Study of Knowledge and Innovation
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This assignment is mainly based on a study of knowledge and innovation of a population of 10,000 people. It is not feasible to conduct a study of 10,000 people by considering each of their responses. Thus a sample of 370 people has been considered for the study. A survey questionnaire was prepared and distributed to 370 people selected randomly out of which 152 responses were returned. The analysis in this case will be performed based on these 152 responses.
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Running Head: STUDY OF KNOWLEDGE AND INNOVATION
Study of Knowledge and Innovation
Name of the Student
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Study of Knowledge and Innovation
Name of the Student
Name of the University
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1STUDY OF KNOWLEDGE AND INNOVATION
Table of Contents
1.0 Introduction................................................................................................................................2
2.0 Research Methodology..............................................................................................................2
3.0 Data Analysis.............................................................................................................................2
3.1 Validity Construction.............................................................................................................3
3.2 Reliability Test.....................................................................................................................12
3.3 Factor Analysis....................................................................................................................12
3.4 Multiple Linear Regression.................................................................................................25
Table of Contents
1.0 Introduction................................................................................................................................2
2.0 Research Methodology..............................................................................................................2
3.0 Data Analysis.............................................................................................................................2
3.1 Validity Construction.............................................................................................................3
3.2 Reliability Test.....................................................................................................................12
3.3 Factor Analysis....................................................................................................................12
3.4 Multiple Linear Regression.................................................................................................25
2STUDY OF KNOWLEDGE AND INNOVATION
1.0 Introduction
This assignment is mainly based on a study of knowledge and innovation of a population
of 10,000 people. It is not feasible to conduct a study of 10,000 people by considering each of
their responses. This will give rise to both time and money constraints. Thus a sample of 370
people has been considered for the study. A survey questionnaire was prepared and distributed to
370 people selected randomly out of which 152 responses were returned. The analysis in this
case will be performed based on these 152 responses.
2.0 Research Methodology
To conduct this study, the research methodology that has been adopted is survey
methodology. The mode of analysis will thus be quantitative. Appropriate quantitative
techniques will be used to analyze the subject. The main objective of this research is to
investigate the relationship between knowledge sharing, innovation award and firm
performance. The population that has been targeted are all employees in the
government sector. Information were collected on the demographic profile of
the respondents as well as on other attributes such as knowledge sharing
(both internal and external), innovation awards, innovation performance and
firm performance. In this case, knowledge sharing has been considered as
the dependent variable, innovation awards is the moderator variable and
innovation performance and firm performance are the independent
variables. Several questions were asked to the selected employees under
each of the variable names specified. Thus, in order to consider each of the
variables, a median of the scores given by the respondents have been
1.0 Introduction
This assignment is mainly based on a study of knowledge and innovation of a population
of 10,000 people. It is not feasible to conduct a study of 10,000 people by considering each of
their responses. This will give rise to both time and money constraints. Thus a sample of 370
people has been considered for the study. A survey questionnaire was prepared and distributed to
370 people selected randomly out of which 152 responses were returned. The analysis in this
case will be performed based on these 152 responses.
2.0 Research Methodology
To conduct this study, the research methodology that has been adopted is survey
methodology. The mode of analysis will thus be quantitative. Appropriate quantitative
techniques will be used to analyze the subject. The main objective of this research is to
investigate the relationship between knowledge sharing, innovation award and firm
performance. The population that has been targeted are all employees in the
government sector. Information were collected on the demographic profile of
the respondents as well as on other attributes such as knowledge sharing
(both internal and external), innovation awards, innovation performance and
firm performance. In this case, knowledge sharing has been considered as
the dependent variable, innovation awards is the moderator variable and
innovation performance and firm performance are the independent
variables. Several questions were asked to the selected employees under
each of the variable names specified. Thus, in order to consider each of the
variables, a median of the scores given by the respondents have been
3STUDY OF KNOWLEDGE AND INNOVATION
considered. For the independent variable, knowledge sharing, sum of
internal knowledge sharing and external knowledge sharing has been
considered.
3.0 Data Analysis
3.1 Validity Construction
The first thing that has been performed for the purpose of the data analysis is analysis of
the demographic factors of the respondents. The demographic factors of the 152 includes their
gender, nationality, age, education, level of the job and number of years the person is working
there.
It can be seen that among the participating 152 respondents, 90 were male and 62 were
female. Thus there are 59.2 percent responses from the male point of view and 40.8 responses
from the point of view of the females. The results are shown in table 3.1 and illustrated in figure
3.1.
Table 3.1: Frequency table for Gender
Frequency Percent Valid Percent Cumulative Percent
Valid
Male 90 59.2 59.2 59.2
Female 62 40.8 40.8 100.0
Total 152 100.0 100.0
considered. For the independent variable, knowledge sharing, sum of
internal knowledge sharing and external knowledge sharing has been
considered.
3.0 Data Analysis
3.1 Validity Construction
The first thing that has been performed for the purpose of the data analysis is analysis of
the demographic factors of the respondents. The demographic factors of the 152 includes their
gender, nationality, age, education, level of the job and number of years the person is working
there.
It can be seen that among the participating 152 respondents, 90 were male and 62 were
female. Thus there are 59.2 percent responses from the male point of view and 40.8 responses
from the point of view of the females. The results are shown in table 3.1 and illustrated in figure
3.1.
Table 3.1: Frequency table for Gender
Frequency Percent Valid Percent Cumulative Percent
Valid
Male 90 59.2 59.2 59.2
Female 62 40.8 40.8 100.0
Total 152 100.0 100.0
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4STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.1: Pie Chart showing the percentage of male and female respondents
Again, it can be seen that most of the employees in the government sectors are from the
UAE followed by Egypt, Syria and India. There are also employees belonging to other nations
but to a very less number. The results obtained from the demographic analysis is provided in
table 3.2 and illustrated in figure 3.2 with the help of a bar chart.
Table 3.2: Frequency table for Nationality
Frequency Percent Valid Percent Cumulative Percent
Valid Egypt 14 9.2 9.2 9.2
France 3 2.0 2.0 11.2
India 10 6.6 6.6 17.8
Iraq 6 3.9 3.9 21.7
Jordan 2 1.3 1.3 23.0
Kuwait 1 .7 .7 23.7
Lebanon 4 2.6 2.6 26.3
Oman 3 2.0 2.0 28.3
Sudan 7 4.6 4.6 32.9
Syria 12 7.9 7.9 40.8
Figure 3.1: Pie Chart showing the percentage of male and female respondents
Again, it can be seen that most of the employees in the government sectors are from the
UAE followed by Egypt, Syria and India. There are also employees belonging to other nations
but to a very less number. The results obtained from the demographic analysis is provided in
table 3.2 and illustrated in figure 3.2 with the help of a bar chart.
Table 3.2: Frequency table for Nationality
Frequency Percent Valid Percent Cumulative Percent
Valid Egypt 14 9.2 9.2 9.2
France 3 2.0 2.0 11.2
India 10 6.6 6.6 17.8
Iraq 6 3.9 3.9 21.7
Jordan 2 1.3 1.3 23.0
Kuwait 1 .7 .7 23.7
Lebanon 4 2.6 2.6 26.3
Oman 3 2.0 2.0 28.3
Sudan 7 4.6 4.6 32.9
Syria 12 7.9 7.9 40.8
5STUDY OF KNOWLEDGE AND INNOVATION
UAE 76 50.0 50.0 90.8
UK 5 3.3 3.3 94.1
USA 9 5.9 5.9 100.0
Total 152 100.0 100.0
Figure 3.2: Bar Chart showing the frequency of the nationalities of the respondents
Again, it can be seen that most of the employees in the government sectors are between
20 to 39 years old. The results obtained from the analysis is provided in table 3.3 and illustrated
in figure 3.3 with the help of a pie chart.
