To increase performance of the organization and make its activities more efficient, knowledge management is employed by many organizations and proved it to be an effective and useful tool. Development of the organization requires competitive and actual knowledge.
At a certain point of time, organization inevitably faces situation when it needs to create new knowledge. Knowledge that currently exists in the organization is a platform for all knowledge creation processes that organization can conduct. Therefore it is important to know what knowledge is available, how advance and complicated it is, where it is located and who posses it. The following section will explain how organization‘s existing knowledge can affect and support the knowledge creation process in organization.
1. Knowledge dimensions
Since people became interested in knowledge they wanted to ‗know‘ or ‗understand‘ the world and to describe it first in objective terms and later in terms of complex system. The complexity of system depends on nonlinear interactions between its components (Cilliers, 2005) and these interactions and components form a context where our knowledge exists.
Obviously, the more complex system is, the more difficult is to understand it and to posses knowledge about it. Knowledge about systems will be also different in how well or deep one understands it. The superficial or shallow knowledge is easier to posses or to create than deep and profound knowledge. Holsapple, Raj & Wagner (2008) found that domain complexity influences the quality of acquired knowledge. Turner, Bettis & Burton (2002) used knowledge breadth and knowledge depth to find the best knowledge management strategy. Lin and Wu (2010) viewed knowledge depth of an organization as a portfolio of knowledge repositories and studied its influence on knowledge sourcing strategies. This research considers two dimensions which have influence on knowledge creation, namely complexity (or difficulty) of knowledge and depth of knowledge (or knowledge level).
The complexity dimension differentiates different knowledge areas; organization has knowledge of, from simple to difficult. Complexity of knowledge is a set of skills, experience, education, declarative knowledge, general knowledge about particular topic and interactions within this set. To understand the entire situation with knowledge that organization possesses,
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it is important to distinguish between various types of knowledge that are available to the organization. This classification will not only show existing knowledge, but also can help to find missing knowledge and give the directions for future knowledge creation. Examples of classification knowledge in complexity dimension are: the ability to make routine decisions, tactical decisions, strategic decisions; process of producing CD discs, DVD discs, HD-DVD discs; knowledge of managing small, medium or large projects; knowledge of the motorcycle manufacturing process, car manufacturing process or aircraft manufacturing process, etc.
The depth dimension (level of knowledge) refers to one‘s competence and mastery in a particular knowledge type (Cepeda & Vera, 2007). The depth of knowledge can vary from the absence of knowledge in specific knowledge type through basic level where one has only superficial knowledge to an advance level where one is recognized as a professional and has comprehensive knowledge in that specific knowledge type. For example, if one knows that an artificial neural network can be used for pattern recognition or clustering, but does not know how to build this network and how it works and solves the problem, then one has only basic level in this knowledge area, if on the other hand, he or she knows what this method is and how it works and how it can be modified to produce better result, then one has a profound understanding in this knowledge area.
It should be noted that the level of knowledge tends to be continuous; the person cannot have profound knowledge in a specific area without having basic knowledge in it. In other words it is impossible to be a professional without knowing the fundamentals. On the other hand, complexity of knowledge might be discrete, as one can poses knowledge in one area without having knowledge in similar or close knowledge fields. For example, one can know how to drive a car and not know how to ride a motorcycle.
2. Factors that support knowledge creation
The knowledge creation process can be initiated by the organization when it requires a new type of knowledge, or it needs to increase the level of existing knowledge. Figure 9 gives an example of the existing and new desirable knowledge, where existing knowledge can be found in knowledge types 1, 2, 3, 4, 5, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18 and 20, and their corresponding knowledge levels, and new desirable knowledge can be found in types 5, 8, 9, 14, 19. The knowledge creation process can be implemented by individuals or by teams.
Team work is more efficient than individual efforts in many different areas of human activities,
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and it is also true for the creation of knowledge. Outcome of team work is greater than just the sum of the outcomes individuals will produce (Plaza, Ngwenyama, & Rohlf, 2010).
Figure 9 Existing and Desirable Knowledge
It is true in the case of knowledge creation, because working in team, individuals can absorb experience and knowledge that a single person can‘t posses. With increasing complexity of knowledge, it becomes more difficult and time consuming to learn that knowledge and to create new knowledge and almost impossible for one person. Complexity in products and technique lead to the narrowing of knowledge that person has. Before, one or two persons can have all needed knowledge to produce some products or provide some services, but nowadays it is almost impossible. Knowledge of one single isolated person will be not enough now to make a pencil. This is simple and evident example how specialized and narrow became knowledge possessed by one person. Therefore importance of teams increases.
To achieve desirable results, the organization needs to analyze the possibility knowledge workers can complete an assigned task. In some cases, existing teams with high efficiency must be used, and in others cases, new teams or community of practice must be formed. To increase effectiveness of the team, it should be balanced with individuals whose existing knowledge and personal qualities match with each other. Some people are brilliant idea generators, but they cannot or are not willing to implement these ideas. To make ideas become a new knowledge, they need other individuals from the team who can help to fully develop them.
