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Section 2 Measuring the effect of existing knowledge on the
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Step 5. Calculate supporting effect of each existing knowledge type on creation new desirable knowledge using Equation (7).
Figure 12 Existing and desirable knowledge.
Based on Equation (7) and data gained from the previous steps, supporting effect of each knowledge type that every team has was calculated. The results are shown in Table 11, where Gkicorresponds to the supporting effect of knowledge type k on creation desirable knowledge i (i=44).
Table 10
Cumulative Effect Knowledge type
Cumulative effect
Knowledge type
Cumulative effect
Knowledge type
Cumulative effect
12 1.1 27 -3 40 0
15 3 28 3 41 1.5
19 1 31 2 42 2
22 3 32 2.3 48 -7.2
23 -2 35 -3 50 5
24 -4 37 -3 52 2
26 2 38 5 57 3
Knowledge type
Knowledge level
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Table 11
Supporting Effect of Different Knowledge Types
Team1 Team2 Team3 Team4
Knowledge
type Gki Knowledge
type Gki Knowledge
type Gki Knowledge
Type Gki
12 3.11 19 9.92 12 1.49 15 0.05
19 0.66 22 0.31 24 4.05 22 0.70
23 3.16 27 82.80 32 28.09 26 9.94
26 0.21 31 2.92 35 189.98 31 11.16
28 5.07 38 0.50 40 4.92 37 9.50
37 108.79 41 6.55 50 354.88 42 61.17
52 50.55 48 1694.12 52 53.47 57 384.11
Step 7. Determine conjoint effect of existing knowledge and desirable new knowledge.
Conjoint effect is determined as a follow sum of biquadrate functions:
) ( ) ( )
( l m
,where l and m represents difference between knowledge types of less complex knowledge than desirable knowledge, and more complex knowledge than desirable knowledge respectively;
and biquadrate function is determine as:
(x)1.2x4 2x32.7x2 0.7x10 Step 8. Determine norm of the level of knowledge.The norm ofknowledge depends on relation between the levels of existing supporting knowledge as is specified as listed in Table 12
Step 9. Determine weight coefficient for Equation (8).
Weight coefficient is specified as followed: 0.4 and 0.6
Step 10. For each team 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.
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Table 12
Norm of Level of Knowledge
Norm of level of knowledge Situations
1 level of more and less complex knowledge is higher than that of the desirable knowledge
2 level of more complex is higher than and level of less complex is equal to that of desirable knowledge
3 level of more complex is equal to and level of less complex is higher than that of desirable knowledge
4 level of more and less complex are equal to that of desirable knowledge
5 level of more complex is higher than and level of less complex is lower than that of desirable knowledge
6 level of less complex is higher than and level of more complex is lower than that of desirable knowledge
7 level of more complex is equal to and level of less complex is lower than that of desirable knowledge
8 level of less complex is higher than and level of more complex is lower than that of desirable knowledge
9 level of more complex and less complex are lower than that of the desirable knowledge
Using Equation (8) and mentioned above data, maximum joint effect for every team and their existing knowledge was calculated and results are shown in Table 13. For every knowledge type that is less complex than desirable knowledge,
i m G G
L ki m i
m ( , ),
max was calculated. Teams 1, 2 and 4 have six knowledge types which are less complex than desirable knowledge type (44), Team 3 by turn has 5 knowledge types that satisfy this condition. Therefore, there is one less record in Table 13 for Team 3.
Step 11. Sum maximum joint supporting effect for every team and choose the team with the largest sum.
Sum of maximum joint effect for all teams is shown in Table 13. Evidently existing knowledge of Team 2 can provide the best support for the creation of desirable knowledge.
This team should implement the knowledge creation process.
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Table 13
Total Support for Knowledge Creation
Team1 Team2 Team3 Team4
Knowledge
type maxL Knowledge
type maxL Knowledge
type maxL Knowledge
Type maxL
12 0.39 19 64.67 12 0.40 15 4.66
19 0.70 22 113.25 24 0.52 22 8.13
23 0.56 27 65.14 32 1.78 26 4.08
26 1.16 31 73.52 35 3.09 31 3.73
28 2.11 38 300.42 40 46.14 37 0.77
37 1.85 41 54.48 42 3.37
6.77
671.48
51.93
24.74Section 3 Sensitivity analysis
For better understanding of the proposed model and to explain effects of various parameters, a sensitivity analysis was conducted and its graphical interpretation will be presented in this section to disclose dependencies between parameters. Importance of variables and tradeoffs between the most important of them will be shown.
