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Abstract

Knowledge is an abstract concept without any reference to the tangible world. It is a very powerful concept, yet it has no clear definition so far. From the Greek philosophers up to present experts in knowledge management, people tried to define knowledge but the results are still very fuzzy. This chapter has the intention of showing the most significant aspects of the dispute over the definition of knowledge, and the main conceptual barriers in that endeavor. In the first part of the chapter we discuss about the knowledge nature and the attempts made in epistemology to define knowledge. The well-known definition that knowledge is justified true belief is shown to have the limitations given by the justification condition and the truth nature. In the second part, we consider the metaphorical approach to knowledge explanation and we present the main metaphors used for knowledge in the managerial literature: knowledge as objects, knowledge nuggets, knowledge as an iceberg, and knowledge as stocks and flows. In the last part, we introduce a new paradigm of metaphorical thinking based on the knowledge energy. This metaphor opens new opportunities for understanding knowledge as a multi-field paradigm composed of the rational, emotional, and spiritual knowledge fields.
Chapter 1
The Elusive Definition of Knowledge
Ettore Bolisani and Constantin Bratianu
To cite this document:
Bolisani, E., and Bratianu, C. (2018). The elusive definition of knowledge.
In Bolisani, E. and Bratianu, C. ( 2018). Emergent knowledge strategies:
Strategic thinking in knowledge management (pp. 1-22). Cham: Springer
International Publishing. DOI: 10.1007/978-3-319-60656_1
Abstract
Knowledge is an abstract concept without any reference to the tangible
world. It is a very powerful concept, yet it has no clear definition so far.
From the Greek philosophers up to present experts in knowledge man-
agement, people tried to define knowledge but the results are still very
fuzzy. This chapter has the intention of showing the most significant as-
pects of the dispute over the definition of knowledge, and the main con-
ceptual barriers in that endeavor. In the first part of the chapter we dis-
cuss about the knowledge nature and the attempts made in epistemology
to define knowledge. The well-known definition that knowledge is justi-
fied true beliefis shown to have the limitations given by the justification
condition and the truth nature. In the second part, we consider the meta-
phorical approach to knowledge explanation and we present the main
metaphors used for knowledge in the managerial literature: knowledge as
objects, knowledge nuggets, knowledge as an iceberg, and knowledge as
stocks and flows. In the last part, we introduce a new paradigm of meta-
phorical thinking based on the knowledge energy. This metaphor opens
new opportunities for understanding knowledge as a multi-field paradigm
composed of the rational, emotional, and spiritual knowledge fields.
2
1.1 Searching for Knowledge Definition
1.1.1 Knowledge Nature
Knowing is one of the most specific human processes and knowledge is its
result. That means that knowing and knowledge have been subjects of
human inquiry from the ancient times. Philosophers, starting with Plato
and Aristotle developed Epistemology as a theory of knowledge, trying to
answer to the fundamental question: What is knowledge?There were
many answers and many arguments used in supporting them, but none of
those theories has been accepted so far as being fully satisfactory. Defin-
ing knowledge and explaining its nature proved to be elusive and without
a convincing and universally accepted result (Neta and Pritchard 2009;
Russell 1972). Most of the theories have been integrated into two major
perspectives: rationalism and empiricism. Simplifying, we may say that
both theories accept that knowledge is a justified true belief, but they de-
part in showing the ways through which one can find the truth or justify-
ing the true belief.
Rationalism, for which Plato is a pioneering philosopher, argues
thatknowledge is a result of a reasoning process and that our sensory ex-
perience plays no role. Knowledge can be obtained only from rational
reasoning grounded in axioms, like in mathematics, and it should be dis-
tinguished from opinion which is a product of our senses. In his theory
about ideas, Plato makes a difference between a “cat” which represents a
particular object in the real world and the concept of “cat” coming from
the eternal world of cattyness. While the real “cat” is born and sometimes
will die, the concept of “cat” remains in the eternal world of ideas.
Knowledge belongs to that eternal world. Explaining the Plato’s frame-
work of knowledge, Bertrand Russell (1972, p.152) shows that “We per-
ceive hard and soft through touch, but it is the mind that judges that they
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exist and that they are contraries. Only the mind can reach existence, and
we cannot reach truth if we do not reach existence”. We cannot know the
real world through senses alone since they can mislead us. In conclusion,
“knowledge consists in reflection, not in impressions, and perception is
not knowledge” (Russel, 1972; p.153). We may agree with Plato when
discussing about mathematics and mathematical propositions. To under-
stand that y = a + bx reflects a correlation between two variables we
don’t need any sensory perception. We need only a reasoning process
with abstract symbols. But that is just a particular domain of science and
cannot be generalized over the whole human existence.
René Descartes made rationalism the basis of modern philosophy
by integrating in his conceptual universe many new scientific discoveries.
He founded the famous method of doubting everything and searching for
certainty: “I can do nothing else, until I have learned for certain that there
is nothing in the world that is certain” (Descartes, 1997; p. 139). By ana-
lyzing comparatively his thoughts coming from the mind and the infor-
mation coming from the sensory system, Descartes reached the conclu-
sion that thought is the only attribute that belongs to him that cannot be
detached of him: “What of thinking? I find here that thought is an attrib-
ute that belongs to me; it alone cannot be separated from me. I am, I ex-
ist, that is certain” (Descartes, 1997; p. 141). That means that the only
test of our existence is the fact that we think and through thinking we ac-
quire knowledge. In his famous formulation Cogito, ergo sum!”, mind
and body are like two different worlds, and while bodily sensations fail
the reliability test, thinking proves to be the unique characteristic that is
reliable and certain. Finally, he remarks: “I am, however, a real thing and
really exist; but what thing? I have answered: a thing which thinks” (Des-
cartes, 1997; p. 142). This dualism of mind and body had a great impact
on science, philosophy and education in Europe, and later on in America.
Even today, many authors consider knowledge to be rational and based
on solely mental processes.
Empiricism emerged as an opposable perspective to rationalism.
Aristotle, a former student of Plato, considered that ideas and forms can-
not be separated from physical objects and sensory information.
4
Knowledge is not created a priori and is not innate in a deterministic
form. It is created through our sensory interface with the real world, and
it is processed finally by our mind. John Locke continued that approach
emphasizing that objects do exist in the outer world and that our sensory
perception is the most important source of our knowledge. Many con-
temporary philosophers tried to bridge the gap between rationalism and
empiricism by generating conceptual frameworks based on different syn-
theses between them.
In sharp contrast with the Cartesian dualism of mind and body,
the Japanese intellectual tradition based on Buddhism and Confucianism
created an integrated perspective of mind and body with three overarch-
ing premises (Nonaka and Takeuchi, 1995; p. 27): “(1) oneness of humani-
ty and nature; (2) oneness of body and mind; and (3) oneness of self and
other. These traits have formed the foundation of the Japanese view to-
ward knowledge as well as the Japanese approach toward management
practices”. That means that knowledge is rooted in the sensory system
and only in its final processing stage is open to abstract considerations.
Their relation with the real world is through their senses and they don’t
need to make appeal to any eternal or metaphysical world in order to un-
derstand the nature of knowledge. Mind and body are not two distinct
realities but an integrated one which creates the whole personality of
people. “For the Japanese, knowledge means wisdom that is acquired
from the perspective of the entire personality. This orientation has
proved a basis for valuing personal and physical experience over indirect,
intellectual abstraction” (Nonakaand Takeuchi, 1995; p. 29). This inte-
grated view can be seen in the samurai education, where internal medita-
tion was used together with physical training, and in the knowledge man-
agement practices developed within Japanese companies where the focus
is on tacit knowledge which reflects the best people’s direct experience. It
is interesting to see how Miyamoto Musashi, the legendary Japanese
martial artist, emphasizes in his famous Book of five rings the importance
of learning with the whole body the correct motion during a fight (Kauf-
man, 1994; p. 31): “Proper movement of the body depends entirely on
the manner in which you carry yourself. The feet carry the body and the
5
body directs the feet. Tread firmly with the heel touching the ground first
and then roll forward to the ball of your foot. Practice this until you ap-
pear to move without motion”.
