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University of Groningen

The knowledge dynamics of organizational innovationSjarbaini, Vivyane Larissa Ratna Nirma

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Publication date:2009

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Citation for published version (APA):Sjarbaini, V. L. R. N. (2009). The knowledge dynamics of organizational innovation: understanding theimplementation of decision support for planners Enschede: PrintPartners Ipskamp B.V., Enschede, TheNetherlands

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Chapter 3

Studying Knowledge Dynamics in Organizations

3.1 Introduction

Following up on the previous chapter, this chapter aims to set the first steps to understanding innovation from a knowledge perspective at the individual level. Therefore, we introduce the concept of representation, and we demarcate knowledge in distinguishing data, information and knowledge [3.1]. We then discuss a model that analyzes information flows within an organizational setting, the Information Space model introduced by Boisot [3.2], after which we will discuss the model by Boisot in light of the purpose of the present study [3.3]. We conclude this chapter with a summary [3.4].

3.2 General introduction to knowledge

The philosophical question about the essence of knowledge has puzzled great thinkers from all over the world for centuries. Two prominent philosophers on knowledge are Confucius and Plato. Confucius, an Eastern philosopher born 551 B.C., said that true knowledge is to know what you do not know; he focused on the practical side of life [Heijloo & Eskens 2005]. Plato, a Western philosopher born 427 B.C., takes a different stand in that unambiguous [true] knowledge exists in another world apart from ours and that we only have a dim recollection of the true knowledge from this other world [Bostock 1999]. Confucius and Plato differ in that Confucius focuses on the knowledge obtained in every day life and Plato argues for the true knowledge in a world outside of our own [see Encyclopedia of Philosophy, Edwards 1972, for a more detailed treatment of knowledge in philosophy and science]. So, what do we want to discuss here to introduce the subject of knowledge? We want to clarify how we use the concept of knowledge and our motivation to use it this way in studying knowledge dynamics during innovation.

The Knowledge Dynamics of Organizational Innovation

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3.2.1 Introducing representation

Individuals have knowledge. The way to study knowledge in humans is in cognitive science defined in terms of representation. Representation is a basic concept in the study of the mind [e.g. Posner 1993; Jorna 1990]. As the next chapter will show, cognitive science and semiotics are two disciplines that use this concept of representation to study the mind; cognitive science focuses on the nature of intelligence [Posner 1993] and semiotics focuses on the use of signs and on meaning [Jorna, van Heusden, & Posner 1993]. In general we can say that a representation is something that 'stands for something else' [e.g. Haberlandt 1994; Jorna 1990]. Johnson-Laird [1993] argues that a mental representation is essential to understanding phenomena of knowledge. Many forms of representations have been distinguished and studied varying from the propositional representation, the pictorial representation, the procedural representation and the declarative representation to representational content and representation processes [Jorna 1990]. All these representations functionally describe what goes on in our minds. For instance, we can frame a concept with a word, or an image with a picture; we can substitute a procedure for a set of rules and so on. Anderson [1990] in fact even argued that a correct representation is essential in problem solving; a representation enables us to describe the human cognition, to study the structure of knowledge, the meaning of a word or a problem that needs to be solved. Thus, representation can be seen as a crucial concept in studying knowledge and the mind in general. We do want to note that the use of representation is not undisputable. For instance, Blackler [1995] stresses that knowledge is too multi-faceted and complex and therefore unrealistic for such a simple approach.

3.2.2 Data, information, and knowledge

The two concepts of data and information are often used to demarcate the concept of knowledge. Although useful, we do not aim to precisely define either data or information. However, we do want to review some examples that illustrate the differences pointed out between the three concepts within KM

[related] literature. Boisot [1998] says that

Knowledge builds on information that is extracted from data.… whereas data can be characterized as a property of things, knowledge is a property of agents predisposing them to act in

Chapter 3 Studying Knowledge Dynamics in Organizations

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particular circumstances. … Information … establishes a relationship between things and agents [p.12].

