Data Analysis using SPSS - Demographics, Correlation, Reliability and Factor Analysis
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This article presents a data analysis using SPSS on demographics, correlation, reliability and factor analysis. It includes tables and charts on gender, nationality, age, marital status, educational level, organization type, job level, working experience, and field of work.
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Data analysis using SPSS 1
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Data analysis using SPSS 2
DATA ANALYSIS
1. DESCRIPTIVE STATISTICS (DEMOGRAPHICS)
Count Column N %
GENDER FEMALE 106 70.2%
MALE 45 29.8%
Total 151 100.0%
Table 1
Table 1 above shows the demographics of the respondents on the basis of gender. As can be
observed, there were more female respondents than male. They were 106 out of 151 therefore
constituting to 70.2% of the total. Their male counterparts were 45 out of 151 accounting for
29.8%. However, the research could not attribute anything to this disparity.
Count Column N %
NATIONALIT
Y
AUSTRALIA 4 2.6%
CANADA 3 2.0%
CHINA 2 1.3%
EGYPT 5 3.3%
FRANCE 2 1.3%
INDIA 6 4.0%
IRAQ 4 2.6%
IRELAND 4 2.6%
JAPAN 1 0.7%
LEBANON 4 2.6%
MOROCCO 4 2.6%
OMAN 1 0.7%
PALESTINIA
N
6 4.0%
SWEDEN 5 3.3%
SYRIAN 5 3.3%
TUNISIA 4 2.6%
UAE 80 53.0%
DATA ANALYSIS
1. DESCRIPTIVE STATISTICS (DEMOGRAPHICS)
Count Column N %
GENDER FEMALE 106 70.2%
MALE 45 29.8%
Total 151 100.0%
Table 1
Table 1 above shows the demographics of the respondents on the basis of gender. As can be
observed, there were more female respondents than male. They were 106 out of 151 therefore
constituting to 70.2% of the total. Their male counterparts were 45 out of 151 accounting for
29.8%. However, the research could not attribute anything to this disparity.
Count Column N %
NATIONALIT
Y
AUSTRALIA 4 2.6%
CANADA 3 2.0%
CHINA 2 1.3%
EGYPT 5 3.3%
FRANCE 2 1.3%
INDIA 6 4.0%
IRAQ 4 2.6%
IRELAND 4 2.6%
JAPAN 1 0.7%
LEBANON 4 2.6%
MOROCCO 4 2.6%
OMAN 1 0.7%
PALESTINIA
N
6 4.0%
SWEDEN 5 3.3%
SYRIAN 5 3.3%
TUNISIA 4 2.6%
UAE 80 53.0%
Data analysis using SPSS 3
UK 5 3.3%
USA 6 4.0%
Total 151 100.0%
Table 2
Table 2 shows the distribution of respondents by country of origin. As can be observed, majority
of the respondents were United Arab Emirates nationals. They were 80 out of 151 accounting for
53% of the total. The study could only attribute the high percentage to the proximity since it was
conducted in U.A.E. The second nationals were from United States of America who were 6 and
so accounted for only 4% of the total. The least number of respondents came from Oman and
Japan. They each constituted to 0.7% of the total.
Count Column N
%
AGE 20-29 32 21.2%
30-39 52 34.4%
40-49 32 21.2%
MORE THAN
50
35 23.2%
Total 151 100.0%
Table 3
Table 4 shows the distribution of respondents by age. It can be concluded that the number was
generally distributed across the age groups. However, majority were from between the ages of 30
to 39 years. They were 52 out of 151 and constituted 34.4% of the total. This is followed by
those who are more than 50 years old who made 23.2% of the total. The ages between 20 and 29
years and 40 to 49 years were each 21.2%.
UK 5 3.3%
USA 6 4.0%
Total 151 100.0%
Table 2
Table 2 shows the distribution of respondents by country of origin. As can be observed, majority
of the respondents were United Arab Emirates nationals. They were 80 out of 151 accounting for
53% of the total. The study could only attribute the high percentage to the proximity since it was
conducted in U.A.E. The second nationals were from United States of America who were 6 and
so accounted for only 4% of the total. The least number of respondents came from Oman and
Japan. They each constituted to 0.7% of the total.
