Embedded throughout this paper you will find the diversity of opinions that correlates to the diversity of theories, frameworks, case studies and stories that are related to the field of Knowledge Management (KM). We begin by introducing the Sampler Research Call approach and the 13 KM academics and practitioners working in different parts of the world who answered the call. We then provide baseline definitions and briefly explore the process of knowledge creation within the human mind/brain. After a brief (and vastly incomplete) introduction to KM literature at the turn of the Century, the frameworks of Sampler Call participants are introduced, and two early frameworks that achieved almost cult status—the Data-InformationKnowledge-Wisdom (DIKW) continuum and the SECI (socialization, externalization, combination and internalization) model—are explored through the eyes of Sampler Call participants. We then introduce the results of the KMTL (Knowledge Management Thought Leader) Study, which suggest theories consistent with the richness and diversity of thought interwoven throughout this paper. The field of KM is introduced as a complex adaptive system with many possibilities and opportunities. Finally, we share summary thoughts, urging us as KM academics and practitioners to find the balance between the conscious awareness/understanding of higher-order patterns and the actions we take; between the need for overarching theory and the experiential freedom necessary to address context-rich situations.
Keywords: knowledge, knowledge management, theory, information, learning, surface knowledge, shallow knowledge, deep knowledge, neuroscience, mind/brain, decision-making, higher-order patterns, complexity, thought leaders, practitioners, knowledge (proceeding), knowledge (informing), SECI model, DIKW continuum, wisdom, KM research, KM frameworks.
When Kant proposed a Copernican Revolution, he argued that our experiences are structured by the categories of our thought, the way we think about space, time, matter, substance, causality, contingency, necessity, universality, particularity, etc. (Gardner, 1999). Bohm suggests that to achieve clarity of perception and thought “requires that we be generally aware of how our experience is shaped by ... the theories that are implicit or explicit in our general ways of thinking” (Bohm, 1980, p. 6). Bohm emphasizes that experience and knowledge are one process. It is our theories that give shape and form to experience in general, both expanding and limiting us.
The role of theory in the field of Knowledge Management (KM) is indeed controversial (Flock & Mekhilef, 2007). Some studies note that scholarly work in KM played an important role in developing the field (Serenko et al., 2012; Serenko and Bontis, 2013), and other studies point out the disparity between theory and practice (Booker et al., 2008). Many questions remain unanswered (Flock & Mekhilef, 2007). For example, can KM be seen as a discipline? If so, what are its principles, theories and models? Is there an overarching theory for KM? In this paper we explore the relationship of knowledge, theory and KM through the eyes of KM thought leaders and practitioners.
Working across domains, this paper takes a consilience approach, that is, by integrating evidence from independent sources to draw strong conclusions. Further, this exploration is intended as a thought expanding exercise which demonstrates the diversity of the field. Because of this diversity, for each opinion presented in this paper there is undoubtedly a bevy of literature to support it, and an equal amount of disagreement, with research studies often quoted as validation. It is not the intent of this paper to support or question the opinions of the contributors, but to share the different frames of reference these opinions represent. Connecting thought much like the workings of the mind/brain (which is an associative patterner), there is not a bounded literature review as such. Examples of theory or the models that represent theory, and references to supporting literature, are included in this paper.
The thought and findings from three research studies related to KM and practitioners of KM are represented in this paper. In preparation for this paper—to reflect current thought and demonstrate the diversity of opinion—a Sampler Research Call (Sampler Call) went out to KM academics and practitioners working in different parts of the world; there were 13 respondents. Two earlier research studies referenced in this paper are (1) the 2005 Knowledge Management Thought Leader (KMTL) Study which involved in-depth interviews and follow-up with 34 KM thought leaders across four continents (Bennet, 2005), and (2) the 2007 iKMS Global Survey which included responses from over 200 KM practitioners (Lambe, 2008).