Boisot and Canals [2004] refine the above argument into a model in which they argue that the difference between data, information and knowledge is crucial [see also figure 3.1]. They add stimuli as the input for data; perceptual filters convert incoming stimuli to data, conceptual filters convert the data into information. The information in turn becomes knowledge inside the agent. The filters depend on the prior knowledge of the agent. They summarize their differences as follows

Information is an extraction from data that, by modifying the relevant probability distributions, has a capacity to perform useful work on an agent’s knowledge base [2004: 47]

Thus, according to Boisot and Canals, stimuli are the raw material of which data is formed and data is viewed as the raw material for information, which in turn enables knowledge. Information is the intermediary between data and knowledge.

Figure 3.1: The agent-in-the-world [taken from Boisot & Canals 2004: 48]

Zack [1999] formulates a less technical distinction than does Boisot – together with Canals – and says that ‘data represent observations or facts out of context that are, therefore, not directly meaningful. Information results from placing data within some meaningful context, often in the form of a message. Knowledge is that which we come to believe and value on the basis of the meaningfully organized accumulation of information [messages] through experience,


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communication, or inference. Knowledge can be viewed both as a thing to be stored and manipulated and as a process of simultaneously knowing and acting –that is, applying expertise. [As a practical matter, organizations need to manage knowledge both as object and process.]. Thus, Zack also underlines the more or less cumulative relation between data, information and knowledge and he introduces the role of context. Davenport and Prusak’s [2000] approach is even more practical than that of Zack. They say that data is ‘a set of discrete, objective facts about events’. Information in turn is the ‘data that makes a difference’. And finally knowledge

… is a fluid mix of framed experience, values, contextual information, and expert insight that provides a frame work for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. Inorganizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices, and norms. [p.5]

The above shows that data is considered the least ‘advanced’ and knowledge the most advanced of the threesome. Then, data is viewed as building blocks for information, it relates to things [rather than people], deals with facts and signs that have not been interpreted [yet]. Information in turn builds on data and bridges this data to knowledge; it has a context, makes a difference and it has been given meaning. Finally, knowledge builds on information and is linked to people, it consists of values, experiences, insights and inferences, and it has a dynamic character, involving processes.

Our approach to data, information and knowledge shows parallels to the above in that they are cumulatively related from data to information and then to know-ledge. We want to emphasize that we view knowledge as dynamic and exclusive-ly generated by people. Data and information on the other hand can be viewed as input for people. The difference between data and information is in the use of a lens. We illustrate this with an example. The actual occurrence of an accident can itself be viewed as data. The people who witnessed the accident, communica-ting what they have seen tends towards information; the ten different persons that witnessed the accident will have ten different versions of what happened and this results in ten different sets of information. Knowledge in this example is the interaction of the information input with what one already knows, the models that one has. Knowledge is a process of the interaction between information and


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an acting person. This is in line with Boisot who says that the existence of knowledge ‘can only be inferred from the action of agents’ [1998: 12].

In the following section we elaborate on Boisot’s ideas on knowledge dynamics, which form an important inspiration for our cognitive-semiotic model and the accumulation of knowledge discussed in chapter 4.

3.3 The I-Space: A model to understand knowledge dynamics in organizations

The Information Space model or I-Space model enables analysis of information flows within an organizational setting. Therefore, this model is particularly interesting for the purpose of the present study, as our main aim is to understand what factually happens with the knowledge of individuals during organizational innovation.

3.3.1 Introduction

Fundamental in Boisot’s treatment of information [and knowledge] in the I-Space, is his debate with economists. Economists treat information as an asset comparable to other products and goods. According to Boisot this is wrong for two reasons. First, knowledge is intangible and a human [mental] construct, and second, knowledge develops and goes through certain cycles. We will not expound further on Boisot’s discussions with economists in general, but focus on how he treats the dynamics of information [and knowledge].

The growing availability of information underlines the importance of being able to find ones way in the abundance of this information flow [Boisot 1995]. Economizing on data processing becomes vital to effective communication and effective organizational processes. This implies that information needs to be structured in order to become accessible. Boisot stresses that information cannot be treated like any other kind of physical resource, it needs a custom-made economic theory, which one can say most economists do not provide. He uses an evolutionary approach to information and has ‘a primary aim […] to establish information as a resource in its own right, a resource that economists should seriously account for’ [1995]. The dimensions of codification and abstraction are considered to be the key to this information processing. Together with the dimension of diffusion, codification and abstraction form the three dimensions of the I-Space, a conceptual framework that can explore the behavior of information flows to understand the creation and diffusion of knowledge [and by implication


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innovation] within selected populations [1998: 55]. In other words, the I-Space is a tool to understand the different flows of different kinds of information; it helps to understand the creation and the diffusion of information within groups of people. This applies to organizations, but also to societies. Note that Boisot uses the concepts of information and knowledge interchangeably at this point. However, in his publication of 2003 together with Canals he does differentiate between knowledge and information. In the following we will elaborate on [the dimensions of] the I-Space.

