore, from our qualitative ‘database’ of tacit knowledge, we are able to identify the following as
specifically constituting examples of tacit knowledge that cannot, or rather typically do not, lend themselves to
being articulated, what we shall refer to here as inarticulable tacit knowledge ITK:
{a’ … n’} iTK
where {a’ … n’} include the following inarticulable tacit constructs:
{Abilities, Accidental, Accomplishment, Action, Action oriented know how, Action slips,
Ad hoc, Adaptation, After the fact, Analysis, Application, Attention, Automatic,
Automatic knowledge, Awareness, Background knowledge, Between the lines, Body
language, Charisma, Concentration, Coordination, create and enjoy challenges, Diagnostic
closure, Emotions, Executive commitment, Exists, Experience, Expertise, Focal awareness,
Force/tension required, Gaining promotion, Gaining respect, Getting one's feet wet, Hands
on teaching, Have a feeling, Here and now, Hidden, High level goals, Holistic in nature,
How to seek out, Idiosyncratic, Immutable, Implicit, Implied, Indeterminacy, Inferences,
Inferred from actions/statements, Informating, Ingrained, Insight, Inspiration, Instinctive
reaction, Intangibility, Intimacy, Intuition, Involuntary, Know more than we can tell, Know
why, Knowing, Knowledge possessed by itself, Learning by doing, Learning the ropes, Lip
service, Management, Managing relationships, Managing subordinates, Manual dexterity,
Meaning requires tacit component, Mediation, Mental models, Meta-cognitive
understanding, Motivation, Motor skills, Networking, No idea, Noiseless, Non awareness,
Non focus on parts, Orientation, Out of the corner of the eye, Paradigms, Pattern
recognition, Personality, Physical control, Place, Possessed, Power, Practical intelligence,
Practice wisdom, Preciousness, Presuppositions, Principles, Product of process, Proximal
knowledge, Psychomotor skills, Recognition, Recognition of musical note, Reflection in
action, Reflection upon reflection, Relativity, Residual category, Rooted, Second hand,
Second nature, See as' rather than see, Selective comparison, Semiconscious, Sense
perception, Short term, Skill, Smell, Socialisation, Society, Spatial awareness, Spontaneity,
Sub-consciousness, Thinking in practice, Tool, Touch sensitivity, Unanalysed,
Unconscious, Vision, Vivid, Way things ought to be, Weltanschauung, Wholeness} iTK
These subsets are selected from Busch et al's (2001) formal content analysis. They are hereby presented as
identified constituents, which demonstrate the extent of tacit knowledge about any system.
AJIS Special Issue December 2002
It is apparent that there are many statements that describe what tacit knowledge is in terms of its constituents
and components. The first approach above exemplifies the research community's attempts to convey
information by making statements about tacit knowledge. We now take advantage of Weber's work that has
enabled a link to denotational semantics and thus the development of a formal framework for making statements
about tacit knowledge.
In what follows we use the term information structure to refer collectively to the structure in information
system models and constructs.
Weber’s (1997) work applying Bunge's (1979) ontology to information systems defines a basic set of constructs.
These Weber proposed as necessary and sufficient for us to construct and represent the concepts in the reality
we encounter. In short, these ontological constructs provide us a framework for representing the meaning of our
conceptual reality. This meaning, to emphasise, is based on and is relative to the Bunge ontology as interpreted
by Weber and Wand.
We now extend the meaning of a system, as it exists in reality, by adding definitions that make explicit the
underlying assumptions of our formalisation of knowledge. In essence, we add the dimension of the human
mind by which information and then knowledge is realised. Knowledge thus embraces intent, purpose, values
and beliefs formed within the human mind by experience and whatever other mechanisms there might be.
Knowledge therefore is more than the concepts in an immediate encountered reality. Furthermore, though we
have not explored it in this paper, the underlying philosophy of knowledge should extend to an epistemology so
as to embrace learning.
In terms of the semiotic ladder (Stamper, 1991), the extension goes beyond conventional semantics to embrace
pragmatics and belief systems, the higher semiotic levels, by including notions of commitment, intelligent
behaviour and wisdom, which are quintessentially of the human mind, and subjective.