1.1.2 Knowledge Definition
As mentioned before, a frequently adopted definition of knowledge is
that of “justified true belief” (Nonaka and Takeuchi, 1995; p.87). That def-
inition incorporates three basic conditions, fact for which some authors
call it the tripartite account of knowledge. These conditions are the fol-
lowing (Neta and Pritchard, 2009).
The truth condition. It requires that if one knows a proposition then
that proposition must be true. If the proposition is not true, then that
person does not know what he claims to know. The truth condition
makes the difference between opinion and knowledge.
The belief condition. That condition demands that if one knows a prop-
osition then he believes that proposition.
The justification condition. That condition requires a practical way of
justifying that the belief one has is true.
Putting together these conditions for knowing, one may conclude
that “the necessary and sufficient conditions for knowing that something
is the case are first that what one is said to know be true, secondly that
one be sure of it, and thirdly that one should have the right to be sure”
(Ayer, 2009; p. 13). The right to be sure can be earned in different ways
which are culturally and contextual dependent. These conditions are usu-
ally synthesized in a logical format. Considering S to be the subject or the
knower, P to be the proposition the subject is supposed to know, one may
write (Gettier, 2009; p. 14):S knows that P if:
P is true,
S believes that P, and
6
S is justified in believing that P.
However, Gettier (2009) constructed some counter-examples to
demonstrate that this formulation does not constitute a sufficient condi-
tion for the subject S to know that P since justification might not be relia-
ble. A person may be completely justified in believing something (i.e. P)
which can be false. In literature, this case is known as the “Gettier prob-
lem” with respect to justification. Lehrer (2009) introduces a fourth condi-
tion to solve that problem, but it is too abstract to discuss it here. It is
much more appealing to discuss how Nonaka and Takeuchi (1995) con-
sider the justification problem in practice, which means in a company.
In their famous theory of organizational knowledge creation,
Ikujiro Nonaka and Hirotaka Takeuchi (1995) adopted, for knowledge, the
classical definition formulated by Plato that “knowledge is justified true
belief”. However, there is a significant difference in interpreting that defi-
nition. While the Western epistemology focuses on truthfulness as being
the main characteristic of knowledge, Nonaka and Takeuchi (1995, p. 58)
focus on justified belief arguing that: “While traditional epistemology em-
phasizes absolute, static, and nonhuman nature of knowledge, typically
expressed in propositions and formal logic, we consider knowledge as a
dynamic human process of justifying personal belief toward the truth”. In
other words, the authors change the philosophical discussion into a man-
agerial practice and consider that the best way of justification is against
the social context where new knowledge is created and shared, which
means the organizational context. However, by doing this switch the au-
thors show that, in practice,the emergence of new knowledge should be
evaluated with a usefulness metric and not with a logical one: “Justifica-
tion involves the process of determining if the newly created concepts are
truly worthwhile for organization and society” (Nonaka and Takeuchi,
1995; p. 86). They go further giving as practical justification criteria like
cost, profit margin, and degree to which a product can contribute to the
company’s economic performance. However, by means of this switch,
they changed the very nature of justification from a logical construct to an
economic one, implemented by managers. Top managers would ask for a
concordance with the strategic vision of the company, while the middle
7
managers would be looking for some practical requirements. In conclu-
sion, the approach of Nonaka and Takeuchi clearly changed the nature of
the problem and offered solutions for the practical organizational context
instead of solving the original truthfulness problem formulated by Plato
and refined by the Western epistemology. It is like Alexander the Great
who not being able to unfold the famous Gordian knot cut it with his
sword and changed the history of the world.
We see that truth and its justification is mostly a matter of inter-
pretation, and although the epistemological approach looks like a precise
and logical formulation the final definition of knowledge may be just an il-
lusion. The truth is far away and can be distorted by the justification at-
tempt due to misunderstanding of the organizational context. Metaphori-
cally, we may think of the Fata Morgana phenomenon. Fata Morgana is a
mirage that appears on land or at sea, in deserts or in polar regions. It is
an optical phenomenon resulting from the passage of the light rays
through layers of air of different temperatures. In essence, it manifests as
inverted floating images right above the horizon. Metaphorically, defining
knowledge may result in such a mirage since considering the framework
of epistemology we may already have different layers of relative truths.
The definition of knowledge remains a problem, at least in the
managerial sense, since knowledge, becoming a strategic organizational
resource, needs to be defined as an operational concept adequate for a
business environment and not as an abstract one for a transcendental
world of ideas. Knowledge definition is elusive since premises for initial
conditions have been formulated on pure rationalistic grounds and a Car-
tesian perspective on human nature. In the following sections of this
chapter we will change the conceptual paradigm of Greek philosophers
with the new paradigm of cognitive sciences and will continue our jour-
ney to finding a better definition for knowledge.
8
1.1.3 Three Kinds of Knowledge
Adopting an integrated view on the nature of knowledge, some authors
(Dombrowski et al. 2013) explain that there are three kinds of knowledge:
a) experiential knowledge; b) skills; and c) knowledge claims. They are in-
terconnected, but have some specific features of their own.
Experiential knowledge is what we get from the direct connection
with the environment, through our sensory system, and then it is pro-
cessed by the brain. For instance, if we want to know what snow is then
we must go where there is snow and touch it, smell it, taste it and play
with it. We cannot get that knowledge only from books or seeing some
movies with people enjoying winter sports in beautiful mountain areas.
People living in geographical zones where there is never snow have real
difficulties knowing what snow is. They lack the experiential knowledge
about snow. Experiential knowledge is personal since it can be acquired
only through direct interface of our sensory system and then processed
by our brain. It is essentially based on perception and reflection. Several
people having together the same experience may acquire different expe-
riential knowledge since reflecting upon a living experience means actual-
ly integrating it in some previous similar experiences and knowledge
structures, if they do exist. “Things are not always as they appear to be
and our own perspectives influence our interpretations. Still, watching
out for errors in thinking can improve tremendously the quality of our re-
flections on our experiences”(Dombrowski et al., 2013; p. 38). As we will
show later, experiential knowledge can be seen as created by a powerful
interaction between emotional, rational and spiritual knowledge since it
is a result of the whole body and mind active participation (Bratianu
2015).
Skills means knowledge about how to do something (know-how).
It is based on experiential knowledge but it is a well-structured and action
oriented knowledge we get by performing repeatedly a certain task and
learning by doing it. This is the way of learning swimming, biking, skiing,
playing piano or doing many other similar activities. It is like learning un-
9
consciously to perform a certain procedure or to follow a given algorithm.
We don’t learn swimming by reading in a book about fluid mechanics and
objects floating. We have to learn by doing it with the whole body and re-
flecting upon it to improve coordination between breathing and moving
our arms. Know-how knowledge is often called procedural knowledge
since it is about performing a task in concordance with a given procedure
or algorithm. We discussed about some skills associated to physical activi-
ties but they can be developed for any kind of task or activities, including
thinking processes. For instance, thinking skills are extremely important
for knowledge workers and decision makers. One of the most important
skill in designing strategies is intuition. According to Klein (2003, p. 36),
“The key to using intuition effectively is experience more specifically,
meaningful experience that allows us to recognize patterns and build
mental models. Thus, the way to improve your intuitive skills is to
strengthen your experience base. The most meaningful type of experi-
ence, naturally, is real-life experience”.