Since opinions from the Sampler Call are embedded throughout this paper—including the definitions section—we introduce the Sampler Research Call approach before laying out foundational definitions and introducing concepts that help develop a common understanding of what is meant by knowledge. We then briefly look at knowledge creation from the viewpoint of the human mind/brain to explore the powerful role that theories—and the frameworks and models emerging from those theories—play in the human decision-making process. Each individual has a self-organizing, hierarchical set of theories (and consistent relationships among those theories) that guide the decision-making process (Bennet & Bennet, 2010a; 2013). We introduce representative KM literature emerging at the turn of the century with a focus on literature and frameworks forwarded by participants in the Sampler Call, then focus on two early frameworks—the Data-Information-Knowledgewisdom (DIKW) continuum and the SECI (socialization, externalization, combination, and internalization) model. These frameworks are viewed through the diverse opinions of Sampler Call participants. Finally, we look at characteristics of the KM field surfaced in the KMTL Study in conjunction with thoughts forwarded by Sampler Call participants and current examples before providing summary thoughts.
The 2014 Sampler Research Call
An email Sampler Research Call went out to 19 geographically-dispersed Knowledge Management academics and practitioners. The intent was to hear from voices who practiced and/or taught KM in different cultures. The primary criteria were that each individual be a practitioner and/or academic in the field and have taken a leadership role through (1) publishing KM-related articles/books and/or recognized as a leader in the field, and (2) speaking at conferences and/or otherwise teaching KM. Due to the need for a short turnaround, ease of contact was also taken into account. For example, fourteen of the 19 individuals approached had participated in the 2005 KMTL Study. This previous relationship facilitated ease of contact. Nine of these participated in the Sampler Research Call.
In addition to meeting the primary criteria, the remaining 5 individuals approached were selected because of (1) their geographic location, and (2) the ease of contact, that is, a previous relationship with the editor of this JEMI special edition or with one of the authors. Four of these participated in the Sampler Call. Listed alphabetically by country, the 13 participants in the Sampler Call are: Charles Dhewa (Africa), Frada Burstein (Australia), Hubert Saint-Onge (Canada), Surinder Kumar Batra (India), Madanmohan Rao (India) Edna Pasher (Israel), Francisco Javier Carrillo (Mexico), Milton Sousa (Portugal), Dave Snowden (UK), and Nancy Dixon, Kent Greenes, Larry Prusak and Etienne Wenger-Trayner (across the US). The names and reputations of these practitioners and academics will be familiar to many readers. Short descriptions are included at the end of this paper following the authors’ bios.
Each participant was provided a copy of the call for papers that went out for this special JEMI issue. The intent of the Sampler Research Call was to “explore the connections between knowledge, KM and theory”. Each individual was asked to provide the answers as appropriate to six questions, and to provide other thoughts “of significance in regards to this focus area”. The six questions dealt with: KM practitioners trust of theoretical approaches and frameworks; why some KM frameworks (such as SECI and DIKW) had achieved cult status; favorite theories and their application; personal theories and how these personal theories serve them; authoring of papers/articles and the theories referenced in this work; and the tenuous connections in published works between KM research and KM practice.
Five of the responders chose to focus their thoughts on the relationship of knowledge, KM and theory rather than answer the specific questions; and three others left one or more questions unanswered. Thus this qualitative response was organized by related topics, with the thoughts and words attributed to these participants embedded throughout this paper. Where embedded, following each participant’s name is the reference: “(Sampler Call, 2014)”. While it is acknowledged that these are opinions that reflect a small number of academics/practitioners, a limitation of the Sampler Call approach, they demonstrate the diversity of thought related to the KM field, and the deliberate geographic spread should reduce region-specific bias. The authors do not propose to support or oppose these opinions, rather providing them for the reader’s reflection.
The terms used in this paper are explicated below in order to provide a common language within the bounds of this paper to explore the relationship of knowledge, theory, and knowledge management. Through these definitions we will see that the characteristics of knowledge in action underpin the way that knowledge management plays out in practice, specifically in the interplay between theory and practice, and the critical role of context in determining how knowledge is applied.
The brain consists of an atomic and molecular structure and the fluids that flow through this structure. The mind is the totality of the patterns in the brain created by neurons and their firings and connections. These patterns encompass all of our thoughts. The term mind/brain refers to both the structure and the patterns emerging within the structure (Bennet & Bennet, 2010).