Representation and Denotation
Representation is at the heart of Weber's work. Representability is central also to the denotational semantics of
programming languages. The link between representing the information systems structure of a conceptual reality
and denotational semantics proves to be important. Previous work by Dampney (1998) showed that Weber's
static and dynamic models3 could be formalised using category theory constructs that turn out to be used also in
programming language denotational semantics. Since then Jacob's (1999) monograph on categorical logic and
type theory, which focuses on fibration, a means proposed by Colomb, Dampney and Johnson (2001) for
composing information structures, provides substantiating evidence linking the composition of information
structures, (c.f. Weber's composition model) and computation structures.
Representability requires that an information system σ and changes in the information system [σ Æ σ] both be
representable in a Sets category. That both σ and [σ Æ σ] be representable in the same mathematical domain
requires a constraint on the changes as expressed by the operator “Æ”.
The constraint is that the mapping to the Sets category satisfies denotation as formalized by denotation
semantics (e.g. van Leeuwen, 1990). Thus a slightly stronger condition than representable, and than Weber's
formalization in sets and relations implies, is required. This constraint is satisfied if the category satisfies a
property called cartesian closure. We defer further discussion of this issue in this context as we change context
below to statements about knowledge systems, for which different operators apply, but which are still
formalized as a category satisfying cartesian closure and the requirements of denotational semantics.
This enables us to express statements about an information system in a formal theory and consequently provide
mathematically sound and valid definition.
An Exercise in Formalization
We are now in a position to develop a theory within which (our understanding of) knowledge can be formally
described and to take into account the dimension of the human mind.
We begin formally by declaring the existence of data (D), information (I), knowledge (K), tacit knowledge
(TK), and articulable tacit knowledge (aTK). The term aTK some may feel more comfortable labelling implicit
knowledge, and for completeness we also identify inarticulable tacit knowledge (iTK) as truly tacit and not able
to be passed on through person-to-person interaction.
This requires, from a formal denotational semantic perspective, several assumptions about how knowledge may
be represented in Sets so as to be subject to analysis. We say that there exist sets of whatever constitutes or
characterises these various elements of knowledge and that there is a partial order, ““, on the sets which
3 The representation model (static), the state-tracking model (dynamic), and the compositional model.
AJIS Special Issue December 2002
enables us to say that knowledge objects contain other knowledge objects. Mathematically, a partial order
defines a lattice (complete partial ordering (CPO)). Lattices have proved to be an effective means of
representation in formal concept analysis used by knowledge analysts, in computational theory (denotational
semantics) and in many other areas.
In a style suggested by denotational semantics, we begin by identifying the objects with which we may
formulate tacit knowledge in a domain:
D, K, I, TK, aTK, iTK
together with the two combinators appropriate to describing knowledge – “” (conjunction - also
called “meet”, “intersection”), and ““ (disjunction - also called “join”, “union”). The combinators
enable us to combine any two objects, say A and B, to form new objects AB and AB. These
objects are governed according to a partial ordering ““ which in our case could be called
“containment”. So for example we can say that AB C.
It would be useful if we have a means for expressing implication over sets. Overloading our
symbolism for the moment - suppose B represents some population that satisfies some condition B,
similarly for C, and that {B=>C] represents the population that satisfies the condition that if B is
satisfied then C is satisfied. Not necessarily all of C. We can express this as ([B=>C] B) C
It turns out that satisfying denotation requires that we introduce a new operator “=>“4, which we now
identify as implication, and which satisfies ([B=>C] B) C. This enables the domain to be
Cartesian closed, that is, denotable.
The denotation constraint is satisfied if for every partial order (AB) C there is a partial order
A [B=>C] and vice versa as illustrated by the diagram. [The arrow represents a partial order “”.].
This correspondence proves useful.
We can now propose definitions concerning the information structure of knowledge, which are derived from
assertions made by various experts. These definitions will at least be sound and valid in the formal sense.