Count Column N
%
AGE 20-29 32 21.2%
30-39 52 34.4%
40-49 32 21.2%
MORE THAN
50
35 23.2%
Total 151 100.0%
Table 3
Table 4 shows the distribution of respondents by age. It can be concluded that the number was
generally distributed across the age groups. However, majority were from between the ages of 30
to 39 years. They were 52 out of 151 and constituted 34.4% of the total. This is followed by
those who are more than 50 years old who made 23.2% of the total. The ages between 20 and 29
years and 40 to 49 years were each 21.2%.
Data analysis using SPSS 4
Count Column N %
MARITAL_STATUS MARRIED 79 52.3%
SINGLE 67 44.4%
DIVORCED 5 3.3%
Total 151 100.0%
Table 4
The distribution of respondents by marital status showed that 79 out of 151 respondents are
married. They made up 52.3% of the total. They were followed by the singles that were 67 out of
151 and made up 44.4% of the total. The rest were the divorced respondents who were only 5 in
number and only constituted to 3.3% of the total.
Count Column N %
EDUCATIONAL_LEVE
L
BACHELORS 56 37.1%
DOCTORATE 61 40.4%
HIGH DIPLOMA 6 4.0%
HIGH SCHOOL 6 4.0%
MASTERS
DEGREE
22 14.6%
Total 151 100.0%
Table 5
The table above shows the distribution of respondents by their educational levels. The highest
number as can be observed was the doctorates who were 61 out of 151 constituting to 40.4%.
They were followed by holders of bachelor’s degree who were 56 and making 37.1% of the total.
Respondents having master’s degree were 22 constituting to 14.6%. The least number were
respondents who were holders of high diploma and high school education. They were 6 each in
number constituting to 4% each.
Count Column N %
MARITAL_STATUS MARRIED 79 52.3%
SINGLE 67 44.4%
DIVORCED 5 3.3%
Total 151 100.0%
Table 4
The distribution of respondents by marital status showed that 79 out of 151 respondents are
married. They made up 52.3% of the total. They were followed by the singles that were 67 out of
151 and made up 44.4% of the total. The rest were the divorced respondents who were only 5 in
number and only constituted to 3.3% of the total.
Count Column N %
EDUCATIONAL_LEVE
L
BACHELORS 56 37.1%
DOCTORATE 61 40.4%
HIGH DIPLOMA 6 4.0%
HIGH SCHOOL 6 4.0%
MASTERS
DEGREE
22 14.6%
Total 151 100.0%
Table 5
The table above shows the distribution of respondents by their educational levels. The highest
number as can be observed was the doctorates who were 61 out of 151 constituting to 40.4%.
They were followed by holders of bachelor’s degree who were 56 and making 37.1% of the total.
Respondents having master’s degree were 22 constituting to 14.6%. The least number were
respondents who were holders of high diploma and high school education. They were 6 each in
number constituting to 4% each.
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Data analysis using SPSS 5
Count Column N %
ORGANIZATION_TYPE PUBLIC/GOVERNMENT 132 87.4%
SEMI-GOVERNMENT 14 9.3%
PRIVATE 2 1.3%
OTHER 3 2.0%
Total 151 100.0%
Table 6
From the table above, it can be observed that majority of the respondent were from government
or the public sector. They were 132 out of 151 in numbers thereby constituting to 87.4% of the
total. They were followed by those who work in the semi-government sector who were 14 out of
151 making 9.3% of the total. Those from the private sector were only 2 and the others were 3.
Count Column N %
CURRENTLY_YOU_ARE EMPLOYED 131 86.8%
STUDENT 15 9.9%
UNEMPLOYED 5 3.3%
Total 151 100.0%
Table 7
As can be observed from table 8 above, 131 respondents are currently employed. 15 of them are
students while 5 of them are unemployed.
