The main characteristics of knowledge

2.4.1.1

Defining knowledge

Gutenberg’s invention of the letterpress decreased the cost of access to new ideas dramatically and guaranteed the distribution of insights that were previously reserved for the elite. The Internet has repeated this wonder in the 20th century. There are numerous definitions and taxonomies of knowledge that contribute to theory and practice from a variety of perspectives. However, no generally accepted definition has been agreed upon (Chang & Ahn, 2005; Sveiby, 1997; Teece, 1998b). Knowledge is an elusive term and does not lend itself to a robust description. This means that it is not directly or easily definable, gaugeable or observable (Assudani, 2005; Bontis, 1998; Despres & Chauvel, 2000; Earl & Scott, 1999). Polanyi (1967), Nonaka and Toyama (2005) and Sveiby (1997) argue that knowledge is multifaceted and emerges out of subjective views of the world. Similarly, Fahey and Prusak (1998) argue that knowledge is what the knower knows and that there is no knowledge without someone knowing it.

In the contemporary ‘post-industrial’ era the importance of knowledge has only been realized when its characteristics and role in the organisation started to become debated (Drucker, 2006). Grover and Davenport (2001) provide a concise definition by saying that knowledge consists of truths and beliefs, perspectives and concepts, judgments and expectations and that people “who are knowledgeable not only have information, but also have the ability to integrate and frame the information within the context of their

experience, expertise and judgment” (p. 6). Knowledge represents “know-how,

expertise, tradecrafts, skills, ideas, intuitions, and insights” (Desouza & Awazu, 2006, p. 32) and includes an individual’s capacity to take effective action – to make information actionable (Lai & Chu, 2002; Liebowitz & Megbolugbe, 2003; Polanyi, 1967).

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Substantial power of old generations; strong supervision of owner-manager; commitment to status quo; mainly insiders selected for board of directors according to their status and influence within the family.

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Focus on internal operational issues; appointment of family members or close friends for the post of direction; low commitment to change or to find new opportunities; strong orientation toward independence from the environment which is a barrier to growth in terms of lack of external partnerships, inter-organizational relations and cooperative investments.

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Paternalist management style, nepotism characteristics, led by family values; loyalty – keeping the same employees for a long time with a lack of recruitment; strong orientation toward social networking; usually a sound level of trust among family members; strong commitment, cordial, friendly and close ties.

Davenport and Prusak (1998), Nonaka and Takeuchi (1995), and Sveiby (2001) also refer to the understanding that it is impossible to separate knowledge from action.

From a strategic point of view knowledge is seen as a source of value creation and realisation (Earl, 2001). From an individual point of view it can be argued that knowledge resides in the head of a person (Assudani, 2005). However, Nonaka and Toyama (2003) state that it cannot be created in a vacuum. Clarke and Turner (2004) and Despres and Chauvel (2000) also argue that knowledge is generally context specific and depends on a multitude of factors such as time and space or social, socio-economic, psychological, inter-personal, cultural, linguistic or historical variables as the basis for creating meaning. From a collective point of view it stands for what the organisation knows. This comprises “professional intellect, such as know-what, know-how, know why, self-motivated creativity, experience, concepts, values, beliefs and a way of working that can be shared and communicated” (Lai & Chu, 2002, p. 26).

Researchers have dealt with different aspects of knowledge. The most valuable ones in this respect are the distinction between data, information and knowledge and the classification of knowledge into two dimensions: tacit and explicit (Nonaka & Takeuchi, 1995; Polanyi, 1967), as discussed in the following sections.

2.4.1.2

The distinction between data, information and knowledge

Understanding the meaning of data, information and knowledge, that they are not interchangeable concepts and how to get from one to the other, is vital to doing knowledge work successfully (Davenport & Prusak, 1998; Sveiby, 1997). The relations between these three concepts are outlined in the following figure 2.1.

Figure 2.1: The distinction between data, information and knowledge

This shows that knowledge is distinct from both data and information. Data are “raw or

n that is interpreted and blended with

hen patterned in a certain way and

(Assudani, 2005; Davenport & Völpel, 2001; Hitt et al., 2000; Polanyi, 1967; Sveiby, discerned elements” (Liebowitz & Megbolugbe, 2003, p. 189) and represent a set of discrete and objective observations or facts concerning events or recorded measures of certain phenomena. They are not directly meaningful because they are out of context (Davenport & Prusak, 1998; Diakoulakis et al., 2004; Stewart, 1997; Zikmund, 2003).

Information, therefore, results from placing data into context and from giving it some meaningful content (Powell & Swart, 2005; Stewart, 1997; Zack, 1999b). Hence, it refers to a body of facts or data with attributes of relevance and purpose, which is formatted in a way that facilitates decision-making such as tables, symbols or pictures (Diakoulakis et al., 2004; Sveiby, 1997; Zikmund, 2003). The addition of value or meaning can be achieved by contextualizing, categorizing, calculating, correcting or condensing the data (Davenport & Prusak, 1998).

Finally, knowledge can be regarded as informatio

values, experiences and insights (Sveiby, 1997; Zikmund, 2003). Consequently, knowledge is subjective and dynamic in nature (Krogh et al., 2001; Powell & Swart, 2005). Sveiby (1997) concludes that it is important to understand that the focus of a firm needs to be knowledge rather than information.