Definition: The Tacit Components of Knowledge
There is little argument in stating that articulable and inarticulable tacit knowledge are contained within tacit
knowledge, thus:
aTK TK and iTK TK
Furthermore we say that (i) aTK and iTK form disjoint subsets of TK, and (ii) aTK and iTK do not necessarily
form complete subsets of TK. Tacit knowledge in its entirety, necessarily includes both inarticulable and
articulable knowledge, thus:
(aTK iTK) TK
Furthermore, from previous argument, we define tacit knowledge as comprising a subset of knowledge, thus:
Definition: Beyond Knowledge - Choice and Intelligent Behaviour
4 Often denoted by the symbol "". This operator plays the same role as "" used earlier.
AJIS Special Issue December 2002
We extend this representation of knowledge to include Intelligent Behaviour, Choice, Values, Commitment and
Wisdom within our formalisation.
As Tuomi (1999/2000) suggests:
… when the human mind uses this knowledge to choose among alternatives, behavior becomes
intelligent (p.105).
We can restate this assertion as Knowledge (K) combined with () Choice (Ch) is contained within ()
Intelligent Behaviour (IB) and represent it formally by
(K Ch) IB (1)
From the denotation constraint we have:
K [Ch =>IB] (2)
and, to check consistency, evaluate so that from (1) and (2)
(K Ch) ([Ch => IB] Ch) IB
We can now interpret the formal expression K [Ch => IB] as
“(Within our minds) knowledge (K) is contained within () the implication process [Ch
=> IB] of Choice (Ch) over alternatives to gain Intelligent Behaviour.”
Definition: Intelligent Behaviour with Values and Commitment Leads to Wisdom.
Tuomi (1999/2000) further asserts:
… when values [V] and commitment [C] guide intelligent behaviour [IB], behaviour may
be said to be based on wisdom [W] (p. 105).
Restated, this says that Intelligent Behaviour with Values and Commitment is contained within
Wisdom. Representing it formally and subscripting to designate the assertion:
IB V C W(Tuomi). or equivalently IB [V C => W(Tuomi).]
We see that {IB V C} ([V C => W(Tuomi).] V C) W(Tuomi).
In a deeper interpretation, Sternberg (2000) notes that wisdom relies on altruistic principles, as in:
… wisdom is defined as the application of tacit knowledge as mediated by values toward
the goal of achieving a common good through a balance among multiple interests – (a)
intrapersonal, (b) interpersonal, and (c) extrapersonal – in order to achieve a balance
among responses to: (a) environmental contexts, (b) shaping existing environmental
contexts, and (c) selecting new environmental contexts, over both (a) short – and (b) long
terms … [where] … common good refers to what is good in common for all, not just for
those with whom one identifies (pp.253, 254).
From this interpretation we formally represent wisdom thus:
V C IB Al TK W(Sternberg); equivalently
V [C IB Al TK => W(Sternberg);] where Al refers to Altruism
From {IB V C} W(Tuomi). above we have , W(Sternberg) W(Tuomi).
because V C IB Al TK V C IB }
This brings into question whether AlTK is superfluous, which places Sternberg's interpretation in question, or
whether AlTK is required, thus making Tuomi's assertion too broad. A resolution is that the notion of Wisdom
is different in the minds of the two authors. One would expect Wisdom relative to altruism (Al) and tacit
knowledge (TK) to be more focused.
Definition: Data, Information and Knowledge.
We address the concepts of data, information and knowledge, which have been covered at length by many
authors. Many of these authors emphasis the lack of interconnectedness inherent in data, the contextual nature
AJIS Special Issue December 2002
of information and the individual belief systems that heavily bias a person’s interpretation of knowledge. Zack
(1999) presents a useful definition that sums up succinctly these three types of knowledge. It is for this reason
that we use it here:
Data represent observations or facts out of context that are, therefore, not directly
meaningful. Information results from placing data within some meaningful context, often
in the form of a message. Knowledge is that which we come to believe and value on the
basis of the meaningfully organised accumulation of information (messages) through
experience, communication, or inference (Bobrow et al. in Zack, 1999). Knowledge can be
viewed both as a thing to be stored and manipulated and as a process of simultaneously
knowing and acting - that is, applying expertise (Blackler, 1995; Dretske, 1981; Lave,
1988 in Zack, 1999).