A system is a group of elements or objects, the relationships among them, their attributes, and some boundary that allows one to distinguish whether an element is inside or outside the system. a simple system remains the same or changes very little over time. Simple systems have few states, are typically non-organic and exhibit predictable behavior. Examples are an air conditioning system, a light switch, and a calculator. While a complicated system contains a large number of interrelated parts, the connections among the parts are fixed. Complicated systems are non-organic systems in which the whole is equal to the sum of its parts, that is, they do not create emergent properties. Examples are a Boeing 777, an automobile, a computer, and an electrical power system (Bennet & Bennet, 2004).
Complexity is the condition of a system, situation, or organization that is integrated with some degree of order but has too many elements and relationships to understand in simple analytic or logical ways. a complex adaptive system (CAS) is a partially ordered system with many agents (people) that interact with each other as the system unfolds and evolves through time. They are mostly self-organizing, learning and adaptive. Examples are life, ecosystems, economies, organizations, and cultures (Axelrod and Cohen, 1999). As the term is used in this paper, this would infer a nonlinearity and unpredictability among the elements and relationships, thus the difficulty in identifying a single or “best” response or solution to a specific issue or situation.
Embracing Stonier’s description of information as a basic property of the Universe—as fundamental as matter and energy (Stonier, 1990)—we take information to be a measure of the degree of organization expressed by any non-random pattern or set of patterns. The order within a system is a reflection of the information content of the system. Data (a form of information) would then be simple patterns, and while data and information are both patterns, they have no meaning until some organism recognizes and interprets the patterns (Stonier, 1997; Bennet & Bennet, 2008b). Thus information exists in the human brain in the form of stored or expressed neuronal patterns that may be activated and reflected upon through conscious thought.
As a functional definition, knowledge is considered the capacity (potential or actual) to take effective action in varied and uncertain situations (Bennet & Bennet, 2004), a human capacity that consists of understanding, insights, meaning, intuition, creativity, judgment, and the ability to anticipate the outcome of our actions. There is considerable precedent for linking knowledge and action consistent with the emergence of the field of Knowledge Management as a business management approach in the early 1990’s driven by computing, consultants, conferences and commerce (Lambe, 2011). As detailed later in this paper, in the KMTL Study 84 percent of respondents tied knowledge directly to action or use (Bennet, 2005). Similarly, emerging from nearly 20 years of APQC’s leading research in the field of KM, O’Dell and Hubert define knowledge from the practical perspective as “information in action” (O’Dell & Hubert, 2011, p.2).
While recognizing that it is common to define information as processed data, and knowledge as actionable information, Batra (Sampler Call, 2014) finds it interesting that the definitions or interpretations of the term knowledge are contextual. However, he also notes that in another context knowledge gets interpreted as know-what, know-how, know-who and know-why, and in an HR context knowledge includes the competence set of individual skills and attitudes. Further, from a strategic perspective knowledge can be considered as a strategic resource for the firm, taking the form of intellectual capital and intangible capital. Batra finds these differences in interpretation useful to the students of KM in “appreciating that knowledge is not a monolithic entity which can be managed in a prescriptive manner.”
Dhewa (Sampler Call, 2014) likes the notion of “useful knowledge”, which he sees as a way of understanding knowledge as an economic resource, a concept expanded on by Kuznets (1955) and extensively used by Mokyr (2005) in his studies about the role of knowledge in the industrial revolution. As Dhewa suggests, “I am applying this notion in exploring the role of knowledge in the agriculture sector. Unless knowledge solves a specific issue like income growth, it’s not knowledge at all, according to me. When knowledge is applied, it defines itself.”
Linking knowledge and action provides the opportunity to measure knowledge effectiveness (Porter et al, 2003). Outside of its context and the situation in which it is being applied, knowledge itself is neither true nor false. The value of knowledge in terms of good or poor is difficult to measure other than by the outcomes of actions based on that knowledge. Good knowledge would have a high probability (closer to 1 on a 0-1 scale) of producing the desired (anticipated) outcome, and poor knowledge would have a low probability (closer to 0 or a 0-1 scale) of producing the expected result. For complex situations the quality of knowledge (from good to poor) may be hard to estimate because of the system’s unpredictability. After an outcome has occurred, it may be possible to assess the quality of knowledge by comparing the actual outcome to the expected outcome; although it is also possible that there may not be a direct observable causal relationship between a decision made/action taken and the results of that action (Bennet & Bennet, 2013).
