1. Introduction
In the strategic management process based on the resource-based view (RBV), the basic philosophy is that competitive advantage is an integrated result of using efficiently strategic resources and dynamic capabilities of the firm [
1,
2,
3]. Due to its intangible nature and contextual creation, knowledge reflecting the knowing
what in organizations became a strategic resource [
4,
5]. Knowledge may be also used in developing a firm’s capability due to its capacity of showing
how to do things [
6,
7]. The main advantage of knowledge is its invisible contribution to all decision-making and organizational processes. Amongst them, the most important are knowledge creation, knowledge sharing, and organizational learning [
8,
9,
10].
Understanding knowledge is a mental process mediated by metaphorical thinking [
11,
12] since knowledge is an abstract concept. As Andriessen [
13], (p. 5) remarks, “Knowledge can only be analyzed, talked about, and understood by using metaphors”. A metaphor is a semantic construction by which we make a parallel between a physical realm we already know and a conceptual realm we want to understand better. A knowledge metaphor’s structure is composed of a source domain where we place the well-defined concept, a target domain where we place knowledge, and a mapping function which transfers some attributes from the source domain to the target domain, as illustrated in
Figure 1.
In the first generation of knowledge metaphors, researchers used for the source domain
objects or
stocks. Thus, the main meanings transferred to knowledge in the target domain were accumulation, capitalization, delivering, dissemination, distribution, exchanging, measuring, packaging, and storing [
13]. A special case may be considered the
iceberg metaphor used extensively by Nonaka and Takeuchi [
8], because of its capacity of presenting both explicit knowledge and tacit knowledge.
Explicit knowledge is that form of knowledge that we can express by using a natural or a symbolic language. It is the knowledge we get through education and use in our everyday life in social contexts.
Tacit knowledge is personal and acquired through our sensory system by direct experience. “Subjective insights, intuitions, and hunches fall into this category of knowledge. Furthermore, tacit knowledge is deeply rooted in an individual’s action and experience, as well as in the ideals, values, or emotions he or she embraces” [
8], (p. 8). Since tacit knowledge is processed in the cognitive unconscious zone of our brain, we hardly are aware of how much we know. As Polanyi [
14], (p. 4) reveals, “we can know more than we can tell”.
The second generation of knowledge metaphors introduced in the source domain the concept of
flows or
stocks-and-flows [
15,
16,
17]. Knowledge is compared with a flow of fluid from one part of the organization to another one, or with a flow in time. That is a very simple and intuitive metaphor, which was used also in the beginning for understanding heat and electricity. However, applying fluid mechanics to knowledge dynamics yields a mechanical thinking model with its tangibility and linearity limitations. The
flows and
stocks-and-flows metaphors cannot explain organizational learning processes which are based on knowledge transformations. Also, these metaphors cannot explain managerial decision-making processes and organizational changes.
The linearity-induced attribute of these metaphors created real difficulties in evaluating intellectual capital (IC), which is based on knowledge and other intangible organizational resources. Even the definition of the intellectual capital has been influenced by this linearity attribute: “Intellectual capital is the
sum of everything everybody in a company knows that gives it a competitive advantage” [
18], (p. XI). Linearity implies the possibility of using algebraic operations in measuring quantities, and a linear correlation between the output and input variables of a given process. For instance, the measuring systems of the physical properties like mass, length, area, volume, or temperature are based on linearity. Also, the accounting operations in business are based on linear metrics. As Dumay [
19], (p. 205) remarks, “these contemporary IC measurement frameworks are reifying IC in the same manner in which tangible assets are portrayed within accounting, which is akin to attempting to make the intangible tangible. This is what the author defines as an ‘accountingisation’ of IC”. Overcoming those limitations can be done by creating new metaphors and new mental models [
20,
21].
The rest of the paper is structured as follows. In
Section 2, we introduce the metaphor
Knowledge as Energy and show its enlarged semantic field. In
Section 3, we describe the basic
knowledge fields: Rational, emotional, and spiritual. In
Section 4, we explain how we can interpret and use in practice the concept of
knowledge dynamics based on thermodynamics and the entropy law. In
Section 5, we present some theoretical and practical implications of the theory of knowledge fields. In
Section 6, we summarize some concluding remarks with respect to our research.
