Polymorphic Nature of Knowledge: Towards a knowledge creation model
Abou-Bakr A. Moteleb
Middlesex University, UK a.moteleb@mdx.ac.uk
Walaa M. Bakry
Middlesex University, UK w.mohamed@mdx.ac.uk
Abstract
The ability to create and communicate knowledge represents a major challenge for organisations today Yet, a gap is apparent between different notions of knowledge creation in epistemology and models of knowledge management in literature This paper attempts at narrowing this gap by studying the nature of knowledge and presenting a holistic generic model for knowledge creation based on different epistemological notions in literature.
Keywords: knowledge, knowledge creation, eKnowledge, knowledge management This paper explores
1. Introduction
Knowledge has always represented an important component in civilisations and an interesting topic of discussion. Ever since ancient Egyptians, to Greek philosophers, to modern scholars, ‘Epistemology’, the study of knowledge, occupied people minds for millennia. Today, knowledge is considered “the key personal resource and the key economic resource” (Drucker P. 1993). Recognising this fact, many organisations have initiated Knowledge Management programmes (Nonaka I. and Takeuchi H. 1995; Davenport T. and Prusak L. 2000; Nonaka I. et Al. 2000). However, there are few examples in literature that bridge the gap between knowledge and knowledge application (Roth J. 2003).
Knowledge Management (KM) is a multi oriented subject that has roots and is influenced by a variety of disciplines such as philosophy, cognitive, social, economics, and management sciences, as well as knowledge and information systems engineering and artificial intelligence. As a result, a multitude of variant philosophies, definitions, theories and models of KM are circulating in literature as well as in practice today (Kakabadse N. et Al. 2003). However, KM models are often criticised of supporting one notion of knowledge creation over another For example, Gebert et Al. (2003) criticises most KM models developed within the last decade; as it either takes an epistemological approach “view[ing] knowledge as an entity with distinctive
attributes that can be deconstructed and its details analyzed”, or an ontological approach “view[ing] it as an integrated whole and focus on its relations with the surroundings”
Consequently, two types of knowledge management systems (KMS) are generally discussed in literature: analytical and actor models (Herder P.M. et Al. 2003). The analytical model focuses on the codification of knowledge into Information and Communication Technologies (ICT) based information systems, emphasising the “importance of explicit knowledge and information, and the technological infrastructure to share it” (e.g. Intranet) The actor model focuses on people and business processes, emphasising the “importance of tacit knowledge and the social infrastructure to share it” (e.g. communities of practice). It is often argued that the weakness of many traditional KMS is an overemphasis on technology and tools, or an overemphasis on people and knowledge (Moffett S. et Al. 2003; Herder P.M. et Al. 2003) Therefore, a proper KM approach has to strike and maintain a balance between the two paradigms. (De Bruijn and De Nere´ e tot Babberich 2000; cited in Herder P.M. et Al. 2003).
Accordingly, more research addressing the relationship between the cultural and technological aspects of KM is required (Moffett S. et Al. 2003). Some scholars, such as Milton et al., (1999), took this notion further by stating that the creation of knowledge technologies from the methods of knowledge engineering will produce better knowledge management (cited in Beveren J. V. 2002) In order to engineer effective KMS, it is essential to understand the characteristics of knowledge and the methods of knowledge creation. “The challenge in knowledge management is to determine how each knowledge type can be codified and transferred in an organization” (Goh S. C. 2002). This paper explores the nature of knowledge and presents a holistic generic model for knowledge creation based on different epistemological notions in literature. First a theoretical investigation on the different notions of knowledge creation is presented in Section (2). Then, the implications of these different notions to knowledge systems engineering are discussed, and a generic model of knowledge creation is presented in Section (3). Finally, the paper concludes with implications to organisations in Section (4)
2. Knowledge Creation Controversies
Despite a strong consensus, among scholars, on the general importance of knowledge, controversies on its reality and origins are widely debated in a wide range of disciplines. For example, the argument whether knowledge finds its basis in perception and experience (Empiricism) or whether it finds its basis in ratio and reasoning (Rationalism) has kept philosophers occupied for many millennia (Beijerse R. P. 1999).