If we take the above definitions as a starting point, then we may say that information is derived from data. It is
the useful combination of data with context that provides us with information, the emphasis being placed on
useful. The FRISCO report’s (Falkenberg et al, 1998) definition of knowledge as “a relatively stable and
sufficiently consistent set of conceptions possessed by single human actors” (p.66), whereas “the term data
denotes any set of representations of knowledge, expressed in a language.” In other words, data are meaningful
symbolic constructs (expressed in a language), that can be qualified as “knowledge bearing” (p.66); whereas
information “is the knowledge increment brought about by a receiving action in a message transfer, i.e. it is the
difference between conceptions interpreted from a received message and the knowledge of the receiving action
…. An important aspect of information is how a receiver uses it” (p.68).
What appears in this instance to be unique about the term tacit knowledge is that if we take the definitions of
knowledge and information given above, then tacit knowledge is actually a combination of the two. It is a
prerequisite of tacit knowledge that it be understood by the receiver (information) or make sense to the receiver,
yet tacit knowledge comprises a set of conceptions or interpretations by human actors (knowledge) or meaning
as interpreted by the receiver.
We can thus formalise the concept of context ( Co) and data ( D) being contained within information (I) by: -
[CoD] I
Context we suggest is close to or equivalent in meaning to Weber's (1997) Environment.
Now, as defined above, articulable tacit knowledge and inarticulable tacit knowledge are contained within tacit
{aTK iTK} TK.
Presuming that (in)articulable sense and (in)articulable meaning are disjoint subsets of sense and meaning
respectively, we may express this concept in the following manner:
aSe, aMe, iSe, iMe
{aSe iSe} Se
{aMe iMe} Me
where Se = sense, Me = meaning, aSe = articulable sense, aMe = articulable meaning, iSe =
inarticulable Sense, iMe = inarticulable Meaning
Presuming that articulable sense and articulable meaning comprise articulable tacit knowledge and inarticulable
sense and inarticulable meaning also add to the total definition of tacit knowledge, a more complete definition
may be seen as the following:
(I Se Me) K
This may be interpreted as “sense and meaning need to be combined with information to have
knowledge” and as “ Information is contained in Knowledge”.
Furthermore {(aSe aMe), (iSe iMe), (iSe aMe), (aSe iMe)} K
and we now speculatively identify
(iSe iMe) iTK;
AJIS Special Issue December 2002
{(aSe iMe) (iSe aMe)} aTK; and
(aSe aMe) .
to suggest that the ability to articulate both the sense and meaning of information is required for explicit
The above definitions are a consistent denotable representation in a formal theory relating the various
constituents and associates of data, tacit knowledge, information, knowledge and wisdom. Perhaps our major
contribution has been to recognise that the elements of knowledge inter-relate within a lattice of containments,
suggesting therefore that the elements of knowledge are both inter-dependent and form themselves into
overlapping hierarchies.
Properties as Constituents or Characterizations of Tacit Knowledge
In the argument presented in the formalisation, we deferred definition of the elements, if any, of what denotes,
that is constitutes or characterizes, tacit knowledge. Properties are a candidate as they are elemental in both the
Chisholm (1996) and the Bunge ontologies. One may say that ‘shop talk’, ‘work experience’, ‘skills’ and so
forth constitute knowledge of properties of systems. The properties may be of the various types identified by
Weber (1997) from general, particular, intrinsic, mutual, emergent, and hereditary.
We denote tacit knowledge (TK) perceived within the human mind η of H5 as (a subset, more strictly a sub-
object lattice, i of) properties within of a system σ of 6. Thus:
Denote_TK: ( ( )) whereis the powerset symbol and ( ) forms a lattice]
Thus the tacit knowledge TK(σ,η) by a human mind η of a system σ is about properties pj singly or associated
by conjunction j pj) and disjunction (j pj). Thus TK(η,σ) maps to a sublattice i of properties. This means that
some properties contain more elemental properties, and this we argue is in keeping with the way we think and
reason about our reality.