Knowledge claims are what we know, or we think we know. We
don’t know how much we know since knowledge means both explicit
knowledge and tacit knowledge, which means experience existing in our
unconscious zone and manifesting especially as intuition. Explicit
knowledge is something we learn in schools and reading books, or just lis-
tening to some professors or conference speakers. Knowledge claim is
what we frame in an explicit way by using a natural or symbolic language.
Thus, language is an essential component of the transforming our emo-
tional and spiritual experience into rational or explicit knowledge. With
explicit knowledge we are entering the zone of exchange between per-
sonal and shared knowledge. “Because ideas are stated in language, they
can be examined and discussed, questioned, evaluated, refuted, or pub-
lished and passed on. Knowledge claims enable us to learn from each
other and built our shared knowledge” (Dombrowski et al., 2013; p. 44).
10
1.2 Knowledge Metaphors
1.2.1 MetaphoricalThinking
Cognitive scientists discovered that thinking is a conceptual process which
is primarily metaphoric. That means that metaphors represent much
more than just linguistic expressions. They are involved in our thinking
process, helping us to understand new concepts and ideas. Steven Pinker,
a famous cognitive scientist and professor in the Department of Psychol-
ogy at Harvard University, explains that: “Conceptual metaphors point to
an obvious way in which people could learn to reason about new, ab-
stract concepts. They would notice, or have pointed out to them, a paral-
lel between a physical realm they already understand and a conceptual
realm they don’t yet understand” (Pinker, 2008; p. 241).
Fundamentally, metaphors are embodied in our experience and
through a progressive abstraction process they lead to new meanings for
less known objects or concepts. As underlined by Lakoff and Johnson
(1999) in their captivating book Philosophy in the flesh. The embodied
mind and its challenges to western thought, any complex metaphor can
be decomposed into primary metaphors, and “each primary metaphor is
embodied in three ways: (1) It is embodied through bodily experience in
the world, which pairs sensorimotor experience with subjective experi-
ence. (2) The source-domain logic arises from the inferential structure of
the sensorimotor system. An (3) it is instantiated neutrally in the synaptic
weights associated with neutral connections” (Lakoff and Johnson, 1999;
p.73).
Metaphors are similar with analogies which create comparisons
between a known object or concept and a less known one. They allow us
to map one experience in terms of another experience, making it possible
to understand complex and new situations in terms of what we already
know. A metaphor is composed of two different semantic domains: a) a
source domain where we describe the known object or concept with its
11
structural and functional attributes, and b) a target domain where we
place the less known object or concept. Metaphorical thinking means to
analyze the attributes and relationships from the source domain and to
compare them with the situation from the target domain trying to identi-
fy which of these elements can be transferred from the source domain in-
to the target domain. Theoretically, we perform a structural mapping of
the known attributes and relationships from the source domain onto the
target domain (see Figure 1.1).
Figure 1.1 The structure of a conceptual metaphor
As a result of this process, the less known object or concept re-
ceives new semantic attributes which lead to its better understanding. As
Lackoff (1990) suggested, metaphors can create meaning and enlarge the
semantic horizon of the less known object or concept. That means that in
a metaphorical process a conceptual systemis projected from one domain
to another, which is usually more abstract. It is a progressive abstraction
effort, which will be clearly demonstrated in the case of knowledge met-
aphors (Gentner et al. 2001). However, not all structural and functional
attributes from the known semantic domain can be transferred into the
less known semantic domain which means that we discuss about a selec-
tive mapping based on some sound hypotheses and principles. For in-
stance, in the well-known metaphor Time is money, the source domain
contains the semantic field of the concept money, and the target domain
contains the semantic field of the concept time. In this metaphor, money
represents a tangible object with some physical or structural attributes
Source
Domain
Target
Domain
Mapping
12
and some functional or intangible ones. Time represents an intangible ob-
ject only with intangible structural and functional attributes. Thus, the
metaphor cannot map the physical attributes of money onto the target
domain, but it can map the functional intangible attributes like spending
and saving. For instance: I saved one hour by driving the car on a different
route.
The process of structural mapping from the source domain onto
the target domain is unidirectional and asymmetric. It is unidirectional
since mapping is done only in one way according to our purpose to en-
large the semantic field of the less known concept. It is asymmetric since
the target domain has a deficit of semantic attributes by comparison with
the source domain. By means of structural mapping, the degree of asym-
metry is decreased and the target domain is enriched with new semantic
attributes. We will illustrate this phenomenon in the following sections
with some significant knowledge metaphors. Knowledge is an abstract
concept with no physical counterpart. Defining knowledge from pure the-
oretical point of view proved to be difficult and fuzzy, especially when in-
terpreting the justification condition. Metaphorical thinking opens a new
way of understanding and defining knowledge by placing it in the target
domain and searching for meaningful tangible or intangibles entities
placed in the source domains. But that means that there is an endless se-
ries of objects and concepts which can be used in the source domain, and
that knowledge definition depends on the metaphor used for its explana-
tion. As Andriessen and Boom show, “Knowledge is not a concept that has
a clearly delineated structure. Whatever structure it has it gets through
metaphor. Different people from different cultures use different meta-
phors to conceptualize knowledge. They may be using the same word;
however, this word can refer to totally different understandings of the
concept of knowledge” (Andriessen and Boom, 2007; p. 3). That is a fun-
damental idea in defining knowledge and using that definition for re-
search purposes. It would be a mistake to take for granted a knowledge
definition without understanding the supporting metaphor and its seman-
tic limitations. Unfortunately, many researchers in knowledge manage-
ment use knowledge definitions formulated by famous authors without
13
checking for their metaphorical framework and their semantic limits. For
instance, one of the most frequently cited working definition of
knowledge has been formulated by Thomas Davenport and Laurence
Prusak (2000, p. 5): “Knowledge is a fluid mix of framed experience, val-
ues, contextual information, and expert insight that provides a framework
for evaluating and incorporating new experiences and information. It orig-
inates and is applied in the minds of knowers. In organizations, it often
becomes embedded not only in documents or repositories but also in or-
ganizational routines, processes, practices, and norms”. It is a descriptive
definition trying to capture the main attributes of knowledge in an organ-
izational context. Although we need such a working definition for
knowledge, we should see the supporting metaphor and the limitations
induced by it in using that concept of knowledge. In this particular case,
the metaphor used is that of stocksandflows which will be discussed in
one of the following sections of this chapter.
1.2.2 Knowledge as Objects
The first class of metaphors developed by people who were in search for
knowledge understanding and using it in practical organizational contexts
is that of knowledge as objects, stocks, or resources. The explanation
comes from the fact that objects are tangible with clear and easily identi-
fiable attributes. In a research on the nature of intellectual capital and on
the metaphors used by different authors, Andriessen (2006) shows that
Davenport and Prusak used this kind of metaphors in the first chapter of
their book Working knowledge. How organizations manage what they
know in proportion of 59% of the total number of all metaphors used in
that chapter, and Nonaka and Takeuchi used in chapter 5 of their book
The knowledge-creating company. How Japanese companies create the
dynamics of innovation metaphors based on physical objects in propor-
tion of 29% of the total number of metaphors used in that chapter. We
14
provide these examples because both books have been very influential
among all academics and practitioners involved in knowledge manage-
ment and intellectual capital, andhave contributed significantly to pro-
moting knowledge metaphors based on physical objects and their attrib-
utes. The followings are just some examples of such metaphors, where
we introduced italics to underline the main elements of these metaphors:
(1) “The idea of dealing with knowledge as an object has been al-
ready exploited in a variety of areas across knowledge manage-
ment and information technology” (Borgo and Pozza, 2012;
p.229).