This paper is not trying to support one ideology over another; it only highlights these controversies to allow for a better understanding of the knowledge creation process. In addition, studying how these ideologies reflect on KMS design would allow the development of a more holistic model for knowledge creation
2.1
External Knowledge Creation (Outside In)
Divine vs. Earthly
Ancient Egyptians believed all knowledge is divine. Since the pharaohs ruled Egypt, from about 3000 BC, Knowledge was highly recognised by ancient Egyptians. According to Egyptian mythologies, ‘Thoth’, the worshiped god of knowledge and learning, represented the source of all knowledge and transmitted it to ancient Egyptians through the wisdom of kings and priests. Ancient Egyptians conceived him as the “personification of the mind of god, as the author of every work on every branch of knowledge" (Hancock G. 2001). Nevertheless, ancient Egyptians organised their social structure around what is described today as “knowledge workers” (Drucker P. 1993). Beside traditional workers such as stone workers, wood workers and metal workers, there were the “greatest of all jobs”, the Scribe (McNeill S. 1996). Scribes were privileged workers, who were responsible for recording earthly and divine knowledge, from geometrical designs and medical cures to mummification techniques (McNeill S. 1996).
Rationalism vs. Empiricism
These notions of knowledge were taken a step further by Greek philosophers in open discussions about knowledge. Two main ideologies emerged in the last four centuries BC. “Rationalism”, led by Plato, argues that “knowledge is a priori (comes before experience), and “empiricism”, led by Aristotle, argues that “knowledge is posteriori (comes after experience) (Kreis S. 2001). The first essentially claims that “knowledge can be obtained deductively by reasoning” and the second essentially claims that “knowledge can be obtained inductively from sensory experiences” (Nonaka I. and Takeuchi H. 1995).
The Platonic and Aristotelian ideologies were inherited in modern epistemology, through debating ‘Rationalism’ vs. ‘Empiricism’, from Descartes and Locke in the 17th century, to Kant, Hegel, and Marx in the 18th and 19th century (Nonaka I. and Takeuchi H. 1995) ‘Rationalists’, such as Descartes, argued that “many aspects of our knowledge are based on innately given characteristics of the human mind” (Beveren J. V. 2002), and hence, true knowledge can be obtained by the mind, not by the senses (Nonaka I. and Takeuchi H. 1995). ‘Empiricists’, such as John Locke, maintained that we are born with the human mind as an empty vessel or a white paper ‘tabula rasa’, and that all knowledge is acquired through the sensors, and hence, only experience can provide the mind with ideas or concepts (Nonaka I. and Takeuchi H. 1995, Beveren J. V. 2002)
Kant, Hegel, and Marx, attempted to synthesize the two ideologies (Nonaka I. and Takeuchi H. 1995). For example, Kant realised that knowledge cannot come from sensory input alone and that there must be certain pre existing ‘‘categories’’ according to which this sensory material is ordered and organized (Nonaka I. & Takeuchi H. 1995; Beveren J. V. 2002). He argued that knowledge arises only when both logical thinking (Rationalism), and sensory experience (Empiricism) work together (Nonaka I. and Takeuchi H. 1995). Hegel acknowledged that knowledge begins with sensory perceptions, while becoming more subjective and rational through a dialectic purification of the senses (Nonaka I. and Takeuchi H. 1995)
Similarly, Marx reached that perception is the interaction between the knower (Subject) and the known (Object) (Nonaka I. and Takeuchi H. 1995).
Positivism vs. Relativism
“Rationalism’ and ‘Empiricism’ continued to impact epistemology until today. The paradox between the two ideologies was inherited in the research domain through debating ‘Relativism’ vs. ‘Positivism’ According to Robson C. (2002) the ‘Positivist’ notion maintains that “objective knowledge (facts) can be gained from direct experience or observation, and is the only knowledge available to science”. The ‘Relativism’ notion maintains “that there is no external reality independent of human consciousness; there are only different sets of meanings and classifications which people attach to the world” (Robson C. 2002).
Similar to what happened with ‘Rationalism’ and ‘Empiricism’, new approaches in the research domain attempted to synthesise the two ideologies in the last decade ‘Post Positivism’, while maintaining a commitment to objectivity, acknowledges the influence of the mental model (i.e. background knowledge and values) of the observer on the observed On the other hand, ‘Constructivism’, although still maintains that knowledge is subjective, it posits that knowledge is socially constructed (Robson C. 2002, Hwang A. 2003). Constructivists view knowledge as a structure that is subjectively constructed by individual minds (Hwang A. 2003). Hence, it is essential to understand the multiple social constructions of meaning and knowledge.
A comparison between key characteristics of the two notions is presented in Table (1).
