Knowledge: Concept Underpinning

The use of the words ‘knowledge’, ‘information’, and ‘data’ in current research is confusing; central to this confusion is an understanding of what knowledge is, how it relates to, or differs from, information and data, and how this shapes and impacts its management (McInerney, 2002; Grover and Davenport, 2001). As clarity of terminology is a critical factor in any study, understanding what knowledge means is important in exploiting knowledge management in organisations (Blair, 2002; Southon et al., 2002).

In understanding what knowledge is, one needs to start with the previously more widely used terms: data and information. The assumption seems to be that if knowledge is not something that is different from data or information, then there is nothing new or interesting about knowledge management (Fahey and Prusak, 1998). Levitin and Redman (1998) argue that data are used in, and created by, all daily operations, from serving customers to manufacturing a product, to tracking inventory. They further suggest that data are collected according to the status of events, and that they support managerial and professional work. This corresponds to the well-accepted definition that data are representations of events that people notice and bring to the attention of others in the organisation (Sanchez, 2001a). They are mainly a compilation of facts and figures (Blair, 2002; Drucker, 1995). Davenport (1994) stresses that data are not information.

Boisot and Griffiths (2001) describe information as what is extracted from data when incoming data can be related in a meaningful way to an observer’s prior expectation. Or simply put, as Prusak (1996) proposes, information is a message that is bounded. This leads to an understanding that there must be a meaning bound to the data that makes data information; therefore, Sanchez (2001a) defines information as the meaning that is imputed to some data by evaluating the data in an interpretive framework. Drucker (1995) argues that information is data that has been organised for a particular purpose. To make data into information, a particular use must be identified so that it can be structured in as readily an accessible form as possible. Taking an example of customers: customer data are mainly a compilation of facts such as addresses, items purchased, time of purchase, total value of purchase, and others. To turn this into information, a particular purpose must be defined. The purpose can be exemplified as targeting credit control in a specific group. To become information, the data can then be categorised into customers with receivables of more than a certain period. This information needs to be used to create a difference (Styhre, 2002). As soon as a piece of information is used, it is, as Luhmann (2000) points out, turned into non-information.

While there is a wide agreement as to what data and information are, there appear to be various definitions and concepts of knowledge. Transformation of information into knowledge is by no means linear or uncomplicated (Styhre, 2002). Knowledge is the intelligent use of data and information, and the more knowledge is

exercised and shared the greater it becomes. Adler states that, “Knowledge is a remarkable substance. Unlike other resources, most forms of knowledge grow rather than diminish with use” (Adler, 2001:45).

Philosophical analysis and debate relating to the meaning of knowledge began in the ancient Greek period and continues today, with a multitude of different and often competing approaches. A difference exists within the Western tradition, between Platonic idealism and Aristotelian empiricism. There is also a difference between the Western tradition, separating the subject who knows and the object that is known, and the Eastern or Japanese tradition of unity of body, mind, nature and other (Nonaka and Takeuchi, 1995). This reflects and illustrates the importance of cultural factors in the way the world is understood and knowledge interpreted.

Kogut and Zander (1992) include information within their definition of knowledge. According to them, knowledge consists of information and know-how. Information is knowledge that can be transmitted without loss of integrity once the syntactical rules for deciphering it are known. Know-how is the accumulated practical skill or expertise that allows one to do something smoothly and efficiently. On the other hand, Nonaka and Takeuchi (1995) assert that knowledge - unlike information - is about beliefs, commitment and action; knowledge, like information, is about meaning.

Blair (2002) makes out the case that by using the “let the use teach the meaning” method introduced by Ludwig Wittgenstein in 1953, knowledge is not something tangible that we can possess, exchange, or lose in the way that we can with data or information. When we lose knowledge, we lose an ability to do something. Therefore, knowledge is something intangible attached to an individual that in a normal healthy situation cannot disappear at once but can “erode” over time when not exercised. Applying knowledge often depends on having relevant data or information. But data or information that enables a knowledgeable person to exercise expertise are insufficient by themselves to enable someone else to exercise that expertise.

