Research Methodology

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.

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 considered. For the independent variable, knowledge sharing, sum of internal knowledge sharing and external knowledge sharing has been considered.

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 table 3.1.

Table 3.1: Frequency table for Gender Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper
	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

Table 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 table 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
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

Data Analysis

Tabel 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

Table 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

Table 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

Table 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

Table 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
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

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
Sig. (2-tailed)	.000	.000	.000	.000
N	152	152	152	152	152
**. Correlation is significant at the 0.01 level (2-tailed).

Validity Construction

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

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 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
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.  

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
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
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 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