Rationalism
Empiricism
Other labels Naturalistic Relativism Positivism scientific Notion evolution Constructivism Post Positivism
Knowledge source Ideal mental process Product of sensory experience
Knowledge occurrence Priori (comes before experience) Posteriori (comes after experience)
Nature of Knowledge Subjective Objective
Methodology of knowledge creation
Deductive inference: by appealing to mental constructs such as concepts, laws, or theories
Inductive inference: from particular sensory experiences
Table (1): Contrasting knowledge creation in Rationalism vs. Empiricism
2.2 Internal Knowledge Creation (Inside Out)
Another way to look at knowledge creation is how knowledge is created inside the human mind. Beijerse R. P. (1999) states that “whereas traditional epistemology finds its basis in knowledge creation as a result of the difference between objects and subjects (human subjects acquire knowledge of (on) human objects by processes of induction and deduction), Polanyi states that people create knowledge by becoming involved in the object.” In fact, Polanyi (1966) argues that people actually acquire knowledge by the active (re)creation and organization of their own experience (Beijerse R. P. 1999).
Researchers such as Nonaka I. and Takeuchi H. (1995) have used the notions of Tacit and Explicit knowledge first introduced by Polanyi, to explain the process of
knowledge creation among people from an internal perspective of the organisation. In their book “The knowledge Creating Company”, Nonaka I. and Takeuchi H. (1995) argue that knowledge creation in organisations is actually done from the inside out rather than simply processing information from the outside in. Accordingly, they define two forms of knowledge Tacit and Explicit, and study the conversion processes between the two notions.
Tacit vs. Explicit
According to Nonaka I. and Takeuchi H. (1995), Tacit knowledge is subjective and experience based, while Explicit knowledge is objective and reason based. Tacit knowledge includes cognitive skills (e.g. beliefs and mental models) and technical skills (e.g. crafts and ‘know how’). Therefore, it is difficult to articulate, codify, and distribute Tacit knowledge. In contrast, Explicit knowledge includes theoretical approaches, problem solving manuals and databases. Therefore, Explicit knowledge is easy to articulate, codify, and distribute in different formats. The following table summarises the different characteristics of the two notions.
Tacit Knowledge
Explicit Knowledge
Knowledge source Knowledge of Experience Knowledge of rationality Knowledge occurrence Simultaneous
Sequential Nature of Knowledge Subjective Objective
Knowledge Type Analog (Practice) Digital (Theory
Table (2): Tacit vs. Explicit Knowledge (Adapted from Nonaka I. and Takeuchi H. 1995)
Knowledge Conversion
Nonaka I. and Takeuchi H. (1995) suggest that knowledge is created through the interaction between tacit and explicit knowledge. Accordingly, four modes of knowledge conversion are discussed: (1) Socialisation from tacit to tacit, (2) Externalisation from tacit to explicit, (3) Combination from explicit to explicit, and (4) Internalisation from explicit to tacit. The four modes of interaction between tacit and explicit knowledge are illustrated in figure (1):
Tacit Knowledge
KnowledgeTo
Tacit Knowledge
From
Explicit Knowledge
Knowledge
Knowledge
Knowledge
Knowledge
Fig (1): Knowledge created in the four modes of knowledge conversion
(Source: Nonaka I. and Takeuchi H. 1995)
(1) Socialization: The process of sharing experiences whereby Sympathised knowledge is being created in the form of mental models and technical skills (Nonaka I. and Takeuchi H. 1995). Examples of situations where socialisation can happen are master fellow relationships, mentoring, on the job training, trial and error policy, imitating others, constructive brainstorm sessions, practising and training, exchanging of ideas and ‘water coolers’ talking (Beijerse R. P. 1999).
(2) Externalization: The process of articulating tacit knowledge into explicit concepts whereby Conceptual knowledge is being created using metaphors, analogies, hypotheses and models (Nonaka I. and Takeuchi H. 1995). Externalisation usually happens in a design process, when conversations and collective consideration are used to boost this design process (Beijerse R. P. 1999).
(3) Combination: The process of systemising concepts into a knowledge system whereby Systemic knowledge is being created such as a prototype and new component technologies (Nonaka I. and Takeuchi H. 1995). Combination can happen when the knowledge exchanged between people is combined through documents, meetings, telephone conversations and the exchange of information via media like computer networks. Also, new knowledge can be created through reorganising existing information by sorting, adding, combining and categorizing explicit knowledge (Beijerse R. P. 1999).