2. Knowledge as Energy: A Mental Challenge
Since any object or stuff positioned in the source domain would induce the attributes of tangibility and linearity to the concept of knowledge, Bratianu and Andriessen [
22] suggest using
energy as a source of analogy,
thermodynamics as an inspirational domain for explaining energy
transformations, and
entropy as a measure of the irreversibility of these transformations. The most important attribute of energy we are interested in is the fact that energy is a
field. A field is not tangible. It cannot be seen and it cannot be touched. However, it can be felt in some specific conditions, like the gravity field when somebody jumps or a thermal field as a result of the temperature variation. Unlike physical objects which have well-defined geometries and sizes, a field represents a
continuum of forces. Due to its semantic power, the
field concept has been extended to social sciences [
23,
24,
25]. Although linearity may be present in some simple fields of mechanical forces, fields are non-uniform and nonlinear continua. Their non-uniform intensity in space generates forces and fluxes directed against the field gradient. That is another type of dynamic other than fluid flows, and it can be explained by the second law of thermodynamics. For instance, heat which represents a thermal flux is directed from a region with a high temperature towards another region with a lower temperature. If we map these attributes onto the knowledge domain, we get that
knowledge is a field with a non-uniform distribution within our brain and body, or within a social context like an organization. This interpretation overcomes the limitations imposed by the Newtonian mechanics.
Energy manifests in different forms like mechanical energy, thermal energy, electrical energy, or nuclear energy. Mapping this structure from the source domain onto the target domain of the
energy metaphor leads to the idea that
knowledge may exists in different forms. The well-known forms of
tacit knowledge and
explicit knowledge used frequently in knowledge management [
8,
9] should be re-considered since tacit knowledge represents a conglomerate of forms of knowledge, which makes it difficult to work with it. Starting with the energy metaphor and the well-known mechanical, thermal, and electrical fields, and being inspired by the cognitive sciences we shall consider three fundamental fields of knowledge:
Rational knowledge field, emotional knowledge field, and spiritual knowledge field. These fields can be identified at individual level, by the analogy with the traditional belief in mind, heart, and spirit, and at organizational level by the analogy with the nonlinear integrators of management, organizational culture, and leadership.
A comparable view has been presented by Vemuri and Bellinger [
26] in their adoption of systems thinking in organization. However, there are some differences in defining and interpreting the knowledge systems. The framework presented by Vemuri and Bellinger [
26] is composed of: Systems of spirit, systems of mind, and systems of body. The analogy with the human body is obvious, but the functional interpretation of all these systems may reveal some contradictions because they ignore the quality of information and knowledge processed. For instance, systems of the body include knowledge management systems although some of them could be better associated with the systems of the mind. Also, management, leadership, and organizational culture do not appear although they are essential in any organization.
Our approach closely follows the energy realm and the way we think and process knowledge. Kahneman [
27], (pp. 20–21) explains that there are two systems of thinking, called generically System 1 and System 2: “
System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control.
System 2 allocates attention to the effortful mental activities that demand it, including complex computations”. System 1 represents the sensory system which can “perceive the world around us, recognize objects, orient attention, avoid losses and fear spiders” [
27], (p. 21). System 1 processes
emotional information received by the sensory system and transforms it into
emotional knowledge which creates impressions, intuitions, and feelings. System 2 processes
rational information and transforms it into
rational knowledge which creates concepts, ideas, theories, and mental models [
20,
28,
29]. These two thinking systems interact continuously across the invisible interface between the cognitive unconscious and our consciousness [
30,
31].
Social life developed in time a set of
moral and ethical values as a reference system in human behavior. In any decision-making process, we find beyond rational and emotional knowledge a contribution coming from
spiritual knowledge, a type of knowledge focused on our existential values and vision for the future [
32,
33,
34]. We learn from the energy metaphor that these three fields of knowledge interact and can be transformed one into another, a property which will be discussed in the next sections.
4. Knowledge Dynamics and the Entropy Law
Thermodynamics developed as a science with the emergence of technological revolution of the 19
th century and the construction of the heat engines [
52,
53]. Sadi Carnot was amongst the first engineers interested in the transformation of heat in mechanical work to generate power, and he studied the necessary conditions for improving the efficiency of the thermal cycles. Rudolf Clausius made interesting observations concerning the spontaneous heat flow from a hot body toward a cold one, and stated that “heat does not pass from a body at low temperature to one at high temperature without an accompanying change elsewhere” [
54], (p. 42). Since classical mechanics could not help him in describing the energy transfer, he introduced the concept of
entropy, defining the
change in entropy (dS) of an isolated system as being the ratio between the heat transferred (dQ), and the absolute temperature (T). He postulated that
entropy is a state function associated to any system and “when a spontaneous process occurs, the entropy always increases” [
53], (p. 6). Lord Kelvin studied the transformation of heat into mechanical work and stated that “no cyclic process is possible in which heat is taken from a hot source and converted completely into work” [
54], (p. 41). Although these statements look different, they reflect the same law of thermodynamics applied to macrosystems, like machines and complex thermal and nuclear technologies. Atkins suggests that both statements formulated by Clausius and Kelvin for the second law of thermodynamics can be synthetized by using the concept of entropy: “the entropy of the universe increases in the course of any spontaneous change” [
54], (p. 49).