3.3.2 Codification

The first step to data reduction is through codification. Within the I-Space codification forms the first dimension [see also figure 3.2]. The codification process prepares the incoming information for the coding process; a more effective codification process facilitates the coding. Codification is not to be taken lightly. For instance, for this dimension it is important [how] to choose the codes or categories; choosing too many categories is not efficient and choosing too few categories goes with a loss of power in using the codes. The timing of the codification is also important, because ‘once codified, standards often create a lock-in effect that over time become irreversible’ [1998: 45]. So, once you choose it is not easy to reverse this decision. And the gain that comes with codifying in terms of data reduction is paid for with a loss of flexibility. Codification of an experience can, to a certain extent, be described as committing ‘oneself to a particular view of the world’ [Boisot 1983: 163]. Boisot also remarks about codification that it [see also figure 3.2]:

… creates … perceptual and conceptual categories that facilitate the classification of phenomena. The act of assigning phenomena to categories once these have been created is known as coding [1998: 42].

Figure 3.2: The codification process precedes the process of coding


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In terms of complexity, Boisot defines this dimension of codification as ‘the number of bits of information required to carry out a given data-processing task’ [1998: 46]; the dimension runs from uncodified to codified.

3.3.3 Abstraction

The second dimension within the I-Space is that of abstraction. Abstraction is closely related to codification as it is an extended form of data reduction. In structuring the phenomena that have been codified, the number of categories is again reduced. However, abstraction essentially differs from codification; the process of codification gives form to phenomena, and the process of abstraction structures these phenomena. On the abstraction scale abstract opposes concrete, in which abstract stands for conceptual and non-local knowledge – abstract thought – and concrete stands for perceptual and local knowledge – highly concrete experiences.

3.3.4 Diffusion

The third dimension is covered by diffusion; it establishes ‘the availability of data and information for those who want to use it’ [1998: 52]. Diffusion ‘can be scaled to refer to the proportion of a given population of data-processing agents that can be reached with information operating at different degrees of codification and abstraction’ [1998: 52]. In other words, the diffusion expresses the ratio of a certain population compared to that part of this population that is susceptible to the way that the information is codified and abstracted. This diffusion scale ‘establishes the availability of data and information for those who want to use it. It does not measure adoption: information may be widely diffused and yet remain unused’ [1998: 52]. On the relation between diffusion and effective communication Boisot remarks:

… A shared context is essential to the formulation of meaningful messages; push the requirement of sharing too far, however, and a message becomes banal and uninformative; on the other hand make sharing too tenuous, and a message becomes meaningless. Interesting messages must navigate between intelligibility and banality and effective communication within a social group therefore depends upon a partial, rather than a total diffusion of knowledge and experience among its members [1983: 163-164].


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So diffusion merely signifies the availability of information; diffusion precedes adoption. In this way Boisot depersonalizes the information; information can be codified and structured by ways of abstraction in which sense it becomes available to a certain population. The actual adoption of that information concerns a next step. We would like to note that, in stating that diffusion precedes adoption, we see a possible discrepancy with the statement that codification can be described as committing oneself ‘to a particular view of the world’ [1983: 163], also quoted on the previous page. We argue that the former statement about diffusion implies a depersonalization of knowledge, whereas the latter statement in particular implies a personalization of knowledge.

3.3.5 The I-Space

The three dimensions of codification, abstraction and diffusion together form the I-Space, a tool to understand the different flows of different kinds of information. Boisot hypothesizes that codification and abstraction together facilitate the diffusion of information and that they reinforce each other. This hypothesis can be visualized in the I-Space [see figure 3.3a below]. For instance, the curve in figure 3.3a moving from point A to point B indicates ‘that the more codified and abstract an item of information becomes, then, other things being equal, the larger the percentage of a given population it will be able to reach in a given period of time’ [1998: 55]. Boisot considers this curve to be static, ‘depicting a function relationship between codification, abstraction, and diffusion at a single instant in time’ [p.58] and remarks that the I-Space can also be used more dynamically.