Table 3.3: Frequency table for Age
Frequency Percent Valid Percent Cumulative Percent
Valid
20 - 29 50 32.9 32.9 32.9
30 - 39 57 37.5 37.5 70.4
40 - 49 27 17.8 17.8 88.2
More than 50 18 11.8 11.8 100.0
Total 152 100.0 100.0
UAE 76 50.0 50.0 90.8
UK 5 3.3 3.3 94.1
USA 9 5.9 5.9 100.0
Total 152 100.0 100.0
Figure 3.2: Bar Chart showing the frequency of the nationalities of the respondents
Again, it can be seen that most of the employees in the government sectors are between
20 to 39 years old. The results obtained from the analysis is provided in table 3.3 and illustrated
in figure 3.3 with the help of a pie chart.
Table 3.3: Frequency table for Age
Frequency Percent Valid Percent Cumulative Percent
Valid
20 - 29 50 32.9 32.9 32.9
30 - 39 57 37.5 37.5 70.4
40 - 49 27 17.8 17.8 88.2
More than 50 18 11.8 11.8 100.0
Total 152 100.0 100.0
6STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.3: Pie Chart showing the frequency of the age of the respondents
Again, it can be seen that most of the employees in the government sectors are have
completed bachelor’s degree and some have completed masters’ degree as well. The results
obtained from the analysis is provided in table 3.4 and illustrated in figure 3.4 with the help of a
pie chart
Table 3.4: Frequency table for Education
Frequency Percent Valid Percent Cumulative Percent
Valid
High School 1 .7 .7 .7
Bachelor Degree 79 52.0 52.0 52.6
Master Degree 53 34.9 34.9 87.5
Doctorate Degree 19 12.5 12.5 100.0
Total 152 100.0 100.0
Figure 3.3: Pie Chart showing the frequency of the age of the respondents
Again, it can be seen that most of the employees in the government sectors are have
completed bachelor’s degree and some have completed masters’ degree as well. The results
obtained from the analysis is provided in table 3.4 and illustrated in figure 3.4 with the help of a
pie chart
Table 3.4: Frequency table for Education
Frequency Percent Valid Percent Cumulative Percent
Valid
High School 1 .7 .7 .7
Bachelor Degree 79 52.0 52.0 52.6
Master Degree 53 34.9 34.9 87.5
Doctorate Degree 19 12.5 12.5 100.0
Total 152 100.0 100.0
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7STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.4: Pie Chart showing the frequency of the education of the respondents
Again, it can be seen that most of the employees in the government sectors are senior
employees. The results obtained from the analysis is provided in table 3.5 and illustrated in
figure 3.5 with the help of a pie chart
Table 3.5: Frequency table for Job_Level
Frequency Percent Valid Percent Cumulative Percent
Valid
Junior Employee 39 25.7 25.7 25.7
Senior Employee 59 38.8 38.8 64.5
Lower Management 21 13.8 13.8 78.3
Middle Management 22 14.5 14.5 92.8
Top Management 11 7.2 7.2 100.0
Total 152 100.0 100.0
Figure 3.4: Pie Chart showing the frequency of the education of the respondents
Again, it can be seen that most of the employees in the government sectors are senior
employees. The results obtained from the analysis is provided in table 3.5 and illustrated in
figure 3.5 with the help of a pie chart
Table 3.5: Frequency table for Job_Level
Frequency Percent Valid Percent Cumulative Percent
Valid
Junior Employee 39 25.7 25.7 25.7
Senior Employee 59 38.8 38.8 64.5
Lower Management 21 13.8 13.8 78.3
Middle Management 22 14.5 14.5 92.8
Top Management 11 7.2 7.2 100.0
Total 152 100.0 100.0
8STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.5: Pie Chart showing the frequency of the job level of the respondents
Again, it can be seen that most of the employees in the government sectors are employed
for 9 to 15 years. The results obtained from the analysis is provided in table 3.6 and illustrated in
figure 3.6 with the help of a pie chart
Table 3.6: Frequency table for Working_Years
Frequency Percent Valid Percent Cumulative Percent
Valid
1 Year or less 21 13.8 13.8 13.8
2 to 8 Years 50 32.9 32.9 46.7
9 to 15 Years 68 44.7 44.7 91.4
More than 15 Years 13 8.6 8.6 100.0
Total 152 100.0 100.0
Figure 3.5: Pie Chart showing the frequency of the job level of the respondents
Again, it can be seen that most of the employees in the government sectors are employed
for 9 to 15 years. The results obtained from the analysis is provided in table 3.6 and illustrated in
figure 3.6 with the help of a pie chart
Table 3.6: Frequency table for Working_Years
Frequency Percent Valid Percent Cumulative Percent
Valid
1 Year or less 21 13.8 13.8 13.8
2 to 8 Years 50 32.9 32.9 46.7
9 to 15 Years 68 44.7 44.7 91.4
More than 15 Years 13 8.6 8.6 100.0
Total 152 100.0 100.0
9STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.6: Pie Chart showing the frequency of the years of employment of the respondents
Further, descriptive analysis has been performed on the independent variables, dependent
variable and the moderator variable. As it can be seen from the analysis that all the variables
have a mean rating score close to each other and which is quite high. The standard deviation for
the scores are quite close to one which indicates that the scores given by the respondents on the
chosen issues are quite close to the average value. Thus, it can be said that most of the employees
have given very high ratings. 50 percent of the people have rated 4 or higher in each of the
aspects and most of the people have rated 4 in the aspects. Table 3.7 gives the descriptive
summary of the variables followed by the histograms for each of the variables showing their
distributions.
Table 3.7: Summary of Descriptive Statistics
Knowledge_Sharing Moderator Value Performance Growth
N Valid 152 152 152 152 152
Missing 0 0 0 0 0
Figure 3.6: Pie Chart showing the frequency of the years of employment of the respondents
Further, descriptive analysis has been performed on the independent variables, dependent
variable and the moderator variable. As it can be seen from the analysis that all the variables
have a mean rating score close to each other and which is quite high. The standard deviation for
the scores are quite close to one which indicates that the scores given by the respondents on the
chosen issues are quite close to the average value. Thus, it can be said that most of the employees
have given very high ratings. 50 percent of the people have rated 4 or higher in each of the
aspects and most of the people have rated 4 in the aspects. Table 3.7 gives the descriptive
summary of the variables followed by the histograms for each of the variables showing their
distributions.
Table 3.7: Summary of Descriptive Statistics
Knowledge_Sharing Moderator Value Performance Growth
N Valid 152 152 152 152 152
Missing 0 0 0 0 0
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10STUDY OF KNOWLEDGE AND INNOVATION
Mean 7.2237 3.66 3.69 3.56 3.81
Median 8.0000 4.00 4.00 4.00 4.00
Mode 8.00 4 4 4 4
Std. Deviation 1.99734 1.151 1.016 1.211 1.105
Figure 3.7
Figure 3.8
Mean 7.2237 3.66 3.69 3.56 3.81
Median 8.0000 4.00 4.00 4.00 4.00
Mode 8.00 4 4 4 4
Std. Deviation 1.99734 1.151 1.016 1.211 1.105
Figure 3.7
Figure 3.8
11STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.9
Figure 3.10
Figure 3.9
Figure 3.10
12STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.11
The next analysis that will be performed is the correlation analysis between all the
selected variables. It can be seen that the variable knowledge sharing has a strong association
with the other variables, moderator, firm value, performance and growth. Thus, these factors can
be considered for predicting the knowledge sharing between the employees.