(2) “A knowledge map can also serve as an inventory It therefore
can be used as a tool to evaluate the corporate knowledge stock,
revealing strengths to be exploited and gaps to be filled” (Daven-
port and Prusak, 2000; p. 72).
(3) “The realization that knowledge is the new competitive resource
has hit the West like a lightning” (Nonaka and Takeuchi, 1995;
p.7).
(4) “Codification can be defined as a process of storage, indexation
and distribution of formal knowledge independently of any con-
text” (Janicot and Mignon, 2012; p. 6).
(5) “Just as food and manufactured goods can be packaged and sold,
there are ways to package knowledge for commercial benefit, us-
ing the intellectual property laws” (Sullivan, 1998: p. 143).
The first example shows explicitly that knowledge should be understood
in terms of an object, which means that the metaphor defines a frame-
work with some structural and functional attributes coming from objects.
That is confirmed by the second example where knowledge is considered
to be like astock, and the third example where knowledge is considered
like a tangible resource in a company. The last two examples refer to the
functional attributes of objects which have been transferred to the target
domain. Thus, knowledge can be stored, indexed, distributed and packed
like physical objects. Although these properties are very intuitive in de-
scribing knowledge, they induce the idea of considering knowledge like
some individual entities which can be stored on a shelf, can be distributed
15
like physical objects and it can be subject to packaging operation like any
commercial product. Some people may ask what is wrong with such a
perspective or why should we be careful in treating knowledge in this
way. First, if we consider knowledge existing as individual entities like
products in a supermarket which can be arranged on shelves and stored
one upon the other, then we accept the idea of linearity and the summa-
tion operation. That leads to the idea of measuring the quantity of
knowledge by counting the number of knowledge entities and performing
the summation mathematical operation. Actually, this kind of attitude has
been already produced and most of the metrics designed to evaluate
knowledge and other intangible resources in organizations are linear met-
rics (see Chapter 8 for an additional discussion about this issue). Second,
when distributing physical objects the initial quantity of them is progres-
sively diminishing. In reality, when a person shares her/his knowledge
with somebody else or disseminates it to a group of people, the initial
quantity of knowledge does not diminish; it remains at the same level
since knowledge is not composed of individual well-defined pieces which
are removed from the initial inventory. Third, when physical objects are
used frequently and for a long time they suffer a degrading process.
Knowledge can be used as much as we need it without any process of los-
ing any of its properties. Just think of the Pythagorean theorem in math-
ematics or the Newtonian laws of physics.
These metaphors have been promoted mostly by researchers
coming from information science and engineering who work with the
Shannonian concept of information, which is devoid of any meaning (Bra-
tianu, 2015) and is a pure mathematical concept reflecting a certain dis-
tribution of probabilities. Due to its mathematical nature, this concept of
information is objective, and its objectivity inspired some researchers to
extend mathematical methods to the concept of knowledge and to find
ways of its objectification. In this perspective, Bolisani, Borgo and Oltra-
mari (2012, p. 203) remark that if “knowledge can be objectified, this
means that it can be handled, reproduced, stored and transferred, largely
independently from the individual that produces or possesses it”. That
objectified knowledge can be embedded into documents, software codes,
16
databases, and different platforms for sharing it among the employees
with a high probability of getting the same interpretation.
1.2.3 Knowledge Nuggets
The temptation of using simple and intuitive metaphors leads to the crea-
tion of the interesting expression of knowledge nuggets. From the well-
known chicken nuggets you can order in McDonald’s fast food restau-
rants, knowledge nuggets captured the imagination of IT experts who use
it quite frequently in data processing, especially in data mining,
knowledge discovery, and knowledge production processes (Carayannis
and Campbell, 2011; Delen and Al-Hawamdeh, 2009; Williams and Huang,
1997). According to the Oxford Advanced Learner’s Dictionary (2004), the
word nugget may have the following meanings: a) a small lump of a valu-
able metal or mineral, especially gold, that is found in the earth; b)a small
round piece of some type of food: chicken nuggets; c) a small thing such
as an idea or a fact that people think of as valuable: a useful nugget of in-
formation. Thus, the concept of knowledge nuggets reflects the meta-
phorical thinking based on small and usually valuable objects. Also, it sug-
gests an extension of the concept of shannonian information toward
semantic information, although the first one is a mathematical concept
without embedding any concrete meaning. The concept of knowledge
nuggets leads intuitively to the idea of defining small pieces of infor-
mation or knowledge which can be aggregated into larger structures,
stored, retrieved, distributed and used. The exponential increase in data
gathered and stored in huge databases generated a great conceptual ef-
fort to create new models and technologies for searching and retrieving
useful information. In this context, Data Mining is “the process of identify-
ing valid, novel, potentially useful, and ultimately understandable pat-
terns in data stored in structured databases, where the data are orga-
nized in records structured by categorical, ordinal and continuous
17
variables” (Delen and Al-Hawamden, 2009; p. 142). By novel information
experts in data mining mean new correlations, trends, or patterns that
can be discovered in the very large databases of the Big Data systems.
The novel information is structured as knowledge nuggets which can be
delivered to the interested users. The concept of knowledge nuggets is al-
so used in e-learning and micro-learning programs, where it represents
well-defined and meaningful structures of knowledge. Here, we have to
make a clear distinction between the string of signs which corresponds to
a knowledge nugget and the semantic content of that nugget. For exam-
ple, we may consider as a knowledge nugget a trend found in a large da-
tabase, expressed as a sentence. We may put together such sentences
and sum them up into a paragraph. That is a linear operation applied to
the strings of letters or to their digital correspondents which can be
stored, retrieved, transferred or distributed. However, the meanings of
the nuggets cannot be aggregated on the same principle, since meaning is
nonlinear and the result of such an aggregation may have no meaning at
all. The conflicting situation is generated by the different significance of
the concept of shannonian information used in computer science as a
pure mathematical construct without any semantic content, and the con-
cept of semantic information used in knowledge management.
In practical terms, knowledge nuggets can be the result of pre-
senting some ideas, tips, rules, or practical suggestions very synthetically,
by using both texts and images, like in a series of humorous videos for
field sales agents which are posted on Youtube. Also, the Organization
Migration4Development (M4D) uses knowledge nuggets as extractions of
key concepts and ideas from projects, e-discussions, live chats and reports
to inform the community with M4D issues at the local level. In a larger
sense, knowledge nuggets may be conceived as a result of piecewise dis-
cretization process of a continuum of knowledge contained in a book, pa-
per, program, conference or live chat and selection of most significant of
them for the users. We can make a parallel with complex nonlinear phe-
nomena in mathematics which cannot be solved as they are, and experts
use different discretization methods to transform those continuum fields
18
into discrete ones for which can be applied numerical methods to get use-
ful solutions.