Figure 3.3a: The diffusion curve in the I-Space


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This happens when we consider the model to have intrinsic dynamics. In other words, the better information is codified and the more structured that information is, the higher the percentage of the population to which this information will become available. Then, formulated this way, the diffusion of the information is a resultant of the degree of codification and the abstraction of information. But the diffusion also influences the abstraction and codification. As Boisot puts it

… data is … constantly on the move in the I-Space: much uncodified data sooner or later gets codified, much concrete data gradually increases in abstraction, and data that was the proprietary possession of a few individuals gradually becomes the common possession of all [p.58]

The movement can also be in opposite directions on all three dimensions

… codified data over time gets internalized and becomes tacit, abstract data gets applied to concrete problems, and diffused data gives rise to unique insights which are appropriated by well-placed individuals [p.58]

Adding up these forces in movements, Boisot constructs a schematic sequence of movements, which he calls the social learning cycle or SLC shown in figure 3.3bbelow. The figure shows six phases. The cycle starts with scanning fuzzy data and weak signals through which it becomes the possession of individuals [1]. Subsequently these data are codified through problem solving, reducing uncertainty [2], which in turn leads to generalizing the application through abstraction, capturing its essence [3]. The newly created insights are shared with a target-population through diffusion [4]. Next these new insights are applied in different situations in a process of ‘learning-by-doing’ called absorption [5]. Finally, the abstract knowledge is embedded into concrete practices in for instanceorganizational rules, impacting [6]. There are many variations to this schematic knowledge flow.

All in all, the I-Space identifies knowledge flows of information; these ‘pathways’, as Boisot calls them, are shaped by the culture of the population in question, for instance the organization or the industry. The other way around is also possible; the pathways shape the culture of the organization.


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Figure 3.3b: Movement of knowledge in the I-Space: The social learning cycle [SLC]1: Scanning – 2: Problem solving – 3: Abstraction

4: Diffusion – 5: Absorption – 6: Impacting

3.3.6 Two ways to value knowledge

Boisot distinguishes two perspectives on knowledge: 1] knowledge is cumulative in an absolute sense, and 2] knowledge is cumulative, but confined in a paradigm. In relation to learning and knowledge dynamics the first perspective is referred to as Neoclassical learning or N-learning, and the second perspective is referred to as Schumperterian learning or S-learning.

Neoclassical or N-Learning

The N-learning strategy implies that knowledge is cumulative, gradually building up to a better quality of knowledge. This is accomplished through the elimination of errors. Therefore, knowledge should be codified and abstracted in a precise way, so that a hierarchical structure can be built [p.93].

An implication of this view is that knowledge is valuable in an absolute sense. Sharing knowledge more or less equals giving away what you have without something in return. Boisot hypothesizes that the N-learning perspective will evoke hoarding strategies. That is, the diffusion of knowledge will be obstructed. In the I-Space model this is visualized in that barriers are set to prevent the knowledge flow from ‘B’ to ‘C’ [see figure 3.3c]. N-learning typically holds the view that learning comes to an equilibrium at point ‘C’ in the I-Space, a neoclassical perspective in economics.


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Figure 3.3c: The four regions activated by an SLC

Schumpeterian or S-Learning

The S-learning strategy also perceives knowledge to be an accumulation, but in a relative way; knowledge building occurs within a paradigm. So, this perspective allows alternative networks of knowledge to either ‘collaborate or compete’ [p.93], a view introduced by Kuhn. This view has an important implication on the essence of knowledge. Boisot [1998] captures this thought in the following citation

These networks are, in effect, patterns that we ourselves impose on the data, and in most cases the data turns out to be consistent with a potentially infinite number of patterns. Networks or paradigms, then, are not inherent in the data and just waiting to be discovered. They are free constructions of the human mind. And just as one can change one’s mind, one can modify the constructions that one overlays on the data of experience. [p.93]