Explicit knowledge is the descriptive term for that which can be called up from memory and described accurately in words and/or visuals (representations) such that another person can comprehend the knowledge that is expressed through this exchange of information. This is consistent with Polanyi’s description as knowledge which can be transmitted in formal systematic language (Polanyi, 1966). Explicit knowledge has historically been called declarative knowledge (Anderson, 1983). Tacit knowledge is the descriptive term for those connections among thoughts that cannot be pulled up in words, a knowing of what decision to make or how to do something that cannot be clearly voiced in a manner such that another person could extract and re-create that knowledge (understanding, meaning, etc.). Consistent with this definition, Polanyi (1966) sees tacit knowledge as personal and context-sensitive, therefore hard to communicate.
We consider knowledge as comprised of two parts: Knowledge (Informing) and Knowledge (Proceeding) (Bennet & Bennet, 2008b). This builds on the distinction made by Ryle (1949) between “knowing that” and “knowing how” (the potential and actual capacity to take effective action). Knowledge (Informing) is the information (or content) part of knowledge. While this information part of knowledge is still generically information (organized patterns), it is special because of its structure and relationships with other information. Knowledge (Informing) consists of information that may represent understanding, meaning, insights, expectations, intuition, theories and principles that support or lead to effective action. When viewed separately this is information even though it may lead to effective action. It is considered knowledge when used as part of the knowledge process. In this context, the same thought may be information in one situation and knowledge in another situation.
Knowledge (Proceeding), represents the process and action part of knowledge. It is the process of selecting and associating or applying the relevant information, or Knowledge (Informing), from which specific actions can be identified and implemented, that is, actions that result in some level of anticipated outcome. There is considerable precedent for considering knowledge as a process versus an outcome of some action. For example, Kolb (1984) forwards in his theory of experiential learning that knowledge retrieval, creation and application requires engaging knowledge as a process, not a product. Bohm reminds us that “the actuality of knowledge is a living process that is taking place right now” and that we are taking part in this process (Bohm, 1980, p. 64). Note that the process our minds use to find, create and semantically mix the information needed to take effective action is often unconscious and difficult to communicate to someone else; therefore, by definition, tacit.
In Figure 1 below, “Justified True Belief” represents the theories, values and beliefs that are generally developed over time and often tacit. “Justified True Belief” is the definition of knowledge credited to Plato and his dialogues (Fine, 2003). The concept is based on the belief that in order to know a given proposition is true you must not only believe it, but you must also have justification for believing it. Justified true belief represents an individual’s truth, that is, the beliefs and values that make up our personal theories, all developed and reinforced by personal life experiences. It is acknowledged that an individual’s justified true belief may be based on a falsehood (Gettier, 1963). However, if it is used to take effective action in terms of the user’s expectations of outcomes, then it would be considered knowledge from that individual’s viewpoint. Note that this is only one part of Knowledge (Informing), and that our beliefs and theories are part of the living process described above (Bohm, 1980; Bennet & Bennet, 2008b; 2014). The term “memory” is used as a singular collective and implies all the patterns and connections accessible by the mind occurring before the instant at hand.
Building on the definitions of Knowledge (Informing) and Knowledge (Proceeding) introduced above, it is also useful to think about knowledge in terms of three levels: surface, shallow and deep. Recognizing any model is an artificial construct, the focus on three levels (as a continuum) is consistent with a focus on simple, complicated and complex systems (Bennet & Bennet, 2013; 2008c) and appropriate in the context of its initial use with the U.S Department of the Navy (DON), the first government organization to be named as a Most Admired Knowledge Enterprise for their extensive work in KM and organizational learning.
Surface knowledge is predominantly but not exclusively simple information (used to take effective action). Answering the question of what, when, where and who, it is primarily explicit, and represents visible choices that require minimum understanding. Surface knowledge in the form of information can be stored in books and computers. Because it has little meaning to improve recall, and few connections to other stored memories, surface knowledge is frequently difficult to remember and easy to forget (Sousa, 2006). Shallow knowledge includes information that has some depth of understanding, meaning and sense-making. To make meaning requires context, which the individual creates from mixing incoming information with their own internally-stored information, a process of creating Knowledge (Proceeding). Meaning can be created via logic, analysis, observation, reflection, and even—to some extent—prediction. Shallow knowledge is the realm of social knowledge, and as such this focus of KM overlaps with social learning theory (Bennet & Bennet, 2010b; 2007). For example, organizations that embrace the use of teams and communities facilitate the mobilization of both surface and shallow knowledge (context rich) and the creation of new ideas as individuals interact, learn and create new ideas in these groups.