0 2 4 6 8 10 12 14
1 3 5 7 9 11 13 15 17 19
Knowledge type
Existing Desirable
Knowledge level
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One of the factors that support knowledge creation is complexity of knowledge types possessed by person. Certainly, if the new knowledge is more complex than the existing knowledge of person, then the existing knowledge will be less useful in supporting the knowledge creation process, and if the existing knowledge is more complex than the desirable knowledge, the supporting effect of the existing knowledge will be greater. The complexity of knowledge, usually, increases exponentially, so does the existing knowledge supporting the knowledge creation process.However, the most complex knowledge does not always provide the greatest support to the creation of new knowledge, thus, a balance between the amounts of new knowledge expected and that of the existing knowledge required must be achieved. If one is used to solve difficult problems with the newest methods and technologies interwoven in a complicated way in his daily activity, he or she might use a sledgehammer to crack a nut and overlook a simpler and easier way used by someone else who doesn‘t have such deep or complicated knowledge.
Another factor that influences the knowledge creation process and balances knowledge of different complexity is the cumulative effect of knowledge. It is effect of interconnections of knowledge of different types. Less and more complex types of knowledge are interconnected and these connections depend on particular industry, way of applying knowledge and fitness of the levels of knowledge. Even if every profession is highly specialized, specific knowledge in different industries is more or less connected to other knowledge in the same or different industries. Communication and discussion between bearers of knowledge of different types leads to the creation of new knowledge. Discussion is very important for knowledge creation, as total agreement will not raise new questions and give new ideas. The cumulative effect of knowledge and nonlinear connections between different types of knowledge influences the effectiveness of the knowledge creation process.
Not only is the neighboring type of existing knowledge critical to creating new knowledge, but also the level of existing knowledge plays an important role in supporting the knowledge creation process. Generally, the deeper the existing knowledge is, or the higher level the existing knowledge is, the more it can help in creating new knowledge.
Considering the above discussion, next section will propose a model which describes how existing knowledge in organization can support knowledge creation through the perspectives of above mentioned knowledge dimension and factors.
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3. Model formulation
Let N be the ii th knowledge type; if i j then Ni Nj and fori j , N is less i complex thanNj. Let L be the level of knowledge, i L is zero if the organization doesn‘t i have that particular knowledge type. For example, in Figure 9, the knowledge type (or complexity of knowledge) increases from 1 to 20 on the horizontal axis, and the level of knowledge increases from 0 to 14 on the vertical axis. Then the supporting effects Gji of the existing related neighboring knowledge type Nj on the creation of the new knowledge typeN can be described by Equation (7). i
a ( L j L i ) b 3 c e dN N i j f N ( i N j )
G ji
where Gji is the supporting effects of the existing knowledge j on the new knowledge i, Lj and L are the level of the knowledge types i Nj and N respectively, i
f(Nj) is a function depicting the cumulative effect of knowledge type Nj, and a, b, c, d are coefficients.
It is assumed that the difference in the level between existing and desirable knowledge influences the knowledge creation process in the way of a cubic function, while the difference in complexity between existing and desirable knowledge affects the knowledge creation process exponentially. Similar to the learning of new knowledge and to learning curves, which have view of the step function where learning from the basic level to some certain middle level is going very fast and then slows down, and accumulation of knowledge became more difficult until it reaches critical point, and learning to the next level occurs fast again, when the level of existing knowledge is close to the level of new desirable knowledge, it will have a moderate influence and give not so big support for the knowledge creation, with increasing difference between knowledge levels, supporting effect become more appreciable. Both learning s-curve and false learning curve (Scholtes, 1998) can be approximated by cubic function with different parameters. The difference in complexity between existing and desirable knowledge affects the knowledge creation process exponentially (it is mentioned (7)
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earlier that complexity of knowledge increases exponentially). Furthermore, the more complex is the desirable knowledge, the lesser will be the supporting effect of the existing knowledge.
This is expressed in Equation (7) by square root of desirable knowledge in the denominator.
And this effect will be higher when the existing knowledge type is more complex and has broader coverage. This is expressed by cumulative effect in Equation (7). In some cases, when the level of existing knowledge is much lower that the level of desirable new knowledge, existing knowledge might impede the knowledge creation process and turn it into wrong direction. This situations should be avoided and persons who posses only that types of knowledge should not be involved into knowledge creation process. Influence of the difference in complexity of knowledge will decrease with the increasing complexity of new desirable knowledge. In other words, the more sophisticated knowledge is needed, the more complicated it become to affect its creation, as it also become more difficult to find connections between existing and desirable knowledge.
Figure 10 depicts how the supporting effect of the existing knowledge changes with the increasing complexity of the existing knowledge. The horizontal axis shows the difference between the complexity of the existing knowledge and desirable knowledge, whereas the vertical axis shows the supporting effect. Decreasing of graph when the difference between complexities is close to 10 shows that more complex knowledge not necessary gives more support to the knowledge creation process.