Table 3.8: Correlations
Moderator Value Performance Growth Knowledge_Sharing
Moderator
Pearson Correlation 1 .732** .566** .643** .774**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Value
Pearson Correlation .732** 1 .612** .680** .718**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Performance
Pearson Correlation .566** .612** 1 .592** .600**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Growth
Pearson Correlation .643** .680** .592** 1 .640**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Knowledge_Sharing Pearson Correlation .774** .718** .600** .640** 1
Figure 3.11
The next analysis that will be performed is the correlation analysis between all the
selected variables. It can be seen that the variable knowledge sharing has a strong association
with the other variables, moderator, firm value, performance and growth. Thus, these factors can
be considered for predicting the knowledge sharing between the employees.
Table 3.8: Correlations
Moderator Value Performance Growth Knowledge_Sharing
Moderator
Pearson Correlation 1 .732** .566** .643** .774**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Value
Pearson Correlation .732** 1 .612** .680** .718**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Performance
Pearson Correlation .566** .612** 1 .592** .600**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Growth
Pearson Correlation .643** .680** .592** 1 .640**
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
Knowledge_Sharing Pearson Correlation .774** .718** .600** .640** 1
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13STUDY OF KNOWLEDGE AND INNOVATION
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
**. Correlation is significant at the 0.01 level (2-tailed).
3.2 Reliability Test
A reliability test was conducted on the questionnaire that was used for this research.
From the analysis, it has been observed that the reliability statistics (Cronbach’s alpha) has been
found to be 0.963, which is close to 1 and is considered very high. Thus, it can be said that the
data collected is quite reliable and can be used further for the analysis of the study of knowledge
and innovation. The results of the test are given in table 3.9
Table 3.9: Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on
Standardized Items
N of Items
.963 .963 32
3.3 Factor Analysis
The Kaiser-Meyer-Olkin measure was found to be 0.930 and hence the sample data used
was deemed to be adequate for factor analysis. The following table shows the result of the KMO
test.
Table 3.10: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .930
Bartlett's Test of Sphericity
Approx. Chi-Square 3062.012
df 496
Sig. .000
Five factors from component 1 to component 5 were found to have eigen values greater
than 1. The first component after rotation had eigen value of 4.480, accounting for 14.001% of
Sig. (2-tailed) .000 .000 .000 .000
N 152 152 152 152 152
**. Correlation is significant at the 0.01 level (2-tailed).
3.2 Reliability Test
A reliability test was conducted on the questionnaire that was used for this research.
From the analysis, it has been observed that the reliability statistics (Cronbach’s alpha) has been
found to be 0.963, which is close to 1 and is considered very high. Thus, it can be said that the
data collected is quite reliable and can be used further for the analysis of the study of knowledge
and innovation. The results of the test are given in table 3.9
Table 3.9: Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on
Standardized Items
N of Items
.963 .963 32
3.3 Factor Analysis
The Kaiser-Meyer-Olkin measure was found to be 0.930 and hence the sample data used
was deemed to be adequate for factor analysis. The following table shows the result of the KMO
test.
Table 3.10: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .930
Bartlett's Test of Sphericity
Approx. Chi-Square 3062.012
df 496
Sig. .000
Five factors from component 1 to component 5 were found to have eigen values greater
than 1. The first component after rotation had eigen value of 4.480, accounting for 14.001% of
14STUDY OF KNOWLEDGE AND INNOVATION
the total variation, the second component accounted for 12.973% with eigen value 4.151. The
third component accounted for 12.316% of the variation with eigen value 3.941. The fourth
component had eigen value of 3.615 with proportion of explained variation being 11.298 and
finally the fifth and final factor with eigen value 3.484 explained 10.887%. The sum total
variation explained by the five factors was found to measure up to 61.474% of the total variation
in the data. The following table shows the “Total Variance Explained” table output from SPSS.
Table 3.11: Total Variance Explained
Component Initial Eigenvalues Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 15.005 46.892 46.892 4.480 14.001 14.001
2 1.378 4.307 51.198 4.151 12.973 26.974
3 1.164 3.638 54.837 3.941 12.316 39.290
4 1.095 3.423 58.260 3.615 11.298 50.587
5 1.029 3.214 61.474 3.484 10.887 61.474
6 .908 2.838 64.312
7 .905 2.828 67.140
8 .847 2.648 69.788
9 .782 2.444 72.232
10 .727 2.273 74.505
11 .705 2.202 76.707
12 .626 1.957 78.665
13 .607 1.898 80.562
14 .557 1.741 82.303
15 .520 1.624 83.927
16 .494 1.543 85.470
17 .475 1.485 86.955
18 .443 1.383 88.338
19 .429 1.341 89.679
20 .395 1.234 90.913
21 .384 1.201 92.114
22 .343 1.070 93.184
23 .322 1.008 94.192
24 .290 .905 95.097
the total variation, the second component accounted for 12.973% with eigen value 4.151. The
third component accounted for 12.316% of the variation with eigen value 3.941. The fourth
component had eigen value of 3.615 with proportion of explained variation being 11.298 and
finally the fifth and final factor with eigen value 3.484 explained 10.887%. The sum total
variation explained by the five factors was found to measure up to 61.474% of the total variation
in the data. The following table shows the “Total Variance Explained” table output from SPSS.
Table 3.11: Total Variance Explained
Component Initial Eigenvalues Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 15.005 46.892 46.892 4.480 14.001 14.001
2 1.378 4.307 51.198 4.151 12.973 26.974
3 1.164 3.638 54.837 3.941 12.316 39.290
4 1.095 3.423 58.260 3.615 11.298 50.587
5 1.029 3.214 61.474 3.484 10.887 61.474
6 .908 2.838 64.312
7 .905 2.828 67.140
8 .847 2.648 69.788
9 .782 2.444 72.232
10 .727 2.273 74.505
11 .705 2.202 76.707
12 .626 1.957 78.665
13 .607 1.898 80.562
14 .557 1.741 82.303
15 .520 1.624 83.927
16 .494 1.543 85.470
17 .475 1.485 86.955
18 .443 1.383 88.338
19 .429 1.341 89.679
20 .395 1.234 90.913
21 .384 1.201 92.114
22 .343 1.070 93.184
23 .322 1.008 94.192
24 .290 .905 95.097
15STUDY OF KNOWLEDGE AND INNOVATION
25 .277 .864 95.961
26 .240 .750 96.712
27 .222 .695 97.407
28 .206 .645 98.052
29 .188 .588 98.640
30 .179 .561 99.201
31 .151 .473 99.674
32 .104 .326 100.000
Extraction Method: Principal Component Analysis.
An inflection point is a point of change in a situation or in this case a curve. The
inflection point in this context is the point which marks the significant drop in rate of change in
eigen values as one moves across the factor components. Two inflection points can be seen in the
Scree plot shown below. A Scree plot, plots the eigen value of a component against the
corresponding component number. It is used to give an idea of the number of factors that ought
to be taken into consideration. The first inflection point is at component 2 which is obviously
apparent and the second one is at 5. Since the criteria for eigen value is >1, and a small inflection
can be seen at component 5 as well after which the curve decreases without any other apparent
inflection, the inflection point is taken to be 5 and five of the first factors are taken into
consideration.
25 .277 .864 95.961
26 .240 .750 96.712
27 .222 .695 97.407
28 .206 .645 98.052
29 .188 .588 98.640
30 .179 .561 99.201
31 .151 .473 99.674
32 .104 .326 100.000
Extraction Method: Principal Component Analysis.