Looking from a microuniverse framework (e.g., the motion of molecules within a gas), Ludwig Boltzman adopted a probabilistic approach in defining the concept of
entropy and stated that
entropy is proportional with the logarithm of the total number of microstates which can define a macrostate of the system [
53,
54]. From a mathematical viewpoint, entropy may be considered as a measure of a given distribution of probabilities and this very abstract meaning linked entropy to order and disorder and contributed to its extension to many other research areas like engineering communications, information systems, and economics [
52,
53,
55].
The first idea we may get in interpreting knowledge fields through thermodynamics lenses is that knowledge flows always from a higher level of knowledge intensity toward a lower level of knowledge intensity of a certain field. For the moment, we may call metaphorically this intensity knowledge temperature. That is a very important insight which underlines the non-uniformity of the knowledge field and the creation of a knowledge flux directed against the gradient of the field. Processes like knowledge sharing and organizational learning become possible due to that gradient of the knowledge field.
A second idea is related to the
order existing in each field of knowledge.
Emotional knowledge is like motion of free molecules in a gas, since there is almost no imposed structure on all the information coming through our sensory system. They go directly to the cognitive unconscious brain and orient the body to a fast reaction, if necessary. That is a very low level of order, or high level of disorder, which implies a high level of entropy.
Rational knowledge is a result of reflection and of using a natural language. “Language serves not only to express thoughts, but to make possible thoughts which could not exist without it” [
56], (p. 92). That means to create structures which may have a high level of order, depending on the type of rational knowledge. For instance, a mathematical equation implies a higher level of order than a simple written text. Generally speaking, scientific knowledge involves much more order than daily operational knowledge. Transforming emotional knowledge into rational knowledge, or codifying rational knowledge based on some scientific principles, can be done only by consuming some cognitive work like thinking and learning.
Spiritual knowledge represents a condensed knowledge with a very high level of order, and with a low level of entropy, respectively. If we compare the order of emotional knowledge with that of a gas, and the order of rational knowledge with that of a fluid, then the order of spiritual knowledge looks like that of a solid. If we would like to keep the framework of the energy metaphor, then we compare emotional knowledge with thermal energy, rational knowledge with mechanical energy, and spiritual knowledge with the electrical energy. This understanding of knowledge goes beyond a simple classification or codification model of knowledge.
The third idea comes directly from the second law of thermodynamics which states that any transformation of knowledge implies a change in the entropy of the universe considered (i.e., personal knowledge or organizational knowledge). Knowledge management becomes, in this perspective,
entropy management. If we make the analogy between the motion of molecules in a gas and their distribution of probability and the personal knowledge and the dynamics of people within an organization, we can analyze the probability distribution of them and to compute the
knowledge entropy for a given state of the organization. Thus, managing knowledge means actually to manage that knowledge entropy such that we can obtain the configuration we want. Knowledge sharing does not create new knowledge. It changes the knowledge distribution within organization, by offering access to a larger number of employees to the existing knowledge. Knowledge sharing is like a diffusion process which increases the overall disorder of organizational knowledge.
Figure 2 illustrates how a well-structured knowledge field within an organization can be re-structured as a result of knowledge sharing. The final state of organizational knowledge has an increased level of disorder, i.e., an increased level of knowledge entropy.
By increasing the probability of any employee to access needed knowledge, at a given time and in a given place, knowledge sharing increases the organizational entropy and positively influences innovation. Thus, we may say that by managing knowledge entropy we can positively influence innovation and firm performance (see
Figure 3).
Mechanical energy can be transformed into thermal energy and electrical energy. Thermal energy can be transformed into mechanical energy and electrical energy. Electrical energy can be transformed into mechanical energy and thermal energy. All of these transformations can be mapped onto the knowledge fields and get by analogy similar transformations. The transformations between emotional knowledge and rational knowledge are governed by
experience and expertise. The transformations between emotional knowledge and spiritual knowledge are governed by
culture, and those between rational knowledge and spiritual knowledge by
wisdom. Thus, we obtain a holistic view of knowledge dynamics (see
Figure 4).
In economics and management, decision-making is considered as a rational process [
57,
58], supported by mathematical models and software applications. However, in many situations, under pressure of time and uncertainty, managers use mental shortcuts which can be put under the umbrella of
intuition [
59]. While rational decision-making is based on rational knowledge, intuition is based on emotional knowledge. Although the two systems of thinking (i.e., System 1 and System 2) explained by Kahneman [
27] interact, it is hard to conceive the direct switch between rational and emotional knowledge. Our model of knowledge dynamics may be useful in understanding decision-making as a complex process influenced by all the knowledge fields (i.e., rational, emotional, and spiritual) and their dynamics, based on the unity of knowledge: “A most methodological study is of the multi-causal circular causation relations in the epistemic perspective of unity of knowledge” [
60], (p. 380).