Count Column N %
ORGANIZATION_TYPE PUBLIC/GOVERNMENT 132 87.4%
SEMI-GOVERNMENT 14 9.3%
PRIVATE 2 1.3%
OTHER 3 2.0%
Total 151 100.0%
Table 6
From the table above, it can be observed that majority of the respondent were from government
or the public sector. They were 132 out of 151 in numbers thereby constituting to 87.4% of the
total. They were followed by those who work in the semi-government sector who were 14 out of
151 making 9.3% of the total. Those from the private sector were only 2 and the others were 3.
Count Column N %
CURRENTLY_YOU_ARE EMPLOYED 131 86.8%
STUDENT 15 9.9%
UNEMPLOYED 5 3.3%
Total 151 100.0%
Table 7
As can be observed from table 8 above, 131 respondents are currently employed. 15 of them are
students while 5 of them are unemployed.
Data analysis using SPSS 6
Count Column N
%
FIELD_OF_WORK BUSINESS 45 29.8%
EDUCATION 26 17.2%
ENGINEERING 14 9.3%
INFORMATION
TECHNOLOGY
14 9.3%
MEDICINE & HEALTH
SCIENCES
13 8.6%
AGRICULTURE 4 2.6%
COMMUNICATION/
PUBLIC RELATIONS
8 5.3%
LEGAL 2 1.3%
PILOT/AIRFORCE 1 0.7%
OTHER 24 15.9%
Total 151 100.0%
Table 8
From the table above, it can be observed that majority of the respondent work in the field of
business. They were 45 out of 151 in numbers thereby constituting to 29.8% of the total. They
were followed by those who work in other sectors other than the ones that were listed who were
24 out of 151 making 15.9% of the total. The least number was from pilot/air force. There was
only one person in this category.
Count Column N %
Count Column N
%
FIELD_OF_WORK BUSINESS 45 29.8%
EDUCATION 26 17.2%
ENGINEERING 14 9.3%
INFORMATION
TECHNOLOGY
14 9.3%
MEDICINE & HEALTH
SCIENCES
13 8.6%
AGRICULTURE 4 2.6%
COMMUNICATION/
PUBLIC RELATIONS
8 5.3%
LEGAL 2 1.3%
PILOT/AIRFORCE 1 0.7%
OTHER 24 15.9%
Total 151 100.0%
Table 8
From the table above, it can be observed that majority of the respondent work in the field of
business. They were 45 out of 151 in numbers thereby constituting to 29.8% of the total. They
were followed by those who work in other sectors other than the ones that were listed who were
24 out of 151 making 15.9% of the total. The least number was from pilot/air force. There was
only one person in this category.
Count Column N %
Data analysis using SPSS 7
JOB_LEVEL JUNIOR EMPLOYEE 13 8.6%
LOWER
MANAGEMENT
3 2.0%
MIDDLE
MANAGEMENT
30 19.9%
SENIOR EMPLOYEE 30 19.9%
TOP MANAGEMENT 59 39.1%
UNEMPLOYED 10 6.6%
OTHER 6 4.0%
Total 151 100.0%
Table 9
From table 11 above, it can be observed that 59 respondents worked as the top management
level. They were the majority constituting to 39.1% of the total. This is followed by those who
work middle management and senior employee levels who were 30 each. The least number of
respondents (3) worked in the lower management level.
Count Column N %
WORKING_EXPERIENCE 1 YEAR OR LESS 5 3.3%
2 TO 6 YEARS 25 16.6%
7 TO 11 YEARS 36 23.8%
12 TO 16 YEARS 46 30.5%
MORE THAN 17
YEARS
27 17.9%
UNEMPLOYED 12 7.9%
Total 151 100.0%
Table 10
It can be observed from the table above that most of the respondents (46 out of 151) had worked
for between 12 to 16 years. They were followed by those who had worked for more than 17
years. Those who had worked for between 2 to 6 years were 25 in number.
Summary statistics for work experience
Table of summary statistics
JOB_LEVEL JUNIOR EMPLOYEE 13 8.6%
LOWER
MANAGEMENT
3 2.0%
MIDDLE
MANAGEMENT
30 19.9%
SENIOR EMPLOYEE 30 19.9%
TOP MANAGEMENT 59 39.1%
UNEMPLOYED 10 6.6%
OTHER 6 4.0%
Total 151 100.0%
Table 9
From table 11 above, it can be observed that 59 respondents worked as the top management
level. They were the majority constituting to 39.1% of the total. This is followed by those who
work middle management and senior employee levels who were 30 each. The least number of
respondents (3) worked in the lower management level.