An inflection point is a point of change in a situation or in this case a curve. The
inflection point in this context is the point which marks the significant drop in rate of change in
eigen values as one moves across the factor components. Two inflection points can be seen in the
Scree plot shown below. A Scree plot, plots the eigen value of a component against the
corresponding component number. It is used to give an idea of the number of factors that ought
to be taken into consideration. The first inflection point is at component 2 which is obviously
apparent and the second one is at 5. Since the criteria for eigen value is >1, and a small inflection
can be seen at component 5 as well after which the curve decreases without any other apparent
inflection, the inflection point is taken to be 5 and five of the first factors are taken into
consideration.
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16STUDY OF KNOWLEDGE AND INNOVATION
Figure 3.12: Scree Plot
The rotated component matrix consists of the factor loading values of the variables for each of
the selected factor components. The “Rotate Component Matrix” table obtained from SPSS in
this case has five components and the loadings for the variables in each component which are
above 0.4 were considered for simplification purposes. The matrix shows that component 1 or
factor one has 6 variables which contribute more than 0.4 loadings. Component 2 has 7 variables
separate from factor 1, component 3 has 6 variables, component 4 and 5 have 4 variables each
which contribute substantially to the respective factors.
Table 3.12: Rotated Component Matrixa
Component
1 2 3 4 5
Figure 3.12: Scree Plot
The rotated component matrix consists of the factor loading values of the variables for each of
the selected factor components. The “Rotate Component Matrix” table obtained from SPSS in
this case has five components and the loadings for the variables in each component which are
above 0.4 were considered for simplification purposes. The matrix shows that component 1 or
factor one has 6 variables which contribute more than 0.4 loadings. Component 2 has 7 variables
separate from factor 1, component 3 has 6 variables, component 4 and 5 have 4 variables each
which contribute substantially to the respective factors.
Table 3.12: Rotated Component Matrixa
Component
1 2 3 4 5
17STUDY OF KNOWLEDGE AND INNOVATION
Innovation Award
competition shall raise the
awareness of the
importance of innovations.
.736
The award will provide
creative and cutting-edge
solutions to counter any
challenges.
.694
The work environment
encourages innovation and
increases productivity of the
organization.
.582
The policies and procedures
of the organization support
and encourage creativity
and innovation.
.565
Knowledge-sharing culture
provides innovative
solutions.
.562
The award will engage
employees within a
framework that supports
innovative thinking which will
deepen both existing and
new innovations.
.531
Applying the excellence or
innovation award in the
organization will encourage
employees to work better in
a knowledge-sharing
environment.
The leadership of the
organization believes in
creativity, honors and
motivates creative
employees.
External communication and
knowledge-sharing are very
important between
organizations.
.690
Innovation Award
competition shall raise the
awareness of the
importance of innovations.
.736
The award will provide
creative and cutting-edge
solutions to counter any
challenges.
.694
The work environment
encourages innovation and
increases productivity of the
organization.
.582
The policies and procedures
of the organization support
and encourage creativity
and innovation.
.565
Knowledge-sharing culture
provides innovative
solutions.
.562
The award will engage
employees within a
framework that supports
innovative thinking which will
deepen both existing and
new innovations.
.531
Applying the excellence or
innovation award in the
organization will encourage
employees to work better in
a knowledge-sharing
environment.
The leadership of the
organization believes in
creativity, honors and
motivates creative
employees.
External communication and
knowledge-sharing are very
important between
organizations.
.690
18STUDY OF KNOWLEDGE AND INNOVATION
The innovative solutions can
equip the employee with
innovation skills such as
critical thinking, analytical
thinking, problem-solving
and creativity.
.633
There is a good relationship
between my organization
and other organizations in
terms of exchange and
knowledge-sharing.
.627
Do you think there is a
relationship between
innovation thinking and the
academic qualifications of
the employee?
.591
Our organization accepts all
creative ideas from
employees to improve the
work process.
.572
As an employee, I accept
any feedback and learn from
mistakes, which is part of
our internal knowledge –
sharing environment to
develop the organization.
.548
The effective communication
and knowledge-sharing with
other staff are easy and
clear.
.668
Our work environment
encourages creativity and
innovation.
.619
All staff members show
willingness and positiveness
in sharing knowledge, and I
don’t personally find any
difficulty with my team on
that matter.
.618
The innovative solutions can
equip the employee with
innovation skills such as
critical thinking, analytical
thinking, problem-solving
and creativity.
.633
There is a good relationship
between my organization
and other organizations in
terms of exchange and
knowledge-sharing.
.627
Do you think there is a
relationship between
innovation thinking and the
academic qualifications of
the employee?
.591
Our organization accepts all
creative ideas from
employees to improve the
work process.
.572
As an employee, I accept
any feedback and learn from
mistakes, which is part of
our internal knowledge –
sharing environment to
develop the organization.
.548
The effective communication
and knowledge-sharing with
other staff are easy and
clear.
.668
Our work environment
encourages creativity and
innovation.
.619
All staff members show
willingness and positiveness
in sharing knowledge, and I
don’t personally find any
difficulty with my team on
that matter.
.618
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19STUDY OF KNOWLEDGE AND INNOVATION
The award will engage
employees at the
organization within a
framework that supports
innovative thinking.
.558
My organization facilitates a
work environment that
enhances the concepts of
innovation and consolidation
of creative practices.
.549
My organization creates the
knowledge-sharing culture
by changing employee
attitudes and ehaviours to
promote willingness and
consistency in the
environment of knowledge
sharing.
.515
My organization follows
effective communication
methods and adapts a
variety of knowledge-sharing
channels with all our
stakeholders (staff, partners,
vendors, …etc.).
Knowledge-sharing culture
provides innovative
solutions to improve
products and enhance
services.
.797
Using innovation techniques
will increase the customer
satisfaction from the
organization which is
important to rise the growth.
.747
Using innovation techniques
will improve the operational
work and rise the growth of
the organization.
.656
The award will engage
employees at the
organization within a
framework that supports
innovative thinking.
.558
My organization facilitates a
work environment that
enhances the concepts of
innovation and consolidation
of creative practices.
.549
My organization creates the
knowledge-sharing culture
by changing employee
attitudes and ehaviours to
promote willingness and
consistency in the
environment of knowledge
sharing.
.515
My organization follows
effective communication
methods and adapts a
variety of knowledge-sharing
channels with all our
stakeholders (staff, partners,
vendors, …etc.).
Knowledge-sharing culture
provides innovative
solutions to improve
products and enhance
services.
.797
Using innovation techniques
will increase the customer
satisfaction from the
organization which is
important to rise the growth.
.747
Using innovation techniques
will improve the operational
work and rise the growth of
the organization.
.656
20STUDY OF KNOWLEDGE AND INNOVATION
Innovative performance will
improve and support the
collaborative work
environment at the
organization.
.502
The strategic plan of the
organization is clear and
encourages employees to
engage in a more creative
and innovative way of
thinking.
The strategic goals of the
organization express the
creativity and innovation.
.576
A documented methodology
of creativity and innovation
is known by all employees.
.569
The competitive
environment to win the
innovation award shall result
in a successful performance
between the staff.
.506
The effectiveness of
decision-making process
becomes stronger through
innovation and knowledge-
Sharing.
.505
Sharing knowledge between
departments will add more
value to the service and the
processes of the
organization.
Directors of departments at
the organization provide a
typical example of creativity
at work.
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 20 iterations.
Innovative performance will
improve and support the
collaborative work
environment at the
organization.