5. Discussion
The theory of knowledge fields impacts both research and practice. In research, there is a change from the Nonakian dyad of tacit–explicit knowledge to the triad of rational–emotional–spiritual knowledge, and from the SECI (Socialization-Externalization-Combination-Internalization) model for knowledge dynamics to the thermodynamics interpretation of transforming one form of knowledge into another one. In practice, there are many consequences since management complexity is not reduced anymore to only rational knowledge and rationality. The most important implication of the theory of knowledge fields is the integration of managerial decision-making with the recognition and motivational system of employees, organizational culture, and organizational behavior. Also, it is necessary to develop a new working spirituality and use it for supporting corporate social responsibility and organizational change. Managers act as a result of their economic education mostly on rational knowledge field. However, motivating people and managing change require thinking models which include emotional and spiritual knowledge. Leaders influence people mostly through emotional knowledge and develop their vision by using both rational and spiritual knowledge. Managers and leaders act as organizational nonlinear integrators of all these knowledge fields, i.e., rational, emotional, and spiritual knowledge fields.
Knowledge dynamics can explain complex social phenomena like organizational change [
45], cross-emotional infection among multi-flight groups in mass flight delays [
61], epidemics of violence, and crime surge in a certain area or city [
62]. Kotter [
63], (p. 20) shows that in change management,
complacency is a powerful inertial force which reflects the interactions between the rational and emotional knowledge fields: “Complacency is not only a thought. It’s very much a feeling. It is usually less a matter of conscious, rational analysis than unconscious emotion”. Knowledge dynamics can explain many irrational economic phenomena and consumers behavior [
64,
65], as well as bad strategy design and implementation [
66,
67]. For instance, one of the frequent mistakes some companies make is keeping the same marketing strategies throughout the world, regardless of the cultural differences between different countries. For instance, when Taiwan Pepsi used the generic slogan “Come alive with the Pepsi generation”, the uninspired translation “Pepsi will bring your ancestors back from the dead” produced a strong negative buying reaction in the potential consumers of pepsi-cola. A similar emotional effect happened when General Motors wanted to sell “Chevy Nova” in South America, since “nova” means “It doesn’t go” in Spanish [
66].
A famous psychological experiment which demonstrates the influence of knowledge dynamics on decision-making was performed at an old British university, where students used to go in their free time to a tea room to prepared themselves a tea or a coffee and eat some snacks. In that room, there was a list with suggested prices and an “honesty box” for students to pay for their consumption. “One day a banner poster was displayed just above the price list, with no warning or explanation. For a period of ten weeks, a new image was presented each week, either flowers or eyes that appeared to be looking directly at the observer” [
27], (p. 57). The result showed that students were influenced in their decision of how much to pay by those images. When they felt the eyes looking at them, they put in the “honesty box” more money than when they saw flowers. That explains how emotional knowledge generated by the flowers or eyes transformed into rational knowledge and influenced students in their decision for payment. This emotional–rational knowledge dynamic appears important also in investment decisions, since people accept with difficulty the idea of losing money: “They are reluctant to take risk when there is a huge change of losing because it is hard to overcome the psychological burden of it” [
68], (p. 1056).
The theory of knowledge fields may be very useful in the design and realization of virtual agents [
68], and emotional robots. Although it looks like a paradox to discuss love relationships between humans and robots, there is a developing research in this new domain called
lovotics (love + robotics). It is a multidisciplinary domain utilizing concepts and ideas from several research areas like biology, psychology, neuroscience, artificial intelligence, and robotics [
69,
70]. This theory can explain also the complex process of managing knowledge entropy and influencing the innovation process and firm’s performance [
71,
72,
73].
6. Conclusions
The aim of this paper is to present a new approach to understanding the complex concepts of knowledge and knowledge dynamics by analogy with thermodynamics, targeting managerial applications. All the knowledge metaphors based on objects, stocks, flows, and stocks-and-flows induce in the semantic field of knowledge the idea of linearity and sometimes that of tangibility. These attributes are serious limitations of understanding the practical value of these concepts, and in designing metrics for intangible evaluations.
Thermodynamics suggests the idea of knowledge fields and that of transformation of one field into another, like in the energy case. Based on that, we define three fundamental knowledge fields: Rational, emotional, and spiritual. We show their characteristics and the transformational processes between them. The whole conceptual model is based on the hypothesis of knowledge unity and oneness philosophy between mind and body. Understanding knowledge fields and knowledge dynamics as a transformational process helps managers and leaders in making decisions and motivating much better the employees. Also, knowledge dynamics plays a key role in leading change and developing a dynamic knowledge ecosystem. Knowledge sharing, a key process in knowledge management, changes the knowledge distribution within an organization, which contributes to increasing knowledge entropy. Thus, knowledge management can be interpreted as organizational entropy management.