Denotation introduces implication and the ability to express a richer set of propositions over the properties. The
properties will include constraints as needed to satisfy the ontology of the domain.
The properties must finally be attributed and the population of the system satisfying the attributed property
determined. How well properties are attributed is a separate issue and this will inherently cause a level of
uncertainty in transference of human knowledge. Such issues are beyond the scope of this paper.
The propositions about properties may now be regarded as statements about the system for knowledge analysis
We have examined a way to formalise tacit knowledge along the lines proposed by Weber (1997) for systems
theory in general. As a balance to formalisation we also present some results from a content analysis of the tacit
knowledge literature, which reveals themes that best interpret this special type of knowledge. Tacit knowledge,
at least in practice, encompasses a component that lends itself to eventual articulation.
The formalization has been applied to infer a number of conclusions from more basic conjectures derived from
assertions in the literature:
Without sense, meaning and context properties are just recorded data. With context data becomes
information. Adding sense and meaning with the human mind provides the means for understanding. By
unknown processes, the evidence from experience is that sense and meaning, evolves within our minds,
first inarticulable and tacit, then expanding to become articulable and explicit.
Knowledge is combined with other intelligence to define wisdom. Wisdom involves knowledge, but
within a human value system that enables intelligent behaviour and choice. The cultural context, within
which tacit knowledge is acquired, therefore bears consideration, as does the competitive nature of the
knowledge, which indicates why tacit knowledge is not so easily, or rather readily, transferred.
Whether or not these conclusions withstand closer scrutiny remains to be seen. But we now assert that the
formalization has at least provided a means for closer scrutiny.
5 η of H is a human mind η from amongst all human minds. Similarly for σ of Σ, where σ is a system
and Σ is all systems.
6 See previous footnote.
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Finally at the core of this paper is the conjecture that information space and computational space are governed
by the same essential structure. Knowledge adds the human dimension that may ultimately be unfathomable, at
least by us.
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... Deep-level attributes include individual psychological constructs such as values, attitudes, preferences, beliefs, worldviews, and assumptions [14]. These not so readily observable characteristics are forms of tacit knowledge which are resident within the mind and perceptions of individuals [17,18]. Gosselin et al. [16] refers to these characteristics collectively as motivational drivers in that they are the "why behind the how and what we do". ...
... How do you prefer to be communicated with? One of the most common mechanisms for making sense of your own as well as others deep-level characteristics are person-to-person interactions using stories, analogies, metaphors, and discussion [17] General resource on reflection ...
The development of relationships between individual team members is critical to effective collaboration. Whether you are a person who considers themselves a team member or a team facilitator, the development of quality relationships influences the extent to which the shared goals of the project are achieved by the team. It is important to intentionally facilitate the emergence and growth of relationships using a variety of processes whereby team members can learn more about each other’s characteristics—behavioral styles, approaches to research, motivational drivers, world views, values, talents, and interests. The extent to which these characteristics are explored will be dependent on the context/complexity of the project and the extent to which team members have worked with each other in the past (i.e., team history). This chapter focuses on the importance of accounting for the compositional characteristics of team members—e.g., behavior patterns, motivational drivers, personality, dispositions, demographics, cultural heritage, etc.—as an inherent part of the collaborative process. Learning to respect, manage, and navigate the differences in these characteristics in your specific context is important to team development and its long-term effectiveness.KeywordsCollaborationDispositionsSurface-level characteristicsDeep-level characteristicsCognitive and disciplinary characteristicsPersonal characteristics
... The approach used is a tacit knowledge case study to describe the results of research and find a comprehensive picture of a situation. The knowledge contained in a person's brain or mind corresponds to the person's understanding and experience (Dampney et al., 2002). Usually, this knowledge is unstructured, difficult to define, and told in formal language to others, and its content includes personal understanding. ...