.502
The strategic plan of the
organization is clear and
encourages employees to
engage in a more creative
and innovative way of
thinking.
The strategic goals of the
organization express the
creativity and innovation.
.576
A documented methodology
of creativity and innovation
is known by all employees.
.569
The competitive
environment to win the
innovation award shall result
in a successful performance
between the staff.
.506
The effectiveness of
decision-making process
becomes stronger through
innovation and knowledge-
Sharing.
.505
Sharing knowledge between
departments will add more
value to the service and the
processes of the
organization.
Directors of departments at
the organization provide a
typical example of creativity
at work.
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 20 iterations.
21STUDY OF KNOWLEDGE AND INNOVATION
The following table shows the component scores of each variable in each of the five
components. These are the factor scores.
Table 3.13: Component Score Coefficient Matrix
Component
1 2 3 4 5
The effective communication
and knowledge-sharing with
other staff are easy and
clear.
-.183 -.067 .376 -.176 .153
All staff members show
willingness and positiveness
in sharing knowledge, and I
don’t personally find any
difficulty with my team on
that matter.
-.211 -.013 .301 -.146 .189
Our organization accepts all
creative ideas from
employees to improve the
work process.
-.002 .220 -.079 -.078 .036
As an employee, I accept
any feedback and learn from
mistakes, which is part of
our internal knowledge -
sharing environment to
develop the organization.
.080 .236 -.088 .005 -.162
My organization facilitates a
work environment that
enhances the concepts of
innovation and consolidation
of creative practices.
.142 -.132 .223 -.003 -.139
Our work environment
encourages creativity and
innovation.
.024 -.161 .312 -.022 -.060
The following table shows the component scores of each variable in each of the five
components. These are the factor scores.
Table 3.13: Component Score Coefficient Matrix
Component
1 2 3 4 5
The effective communication
and knowledge-sharing with
other staff are easy and
clear.
-.183 -.067 .376 -.176 .153
All staff members show
willingness and positiveness
in sharing knowledge, and I
don’t personally find any
difficulty with my team on
that matter.
-.211 -.013 .301 -.146 .189
Our organization accepts all
creative ideas from
employees to improve the
work process.
-.002 .220 -.079 -.078 .036
As an employee, I accept
any feedback and learn from
mistakes, which is part of
our internal knowledge -
sharing environment to
develop the organization.
.080 .236 -.088 .005 -.162
My organization facilitates a
work environment that
enhances the concepts of
innovation and consolidation
of creative practices.
.142 -.132 .223 -.003 -.139
Our work environment
encourages creativity and
innovation.
.024 -.161 .312 -.022 -.060
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22STUDY OF KNOWLEDGE AND INNOVATION
There is a good relationship
between my organization
and other organizations in
terms of exchange and
knowledge-sharing.
-.085 .305 -.051 -.147 .058
External communication and
knowledge-sharing are very
important between
organizations.
-.103 .366 .116 -.029 -.285
My organization follows
effective communication
methods and adapts a
variety of knowledge-sharing
channels with all our
stakeholders (staff, partners,
vendors, ...etc.).
.031 .102 .133 .082 -.267
My organization creates the
knowledge-sharing culture
by changing employee
attitudes and behaviors to
promote willingness and
consistency in the
environment of knowledge
sharing.
-.060 .048 .211 -.014 -.090
The award will engage
employees at the
organization within a
framework that supports
innovative thinking.
-.007 -.036 .232 -.002 -.085
The award will engage
employees within a
framework that supports
innovative thinking which will
deepen both existing and
new innovations.
.154 -.094 .149 .005 -.118
The award will provide
creative and cutting-edge
solutions to counter any
challenges.
.332 -.074 -.006 -.002 -.194
There is a good relationship
between my organization
and other organizations in
terms of exchange and
knowledge-sharing.
-.085 .305 -.051 -.147 .058
External communication and
knowledge-sharing are very
important between
organizations.
-.103 .366 .116 -.029 -.285
My organization follows
effective communication
methods and adapts a
variety of knowledge-sharing
channels with all our
stakeholders (staff, partners,
vendors, ...etc.).
.031 .102 .133 .082 -.267
My organization creates the
knowledge-sharing culture
by changing employee
attitudes and behaviors to
promote willingness and
consistency in the
environment of knowledge
sharing.
-.060 .048 .211 -.014 -.090
The award will engage
employees at the
organization within a
framework that supports
innovative thinking.
-.007 -.036 .232 -.002 -.085
The award will engage
employees within a
framework that supports
innovative thinking which will
deepen both existing and
new innovations.
.154 -.094 .149 .005 -.118
The award will provide
creative and cutting-edge
solutions to counter any
challenges.
.332 -.074 -.006 -.002 -.194
23STUDY OF KNOWLEDGE AND INNOVATION
Applying the excellence or
innovation award in the
organization will encourage
employees to work better in
a knowledge-sharing
environment.
.112 -.010 -.111 -.045 .160
Innovation Award
competition shall raise the
awareness of the
importance of innovations.
.360 -.022 -.147 -.133 -.002
The leadership of the
organization believes in
creativity, honors and
motivates creative
employees.
.059 -.035 .069 -.035 .054
The competitive
environment to win the
innovation award shall result
in a successful performance
between the staff.
.045 -.127 -.003 -.032 .233
The policies and procedures
of the organization support
and encourage creativity
and innovation.
.197 -.039 .006 -.043 -.035
Knowledge-sharing culture
provides innovative
solutions.
.229 -.147 -.092 -.039 .137
Innovative performance will
improve and support the
collaborative work
environment at the
organization.
-.035 .031 .054 .173 -.102
Directors of departments at
the organization provide a
typical example of creativity
at work.
.020 .043 -.045 -.062 .151
Applying the excellence or
innovation award in the
organization will encourage
employees to work better in
a knowledge-sharing
environment.
.112 -.010 -.111 -.045 .160
Innovation Award
competition shall raise the
awareness of the
importance of innovations.
.360 -.022 -.147 -.133 -.002
The leadership of the
organization believes in
creativity, honors and
motivates creative
employees.
.059 -.035 .069 -.035 .054
The competitive
environment to win the
innovation award shall result
in a successful performance
between the staff.
.045 -.127 -.003 -.032 .233
The policies and procedures
of the organization support
and encourage creativity
and innovation.
.197 -.039 .006 -.043 -.035
Knowledge-sharing culture
provides innovative
solutions.
.229 -.147 -.092 -.039 .137
Innovative performance will
improve and support the
collaborative work
environment at the
organization.
-.035 .031 .054 .173 -.102
Directors of departments at
the organization provide a
typical example of creativity
at work.
.020 .043 -.045 -.062 .151
24STUDY OF KNOWLEDGE AND INNOVATION
Sharing knowledge between
departments will add more
value to the service and the
processes of the
organization.
-.115 .091 -.106 .111 .147
The effectiveness of
decision-making process
becomes stronger through
innovation and knowledge-
Sharing.
-.080 .129 -.083 -.078 .226
A documented methodology
of creativity and innovation
is known by all employees.
.089 -.158 -.032 -.092 .304
The strategic goals of the
organization express the
creativity and innovation.
-.122 -.100 -.061 .121 .301
The strategic plan of the
organization is clear and
encourages employees to
engage in a more creative
and innovative way of
thinking.
-.068 -.077 -.017 .141 .160
Do you think there is a
relationship between
innovation thinking and the
academic qualifications of
the employee?
-.128 .236 -.037 -.072 .113
The innovative solutions can
equip the employee with
innovation skills such as
critical thinking, analytical
thinking, problem-solving
and creativity.