(4) Internalization: The process of embodying explicit knowledge into tacit knowledge whereby Operational knowledge is being created about project management, production process, new product usage, and policy implementation (Nonaka I. and Takeuchi H. 1995) Examples of situations where internalisation can happen are learning by doing, ‘reliving’ a project by studying its archives, and experts’ lectures (Beijerse R. P. 1999).
3. Implications to Knowledge Systems Engineering
Some researchers suggest that a KMS is a sub system of the organisation’s management information system (MIS), “specifically a firm based network that enables the acquisition, storage, distribution, and retrieval of organizational knowledge and information (Alavi and Leidner, 2001; Huber, 1991; Zack, 1999 cited in Adams G. L. & Lamont B. T. 2003). So far, ICT based MIS are successful at simulating the relation between data and information, while knowledge is applied externally at different stages of the information system. “The prior knowledge contained in human brains is required for the creation of information, just as the creation of knowledge often requires the input of information through the sensors to the brain.” Beveren J. V. (2002). Yet, ICT based KMS need to simulate the relationship between knowledge, information, and data, in order for ICT to prove successful in enabling knowledge creation Lueg C. (2001) argues that the applicability of computer based information management tools reveals that the scope is rather limited and so is the applicability of such tools in the KM domain. The following sections study the relationship between knowledge, information, and data in literature, and present an integrated model for knowledge creation accordingly.
3.1 Data, Information, and Knowledge
It is widely accepted, in literature as well as in practice, that knowledge is different from data, and information (Lang J. C. 2001, Stenmark D. 2002, Yahya S. & Goh W.
2002). However, there is a lack of a definition that clearly distinguishes between the terms (Stenmark D. 2002). In fact, the terms are usually used interchangeably in literature (Stenmark D. 2002, Vouros G. A. 2003). The following table presents some of the definitions currently used by scholars.
Author(s) Data Information Knowledge
Nonaka I. & Takeuchi H. (1995)
Davenport T. H. & Prusak L. (2000)
A flow of meaningful messages
A set of discrete facts A message meant to change the receiver’s perception
Commitments and beliefs created from these messages
A fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experience and information.
Bhatt G. D. (2001) Pe´rez M. P. et Al. (2002)
Bollinger A. S. & Smith R. D. (2001)
Raw facts
Organized set of data Meaningful information.
Processed data that can reside within computers
The understanding, awareness, or familiarity acquired through study, investigation, observation, or experience over the course of time
Morey D. (2001)
Beveren J. V. (2002)
Information that relates to the attributes of an object or event
Raw facts that can be shaped and formed to create information
Herder P.M. et Al. (2003) Discrete, objective, non contextual facts about events. Usually codified, these facts describe or quantify events
Patterns or inferences about data
Data within a context
Data that has been endowed with relevance and purpose. That is, adding context by organizing, or categorizing it with a particular purpose in mind. Information requires a sender and receiver.
Inference rules that create the capacity for effective action
Stock of information, skills, experience, beliefs and memories
Information becomes knowledge when meaning is given to a piece of information depending on the skills and the context in which the person involved judges the information. Knowledge has, therefore, a subjective dimension.
British Standards Institution (2003) Facts, statistics that can, frequently, be analysed to derive information
Collection of data organized and translated into a form that conveys structure and understanding
No single definition (They rather defined different types of knowledge such as conceptual, operational, explicit, tacit, sympathised and systemic knowledge)
Table (3): Definitions of knowledge, information, and data in literature
From the above table it can be noticed that there is no clear distinction between the terms, as it is usually defined in relation to each other. In fact, a linear asymmetrical relationship between the terms is often implied in literature, suggesting that data can be transformed to information to knowledge, but not the other way around (Stenmark D. 2002). However, this oversimplified notion is criticised since knowledge could be used to derive information, as well as to create data from information (Stenmark D. 2002) Accordingly, it could be argued that a continuous interaction between knowledge, information, and data exists in the knowledge creation process.
3.1 Generic Model for Knowledge Creation
The discussion in Section 2, distinguishes between two types of knowledge creation in organisations: knowledge exploration flowing from the outside in and knowledge exploitation flowing from the inside out. De Pablos (2002) argues that organisations engage in exploration (the pursuit of new knowledge, of things that might come to be known) and exploitation (the use and development of things already known).