1.2.4 The Iceberg Metaphor
The iceberg metaphor has been used extensively by Ikujiro Nonaka and
his colleagues since it is very simple and very intuitive for the conceptual
dyad of explicit knowledge - tacit knowledge (Nonaka1994; Nonaka et al.,
2008; Nonaka and Takeuchi, 1995; Nonaka and Von Krogh, 2009). As they
recognize, the fundamental aspect of their epistemology is the distinction
between explicit and tacit knowledge, distinction that can be easily un-
derstood by using the iceberg metaphor. “Thus, knowledge that can be
expressed in words and numbers represents only the tip of the iceberg of
the entire body of knowledge” (Nonaka and Takeuchi, 1995; p. 60). Explic-
it knowledge is the rational knowledge that can be formulated by using
any natural or symbolic language, and can be easily transferred in a social
context. It is like the visible part of the iceberg. Tacit knowledge is per-
sonal knowledge and comes mostly from direct experience, which is pro-
cessed by the cognitive unconscious. According to Nonaka and Takeuchi
(1995; p. 8), “Tacit knowledge is personal and hard to formalize, making it
difficult to communicate or to share with others. Subjective insights, intu-
itions, and hunches fall into this category of knowledge. Furthermore, tac-
it knowledge is deeply rooted in an individual’s action and experience, as
well as in the ideals, values, or emotions he or she embraces”. Metaphor-
ically, tacit knowledge can be represented by the hidden part of the ice-
berg. We know that it is there, under the water line, but we cannot see it
and we have no idea how big that part of the iceberg is.
The iceberg metaphor captures our attention since it is simple
and intuitive, but on the other hand it has serious limitations since the
iceberg is a solid and there is no flow between its visible and hidden parts.
Thus, there is no dynamics in the source domain which can be mapped
19
onto the target domain to illustrate the conversion process of tacit
knowledge into explicit knowledge. In addition, the split of the iceberg in-
to two distinct parts can be only seen from an observer’s perspective,
since the iceberg is a homogeneous solid without any intrinsic differences
between the upper and the lower parts. Thus, the distinction between
tacit and explicit knowledge comes from a contextual attribute of the ice-
berg seen in the ocean’s water and not from a material distinction be-
tween the upper and the lower parts, which questions the effectiveness
of the structural mapping from the source domain onto the target domain
of the metaphor.
1.2.5 Knowledge Flows
In order to eliminate the limitations introduced by the discrete nature of
objects and their static forms, a new metaphor has been created by using
the image of fluid flows. Thus, in the source domain, we have the seman-
tic field associated to flow of fluids,while we have the semantic field of
knowledge in the target domain. In the source domain, in a more ad-
vanced and complex metaphor, some authors consider both stocks and
flows combining the attributes of the two semantic fields. Thus,
knowledge as stocks and flows constitutes one of the most frequently
used metaphors. Bolisani and Oltramari (2012; p.280) explain the essence
of this metaphor effectively: “We can denote knowledge stock as the
amount or ‘level’ of knowledge possessed at a particular time in an organ-
ization, while knowledge flows identify knowledge that is transferred
from one economic player to another. According to this interpretation,
knowledge flows can affect the amount of knowledge stocked by the two
players”. We shall illustrate these metaphors with some examples taken
from literature. We introduced italics for the metaphors used in the texts.
“For this flow of knowledge to prevail, the organizational culture must
be extraordinary” (Davenport and Prusak, 2000; p. 109).
20
“The way knowledge flows in organizations is often a hidden process”
(O’Dell and Hubert, 2011; p. 109).
“Rapid and reliable flows of knowledge across people, organizations,
times, and places are critical to enterprise performance. Unfortunately,
the leader and manager have negligible current guidance for assessing
and enhancing knowledge flows in practice. A dearth of contemporary
research addresses the dynamics of knowledge, which are fundamental
to understanding knowledge flows” (Nissen, 2006;p.IX).
“With the wider view I am taking, I claim that managing knowledge
flows is something that can be applied and used in almost any type of
organization” (Leistner, 2010; p. 6).
“So flow of knowledge from individuals depends on three broad fac-
tors: individual preferences, the social situation and organizational fac-
tors” (Oliver, 2013; p. 19).
Fluid flows are well-known phenomena, easily to understand and
explain. Unlike objects that have limited and well-defined geometries that
are static and unchangeable in a uniform and constant field of forces, flu-
ids have changeable geometries and have the property of flowing under
the influence of a pressure field. They are dynamic. Fluids can be accumu-
lated and stored in reservoirs, and distributed through channels or indus-
trial piping systems. In nature, fluid flows in channels or rivers as a result
of the gravity field, which means from a higher altitude to a lower one. In
industry, cities or buildings, fluid flows through ducts and pipes from a
higher pressure level created by a pump toward a lower pressure level.
That motion of flow has been used many times in science to explain new
phenomena like electrical current and heat flux. Even today, some people
think that heat is flowing from a hot physical object toward a cold one,
and that electricity is flowing through a wire. Why not to consider that
knowledge is flowing through an organizational structure from well-
informed people toward less-informed ones?
Knowledge as stocks and flows is a complex metaphor composed
of several simple ones which form analogies with fluids, their physical
property of being a continuum and their functional attribute of flowing.
Nissen (2006; p. XX) associated the fluid flow through a piping system
21
with the knowledge flow through an organizational structure: “To the ex-
tent that organizational knowledge does not exist in the form needed for
application or at the place and time required to enable work perfor-
mance, then it must flow from how it exist and where it is located to how
and where it is needed. This is the concept knowledge flows”. The model
proposed by Nissen is an extension of the dynamic model developed by
Nonaka and his colleagues (Nonaka, 1994; Nonaka and Takeuchi, 1995)
since it is based on the SECI construct, but it contains time as a new di-
mension. While Nonaka’s model is an inertial model, Nissen’s one is really
a dynamic model because it includes time. Nissen introduces two new
dimensions: life cycle and flow time. “Life cycle refers to the kind of activi-
ty (e.g. creation, sharing, application) associated with knowledge flows.
Flow time pertains to the length of time (e.g. minutes, days, years) re-
quired for knowledge to move from one person, organization, place, or
time to another” (Nissen, 2006; p. 35). It is useful to underline the fact
that knowledge flows in the Nissen’s perspective refers not only to the
motion of knowledge from one part of organization to another one, but
also from one moment of time to another one. Flow of time is important
especially for intergenerational knowledge transfer and databases crea-
tion. However, Szulansky (1996; 2000) reveals that knowledge flows im-
plies also knowledge stickiness manifested as a difficulty in the process of
knowledge transfer. He says that knowledge can be sticky: “To a large ex-
tent, this is because internal transfer of knowledge, rather than fluid, is
often ‘sticky’ or difficult to achieve” (Szulansky, 2000; p. 10).
We have to observe the fact that the metaphor knowledge as ob-
jects can be used only for explicit knowledge, while the metaphor
knowledge as stocks and flows can be used for both explicit and tacit
knowledge. Davenport and Prusak (2000; p.5) used this metaphorical en-
tailment in their famous definition: “Knowledge is a fluid mix of framed
experience, values, contextual information and expert insight that pro-
vides a framework for evaluating and incorporating new experiences and
information”. However, the knowledge flow metaphor cannot explain the
conversion of tacit knowledge into explicit knowledge which constitutes
the essence of knowledge creation in the Nonaka’s model. Also, the met-
22
aphor is still based on the Newtonian physics which implies motion in
space and linearity when dealing with knowledge. There is no transfor-
mation of phase or other type of changing the nature of the fluid to sup-
port the knowledge conversion processes postulated by Ikujiro Nonaka in
his famous SECI model. For overcoming these limitations we need to
change the paradigm of Newtonian logic into the paradigm of entropic
transformations as we shall explain in the next sections of this chapter.
1.3 The Energy Metaphor
1.3.1 Knowledge as Energy
In his seminal book Corporate longitude: What you need to know to navi-
gate the economy (2002), Leif Edvinsson considers that we need to ad-
vance in understanding and explaining knowledge by developing new
models and new metaphors.Such a new metaphor is knowledge as energy
(Bratianu 2011, 2013, 2015; Bratianu and Andriessen 2008). In the source
domain we consider energy with all its attributes, and in the target do-
main we consider knowledge. There are three main attributes we are in-
terested in mapping them onto the knowledge domain:
Energy is a field.