-.044 .300 -.221 .088 -.022
The work environment
encourages innovation and
increases productivity of the
organization.
.217 .076 -.184 -.023 -.003
Sharing knowledge between
departments will add more
value to the service and the
processes of the
organization.
-.115 .091 -.106 .111 .147
The effectiveness of
decision-making process
becomes stronger through
innovation and knowledge-
Sharing.
-.080 .129 -.083 -.078 .226
A documented methodology
of creativity and innovation
is known by all employees.
.089 -.158 -.032 -.092 .304
The strategic goals of the
organization express the
creativity and innovation.
-.122 -.100 -.061 .121 .301
The strategic plan of the
organization is clear and
encourages employees to
engage in a more creative
and innovative way of
thinking.
-.068 -.077 -.017 .141 .160
Do you think there is a
relationship between
innovation thinking and the
academic qualifications of
the employee?
-.128 .236 -.037 -.072 .113
The innovative solutions can
equip the employee with
innovation skills such as
critical thinking, analytical
thinking, problem-solving
and creativity.
-.044 .300 -.221 .088 -.022
The work environment
encourages innovation and
increases productivity of the
organization.
.217 .076 -.184 -.023 -.003
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25STUDY OF KNOWLEDGE AND INNOVATION
Using innovation techniques
will improve the operational
work and rise the growth of
the organization.
-.015 -.073 .031 .304 -.122
Knowledge-sharing culture
provides innovative
solutions to improve
products and enhance
services.
-.002 -.139 -.175 .442 .009
Using innovation techniques
will increase the customer
satisfaction from the
organization which is
important to rise the growth.
-.124 .023 -.062 .410 -.122
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Therefore 5 factors could be identified, each representing a particular aspect of the
company’s workings and attitudes. The score values of each variable in each factor is given in
the table above.
3.4 Multiple Linear Regression
In order to test the stated objective of the study, to investigate the relationship
between knowledge sharing, innovation award and firm performance,
regression analysis has been performed. The following null and alternative
hypothesis can be framed in order to run the analysis.
Alternate Hypothesis (H1): Innovation award toward knowledge sharing will influence the firm
performance.
Null Hypothesis (H0): Innovation award toward knowledge sharing has no influence on the
firm performance.
Using innovation techniques
will improve the operational
work and rise the growth of
the organization.
-.015 -.073 .031 .304 -.122
Knowledge-sharing culture
provides innovative
solutions to improve
products and enhance
services.
-.002 -.139 -.175 .442 .009
Using innovation techniques
will increase the customer
satisfaction from the
organization which is
important to rise the growth.
-.124 .023 -.062 .410 -.122
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Therefore 5 factors could be identified, each representing a particular aspect of the
company’s workings and attitudes. The score values of each variable in each factor is given in
the table above.
3.4 Multiple Linear Regression
In order to test the stated objective of the study, to investigate the relationship
between knowledge sharing, innovation award and firm performance,
regression analysis has been performed. The following null and alternative
hypothesis can be framed in order to run the analysis.
Alternate Hypothesis (H1): Innovation award toward knowledge sharing will influence the firm
performance.
Null Hypothesis (H0): Innovation award toward knowledge sharing has no influence on the
firm performance.
26STUDY OF KNOWLEDGE AND INNOVATION
The multiple regression equation that can show the relationship of the knowledge sharing
with growth, performance and value of the firm in the presence and the absence of the moderator
variable (Innovation awards) are given as follows:
Knowledge Sharing = 1.310 + (0.883 * Value) + (0.320 * Performance) + (0.398 * Growth)
Knowledge Sharing = 1.120 + (0.426 * Value) + (0.226 * Performance) + (0.205 * Growth) +
(0.806 * Innovation Awards)
The coefficient of R2 (Coefficient of determination) for the first model, in the absence of
the moderator variable has been found to be 0.580. This indicates that the model can explain 58
percent of the variability in the knowledge sharing in the absence of the moderator variable
innovation awards. On the other hand, the coefficient of R2 (Coefficient of determination) for the
second model, in the presence of the moderator variable has been found to be 0.670. This
indicates that the model can explain 67 percent of the variability in the knowledge sharing in the
presence of the moderator variable innovation awards. Thus, model 2 will be considered as a
much better model in the prediction of knowledge sharing.
The normal probability plot shows that there is almost a linear relationship between the
variables, which supports the assumption of normality of the residuals in the model. Thus, it can
be said that the first assumption of regression has been satisfied and it can be seen from the
figure 3.13 that there are no outliers present to the data.
From the ANOVA table given in table 3.16, it can be seen that the significance value
obtained is well below the stated 5% level. Thus, it can be said that the models are useful enough
in predicting the response. Thus, the null hypothesis stated previously is rejected.
The multiple regression equation that can show the relationship of the knowledge sharing
with growth, performance and value of the firm in the presence and the absence of the moderator
variable (Innovation awards) are given as follows:
Knowledge Sharing = 1.310 + (0.883 * Value) + (0.320 * Performance) + (0.398 * Growth)
Knowledge Sharing = 1.120 + (0.426 * Value) + (0.226 * Performance) + (0.205 * Growth) +
(0.806 * Innovation Awards)
The coefficient of R2 (Coefficient of determination) for the first model, in the absence of
the moderator variable has been found to be 0.580. This indicates that the model can explain 58
percent of the variability in the knowledge sharing in the absence of the moderator variable
innovation awards. On the other hand, the coefficient of R2 (Coefficient of determination) for the
second model, in the presence of the moderator variable has been found to be 0.670. This
indicates that the model can explain 67 percent of the variability in the knowledge sharing in the
presence of the moderator variable innovation awards. Thus, model 2 will be considered as a
much better model in the prediction of knowledge sharing.
The normal probability plot shows that there is almost a linear relationship between the
variables, which supports the assumption of normality of the residuals in the model. Thus, it can
be said that the first assumption of regression has been satisfied and it can be seen from the
figure 3.13 that there are no outliers present to the data.
From the ANOVA table given in table 3.16, it can be seen that the significance value
obtained is well below the stated 5% level. Thus, it can be said that the models are useful enough
in predicting the response. Thus, the null hypothesis stated previously is rejected.
27STUDY OF KNOWLEDGE AND INNOVATION
From the value of the VIF (Variance Inflation Factor), it can be seen clearly that the
value lies within 5, which indicates that there is no problem of multicollinearity in the problem.
The 95% confidence intervals for the slopes, β i, of the population regression
line that relates innovation awards have been obtained and provided in table
3.17.
Table 3.14: Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 Growth, Performance, Valueb . Enter
2 Moderatorb . Enter
a. Dependent Variable: Knowledge_Sharing
b. All requested variables entered.
Table 3.15: Model Summaryc
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .761a .580 .571 1.30770
2 .818b .670 .661 1.16369
a. Predictors: (Constant), Growth, Performance, Value
b. Predictors: (Constant), Growth, Performance, Value, Moderator
c. Dependent Variable: Knowledge_Sharing
Table 3.16: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 349.302 3 116.434 68.087 .000b
Residual 253.093 148 1.710
Total 602.395 151
2
Regression 403.333 4 100.833 74.462 .000c
Residual 199.062 147 1.354
Total 602.395 151
a. Dependent Variable: Knowledge_Sharing
b. Predictors: (Constant), Growth, Performance, Value
c. Predictors: (Constant), Growth, Performance, Value, Moderator
From the value of the VIF (Variance Inflation Factor), it can be seen clearly that the
value lies within 5, which indicates that there is no problem of multicollinearity in the problem.
The 95% confidence intervals for the slopes, β i, of the population regression
line that relates innovation awards have been obtained and provided in table
3.17.