Count Column N %
WORKING_EXPERIENCE 1 YEAR OR LESS 5 3.3%
2 TO 6 YEARS 25 16.6%
7 TO 11 YEARS 36 23.8%
12 TO 16 YEARS 46 30.5%
MORE THAN 17
YEARS
27 17.9%
UNEMPLOYED 12 7.9%
Total 151 100.0%
Table 10
It can be observed from the table above that most of the respondents (46 out of 151) had worked
for between 12 to 16 years. They were followed by those who had worked for more than 17
years. Those who had worked for between 2 to 6 years were 25 in number.
Summary statistics for work experience
Table of summary statistics
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Data analysis using SPSS 8
Statistics
WORKING_EXPERIENCE
N Valid 151
Missing 0
Mean 3.6689
Median 4.0000
Mode 4.00
Std. Deviation 1.26344
Table 11
As can be seen from the table above, the mean work experience is 3.6 while the modal work
experience is 4. According to data coding, this indicates that most of the workers had worked for
between 3 to 12 years of their lives.
Statistics
WORKING_EXPERIENCE
N Valid 151
Missing 0
Mean 3.6689
Median 4.0000
Mode 4.00
Std. Deviation 1.26344
Table 11
As can be seen from the table above, the mean work experience is 3.6 while the modal work
experience is 4. According to data coding, this indicates that most of the workers had worked for
between 3 to 12 years of their lives.
Data analysis using SPSS 9
Histogram showing distribution of work experience
Figure 1
As can be observed from the shape of the histogram above, the number of years worked by the
respondents was normally distributed. This is attested by the fact that the normal curve was quite
symmetric.
Correlation
Table of correlation between gender and field of work
Histogram showing distribution of work experience
Figure 1
As can be observed from the shape of the histogram above, the number of years worked by the
respondents was normally distributed. This is attested by the fact that the normal curve was quite
symmetric.
Correlation
Table of correlation between gender and field of work
Data analysis using SPSS 10
Correlations
GENDER FIELD_OF_WO
RK
GENDER
Pearson Correlation 1 .003
Sig. (2-tailed) .969
N 151 151
FIELD_OF_WORK
Pearson Correlation .003 1
Sig. (2-tailed) .969
N 151 151
Table 12
The table above shows correlation results between gender and field of work. As can be observed,
the Pearson correlation coefficient is 0.003. This is an indication that there is a very weak
positive correlation between gender and field of work (Porter, 2008).
2. RELIABILITY TEST (Cronbach test)
Table of reliability test results
Reliability Statistics
Cronbach's
Alpha
N of Items
.841 32
Table 13
The reliability test results above show a Cronbach’s alpha of 0.84. Since this value is above 0.7,
it can be concluded that there is high internal consistency (Richler, 2012).
3. FACTOR ANALYSIS
KMO Value results table
Correlations
GENDER FIELD_OF_WO
RK
GENDER
Pearson Correlation 1 .003
Sig. (2-tailed) .969
N 151 151
FIELD_OF_WORK
Pearson Correlation .003 1
Sig. (2-tailed) .969
N 151 151
Table 12
The table above shows correlation results between gender and field of work. As can be observed,
the Pearson correlation coefficient is 0.003. This is an indication that there is a very weak
positive correlation between gender and field of work (Porter, 2008).
2. RELIABILITY TEST (Cronbach test)
Table of reliability test results
Reliability Statistics
Cronbach's
Alpha
N of Items
.841 32
Table 13
The reliability test results above show a Cronbach’s alpha of 0.84. Since this value is above 0.7,
it can be concluded that there is high internal consistency (Richler, 2012).