Energy manifests in different forms (i.e. mechanical, thermal, electrical
etc.)
One form of energy can transform into another form of energy. The
transformation is irreversible.
The first attribute leads us to a new interpretation of knowledge
which changes the main paradigm of defining it. Knowledge is not consid-
ered like a tangible object or a fluid flow anymore. It is considered like a
field of forces which is intangible and forms a continuum both in space
and time. For instance, we all are aware of the gravity field although we
23
cannot see it and cannot touch it. But if we jump we feel immediately the
attraction force of the earth. That means an intangible field of forces. En-
ergy fields are usually distributed non-uniformly in space and have varia-
tions in time. These properties can be transferred to the knowledge field.
The second attribute is obvious for all of us. Energy can be found
in nature in different existential forms like mechanical energy, thermal
energy, electrical energy, nuclear energy etc. This attribute mapped onto
the target domain leads to the idea that knowledge can manifest in dif-
ferent forms of different nature. The two forms discussed so far (i.e. tacit
and explicit knowledge) are different not due to their nature but due to
their way of being processed by our brain. Tacit knowledge is processed
fundamentally by the unconscious zone of the brain, while the explicit
knowledge is processed by the conscious zone of the brain where natural
language plays an essential role. We consider three fundamental forms of
knowledge: rational knowledge, emotional knowledge, and spiritual
knowledge (Bratianu, 2013; 2015). Rational knowledge is the result of the
reasoning process and expresses concepts and ideas formulated in a nat-
ural or symbolic language. Rational knowledge is the explicit form of
knowledge. Emotional knowledge is a wordless form of knowledge which
is generated by our emotions and feelings. In Nonaka’s theory emotional
knowledge is found in the tacit knowledge mixed with spiritual knowledge
which expresses our cultural values and ethical principles.
The third attribute comes from thermodynamics and reflects the
capacity of energy to transform from one form into another one in some
given conditions. For instance, mechanical energy can transform through
friction into heat. This attribute mapped from the source domain onto the
target domain shows that one form of knowledge can transform into an-
other form in given conditions. For instance, emotions of fear make us to
think of some protection or avoiding a dangerous situation. In such a con-
text, emotional knowledge transforms into rational knowledge. These
transformations are irreversible and they represent the content of the en-
tropic knowledge dynamics, where entropy is a measure of irreversibility.
The energy metaphor allows us to propose a new paradigm for
knowledge based on the multi-field theory of knowledge and the entropic
24
knowledge dynamics. The multi-field theory says essentially that individu-
al and organizational knowledge is represented by three fundamental
fields of knowledge: rational, emotional, and spiritual. The entropic
knowledge dynamics is concerned with the transformation of one form of
knowledge into another one in some given conditions. We shall present
the main ideas of these new domains of research in the next sections.
1.3.2 The Field of Rational Knowledge
The multi-field theory of knowledge states that at the individual level and
organizational level there are three co-existing fields of knowledge: ra-
tional knowledge field, emotional knowledge field, and spiritual
knowledge field. They are fundamental forms of knowledge manifestation
which are generated and constituted in a different way. However, they
are not independent fields but in a continuous interaction and transfor-
mation such that decision making incorporates contributions coming from
all of them (Bratianu, 2013; 2015). We may say that knowledge is a con-
struct similar to the white light which can be decomposed in monochro-
matic lights when passing through a prism. That means that knowledge is
an integrative concept containing rational, emotional, and spiritual
knowledge. The new perspective is in concordance with the multiple in-
telligences model developed by Howard Gardner (1983; 2006). That mod-
el changed completely our idea that intelligence is a single entity which
can be measured and expressed numerically by using the concept of intel-
ligence quotient (IQ) created by Alfred Binet. Gardner defines an intelli-
gence as “a bio-psychological potential to process specific forms of infor-
mation in certain kinds of ways. Human beings have evolved diverse
information - processing capacities I term these ‘intelligences’ – that al-
low them to solve problems or to fashion products” (Gardner, 2006;
p.29).
25
The rational knowledge field contains rational knowledge which
has been considered as the only form of knowledge for centuries by phi-
losophers. We discussed about these epistemological aspects of
knowledge in the beginning of the chapter. Rational knowledge is repre-
sented mainly by explicit knowledge since it is the result of the conscious
cognitive brain. Descartes (1997; p.147) expressed that conviction as fol-
lows: “Even bodies are not properly speaking known by the senses or by
the faculty of imagination, but by the understanding only, and since they
are not known from the fact that they are seen or touched, but only be-
cause they are understood. I see clearly that there is nothing which is eas-
ier for me to know than my mind”. Rational knowledge is considered to
be objective and this attitude made it suitable for developing scientific
and technological knowledge. Also, education in the western countries
has been conceived in objective terms and stressed the importance of
science and technology which means the primacy of rational knowledge.
Rational knowledge is framed into explicit knowledge by using a natural
or symbolic language: “Language serves not only to express thoughts, but
to make possible thoughts which could not exist without it” (Russell,
1992; p. 58). Organizational rational knowledge is obtained by integrating
all individual rational knowledge fields and all documents and databases
which contain data, information, and knowledge. Classical decision mak-
ing theory is based on rational knowledge and expressed mostly in the
symbolic language of mathematics. Knowledge management has been
developed in its first phase on rational knowledge as an extension of the
information management which is centered on the concept of shanno-
nian information and information technology. That is why managers de-
veloped their generic strategies based on rational knowledge and infor-
mation technology.
1.3.3 The Field of Emotional Knowledge
26
The emotional knowledge field contains knowledge generated by emo-
tions and feelings. Emotional knowledge is a wordless form of knowledge
which is processed by the unconscious part of our brain. Emotional
knowledge is generated in the direct contact of our body with the exter-
nal world and integrated into what we call experience. Also, emotional
knowledge can be obtained by processing information coming from our
internal body. Emotional knowledge emerged as a component of tacit
knowledge, especially after the work of Michael Polanyi (1983). In his
seminal book about the tacit dimension of knowledge, Polanyi considers
our direct experience with the environment as a source of knowing. It is a
bodily experience which generates emotional information through per-
ception, information which becomes then emotional knowledge. “I said
that by elucidating the way our bodily processes participate in our per-
ceptions we will throw light on the bodily roots of all thought, including
man’s highest creative powers” (Polanyi, 1983; p. 15).
Human resources management demonstrated that emotional
knowledge plays a crucial role in motivating people for working very hard
and achieving performance. Motivation becomes critical during change
processes when there is a need for greater efforts without immediate re-
wards. Understanding and using emotional knowledge in influencing peo-
ple makes the difference between managers and leaders, since managers
prefer numbers and rational decisions while leaders influence people act-
ing on their emotional and spiritual knowledge fields. John Kotter, who
studied organizational change and leadership involved in performing
them, demonstrated that in any change process emotional knowledge is
much more important than rational knowledge. Kotter showed that ana-
lytics could be interesting, but not always convincing. Rational knowledge
is needed for understanding the logic of change but could be not enough
in determining changing employees’ behavior. Much more convincing
could be for them to feel the need of change as a result of emotional
knowledge transferred to them by the leaders. “The single biggest chal-
lenge in the process is changing people’s behavior. The key to this behav-
ioral shift, so clear in successful transformations, is less about analysis and
thinking and more about seeing and feeling” (Kotter and Cohen, 2002; p.
27
179). In change management, the old paradigm of analyzing-thinking-
changing should be replaced with new one of seeing-feeling-changing.