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"Kabinet Komando I" is the designation of the "Senat Taruna" organization at Jakarta Technical University of Fisheries for the 2018-2019 period. This university, operating under the Ministry of Marine Affairs and Fisheries in Indonesia, offers both applied undergraduate and graduate programs. This study aimed to analyze the strategies implemented by the "Kabinet Komando I" to achieve program success. Utilizing a qualitative case study approach, we explored tacit knowledge from its President, who also held the position of Commander of Battalion 07 Barracuda. The research identified five key strategies that contributed to the cabinet's effective performance: formation of a cohesive team, high and adaptable motivation, effective communication, collaborative efforts, and sound financial management. Additionally, insights from the President emphasized the qualities of visionary, qualified, and intellectual students, echoing the motto that guided their journey: "One Command and One Perception." This study offers valuable insights into the strategic decision-making processes within the organization following the leadership of "Senat Taruna Kabinet Komando I."
... Knowledge claims enable us to learn from each other through an exchange of stated ideas that can be examined and discussed (Dombrowski, Rotemberg and Bick 2013). Explicit knowledge (also referred to as articulate knowledge) is typically acquired through formal education, writings, books, rule sets and legal code (Dampney, Busch and Richards 2007). Whereas tacit knowledge cannot be articulated and is a form of ineffable knowledge which is not expressed through language but through a lived experience (Zappavigna 2014). ...
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Case study research on nurses' perspectives of best interests for patients with advanced dementia
... It is typically shared through discussion, stories, analogies and person-to-person interaction; therefore, it is difficult to capture or represent in explicit form. Because individuals continually add personal knowledge, which changes behaviour and perceptions, tacit knowledge is by definition uncapped" [2]. ...
The context of the paper is, on the one hand, a growing trend of internationalization in higher education, especially focusing on creativity and innovation. Talent development programs play important role in this process. On the other hand, 50% of all employees will need reskilling by 2025, as adoption of technology increases, according to the World Economic Forum’s Future of Jobs Report. Analytical thinking and innovation, creativity, originality, and initiative are on the Top 10 Skills of 2025. The purpose of the paper is to introduce some results of the implementation of a 3-years talent development program at the Budapest Metropolitan University focusing on creative and startup thinking. This program has transdisciplinary approach and modular structure from different domains, such as tourism, business, communication, and arts. This paper introduces some important phenomena and impacts of the program implementation, for instance formulating professional learning community of students and teachers, creating and evaluating business plans, and changing creative and startup thinking skill set at individual, team, and organizational levels.KeywordsTalent development programCreative and startup thinkingTransdisciplinary approach
... Tacit knowledge embedded in Hale design serves to revive and restore the traditional building practices and instil pride that comes from living traditional Hawaiian values (Edwards 2019;Apple, 1971;Buck, 1957). However, such tacit knowledge has been an elusive subject due to its difficulty to be articulated, recorded, and communicated (Dampney et al, 2002). Furthermore, the oral tradition for transferring the tacit knowledge in Hawaii makes more difficult to convert it to explicit knowledge (Nonaka and Takeuchi, 1995) for the future generation of Hawaii. ...
... Compared to traditional interview-based research methods participatory visual elicitation methods offer a number of benefits for researchers and participants, first they draw out conscious and unconscious thoughts, such as a person's unique tacit knowledge which may be difficult to capture or represent in explicit form (Dampney et al., 1969;Meo, 2010;Roger & Blomgren, 2019). Socondly the process balances power asymmetries as participants express themes, thoughts and perspectives in their own way (Meo, 2010;Orr et al., 2020). ...
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Visual stimuli are increasingly used in formal communications and as a qualitative research method, reported to generate a ‘different’ and ‘richer’ response than text or language alone. However, the parameters by which this occurs are underexplored. In our research we use images and text as interview stimuli in a project exploring personal wellbeing to compare differences in communication process and outcomes. A visual tool expressing concepts of human needs and aspirations and comparable text was presented on cards. Forty-five people participated in a sorting process and interview. Participants were presented with the text cards or images first in blocked order and asked to select cards relating to their wellbeing. The process was repeated with the alternate mode. A subsection of six participants were presented with text-image combination cards. Thematic analysis was used to find common themes in participant responses and their experiences of the two modalities. Image and text cards both facilitated communication and rapport but elicited different types of responses. Images more commonly provoked emotive responses, tacit knowledge and greater personal involvement leading to cognitive elaboration and richer narrative. We found that participants more easily selected visual themes and considered images more engaging and open to interpretation than text, which were considered fixed in meaning. When visuals were presented with text, the text dominated the meaning. This paper presents a novel form of researcher generated visual elicitation stimuli, to be called ‘exploratory visuals’; depicting abstract concepts and narrative scenes to aid communication and understanding of complex information. It proposes an analytical framework combining social semiotic, contextual and cognitive perspectives to understand perceptual differences between words and images and the range of responses elicited.