Context is also crucial. One view is that knowledge is contextual and that it will distinguish one person or organisation as more knowledgeable than the other(s) (Blair, 2002). Davenport and Prusak (1998) capture this sense of context within the decisions or movement undertaken as a result of the knowledge available. For Davenport and Prusak, knowledge comprises a person’s experience, truth, and judgment, and may be heuristic. They define knowledge as “a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. In organisations, it often becomes embedded not only in documents or repositories but also in organisational routines, processes, practices, and norms” (1998:5).

Sanchez defines knowledge as “a set of beliefs about causal relationships in the world and an organisation” (2001a:5). He argues that this causal relationship is very relevant in the discussion of knowledge management because it is concerned with forms of knowledge that can be used to cause things to happen. He further emphasises “this concept of knowledge helps to make an important distinction between simply being aware of something, which means having data or information in our framework, and having knowledge, which implies actually knowing how to do things or to cause things to happen” (2001a:6). Knowledge, consequently, resides in the minds of individuals and organisational knowledge exists when individuals in an organisation share sets of beliefs about causal relationships that enable them to work together (Sanchez, 2001a). From a similar perspective, Boisot and Griffiths (2001:214) summarise that “data is something ‘out there’ that an observer notices. The observer

constructs what he believes is information in the form of an interpretation of data that modifies the beliefs that reside ‘in him’ and constitute his or her knowledge.”

This discussion leads to the understanding that there is an implication of a hierarchical view of data, information and knowledge. However, Tuomi (1999) makes the iconoclastic argument that the often-assumed hierarchy from data to knowledge is actually inverse: knowledge must exist before information can be formulated and before data can be measured to form information. He argues that knowledge exists which, when articulated, verbalised, and structured, becomes information which, when assigned a fixed representation and standard interpretation becomes data. Critical to this argument is the fact that knowledge does not exist outside of an agent (a knower): it is indelibly shaped by one’s needs as well as one’s initial stock of knowledge (Tuomi, 1999). Through an in-depth case analysis in a large utility company, Braganza (2004), in agreement with Tuomi, found that the data- information-knowledge hierarchy was of limited practical use. Braganza (2004) further suggests the knowledge-information-data model, which proposes a top-down perspective rather than the traditional bottom-up approach. Knowledge is the result of cognitive processing triggered by the inflow of new stimuli (Alavi and Leidner, 2001). Alavi and Leidner (2001) further posit that information is converted to knowledge once it is processed in the mind of individuals and knowledge becomes information once it is articulated and presented in the form of text, graphics, words, or other symbolic forms.

Alternative Knowledge Perspectives

The accepted philosophical definition of knowledge as the “justified true belief” (Nonaka and Takeuchi, 1995:21) that increases an entity’s capacity for effective action, and the more business-like definition “a capacity to act” by Sveiby in 1997 (cited in Southon et al., 2002:1049) alone do not really help to define the meaning of knowledge in a sense helpful to undertaking knowledge management.

A number of alternative perspectives on knowledge can be explored. Schubert et al. (1998) described knowledge as a state or fact of knowing with knowing being a condition of understanding gained through experience or study; the sum or range of what has been perceived, discovered, or learned. The perspective on knowledge as a state of mind focuses on enabling individuals to expand their personal knowledge and apply it to the organisation’s needs. Carlsson et al. (1996) and Zack (1999) posit that knowledge can be viewed as a thing to be stored and manipulated, or alternatively, knowledge can be viewed as a process of simultaneously knowing and acting. The process perspective focuses on the applying of expertise (Zack, 1999). Grant and Baden-Fuller (2004) suggest that knowledge is that of a condition of access to information. Therefore, organisational knowledge must be organised to facilitate access to and retrieval of content. Gold et al. (2001) view knowledge as a capability with the potential for influencing future action. Davenport and Prusak (1998) suggest that knowledge is the capacity to use information; learning and experience result in an ability to interpret information and to ascertain what information is necessary in decision making.