Table 3.14: Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 Growth, Performance, Valueb . Enter
2 Moderatorb . Enter
a. Dependent Variable: Knowledge_Sharing
b. All requested variables entered.
Table 3.15: Model Summaryc
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .761a .580 .571 1.30770
2 .818b .670 .661 1.16369
a. Predictors: (Constant), Growth, Performance, Value
b. Predictors: (Constant), Growth, Performance, Value, Moderator
c. Dependent Variable: Knowledge_Sharing
Table 3.16: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 349.302 3 116.434 68.087 .000b
Residual 253.093 148 1.710
Total 602.395 151
2
Regression 403.333 4 100.833 74.462 .000c
Residual 199.062 147 1.354
Total 602.395 151
a. Dependent Variable: Knowledge_Sharing
b. Predictors: (Constant), Growth, Performance, Value
c. Predictors: (Constant), Growth, Performance, Value, Moderator
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28STUDY OF KNOWLEDGE AND INNOVATION
Table 3.17: Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. 95.0% Confidence
Interval for B
Collinearity
Statistics
B Std.
Error
Beta Lower
Bound
Upper
Bound
Tolerance VIF
1
(Constant) 1.310 .429 3.055 .003 .463 2.157
Value .883 .153 .449 5.785 .000 .582 1.185 .470 2.126
Performance .320 .117 .194 2.744 .007 .090 .551 .567 1.762
Growth .398 .138 .220 2.886 .004 .125 .670 .488 2.047
2
(Constant) 1.120 .383 2.927 .004 .364 1.877
Value .426 .154 .217 2.766 .006 .122 .730 .366 2.730
Performance .226 .105 .137 2.157 .033 .019 .433 .556 1.798
Growth .205 .126 .113 1.620 .107 -.045 .454 .460 2.175
Moderator .806 .128 .464 6.317 .000 .554 1.058 .416 2.405
a. Dependent Variable: Knowledge_Sharing
Table 3.18: Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition
Index
Variance Proportions
(Constant) Value Performance Growth Moderator
1
1 3.888 1.000 .00 .00 .00 .00
2 .053 8.561 .62 .00 .50 .00
3 .036 10.456 .34 .12 .47 .41
4 .023 12.942 .03 .88 .02 .59
2
1 4.855 1.000 .00 .00 .00 .00 .00
2 .053 9.530 .72 .00 .37 .00 .01
3 .044 10.557 .18 .04 .57 .02 .30
4 .029 12.996 .05 .01 .05 .90 .28
5 .020 15.765 .04 .96 .02 .08 .40
a. Dependent Variable: Knowledge_Sharing
Table 3.19: Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.9332 9.4321 7.2237 1.63434 152
Std. Predicted Value -2.013 1.351 .000 1.000 152
Table 3.17: Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. 95.0% Confidence
Interval for B
Collinearity
Statistics
B Std.
Error
Beta Lower
Bound
Upper
Bound
Tolerance VIF
1
(Constant) 1.310 .429 3.055 .003 .463 2.157
Value .883 .153 .449 5.785 .000 .582 1.185 .470 2.126
Performance .320 .117 .194 2.744 .007 .090 .551 .567 1.762
Growth .398 .138 .220 2.886 .004 .125 .670 .488 2.047
2
(Constant) 1.120 .383 2.927 .004 .364 1.877
Value .426 .154 .217 2.766 .006 .122 .730 .366 2.730
Performance .226 .105 .137 2.157 .033 .019 .433 .556 1.798
Growth .205 .126 .113 1.620 .107 -.045 .454 .460 2.175
Moderator .806 .128 .464 6.317 .000 .554 1.058 .416 2.405
a. Dependent Variable: Knowledge_Sharing
Table 3.18: Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition
Index
Variance Proportions
(Constant) Value Performance Growth Moderator
1
1 3.888 1.000 .00 .00 .00 .00
2 .053 8.561 .62 .00 .50 .00
3 .036 10.456 .34 .12 .47 .41
4 .023 12.942 .03 .88 .02 .59
2
1 4.855 1.000 .00 .00 .00 .00 .00
2 .053 9.530 .72 .00 .37 .00 .01
3 .044 10.557 .18 .04 .57 .02 .30
4 .029 12.996 .05 .01 .05 .90 .28
5 .020 15.765 .04 .96 .02 .08 .40
a. Dependent Variable: Knowledge_Sharing
Table 3.19: Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.9332 9.4321 7.2237 1.63434 152
Std. Predicted Value -2.013 1.351 .000 1.000 152
29STUDY OF KNOWLEDGE AND INNOVATION
Standard Error of Predicted Value .102 .422 .198 .074 152
Adjusted Predicted Value 3.7568 9.4607 7.2261 1.63596 152
Residual -3.87207 3.76786 .00000 1.14817 152
Std. Residual -3.327 3.238 .000 .987 152
Stud. Residual -3.343 3.302 -.001 1.007 152
Deleted Residual -3.90879 3.94588 -.00238 1.19726 152
Stud. Deleted Residual -3.466 3.420 -.001 1.019 152
Mahal. Distance .166 18.843 3.974 3.644 152
Cook's Distance .000 .147 .009 .019 152
Centered Leverage Value .001 .125 .026 .024 152
a. Dependent Variable: Knowledge_Sharing
Figure 3.13
4.0 Discussions
In order to conduct this research, at first, descriptive analysis of the variables has been
conducted. This has been important as the validity of the data is important to evaluate. The
analysis needs to be conducted with a valid data. Thus, for the test of validity of the data, the
Standard Error of Predicted Value .102 .422 .198 .074 152
Adjusted Predicted Value 3.7568 9.4607 7.2261 1.63596 152
Residual -3.87207 3.76786 .00000 1.14817 152
Std. Residual -3.327 3.238 .000 .987 152
Stud. Residual -3.343 3.302 -.001 1.007 152
Deleted Residual -3.90879 3.94588 -.00238 1.19726 152
Stud. Deleted Residual -3.466 3.420 -.001 1.019 152
Mahal. Distance .166 18.843 3.974 3.644 152
Cook's Distance .000 .147 .009 .019 152
Centered Leverage Value .001 .125 .026 .024 152
a. Dependent Variable: Knowledge_Sharing
Figure 3.13
4.0 Discussions
In order to conduct this research, at first, descriptive analysis of the variables has been
conducted. This has been important as the validity of the data is important to evaluate. The
analysis needs to be conducted with a valid data. Thus, for the test of validity of the data, the
30STUDY OF KNOWLEDGE AND INNOVATION
descriptive analysis has been conducted to understand the shape and distribution of the data. The
results have shown that there is no reason to identify the data as invalid.
The variables that are mostly considered for the analysis are innovation awards
(Moderator variable), knowledge sharing (dependent variable) and independent variables such as
value, performance and growth of the firm. The association of the variables with the dependent
variable is the most important event that has to be checked. If there is no association between an
independent variable and a dependent variable, then there is no use to consider that variable in
the prediction model. In this case, the independent variable, knowledge sharing has a positive
correlation with the independent variables as well as with the moderator variable. Thus, all the
variables are important to be considered for testing the impact of knowledge sharing on the
firm’s performance.