In contrast to the N-learning perspective, Boisot [1998] hypothesizes that S-learning will facilitate a strategy of knowledge sharing rather than a hoarding strategy. In terms of the I-Space model S-learning sees

… the SLC as continuing its course beyond region C in the I-Space, and moving down once more into those uncodified and

A

B

C

D


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highly local concrete regions … in which disequilibrating discontinuities originate. [p.99]

Knowledge value of individuals

The two perspectives on knowledge can also be distinguished at the level of the individual. That is, whether or not an individual perceives his or her knowledge as absolute will affect the knowledge dynamics of this individual. For instance, when a person acknowledges that new wisdoms can be obtained in a certain knowledge domain then this person has the potential to change her knowledge. Whereas when a person perceives her obtained knowledge as absolute and, moreover, at the end of the development chain, then this person will very likely not have potential to change her knowledge [in this particular domain].

In other words, the perspective that an individual holds on its own knowledge is an important indicator to the potential knowledge dynamics of this person.

3.4 Evaluating the I-Space for studying knowledge dynamics

3.4.1 Recapitulating

The I-Space can be used dynamically to show movement of knowledge flows. These knowledge flows can be captured in a social learning cycle [SLC] and they are an indication of the culture of the population of which the knowledge flows are represented. The I-Space can also be used more statically as a tool to investigate knowledge assets. This can be done through the scaling of the knowledge of for instance a unit or the units within an organization, on the three dimensions of the I-Space. For example, low codification is hard to articulate, high abstraction is generally applicable and medium diffusion is characterized in that the knowledge is only available to a selected group within the whole population.

3.4.2 Understanding knowledge dynamics during innovation using the I-Space

The I-Space model by Boisot emphasizes the unique nature of information – and knowledge – in comparison to other economic goods that should be considered in its own right. It presents an insightful way to communicate the dynamics and value of information. This makes the I-Space model a powerful tool, which shows the dynamics of information at different organizing levels. When we evaluate the I-Space model for our own purpose – to understand the dynamics of knowledge during innovation – we keep in mind the following six points


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� We study knowledge

� We want to establish the dynamics of knowledge

� We want a tool to study knowledge at the level of the individual

� We want to be able to relate the knowledge and its dynamics to specific tasks

� We want to relate the dynamics of knowledge to the dynamics of the innovation process

� We want a solid foundation for our knowledge perspective, preferably in cognitive science and semiotics to secure a focus on theory and empirical study

When we take these points into account as formulated above and use them to evaluate the I-Space model of Boisot, we come to the following evaluation [see also table 3.1].

The I-Space of Boisot is particularly interesting for our purposes as the I-Space enables exploration of knowledge dynamics within organizations, of whichorganizational innovation is an example. Also, the particular kinds of knowledge can be directly related to specific tasks that are performed within the organization. The I-Space can reveal dynamic as well as static knowledge, although we note that Boisot uses the concept of information and knowledge interchangeably. The possibilities of our model to measure knowledge [dynamics] equal those of the I-Space model, although Boisot does not empirically test his model.

We do however miss an important ingredient in the I-Space model. The aim of the present study is not to understand how knowledge moves within a certain population. Rather, we are interested to know what actually happens to the knowledge within a person who undergoes organizational change in the form of organizational innovation in relation to the type of knowledge; codification and abstraction can be viewed as knowledge types, but diffusion cannot. As Boisot pointed out, codifying involves mastery of codifying skills including ‘an apprecia-tion of how they [codifications] attach to a specific and narrow range of expe-riences’ [1983: 166, between brackets not in original]. Boisot uses one scale for codification, moving from non-codified at one end to codified at the other end. We see the process of codification, from a semiotic perspective which the next chapter will elaborate on, as essentially different from the non-codified skills. We are also interested in the process of non-coding to coding. The I-Space model of


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Boisot therefore is not suited to fit our purpose in all respects. The next chapter presents our cognitive-semiotic model as an alternative to study organizational innovation from a knowledge perspective focusing on the individual level. So, what is missing is the possibility to study knowledge at the individual level.

Table 3.1: Evaluation of the I-Space model by Boisot in comparison with the adjusted cognitive-semiotic model in terms of the aim of our study

Aim Boisot Adjusted c-s model

Knowledge dynamics - / + +

Measurable + +

Empirical - / + +

Individual level - +

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