Scholars have classified organisational knowledge across some dimensions. One common dimension distinguishes between tacit and explicit knowledge. There seems to be a congruence of understanding that knowledge can be either explicit or tacit (Nonaka and Takeuchi, 1995; Polanyi, 1966) and scholars often use the word ‘knowledge’ to mean both. Explicit knowledge or “codified” knowledge refers to knowledge that is transmittable – that is articulated, codified, and communicated – in formal systematic language. An example is an owner’s manual accompanying the purchase of an electronic product. The manual contains knowledge on the appropriate operation of the product. Tacit knowledge, sometimes known as implicit knowledge, is unspoken and hidden. It is personal, context-specific, and therefore hard to formalise and communicate. In Polanyi’s own words “we can know more than we can tell” (1966:4). This tacit knowledge is comprised of both cognitive and technical elements (Nonaka, 1994): the cognitive element refers to an individual’s mental models consisting of mental maps, beliefs, paradigms, and viewpoints, the technical element consists of concrete know-how, crafts, and skills that apply to a specific context. Many scholars suggest that tacit knowledge is more valuable than explicit to create competitive advantage (Alavi and Leidner, 2001).

Another dimension is that knowledge can also be viewed as existing in individuals or in the collective (von Krogh, 1998; Nonaka, 1994; Kogut and Zander, 1992). Individual knowledge is created by and exists in the individuals whereas collective or organisational knowledge is created by and inherent in the collective actions of a group (Heaton and Taylor, 2002; Kogut and Zander, 1992). In their knowledge creation theory Nonaka and Takeuchi (1995) use the interplay between explicit/tacit knowledge and individual/organisational knowledge. They state, “new knowledge always starts with an individual” (1995:13) and suggest that organisations have to mobilise individual knowledge so that it becomes organisationally amplified through the ‘knowledge conversion spiral’ at a higher ontological level.

Matusik and Hill (1998) suggest that although the above two dimensions are important in understanding the nature of organisational knowledge, two other distinctions are particularly germane to arguments of turning knowledge into competitive advantage: 1) private versus public knowledge and 2) architectural versus component knowledge. Figure 2-3 summarises these dimensions and the relationships to each other.

Figure 2-3 A Taxonomy of Organisational Knowledge (Matusik and Hill, 1998:684)

Tacit Explicit Individual Tacit Explicit Collective Component Tacit Collective Architectural Private Firm specific Tacit Explicit Individual Component Public Organisational Knowledge

According to Matusik and Hill (1998) and Barney (1991) private – or firm- specific – knowledge can be a source of competitive advantage if it is valuable, rare, and imperfectly imitable and non-substitutable. By definition, public knowledge cannot be a source of competitive advantage since it is neither unique nor proprietary to any one organisation but is, instead, readily available to everyone. However, Matusik and Hill argue that the failure to apply such knowledge within a given firm can be a source of competitive disadvantage. Private knowledge includes such items as a firm’s unique routines, processes, documentation and trade secrets. Public knowledge consists of knowledge not unique to any one firm. Rather, it resides in the external environment and is, in essence, a public good. Public knowledge includes such items as industry and occupational best practices.

Matusik and Hill (1998) explain that component knowledge is the knowledge that relates to a subroutine or discrete aspect of an organisation’s operations. It is the knowledge that relates to “parts” or “components”, rather than the whole. The knowledge underpinning a firm’s new product development process, technical service delivery process, and so on, could be considered component knowledge (Leonard- Barton, 1998; Amit and Schoemaker, 1993). Each of these processes constitutes just one aspect of a firm’s overall knowledge structure. Component knowledge can be held individually or collectively and it includes both tacit and explicit knowledge. Private component knowledge can be a source of competitive advantage, such as for example, when a firm has developed a superior technical service delivery process.

Matusik and Hill (1998) further explain that architectural knowledge relates to the whole – that is, to organisation-wide routines and schemas for coordinating the various components of the organisation and putting it to productive use. Because it is organisation-wide, architectural knowledge is held collectively. Moreover, often no one individual can comprehend, and articulate the totality of architectural knowledge. Therefore, architectural knowledge tends to be tacit by default. Because no two firms have the same architectural knowledge (Nelson and Winter, 1982), architectural knowledge must be considered private knowledge . Such knowledge may be a source of competitive advantage.

Discussion

An understanding of the concept of knowledge and the different types of knowledge is important because theoretical developments in the knowledge management area are influenced by distinctions among the different types of knowledge.