Thus, the action of seeing creates the perceptions able to generate
through feeling the necessary emotional knowledge needed to contribute
together with rational knowledge to changing people’s behavior. That
means that emotional knowledge contributes significantly to the decision
making both at individual and organizational levels. As Dan Hill (2008; p.
2) remarks, “Breakthroughs in science have revealed that people are pri-
marily emotional decision makers”. Based on this idea and many psycho-
logical investigations of the decision making Malcolm Gladwell introduces
in his famous book Blink the concept of “thin-slicing” decision making:
“Thin-slicing refers to the ability of our unconscious to find patterns in
situations and behavior based on very narrow slices of experiences”
(Gladwell, 2005; p.24). Many people say that is intuition, since intuition is
a result of our condensed and filtered experience powered by emotional
intelligence. These two fields of knowledge are related to the multiple in-
telligences structure of our thinking. In a synthetic way, Daniel Kahneman
(2011) explains the fact that people developed during the history of hu-
manity two modes of thinking that are interacting dynamically: 1) the
emotional system that operates automatically and quickly, with almost no
effort or sense of voluntary control, and 2) the rational system that oper-
ates slowly due to many computations and choices it does. While the
classical management theory in its effort of proving that is a science ig-
nored the work of the first system on the basis of its subjectivity,
knowledge management considers both of them. Looking at the literature
in this domain, we may say that authors coming from western countries
are still emphasizing the role of rational system while authors coming
from Japan emphasize the emotional system and tacit knowledge.
1.3.4 The Field of Spiritual Knowledge
28
Spiritual knowledge has been included by Nonaka and Takeuchi in tacit
knowledge, mixed up with emotional knowledge (Nonaka and Takeuchi;
1995).We consider spiritual knowledge essential for our existence, fact
for which we introduce it as a fundamental field in the new multi-field
theory of knowledge. Spiritual knowledge integrates values and beliefs
about life and about our existence and represents the backbone of the
spiritual capital of any organization (Zohar and Marshall, 2000; 2004).
“Our spiritual capital is our shared meaning, our shared purpose, our
shared vision of what most deeply matters in life and how these are im-
plemented in our lives and in our behavioral strategies. It is the capital
that is increased by drawing on the resources of the human spirit” (Zohar
and Marshall, 2004; p. 27).If rational knowledge reflects the objectivity of
the physical environment we are living in, and emotional knowledge re-
flects the subjectivity of our body interaction with the external world,
spiritual knowledge reflects our understanding about the meaning of our
existence. As Maxwell (2007; p. 274) states, “We have to learn to see as-
pects of the world around us: stones, people, trees, sky. Equally, we have
to learn to see meaning and value in the world around us, in our envi-
ronment, in events, in human actions and lives”.
Individuals working together in a company share their values and
beliefs about life, work and future generating in time an organizational
culture and an working spirituality. It is a way of thinking and feeling inex-
tricably connected with caring, hope, kindness, love and optimism. Spir-
itual knowledge is essential in decision making since rational arguments
are strongly influenced by the value settings. We are all aware of the fact
that positive values correlate directly with the business success, while
negative values lead managers toward business failures. Thus, spiritual
knowledge which reflects positive values and positive spiritual intelli-
gence is essential in conceiving successful strategies and in achieving
competitive advantage. Spiritual knowledge is intrinsically related to the
concept of Corporate Social Responsibility, a concept requesting respon-
sible governance and a vision driven by social values and not profit maxi-
mization (Basu and Palazzo, 2008; Branson, 2011; Pinto et al., 2008; Wang
et al., 2011).
29
1.4 Conclusion
Knowledge is a universal concept which attracted the attention of philos-
ophers from ancient times. There were countless efforts to define it fol-
lowing the rules of scientific inquiry, but always the resulting definition
was not able to integrate all the semantic attributes of knowledge.
Searching for an objective perspective and a rational approach many phi-
losophers eliminated all subjective aspects related to perception and bod-
ily involvement claiming that knowledge is a justified true belief. Howev-
er, the precision and logical coherence used in the theoretical approach
to knowledge generated uncertainty in the practical modalities of justify-
ing the truth. If we agree with Nonaka and Takeuchi (1995; p. 87) that
“justification criteria need not be strictly objective and factual”, then the
philosophical meaning of truth is almost lost. Truth and its justification
cannot have the same degree of objectivity anymore. We may think of
the Heisenberg’s uncertainty principle applicable to quantum mechanics
that states in the case of nuclear particles position and velocity cannot be
measured exactly at the same time. Knowledge is created by human brain
and then it is amplified and integrated into organizational knowledge by
social interaction. That means that knowledge comprises both objective
and subjective attributes. Objective attributes can be conceived as being
independent of the social context, but the subjective attributes are con-
text dependent and cannot be transferred easily to some other similar
contexts. Knowledge sharing can be a good example for such kind of situ-
ations.
Cognitive scientists demonstrated that our mind works metaphor-
ically. That means that we use metaphors to understand and explain a
less known concept or experience in terms of other well-known one. Con-
ceptual metaphors have a simple structure composed of a source domain
30
where we place the well-known concept and a target domain where we
place the new or less known concept. By using structural mapping, some
of the main attributes of the concept framed within the source domain
are transferred to the concept put in the target domain, enlarging this
way its semantic field. Since knowledge is an abstract concept without
any reference to some tangible objects, authors use explicit or implicit
metaphors in dealing with it and with knowledge management. The first
class of metaphors developed for knowledge explanation is based on
those that contain physical objects with tangible attributes in the source
domain. It is the favorite class of metaphors used by authors dealing with
knowledge as strategic resources. Thus, knowledge can be accumulated,
stored, distributed, packed and delivered like tangible objects. From that
class derived lately the iceberg metaphor which has been used extensive-
ly to explain the pair of explicit and tacit knowledge. Knowledge nuggets
are an extension of the same category of metaphors, but resulted from a
discretization of a continuum of knowledge (i.e. a text containing a narra-
tion or a story). The most advanced class of metaphors are those based
on stocks, flows, or stocks and flows used in the source domain. Thus,
knowledge is conceive like a fluid flowing through organizations from
where is created to where it is needed.
All of these metaphors presented above induce a series of limita-
tions in understanding and using the full potential of knowledge. These
limitations derive from the Newtonian logic, the linearity property and
the illusion of measuring knowledge by using the methods developed for
tangible objects and their attributes. In an effort to overcome these limi-
tations, a new metaphor based on energy is proposed in Bratianu and
Andriessen (2008). According to this new perspective, knowledge is con-
ceived like a field without any tangible attributes. Moreover, following
the analogy with co-existence of multiple forms of energy (i.e. mechani-
cal, thermal, electrical, nuclear etc.), the existence of three fundamental
fields of knowledge is postulated: rational, emotional, and spiritual. Ra-
tional knowledge is basically the explicit knowledge since it is framed by
our reasoning mind and natural language. It is a construct following the
Cartesian spirit. Emotional knowledge is a wordless expression of our
31
body response to the external environment and it is a direct result of
emotions and feelings. Emotional knowledge is subjective and context
dependent. Spiritual knowledge contains values and ethical principles and
it is essential in decision making. Both emotional and spiritual knowledge
have been embedded in tacit knowledge and mixed up in the fuzzy de-
scription of experience. The energy metaphor constructs a new paradigm
which allows us to have a better understanding of knowledge and to offer
managers and leaders new opportunities to influence people in times of
change and uncertainty.
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... Organizations treat it as an instrument that enables them to compete effectively in the market [9]. Knowledge is an abstract concept that is free from the material world and has two forms, namely explicit knowledge and tacit knowledge [10]. Explicit knowledge is any knowledge that can be codified, verbalized, communicated, and articulated. ...