Figure 13 depicts changes of the knowledge created by combination with the changes of parameters. Parameters values increase slowly along horizontal axis, vertical axis implies amount of new knowledge created. It can be seen that existing knowledge of person (OLD) and knowledge received from others (KBA) don‘t affect knowledge creation by combination as much as combination coefficient (comb) and knowledge creation ability (KCA) of persons.
Slight changes in last two parameters cause relatively bigger differences in the outcome. In other words, even if a person has big amount of Knowledge and can receive a lot of knowledge from others, but he or she lacks creativity, then it will be just two different set of knowledge that person posses and will not lead to creation of new knowledge. Similar, if combination coefficient is low, it means that two sets of knowledge are difficult to combine with each other, and outcome of the knowledge creation process will be not impressive.
65 0
5 10 15 20 25 30 35
OLD KCA Kb-a comb
Figure 13 Importance of Factors for Knowledge Creation by Combination
0 5 10 15 20 25
0.2 0.24 0.28 0.32 0.36 0.4 0.44 0.48 0.52 0.56 0.6 0.64 0.68 0.72 0.76 0.8 0.84 0.88 0.92 0.96
combination index
NEW
KCA(0.98~0.2) KCA(1~0.4) KCA(0.89~0.5)
Figure 14 Knowledge Created by Combination Tradeoffs.
Figure 14 depicts dependence of the knowledge creation process from these two factors when they change reversely, where combination coefficient increases from 0.2 to 0.96 with step of 0.02 and with different changes of knowledge creation ability. KCA decreases from 0.98 to 0.2 with step of 0.02, from 1 to 0.4 with step of 0.02 and from 0.89 to 0.5 with step of 0.01. As combination coefficient and knowledge creation ability of person are predominant
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factors which affect amount of knowledge created by combination, manager should pay more attention to these factors when choosing persons, and needs to make a decision to choose a person with the higher knowledge creation ability and lower combination coefficient, or a person with the lower knowledge creation ability and higher combination coefficient. As analysis shows the biggest amount of knowledge created by combination not necessarily be created by the person with the highest knowledge creation ability, in stead, an optimal combination of knowledge creation ability and combination coefficient should be found.
Figure 15 depicts changes of the knowledge created by mutation with the changes of parameters. Parameters‘ values increase slowly along horizontal axis, vertical axis implies amount of new knowledge created.
Existing knowledge of person (OLD) and knowledge creation ability of person (KCA) affect amount of knowledge created by mutation less than mutation coefficient (mutation) and amount of knowledge received from others (KBA). Existing knowledge and creativity of person can lead to incremental knowledge creation to make radical changes in new knowledge, one needs external knowledge which with the high probability will interact with existing knowledge, and will be transformed into new knowledge in the qualitatively new dimension.
A reversal changes of mutation coefficient and knowledge received from others (KBA) is shown in Figure 16, where mutation index changes from 0.02 to 0.7 with step of 0.02,
A
KB changes from 7.3 to 0.5, from 8.9 to 2.1, and from 9.7 to 2.9 all with step of 0.2. High probability of mutation of two sets of knowledge is important, but if amount of new set of knowledge is not large enough to implement mutation process completely, the outcome of the creation process will be less than the creation process conducted with the larger amount of new knowledge with lesser probability of mutation. Similar to the situation with knowledge created by combination, it is responsibility of the manager to choose optimal combination of persons, considering all parameters and paying additional attention to mutation coefficient, knowledge which person can receive from others and their balanced values to gain maximum positive effect from available resources.
67 0
200 400 600 800 1000 1200 1400
mutation Kb-a KCA OLD
Figure 15 Importance of Factors for Knowledge Creation by Mutation
0 5 10 15 20 25 30
0.02 0.06 0.1 0.14 0.18 0.22 0.26 0.3 0.34 0.38 0.42 0.46 0.5 0.54 0.58 0.62 0.66 0.7
mutation index
NEW
Kb-a(7.3~0.5) Kb-a(8.9~2.1) Kb-a(9.7~2.9)
Figure 16 Knowledge Created by Mutation. Tradeoffs.
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