For deep knowledge the decision-maker has developed and integrated many if not all of the following seven components: understanding, meaning, intuition, insight, creativity, judgment, and the ability to anticipate the outcome of our actions. Deep knowledge within a knowledge domain represents the ability to shift our frame of reference as the context and situation shift. Since Knowledge (Proceeding) must be created in order to know when and how to take effective action, the unconscious plays a large role, with much of deep knowledge tacit. This is the realm of the expert who has learned to detect patterns and evaluate their importance in anticipating the behavior of situations that are too complex for the conscious mind to understand. During the lengthy period of practice (lived experience) needed to develop deep knowledge in the domain of focus, experts have developed internal theories that guide their Knowledge (Proceeding) (Bennet & Bennet, 2008c).
Building on the definition of knowledge, learning is considered the creation of the capacity (potential or actual) to take effective action. From a neuroscience perspective, this means that learning is the identification, selection and mixing of the relevant neural patterns (information) within the learner’s mind with the information from the situation and its environment to create understanding, meaning and anticipation of the results of selection actions (Bennet & Bennet, 2008e). Each learning experience builds on its predecessor by broadening the sources of knowledge creation and the capacity to create knowledge in different ways. When an individual has deep knowledge, more and more of their learning will continuously build up in the unconscious. In other words, in the area of focus, knowledge begets knowledge. The more that is understood, the more that can be created and understood, relegating more to the unconscious to free the conscious mind to address the instant at hand. The wider the scope of application and feedback, the greater the potential to identify second order patterns, which in the largest aggregate leads to the phenomena of Big Data (MayerSchönberger & Cukier, 2013).
Descriptive definitions of Knowledge Management will be introduced below with the KMTL Study. KM thought leaders, as defined in the KMTL Study, are considered those individuals (a) whose focus has been in the area of KM for several years and continues in this or a related field, (b) who have published or edited books or multiple articles in the field, (c) who have developed and taught academic or certification courses in the area of KM, and (d) who have spoken about KM at multiple symposia and conferences (Bennet, 2005). By definition, this means that thought leaders are both learners and educators. As Durham (2004) points out, thought leadership is as much a social role as the command of knowledge, going beyond subject matter expertise to imply leadership and a willingness to assert direction.
A theory is considered a set of statements and/or principles that explain a group of facts or phenomena to guide action or assist in comprehension or judgment (American Heritage Dictionary, 2006; Bennet & Bennet, 2010a). Based on beliefs and/or mental models and built on assumptions, theories provide a plausible or rational explanation of cause and effect relationships. For purposes of this paper, assumptions are something taken for granted or accepted as true without proof, a supposition or presumption. Principles are considered basic truths or laws; rules or standards; an essential quality or element. Guidelines are a statement or other indication of policy or procedure by which to determine a course of action (how to apply). a framework is a set of assumptions, concepts, values and practices that constitutes a way of viewing reality (American Heritage Dictionary, 2006). Thus a framework is tied closely to action. For purposes of this paper, it is assumed that the frameworks developed and provided by participants in the Sampler Group represent their personal theories as related to KM.
Taken from the Greek word theoria, which has the same root as theatre, theory means to see or view or to make a spectacle (Bohm, 1980). Theories reflect higher-order patterns, that is, not the facts themselves but rather the basic source of recognition and meaning of the broader patterns. Bohm sees theories as a form of insight, a way of looking at the world, clear in certain domains, and unclear beyond those domains, continuously shifting as new insights emerge through experience. While a written theory could be considered information, when understood such that it offers the potential to, or is used by, a decision-maker to create and guide effective action, it would be considered knowledge. Further, while in its incoming form it is Knowledge (Informing), when complexed with other information in the mind of the decision-maker to make decisions and guide action it becomes part of the process that is Knowledge (Proceeding). a framework or model based on a theoretical structure highlights the primary elements of the theory and their relationships.