3. FACTOR ANALYSIS
KMO Value results table
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Data analysis using SPSS 11
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733
Bartlett's Test of Sphericity Approx. Chi-Square 1538.19
2
df 496
Sig. .000
Table 14
As can be observed from table 14 above, the KMO value was 0.733 for all the 32 questions. This
is usually a measure to determine whether the sample used in the study is adequate (Pallant,
2009). The bench mark for adequacy is normally a KMO value of 0.7 and above. Since our value
is 0.733, it can be concluded that the sample in this research study was adequate (Morey, 2015).
In order to extract significant factors, a eigenvalues > 1 was employed to determine which
factors to isolate from the 32 questions. The total variance explained matrix was also analyzed so
as to get those factors that had large values of variance. The results were as in table 15 below.
Total variance explained table
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 6.238 19.494 19.494 6.238 19.494 19.494
2 2.856 8.924 28.418 2.856 8.924 28.418
3 1.849 5.777 34.195 1.849 5.777 34.195
4 1.649 5.153 39.348 1.649 5.153 39.348
5 1.566 4.895 44.243 1.566 4.895 44.243
6 1.439 4.496 48.738 1.439 4.496 48.738
7 1.342 4.194 52.932 1.342 4.194 52.932
8 1.266 3.957 56.890 1.266 3.957 56.890
9 1.103 3.448 60.338 1.103 3.448 60.338
10 1.090 3.406 63.744 1.090 3.406 63.744
11 .976 3.050 66.794
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733
Bartlett's Test of Sphericity Approx. Chi-Square 1538.19
2
df 496
Sig. .000
Table 14
As can be observed from table 14 above, the KMO value was 0.733 for all the 32 questions. This
is usually a measure to determine whether the sample used in the study is adequate (Pallant,
2009). The bench mark for adequacy is normally a KMO value of 0.7 and above. Since our value
is 0.733, it can be concluded that the sample in this research study was adequate (Morey, 2015).
In order to extract significant factors, a eigenvalues > 1 was employed to determine which
factors to isolate from the 32 questions. The total variance explained matrix was also analyzed so
as to get those factors that had large values of variance. The results were as in table 15 below.
Total variance explained table
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 6.238 19.494 19.494 6.238 19.494 19.494
2 2.856 8.924 28.418 2.856 8.924 28.418
3 1.849 5.777 34.195 1.849 5.777 34.195
4 1.649 5.153 39.348 1.649 5.153 39.348
5 1.566 4.895 44.243 1.566 4.895 44.243
6 1.439 4.496 48.738 1.439 4.496 48.738
7 1.342 4.194 52.932 1.342 4.194 52.932
8 1.266 3.957 56.890 1.266 3.957 56.890
9 1.103 3.448 60.338 1.103 3.448 60.338
10 1.090 3.406 63.744 1.090 3.406 63.744
11 .976 3.050 66.794
Data analysis using SPSS 12
12 .940 2.936 69.730
13 .856 2.675 72.406
14 .814 2.543 74.949
15 .770 2.407 77.355
16 .733 2.290 79.646
17 .725 2.267 81.913
18 .646 2.018 83.931
19 .586 1.833 85.764
20 .559 1.746 87.510
21 .495 1.548 89.057
22 .453 1.416 90.473
23 .431 1.348 91.820
24 .414 1.292 93.113
25 .355 1.108 94.221
26 .345 1.077 95.298
27 .311 .972 96.271
28 .288 .901 97.171
29 .268 .836 98.007
30 .255 .797 98.804
31 .210 .657 99.461
32 .173 .539 100.000
Extraction Method: Principal Component Analysis.
Table 15
As can be observed the table indicated 10 factors which cumulatively accounted for the 63.74%
of the total variation of the data. This is an indication that among the 32 questions only the 10
factors can be extracted for the model due to their high variance.
Scree plot
12 .940 2.936 69.730
13 .856 2.675 72.406
14 .814 2.543 74.949
15 .770 2.407 77.355
16 .733 2.290 79.646
17 .725 2.267 81.913
18 .646 2.018 83.931
19 .586 1.833 85.764
20 .559 1.746 87.510
21 .495 1.548 89.057
22 .453 1.416 90.473
23 .431 1.348 91.820
24 .414 1.292 93.113
25 .355 1.108 94.221
26 .345 1.077 95.298
27 .311 .972 96.271
28 .288 .901 97.171
29 .268 .836 98.007
30 .255 .797 98.804
31 .210 .657 99.461
32 .173 .539 100.000
Extraction Method: Principal Component Analysis.