The various definitions and concepts of knowledge capture a number of knowledge’s essential characteristics: 1) knowledge is related to belief and commitment (Nonaka and Takeuchi, 1995), hence it is about meaning and can be intangible (Blair, 2002); 2) knowledge is dynamic and action oriented (Davenport and Prusak, 1998); 3) knowledge is about causal relationships (Sanchez, 2001a); 4) knowledge is contextual – it is a function of situation, experience, culture, and judgment (Davenport and Prusak, 1998); therefore, 5) knowledge may be heuristic (McInerney, 2002; Sanchez, 2001a); and 6) knowledge is a set of routines (Nelson and Winter, 1982).

Knowledge may be viewed from different perspectives: 1) Hierarchical evolution from data and information, 2) a state of mind, 3) an object, 4) a process, 5) a condition of having access to information, 6) a capability. These different views of knowledge lead to different perceptions of knowledge management and the knowledge management system. Table 2-1, adapted from Alavi and Leidner (2001),

summarises this implication. When knowledge is viewed as an evolution of information or state of mind, then knowledge management will focus on enhancing individuals’ understanding. When knowledge is viewed as an object or as a condition of having information access, then knowledge management will focus on building and managing knowledge stocks. When knowledge is a process, then the implied knowledge management focus is on knowledge flow and the processes of creation, mobilisation and diffusion of knowledge. The view of knowledge as a capability suggests building core-competency centred knowledge management. The major implication of these various concepts of knowledge is that each perspective suggests a different strategy for managing the knowledge and a different perspective of the role of systems in facilitating an organisation to manage knowledge.

Perspective of Knowledge

Brief description Implications for Knowledge Management (KM) Implications for Knowledge Management Systems (KMS) Hierarchical relationship: Data, Information, Knowledge Data is compilation of facts, Information is processed data, Knowledge is personalised information KM gives useful information to

individuals and facilitates the assimilation of information into personalised knowledge

KMS is mainly Information Systems with an addition for the use of personalising information to become personalised knowledge State of mind Knowledge is the

states of knowing and understanding KM enhances individual’s understanding through the provision of information KMS is to provide access to sources of knowledge Object Knowledge is an object to be stored and applied

KM builds and applies

knowledge stocks KMS is to capture, store, and diffuse knowledge

Process Knowledge is a

process of applying expertise

KM focuses on

knowledge flows and the process of creation, mobilisation and diffusion of knowledge

KMS is to manage the link among knowledge sources to create better permeability of knowledge flows

Access to

information Knowledge is a condition of access to information

KM focuses on

organising access to and retrieval of content

KMS is to provide effective search and retrieval mechanisms Capability Knowledge is the

ability to take the right decision and action

KM focuses on building core competencies for decision making and actions

KMS is to support the development of individual and organisational competencies

Table 2-1 Knowledge perspectives and the implication to knowledge management and knowledge management systems

The table is inspired and adapted from Alavi and Leidner (2001).

I consider that the question whether tacit or explicit knowledge is more valuable for competitive advantage actually misses the point. The two are not dichotomous states of knowledge, but mutually dependent and reinforcing qualities of knowledge: tacit knowledge forms the background necessary for assigning the structure to develop and interpret explicit knowledge (Polanyi, 1966). The inextricable linkage of tacit and explicit knowledge suggests that only individuals

with a requisite level of shared knowledge can truly exchange knowledge: if tacit knowledge is necessary to the understanding of explicit knowledge, then in order for a person to understand another person’s knowledge, there must be some overlap in their underlying knowledge bases, a shared knowledge space (Tuomi, 1999). This shared space, according to Braganza (2004) could be the business process, which points to a unit of analysis for understanding knowledge management.

Organisational knowledge is understood as socially constructed, therefore, the knowledge that enables competitive advantage must be a combination of tacit and explicit knowledge (Kogut and Zander, 1992). Scholars such as Bessant (2003), Sanchez (2001b), Sawhney and Prandelli (2000), Leonard-Barton (1998) insist that knowledge fuels innovation within an organisation. They further argue that organisations which manage knowledge have better innovative products and services than those which do not. In contrast to the knowledge-based view of the firm, Grant (1996) and Kogut and Zander (1992) also discuss a contract-view theory of the firm. The contract-view perspective charaterises organisations as bundles of contracts that serve to allocate property rights efficiently. It serves to keep a check on the