... Finally, the authors recommend that policy-makers develop an interactive learning program for lessons learned through the independent system integrator. This suggestion is recommended for coping with ongoing technological transformations in the sector and eliciting articulable tacit knowledge, see for example (Dampney, Busch, & Richards, 2002) for subsets of tacit knowledge that are articulable. To obtain feedback and a holistic overview of the sector needs, the authors also suggest a feedback loop among railway organisations and policy-makers, as shown in Fig. 9. ...
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To avoid project delays and cost overruns, it is crucial that organisations' knowledge base on project management is expanded and that experiences in large-scale projects are more widely shared through effective management of lessons learned. However, the management of lessons learned from large scale projects remains a scientific and practical challenge. In addition to their multi-stakeholder setting, the natural loss of experienced knowledge through retirement and the presence of lessons in both tacit and explicit form make their management quite complex. This paper investigates the shortcomings in the current lessons learned management process and contributes to the literature by presenting a formal process for extracting and managing lessons learned from large scale projects. It does this by conducting a case study on the deployment of the European Traffic Management System (ERTMS) in the Netherlands. A nuanced five-step methodological approach is presented, whereby twenty-four structured interviews are conducted with key stakeholders from the Dutch railway sector. The interview data is analysed using the content analysis method and the results are then validated by three independent assessors. To ensure rigour and quality of gathered results, the interrater reliability is determined by calculating Gwet's AC1 coefficients. The paper provides an overview of the key lessons learned from the deployment of ERTMS in the Netherlands and sets out fourteen principles for effective systems management of ERTMS. Finally, the paper also offers six recommendations for policymakers related to their role, such as appointing an independent system integrator, and five recommendations for railways organisations on organ-isational learning and system integration. The results can be used both to facilitate communication for better decision-making and to develop strategies for effective management of lessons learned.
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In an epoch of knowledge explosion, knowledge management is as important as the ability to create new knowledge. As a best practice of knowledge sharing, educational institutions must understand where and in what forms knowledge exists. Plugging into the exact knowledge sources and transform them into a fathomable format is important in knowledge management. Human being knows more than what he can tell.It is the core part of knowledge translation that looks into the Tacit dimensions of knowledge. Tacit knowledge is the collection of intangible abilities and insights gained through experience that are hard to articulate. Tacit and indigenous knowledge sources need to be explicitly expressed to translate knowledge effectively to the learning community. Knowledge, if properly managed and transferred to the needy, can create a leap in educational transformation. This paper investigates the role, dimensions, and prospects of Tacit and indigenous knowledge in educational transformation, while externalized and expressed explicitly.
This paper aims to explore the possibilities of implementing the tacit knowledge transfer process and practices in the metaverse and remote workplaces. It is because of the tacit knowledge transfer) is more crucial for organizational knowledge management and maintaining an organisation's sustainable development. The research team believes that the use of the metaverse in remote workplaces is expected to revolutionize established organizational learning, knowledge management and tacit knowledge transfer models. This research attempts to understand the employees' experiences overlapped by three perspectives, they are (1) the acceptability of both senior and junior staff in using the metaverse for training; (2) the experiences of the tacit knowledge transfer over the metaverse; and (3) the role of immersion and interactivity during the knowledge transfer process. The significance of this research is the theoretical investigation of the tacit knowledge transfer model in the metaverse. This research is explorative in nature; therefore, this research is not going to generalize or prove the effectiveness of using the metaverse in the tacit knowledge transfer process, but rather explore the factors and deepen our understanding of these fundamental attributes. The findings of this research 2 suggest a modification of the classical model SECI Matrix by adding a new component of “integration” in the transfer process.