Table 15
As can be observed the table indicated 10 factors which cumulatively accounted for the 63.74%
of the total variation of the data. This is an indication that among the 32 questions only the 10
factors can be extracted for the model due to their high variance.
Scree plot
Data analysis using SPSS 13
Figure 2
As can be observed from the scree plot above, the point of inflection is at 2 and 3.
Rotated component matrix
Component Matrixa
Component
1 2 3 4 5 6 7 8 9 10
MEDIATOR1 .391 -.537 -.050 -.031 -.012 -.222 .173 -.209 -.241 .120
MEDIATOR2 .435 -.272 .264 -.367 -.171 .101 .129 -.190 -.072 .133
MEDIATOR3 .560 -.236 .061 -.353 .038 -.167 .211 .008 -.105 -.131
MEDIATOR4 .221 .130 .422 .171 .350 .305 -.193 .113 .024 -.120
MEDIATOR5 .468 -.410 -.200 .082 .131 .373 -.073 -.022 .015 .015
MEDIATOR6 .535 -.433 .182 -.117 -.088 -.246 .030 .025 -.256 -.116
MEDIATOR7 .400 .068 .029 .123 -.058 .372 -.288 .269 -.156 .398
Figure 2
As can be observed from the scree plot above, the point of inflection is at 2 and 3.
Rotated component matrix
Component Matrixa
Component
1 2 3 4 5 6 7 8 9 10
MEDIATOR1 .391 -.537 -.050 -.031 -.012 -.222 .173 -.209 -.241 .120
MEDIATOR2 .435 -.272 .264 -.367 -.171 .101 .129 -.190 -.072 .133
MEDIATOR3 .560 -.236 .061 -.353 .038 -.167 .211 .008 -.105 -.131
MEDIATOR4 .221 .130 .422 .171 .350 .305 -.193 .113 .024 -.120
MEDIATOR5 .468 -.410 -.200 .082 .131 .373 -.073 -.022 .015 .015
MEDIATOR6 .535 -.433 .182 -.117 -.088 -.246 .030 .025 -.256 -.116
MEDIATOR7 .400 .068 .029 .123 -.058 .372 -.288 .269 -.156 .398
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Data analysis using SPSS 14
MEDIATOR8 .387 -.397 .147 .112 .209 -.129 -.091 .261 .010 -.284
MEDIATOR9 .241 .127 .250 -.004 .204 -.032 .002 -.178 .462 .105
MEDIATOR10 .274 -.125 -.054 -.078 .112 .371 .350 -.003 .420 .205
MEDIATOR11 .161 .127 .126 .115 .104 -.029 .441 .657 .130 .045
RP1 .517 -.096 .192 -.427 -.112 -.034 -.222 .033 .075 .057
RP2 .481 -.291 -.141 .195 .196 -.063 -.112 .081 .016 .270
RP3 .508 -.263 -.184 .140 .052 -.311 -.163 .099 .250 .336
RP4 .354 -.264 .431 -.266 -.006 .118 -.125 -.118 .204 .076
TRU1 .509 -.002 -.122 .135 .295 -.343 -.407 .011 -.083 .012
TRU2 .544 -.019 .249 .218 .088 -.010 -.168 .036 .151 -.321
TRU3 .374 .080 .357 .389 .008 .075 .153 -.460 -.112 .079
TRU4 .439 .228 .046 .531 .197 .107 .034 -.228 -.264 -.079
KE1 .552 .062 -.083 -.290 .274 .297 .185 .132 -.369 .054
KE2 .397 -.140 -.172 .132 -.510 .161 -.121 -.250 .264 -.266
KE3 .429 .353 -.385 -.334 .233 .136 -.076 -.232 .064 -.148
KE4 .437 .373 -.366 -.178 .396 -.017 -.046 -.184 .063 .041
KE5 .500 .407 -.060 -.316 -.119 .058 -.098 .198 -.067 -.267
GROWTH1 .455 .390 -.003 .026 -.134 -.331 .190 -.071 .138 .197
GROWTH2 .319 .543 .414 .020 -.051 .091 .143 -.033 -.088 -.070
GROWTH3 .482 .512 -.153 .083 -.274 -.247 -.058 .036 -.103 .208
GROWTH4 .414 .425 .443 -.018 -.290 -.181 -.080 .102 .012 .107
GROWTH5 .435 .113 -.277 .080 -.466 .367 -.091 .076 -.130 .130
COMPETITIVE1 .480 -.075 -.153 .219 .019 -.050 .570 -.068 -.015 -.043
COMPETITIVE2 .537 -.334 -.189 .273 -.360 .097 .022 .185 .113 -.239
COMPETITIVE3 .529 .302 -.292 .001 .035 -.135 .103 .064 .155 -.256
Extraction Method: Principal Component Analysis.