Knowledge Exploration Model
Contrasting the two notions of knowledge exploration (i.e. Rationalism and Empiricism), two methodologies for knowledge creation emerge. The first is deductive inference through appealing to mental constructs such as concepts, laws, or theories. The second is inductive inference from particular sensory experiences. Both methodologies require the interaction between knowledge, information, and data. This interaction is illustrated in figure (2), combining both notions of knowledge exploration
Derivation
Information Data
Conceptualisation
Deductive Knowledge Creation
IntegrationKnowledge
Information Data
Inductive Knowledge Creation
Fig. (2): The relationship between knowledge, information, and data in Deductive/Inductive knowledge creation
The right-hand side of the model shows the interaction between knowledge, information, and data in an inductive mechanism. Observed data is collected, then integrated into information (i.e. organised/processed). Information is then put into context to create knowledge. This life cycle representation suggests a continuous interaction between knowledge, information, and data; whether the start point is from observed data (Empiricism) or from priori knowledge used for observation (Rationalism). Accordingly, both notions of knowledge exploration are represented.
The left hand side of the model shows the interaction between knowledge, information, and data in a deductive mechanism. Priori knowledge is conceptualised
into information (i.e. theories/axioms), which is derived to produce relevant constructs/variables. Data is then collected against these constructs/variables and evaluated to reshape priori knowledge Similarly, this life cycle representation suggests a continuous interaction between knowledge, information, and data; whether the start point is from priori knowledge (Rationalism) or observed data (Empiricism). Again, both notions of knowledge exploration are represented in this case.
Knowledge Exploitation Model
Nonaka I. and Takeuchi H. (1995), focus on exploiting knowledge from within the organisation. They argue that knowledge is embedded and constructed from social relationships, and therefore it “cannot be processed as information, because it is continuously re created and reconstituted through dynamic, interactive social networking activity.” Kautz K. & Thaysen K. (2001). Their theory is based on the continuous conversion between tacit and explicit knowledge. “The conversion takes place in an interactive social process through continuous communication and involves different individuals, groups and organisational units.” Kautz K. & Thaysen K. (2001)
According to Nonaka I. and Takeuchi H. (1995), the four modes of interaction between tacit and explicit knowledge (i.e. Socialisation, externalisation, combination, and internalisation) are amplified through expanding communities of interaction, forming a knowledge spiral. However, this paper focuses on the basic level of interaction between individuals, in order to integrate it with the knowledge exploration model The interaction between tacit and explicit knowledge is illustrated in figure (3), based on the theory of Nonaka I. and Takeuchi H. (1995).
Combination
Externalisation
Explicit Tacit Tacit
Explicit
Knowledge Conversion
Fig. (3): The Interaction between tacit and explicit knowledge (Adapted from Nonaka I. and Takeuchi H. 1995)
Hybrid Model for Knowledge Creation
De Pablos (2002) argues that both knowledge exploration and knowledge exploitation are essential for organisations. Therefore, an effective KMS is likely to incorporate both knowledge creation systems. Fig. (4) suggests a generic model for knowledge creation that combines explorative and exploitive knowledge creation.
Information Data
Conceptualisation
Contextualisation
Combination
Derivation Externalisation
Explicit Tacit Tacit
Explicit
Internalisation
Socialisation Evaluation
Knowledge Conversion
Deductive Knowledge Creation
Observation
Information Data
Integration
Inductive Knowledge Creation
5. Conclusion
Knowledge is a polymorphic concept. Continuous interaction between knowledge, information, and data on one level and between tacit and explicit knowledge on another level creates knowledge through exploration and exploitation. Knowledge exploration includes activities such as research and development, risk taking, experimentation, discovery, innovation (De Pablos 2002), and can be created inductively or deductively. Knowledge exploitation includes activities such as refinement, choice, production, efficiency, selection, implementation, execution (De Pablos 2002), and can be created through conversions between tacit and explicit knowledge.
KMS in organisations need to maintain a balance between knowledge exploration and knowledge exploitation. “An organization that engages exclusively in exploration will ordinarily suffer from the fact it never gains the returns of its knowledge. An organization that engages exclusively in exploitation will ordinarily suffer from obsolescence” (De Pablos 2002). Accordingly, the main challenge facing organizations is to engage in sufficient knowledge exploitation to ensure its current viability and, at the same time, to devote enough energy to exploration to ensure its future viability (De Pablos 2002)
This generic model for knowledge creation proposed in this paper allows organisations to engage in both knowledge exploration and knowledge exploitation concurrently In addition, this model integrates both knowledge exploration notions, as well as knowledge conversion theory. Yet this model needs to be empirically evaluated in organisations.
Fig. (4): A generic model for knowledge creation that combine explorative and exploitive knowledge creation
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