... Effective knowledge management allows an organization to become more innovative and effective [30]. For this reason, many organizations treat knowledge management as a strategic resource that enables them to achieve better results than the competition [10]. ...
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... Controlling the current pandemic requires an acceptable level of awareness and knowledge. The compliance of individuals can be improved if they are aware of the disease and the prevention methods [14,15], and it is safe to assume that this applies for COVID-19. There is an abundance of literature where the level of awareness among people about COVID-19 was evaluated. ...
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... Knowledge, by definition, means familiarity with information, data or activities which organization, organizational teams, and individuals use to perform defined tasks as well as achieve defined goals [5]. On the other hand, organizational knowledge is a set of documented and well-known facts as well as well-known practices that organizational employees create when they perform defined tasks [6]. ...
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With changes in organizational environment organizations must adopt their business model to the new conditions that are arising. By adapting to the new conditions, organization create knowledge. The main aim of the paper is to show the possibilities of using AI in managing and creating organizational knowledge within the organization and using once created knowledge for competitive advantage. This paper presents the results of the conducted secondary research on the application of artificial intelligence in knowledge creation, and based on the conducted research, a framework for knowledge creation was proposed. This framework starts with collecting data from different sensors on devices or machines and from employees. Gathering large amount of data then creates Big Data databases from which through data mining knowledge is created. In further research, the proposed framework will be used to conduct primary research on the impact of artificial intelligence on creating knowledge and managing it.
... Before we talk on knowledge transfer, lets discuss the definition of knowledge. According to Bolisani and Bratianu (2018), any definition involves three basic conditions: ...
... Knowledge, as an asset in whichever form, is regarded as key to any organization's present and future growth, and competitive advantage especially in the 21st century (Xue, 2017). Different views have been suggested (Bolisani and Bratianu, 2018) on what "knowledge" is, though many of these definitions still point toward Plato's postulation of knowledge as being "justified true belief " (Nonaka and Takeuchi, 1995). Knowledge, however, can be classically expressed in contemporary times as encompassing a flexible fusion of expert opinion, contextbased information, personal exposure, practices and thoughts or reasoned actions, values and norms, and procedures. ...
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This article illustrates a design approach for capturing, storing, indexing, and search of African traditional herbal medicine (ATHMed) framed on a hybrid-based knowledge model for efficient preservation and retrieval. By the hybrid approach, the framework was developed to include both the use of machine learning and ontology-based techniques. The search pattern considers ontology design and machine learning techniques for extracting ATHMed data. The framework operates on a semantically annotated corpus and delivers a contextual and multi-word search pattern against its knowledge base. In line with design science research, preliminary data were collected in this study, and a proposed strategy was developed toward processing, storing and retrieving data. While reviewing literature and interview data to reflect on the existing challenges, these findings suggest the need for a system with the capability of retrieving and archiving ATHMed in Ghana. This study contributes to SDG 3 by providing a model and conceptualizing the implementation of ATHMed. We, therefore, envision that the framework will be adopted by relevant stakeholders for the implementation of efficient systems for archival and retrieval of ATHMed.
... Yet despite this abundance, the definition of knowledge still remains elusive. 38 Even a very concise list of sources that discuss knowledge and its definitions will make a sizable list of entries. 39 A detailed examination of these definitions is certainly beyond the scope of this article. ...
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This contribution examines the current controversy over research productivity. There are two sides in this controversy. Using extensive data from several industries and areas of research, one side argues that research productivity is currently in decline. The other side disputes this conclusion. It contends that the data used in making this argument are selective and limited; they do not reflect the overall state of research. The conclusion that follows from this critique is that the indicators of research productivity we currently use are not reliable and do not warrant a definitive answer to the problem. The article agrees that we need a new set of indicators in assessing research productivity. It proposes that we should look at global indicators related to knowledge production in general, rather than look at selective data that are inevitably limited in their scope. The article argues that the process of creation plays the essential role in knowledge production. Therefore, the perspective that uses the process of creation as its central organizing principle offers a unique and global view on the production of knowledge and makes a definitive resolution of the controversy possible. The article also outlines some steps for improving research productivity and realizing the full potential of the human capacity to produce knowledge.
... It is a very powerful concept, yet it has no clear definition so far. From the Greek philosophers up to present experts in knowledge management, people tried to define knowledge but the results are still very fuzzy (Ettore and Constantin, 2018). Knowledge is the accumulation of facts and information. ...
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Knowledge is the value connected with each social development and development of economy, as well. It is generally recognised value and, therefore, it is in reference to ius naturale, native rights, generally recognised also. Such values are, nowdays, the part of concept of fundamental freedoms and rights and their protection. However, their protection must be accompanied by the consciousness of wellbeing of everybody. The values like universalisation, globalisation, education, help restauration of society and social and economic progress. In such a progress, there must be place for free economy, market, economic growth, and such a development belongs to people. Without knowledge and education these values could turn to their opposition, under the exuse of achievement of "welfare state". It is why they are protected by the highest domestic and international regulations.
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The purpose of this paper is to perform a metaphorical analysis of knowledge as energy. This paper is based on a theoretical research concerning the nature, perception, basic laws and challenges brought up by these fundamental concepts of knowledge and energy. The metaphorical analysis of knowledge and intellectual capital has been initiated by Daniel Andriessen and his findings have been presented in several seminal works (Andriessen, 2006; 2008; Andriessen and Boom, 2007). In his work, Andriessen concluded we need to find new metaphors for knowledge. In our theoretical research we shall consider the knowledge as energy metaphor, with energy as the source domain, and knowledge as the target domain, and we are interested in identifying the metaphorical semantic kernel and the limitations of this analysis. The semantic kernel contains: (1) the concept of field as a nonuniform and nonlinear distribution of knowledge; (2) dynamics of potential and kinetic forms of manifestations; (3) dynamics of work and heat, and (4) entropy and syntropy process characteristics. Limitations of this analysis come from the conservation laws of energy transformation which cannot be applied to the knowledge domain.
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Promoting organizational knowledge is an important consideration for any business looking toward the future. Understanding the dynamics of knowledge-intensive organizations is a crucial first step in establishing a strong knowledge base for any organization. Organizational Knowledge Dynamics: Managing Knowledge Creation, Acquisition, Sharing, and Transformation introduces the idea that organizational knowledge is composed of three knowledge fields: cognitive knowledge, emotional knowledge, and spiritual knowledge. This book is useful for graduate students, researchers, and practitioners in knowledge management, intellectual capital, human resources management, change management, and strategic management.
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The recent financial crisis, and repeated corporate scandals, raise serious questions about whether a business school education contributes to what some have described as a culture of greed. The dominance of economic-related courses in MBA curricula led us to assess the effects of economics education on greed in three studies using multiple methods. Study 1 found that economics majors and students who had taken multiple economics courses kept more money in a money allocation task (the Dictator Game). Study 2 found that economics education was associated with more positive attitudes toward greed and toward one's own greedy behavior. Study 3 found that a short statement on the societal benefits of self-interest led to more positive ratings of greed's moral acceptability, even for noneconomics students. These effects suggest that economics education may have serious, albeit unintended consequences on our students' attitudes toward greed.
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We focus on two fundamental dimensions of corruption in organizations: (1) whether the individual or the organization is the beneficiary of the corrupt activity and (2) whether the corrupt behavior is undertaken by an individual actor or by two or more actors. We use these dimensions to define a new conceptualization of corruption at the organization level: the organization of corrupt individuals. We contrast this concep-tualization with the prevailing concept of organizational corruption and develop propositions that highlight their differences.
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Cambridge Core - Philosophy Texts - The Philosophical Writings of Descartes - edited by John Cottingham