It is seen that the high loadings variables in factor 1 are seen to be related to the
understanding the need and initiative to facilitate in nurturing innovation. Thus one factor could
be related with the Recognizing need for innovation. The second factor seems to be related to
variables which come together to represent the attitude towards culturing innovative solutions
through knowledge sharing and seeking out competent employees and partnerships through
Internal Knowledge Sharing. The third factor seems to be consisted of variables with high factor
loadings which together seem to speak about inter and intra organizational communication to
facilitate external knowledge sharing. The fourth factor consists of variables that speak about
whether and how practicing innovativeness could lead to better products, services and solutions
Innovative Solutions. The fifth and final factor seems to be consisted of variables with high
factor loadings which together seem to speak about encouragement of innovation and its role in
strategy formation Strategic commitment.
descriptive analysis has been conducted to understand the shape and distribution of the data. The
results have shown that there is no reason to identify the data as invalid.
The variables that are mostly considered for the analysis are innovation awards
(Moderator variable), knowledge sharing (dependent variable) and independent variables such as
value, performance and growth of the firm. The association of the variables with the dependent
variable is the most important event that has to be checked. If there is no association between an
independent variable and a dependent variable, then there is no use to consider that variable in
the prediction model. In this case, the independent variable, knowledge sharing has a positive
correlation with the independent variables as well as with the moderator variable. Thus, all the
variables are important to be considered for testing the impact of knowledge sharing on the
firm’s performance.
It is seen that the high loadings variables in factor 1 are seen to be related to the
understanding the need and initiative to facilitate in nurturing innovation. Thus one factor could
be related with the Recognizing need for innovation. The second factor seems to be related to
variables which come together to represent the attitude towards culturing innovative solutions
through knowledge sharing and seeking out competent employees and partnerships through
Internal Knowledge Sharing. The third factor seems to be consisted of variables with high factor
loadings which together seem to speak about inter and intra organizational communication to
facilitate external knowledge sharing. The fourth factor consists of variables that speak about
whether and how practicing innovativeness could lead to better products, services and solutions
Innovative Solutions. The fifth and final factor seems to be consisted of variables with high
factor loadings which together seem to speak about encouragement of innovation and its role in
strategy formation Strategic commitment.
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31STUDY OF KNOWLEDGE AND INNOVATION
The condition of the firm is represented by the value of the firm, the performance of the
firm as well as with the growth of the firm. The moderator variable innovation award is an
external factor that is expected to have an influence in development of the firm. From the
regression analysis of the both the models, with the mediator and without the mediator, it has
been observed that the explanation of variability in the dependent variable knowledge sharing is
much higher when the mediator variable is involved in the model. Thus, it can be said that,
innovation award is a very important factor. This factor should also be considered for
development for the development of a firm. Thus, the willingness of the employees in sharing in
sharing knowledge and gathering it from others has a significant relationship with the innovation
capability of a firm.
5.0 Practical Implication
There are practical implications of the results obtained from this study. These results can
be useful to different companies as with the help of these results, the companies can develop the
culture of sharing of knowledge. The factors that are mainly responsible for the enhancement of
the innovation capability by sharing of knowledge of a company must have complete focus on.
This will also be helpful in increasing the shares of a company in the market and will also
enhance the performance of the company. There should be arrangement of platforms by the
organizations in which the employees will be having complete freedom in sharing the expertise
they possess, their thoughts, views and opinions about the companies. The employees might also
have some important information at times that can be responsible for the growth of the individual
as well as the company. Opportunity of conducting interactive sessions with the employees is
also important. In these sessions, the authority can bring more confidence in the employees by
sharing motivational thoughts and encouraging them. The results obtained from this study have
The condition of the firm is represented by the value of the firm, the performance of the
firm as well as with the growth of the firm. The moderator variable innovation award is an
external factor that is expected to have an influence in development of the firm. From the
regression analysis of the both the models, with the mediator and without the mediator, it has
been observed that the explanation of variability in the dependent variable knowledge sharing is
much higher when the mediator variable is involved in the model. Thus, it can be said that,
innovation award is a very important factor. This factor should also be considered for
development for the development of a firm. Thus, the willingness of the employees in sharing in
sharing knowledge and gathering it from others has a significant relationship with the innovation
capability of a firm.
5.0 Practical Implication
There are practical implications of the results obtained from this study. These results can
be useful to different companies as with the help of these results, the companies can develop the
culture of sharing of knowledge. The factors that are mainly responsible for the enhancement of
the innovation capability by sharing of knowledge of a company must have complete focus on.
This will also be helpful in increasing the shares of a company in the market and will also
enhance the performance of the company. There should be arrangement of platforms by the
organizations in which the employees will be having complete freedom in sharing the expertise
they possess, their thoughts, views and opinions about the companies. The employees might also
have some important information at times that can be responsible for the growth of the individual
as well as the company. Opportunity of conducting interactive sessions with the employees is
also important. In these sessions, the authority can bring more confidence in the employees by
sharing motivational thoughts and encouraging them. The results obtained from this study have
32STUDY OF KNOWLEDGE AND INNOVATION
shown that all the processes performed by the individuals have an association with the process of
knowledge sharing. The behaviour in sharing knowledge has been influenced by the enjoyment
that is obtained by providing help to others. The enjoyment of the employees leads to their job
satisfaction and it has been observed that job satisfaction has a positive influence towards the
knowledge sharing. Thus, the managers need to keep in mind that the enjoyment of the
employees has to be maintained and increased. The happier the employees are, the more they
will be interested in helping others with the problems they face. This will be helpful for the
growth of the organization. Adaptation of innovative techniques are also important for the
satisfaction of the employees. While performing any work, the work load of the employees can
be reduced if newer technologies are adopted by the companies. The most updated softwares will
reduce the work load of the employees and make the process faster. The employee can work on
more projects if the work spent on each is reduced. This will be satisfactory for the employees as
they will not have to sit with one work for a longer time and can work on various tasks. This will
also be beneficial for the company as more work will be done within a lesser time. This will
increase the productivity of the companies. The employees should be given proper training to
develop their skills. Idea of awareness should also be incorporated in them. The sharing of these
knowledge from the organizational side to the employees is also important. Moreover, in a
company there are multiple departments and a lot of employees work in each of the departments.
The employees in other departments might also have some ideas for the development of the
departments in which they do not work for. These ideas might also be effective for the
development of departments in an organization. Thus, knowledge sharing is extremely important
for an organization and the factors that have been identified to be influencing the knowledge
sharing must be acknowledged by the organizations.
shown that all the processes performed by the individuals have an association with the process of
knowledge sharing. The behaviour in sharing knowledge has been influenced by the enjoyment
that is obtained by providing help to others. The enjoyment of the employees leads to their job
satisfaction and it has been observed that job satisfaction has a positive influence towards the
knowledge sharing. Thus, the managers need to keep in mind that the enjoyment of the
employees has to be maintained and increased. The happier the employees are, the more they
will be interested in helping others with the problems they face. This will be helpful for the
growth of the organization. Adaptation of innovative techniques are also important for the
satisfaction of the employees. While performing any work, the work load of the employees can
be reduced if newer technologies are adopted by the companies. The most updated softwares will
reduce the work load of the employees and make the process faster. The employee can work on
more projects if the work spent on each is reduced. This will be satisfactory for the employees as
they will not have to sit with one work for a longer time and can work on various tasks. This will
also be beneficial for the company as more work will be done within a lesser time. This will
increase the productivity of the companies. The employees should be given proper training to
develop their skills. Idea of awareness should also be incorporated in them. The sharing of these
knowledge from the organizational side to the employees is also important. Moreover, in a
company there are multiple departments and a lot of employees work in each of the departments.
The employees in other departments might also have some ideas for the development of the
departments in which they do not work for. These ideas might also be effective for the
development of departments in an organization. Thus, knowledge sharing is extremely important
for an organization and the factors that have been identified to be influencing the knowledge
sharing must be acknowledged by the organizations.
33STUDY OF KNOWLEDGE AND INNOVATION
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