In summary, data is transformed into information w

when meaning is added. Once certain rules or heuristics are applied and purpose is added to this information it is transformed into knowledge as actionable information (Davidson & Voss, 2002; Kakabadse N. K., Kakabadse A. & Kouzmin, 2003; Liebowitz & Megbolugbe, 2003; Stewart, 1997). Hence, data and information are different from knowledge primarily due to the absence of context, meaning and action (McCann & Buckner, 2004; Powell & Swart, 2005). Albert Einstein agrees with making these distinctions in stating that “information is not knowledge” (Johnston, 2004, p. 2).

2.4.1.3

The two knowledge categories: tacit and explicit knowledge

The most frequently used categorisation of knowledge, which has drawn special interest in the literature, is tacit and explicit knowledge (Diakoulakis et al., 2004; Hitt et al., 2000; Powell & Swart, 2005; Roos, 1998). This distinction was first made by Aristotle (Prusak, 2001) and is also often associated with the philosopher Michael Polanyi. He demonstrated that people can know more than they can tell or explain to others

1997). Some authors suggest that there exists a continuum between explicit and tacit knowledge, as illustrated in figure 2.2.

Figure 2.2: The continuum from explicit to tacit knowledge

Source: Compiled from Bernbom, 2001; Leonard & Sensiper, 1998; McKinlay, 2005

Explicit knowledge is regarded as generic and dynamic, meaning it is not bound to a ittable and can

ledge has physical capabilities (Krogh, 1998) such as the knowledge of how to skilfully use certain tools (Leonard & Sensiper, 1998; certain time, place or workgroup (McKinlay, 2005). Hence, it is transm

be communicated and shared through written specifications in formal, systematic language and can be articulated and expressed in words and numbers (Assudani, 2005; Diakoulakis et al., 2004; Earl & Scott, 1999; Polanyi, 1967). Therefore, it is possible to capture, codify and document explicit knowledge in manuals, patents, reports, documents, and databases (Goh, 2002; Zack, 1999b). In theory, explicit knowledge is “independent of those who developed it and therefore it can be of value for the organisation regardless of the employee who initially produced it” (Apostolou & Mentzas, 1999, p. 135).

Much of our knowing is tacit. Tacit know

Polanyi, 1967), perform craftsmanship such as pottery (Mintzberg, 1987), watchmaking (Krogh, 1998), award-winning cuisine, or the skills of how to play certain types of sports or instruments (Polanyi, 1967; Sveiby, 1997). Secondly, there are perception skills (Krogh, 1998) such as how to recognize a face or the moods of a face or the use of denotative language (Polanyi, 1967). It also encompasses the skill to perceive and interpret certain, possibly new and different, external events or objects (Davenport & Prusak, 1998; Krogh, 1998; Polanyi, 1967), to come up with alternative ways of how to handle them and to anticipate implications and judge their effects (Wiig, 1999a). While both types of knowledge are important, most knowledge generated in organisations is of tacit nature (Egbu et al., 2005; Sveiby, 1997).

In the literature tacit knowledge, which is also called ‘know-how’, ‘subjective knowledge’, ‘personal knowledge’ or procedural knowledge’ (Hitt et al., 2000; Polanyi, 1967), is regarded as more complex: it is local and stubborn, exists in mental models

no, Liebowitz, Buchwalter & McCaw, 2001, p. 306). Experienced employees “develop

missing from passive missing knowledge (Assudani 2005; Davidson & Voss, 2002; Polanyi, 1967)

and is a very dynamic, personal, highly situated, kind of knowledge, which is deeply embedded in an individual’s mind and is, therefore, called ‘sticky’. It is based on individual or collective cumulative perceptions, insights, experiences, intuition, behaviour, commitment and involvement and is influenced by ideals, values and emotions and is, of necessity, embedded in a specific context (Brown & Duguid, 1998; Earl & Scott, 1999; McKinlay, 2005; Nonaka & Toyama, 2003; Polanyi, 1967; Swan & Newell, 2000). This personal quality of tacit knowledge and the fact that it is more challenging to identify, to codify or capture, to formalize and articulate, makes it difficult to change or to share with others (Assudani, 2005; Bannock, 2003; Diakoulakis et al., 2004; Goh, 2002; Hitt et al., 2000; Nonaka & Toyama, 2003; Polanyi, 1967).

Due to the considerable differences between the two categories “tacit knowledge cannot be treated in the same way explicit knowledge is treated” (Rubenstein-Monta

huge repertoires of skills, information, and ways of working that they have internalized to the point of obviousness” (Stewart, 1997, p. 72). If they gain their experience in different parts of an organisation, they have different stocks of tacit knowledge because it is context-dependent. Swan and Newell (2000) argue that abstracting tacit knowledge from its context would mean that much of its intrinsic value and meaning would be lost. Sharing tacit knowledge usually requires the use of metaphors, analogies or unconventional language (Nonaka & Takeuchi, 1995). Roos (2004) argues that it can be more valuable because processes related to sharing, creating or using tacit knowledge can access deeper insights than verbal or written communication.

The following, figure 2.3, depicts four knowledge categories. It suggests that increasing awareness distinguishes explicit from tacit knowledge and active

Figure 2.3: The four categories of explicit and tacit knowledge and active and passive missing knowledge

Sources: Compiled from Davidson & Voss, 2002 and Stewart, 1997

Complexity is another factor that distinguishes the two concepts of explicit and tacit knowledge. The more a “situation is complex [which certainly is the case in strategic decisions], the more the knowledge mobilized is tacit” (Nicolas, 2004, p. 26). Hence, a dilemma is that the concept of tacit knowledge is crucial for strategic decisions but it is hard to grasp, very intangible and, therefore, potentially hard to manage.