In recent times, academic libraries are expected to reposition themselves to maintain their value by introducing innovative services to meet the constant changes in user information needs. This chapter explores the concept of human library and how it may be exploited in the transfer of tacit knowledge in academic institutions and their libraries. Some benefits identified are the interactive nature of human libraries which brings back the natural mode of human communication, the opportunity to promote individual growth, and the collection of living books which serve as an educational resource. The study concludes that although adopting the human library concept has some anticipated challenges, it can help facilitate the transfer of tacit knowledge in academic institutions. Hence, there is a need for academic libraries to adopt this concept as part of their innovative and creative initiatives.
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In knowledge management literature it is often pointed out that it is important to distinguish between data, information and knowledge. The generally accepted view sees data as simple facts that become information as data is combined into meaningful structures, which subsequently become knowledge as meaningful information is put into a context and when it can be used to make predictions. This view sees data as a prerequisite for information, and information as a prerequisite for knowledge. In this paper, I will explore the conceptual hierarchy of data, information and knowledge, showing that data emerges only after we have information, and that information emerges only after we already have knowledge. The reversed hierarchy of knowledge is shown to lead to a different approach in developing information systems that support knowledge management and organizational memory. It is also argued that this difference may have major implications for organizational flexibility and renewal.
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The absence of strict criteria on which to base the acceptance of science shows that the acceptance of scientific knowledge is based on nonstrict criteria. This nonstrict logic rests to an extent on informal elements derived from psychological observations. In these informal elements of science, one can discern the indeterminacy of scientific knowledge, including its indeterminate content and that the coherence that lends validity to a discovery can be only vaguely defined and the data on which a discovery rests are not fully definable. However, forms of tacit knowing, tacit inference and indwelling (interiorization) permit one to see beyond established facts. The lack of clear knowledge of tacit knowing has misguided both philosophy and the knowledge of other minds, universal terms, principles of explanation, and empirical generalization. "Logic" can be seen as the rules for reaching valid conclusions from premises assumed to be true. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
In this Introduction we shall sketch a profile of our field of inquiry. This is necessary because semantics is too often mistaken for lexicography and therefore dismissed as trivial, while at other times it is disparaged for being concerned with reputedly shady characters such as meaning and allegedly defunct ones like truth. Moreover our special concern, the semantics of science, is a newcomer - at least as a systematic body - and therefore in need of an introduction. l. GOAL Semantics is the field of inquiry centrally concerned with meaning and truth. It can be empirical or nonempirical. When brought to bear on concrete objects, such as a community of speakers, semantics seeks to answer problems concerning certain linguistic facts - such as disclosing the interpretation code inherent in the language or explaning the speakers' ability or inability to utter and understand new sentences ofthe language. This kind of semantics will then be both theoretical and experimental: it will be a branch of what used to be called 'behavioral science'.
From the Publisher:Theoretical computer science provides the foundations for understanding and exploiting the concepts and mechanisms in computing and information processing. This handbook will provide professionals and students with a comprehensive overview of the main results and developments in this rapidly evolving field. It consists of thirty-seven chapters in two volumes, all addressing core areas of theoretical computer science as it is practiced today. The material is written by leading American and European researchers, and each volume may be used independently. Volume A covers models of computation, complexity theory, data structures, and efficient computation in many recognized subdisciplines of theoretical computer science. Volume B presents a choice of material on the theory of automata and rewriting systems, the foundations of modem programming languages, logics for program specification and verification, and several chapters on the theoretic modeling of advanced information processing. The organization of each volume reflects the development of theoretical computer science from its classical roots to the modem theoretical approaches in parallel and distributed computing. Extensive bibliographies, a subject index, and list of contributors are included in each volume.
This article describes an important, yet largely neglected, kind of giftedness, namely, wisdom. First, the article discusses how giftedness is identified currently. Second, the article presents an alternative view based on a balance theory of wisdom. The theory is described, and it is shown why the theory offers a useful way of conceptualizinig wisdom. Finally, the article concludes that, given the state of the world, we should have a strong incentive to identify and develop wisdom as a form of giftedness.