Figure 10 Supporting Effect of Existing Knowledge with Increasing Complexity
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Equation (7) describes supporting effect only for one type of existing knowledge. More often there are surrounding types of knowledge that are connected with the desirable new knowledge. The joint effect of more complex existing knowledge and less complex existing knowledge should be considered as they interact with each other. This joint effect Lfrom both Gliand Gm ican be computed as Equation (8)
) ( )
, (
ty connectivi k.
level k.
suppoting between
balance
type k.
g neighborin of
effect supporting
Lmi Lli
Gmi Gli
Gmi Gli
L
where Gli and Gm i are supporting effects of the less and more complex knowledge respectively,
and are weighting coefficient, 1 and
li
mi L
L is a norm of level of knowledge, )
(
is a conjoint effect indicating the connectivity between existing types of knowledge and desirable knowledge.
Joint supporting effect of the two neighboring knowledge types is influenced by the connectivity between these knowledge types. Inadequate knowledge of one or both supporting knowledge types can block the knowledge creation process (Prabhu, Chand. & Ellis, 2005).
Therefore it is important to analyze how the existing knowledge types are connected with each other and with the desirable knowledge type. Method proposed by Jabrouni, Kamsu-Foguem, Geneste & Vaysse (2011) can be used to find connections between the existing knowledge and the missing knowledge.
The norm of the level of knowledge shows the balance between the supporting knowledge levels relative to the desirable level of knowledge. If the levels of supporting knowledge are unbalanced, the knowledge creation process will not be so effective. The ideal situation will happen when both less and more complex supporting knowledge levels are greater than the desirable knowledge. In this situation the existing knowledge will ―pull‖
desirable knowledge type towards the needed level. The worse case is when both supporting knowledge levels are less than the desirable knowledge level, and then the ―push‖ effect will occur, which is more complicated as it requires also increasing of levels of supporting knowledge.
(8)
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The graphical interpretation of the supporting effect of the two surrounding knowledge types is given in Figure 11. It shows how existing knowledge types N and 1 N3 with the knowledge level L and 1 L respectively,3 can support the incremental creation of desirable knowledgeN . 2
According to Equation (7), the difference in knowledge level and knowledge complexity between the desirable knowledge N and the less complex knowledge 2 N will contribute to 1 knowledge creation together with the cumulative of knowledge type N and the effect is 1 obtained byGli. By analogy, the more complex knowledge N will contribute to knowledge 3 creation byGm i. The joint contribution to the creation of new desirable knowledge of the above two effects may be weighed using and, and divided by the norm of the levels of surrounding knowledge and the conjoint effect describing how strong the existing knowledge is connected to the desirable knowledge. The conjoint effect can change with different levels of knowledge complexity. Consequently, an incremental increase in the knowledge level
L from L to 2 L of the desirable knowledge type N can be achieved. Note, the gap 2 betweenL and 2 L may need to be bridged by several iterations within a certain period of time.
Figure 11 Influences of Factors on Desirable Knowledge )
(N1N2
L
) (
li m i
L L
) (N3 f
2
1 L
L
2
3 L
L
2
3 N
N L1
L3
L2
L
G
li G
miN1 N2 N3 Knowledge type
type )
(N1 f
K3
K1
K2
desirable knowledge level L*
Knowledge level
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4. Implementation algorithm II
The algorithm of how to apply the proposed model in practice will be provided in this section. The aim of the algorithm is to choose one team which will implement the knowledge creation process better based on the team‘s existing knowledge from the current teams.
Step 1. Determine type and level of desirable new knowledge.
Step 2. If algorithm is applied the first time, measure knowledge type and knowledge level of each person from all teams. If algorithm was applied before, re-measure person‘s knowledge type and knowledge level.
Step 3. Determine cumulative effect of each knowledge type, possessed by persons, relative to the desirable knowledge type.
Step 4. Determine coefficients for Equation (7) considering nature of knowledge.
Step 5. Calculate supporting effect of each existing knowledge type on creating new desirable knowledge using Equation (7).
Step 6. Determine the threshold and remove knowledge types with supporting effect less than threshold.
Step 7. Determine conjoint effect of existing knowledge and desirable new knowledge.
Step 8. Determine norm of the level of knowledge.
Step 9. Determine weight coefficient for Equation (8).
Step 10. For each team and for every type of existing knowledge less complex than desirable knowledge, calculate join supporting effect with all pairs of more complex existing knowledge and choose the pair with the maximum joint supporting effect. In other words, if i is new desirable type of knowledge for every Gki`, where k<i,
i m G G
L ki m i
m ( , ),
max will be selected.
Step 11. Sum maximum joint supporting effect for every team and choose the team with the largest sum.
Flowchart of this algorithm is show in Appendix B, on Figure 18.
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