a. 10 components extracted.
Table 16
4. Reliability Test (Summary of Cronbach’s Alpha Values)
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total Correlation
Cronbach's
Alpha if Item
Deleted
MEDIATOR1 119.6424 145.618 .312 .838
MEDIATOR2 119.3113 145.602 .373 .836
MEDIATOR3 119.2914 143.835 .481 .834
MEDIATOR4 119.6623 149.838 .204 .841
MEDIATOR8 .387 -.397 .147 .112 .209 -.129 -.091 .261 .010 -.284
MEDIATOR9 .241 .127 .250 -.004 .204 -.032 .002 -.178 .462 .105
MEDIATOR10 .274 -.125 -.054 -.078 .112 .371 .350 -.003 .420 .205
MEDIATOR11 .161 .127 .126 .115 .104 -.029 .441 .657 .130 .045
RP1 .517 -.096 .192 -.427 -.112 -.034 -.222 .033 .075 .057
RP2 .481 -.291 -.141 .195 .196 -.063 -.112 .081 .016 .270
RP3 .508 -.263 -.184 .140 .052 -.311 -.163 .099 .250 .336
RP4 .354 -.264 .431 -.266 -.006 .118 -.125 -.118 .204 .076
TRU1 .509 -.002 -.122 .135 .295 -.343 -.407 .011 -.083 .012
TRU2 .544 -.019 .249 .218 .088 -.010 -.168 .036 .151 -.321
TRU3 .374 .080 .357 .389 .008 .075 .153 -.460 -.112 .079
TRU4 .439 .228 .046 .531 .197 .107 .034 -.228 -.264 -.079
KE1 .552 .062 -.083 -.290 .274 .297 .185 .132 -.369 .054
KE2 .397 -.140 -.172 .132 -.510 .161 -.121 -.250 .264 -.266
KE3 .429 .353 -.385 -.334 .233 .136 -.076 -.232 .064 -.148
KE4 .437 .373 -.366 -.178 .396 -.017 -.046 -.184 .063 .041
KE5 .500 .407 -.060 -.316 -.119 .058 -.098 .198 -.067 -.267
GROWTH1 .455 .390 -.003 .026 -.134 -.331 .190 -.071 .138 .197
GROWTH2 .319 .543 .414 .020 -.051 .091 .143 -.033 -.088 -.070
GROWTH3 .482 .512 -.153 .083 -.274 -.247 -.058 .036 -.103 .208
GROWTH4 .414 .425 .443 -.018 -.290 -.181 -.080 .102 .012 .107
GROWTH5 .435 .113 -.277 .080 -.466 .367 -.091 .076 -.130 .130
COMPETITIVE1 .480 -.075 -.153 .219 .019 -.050 .570 -.068 -.015 -.043
COMPETITIVE2 .537 -.334 -.189 .273 -.360 .097 .022 .185 .113 -.239
COMPETITIVE3 .529 .302 -.292 .001 .035 -.135 .103 .064 .155 -.256
Extraction Method: Principal Component Analysis.
a. 10 components extracted.
Table 16
4. Reliability Test (Summary of Cronbach’s Alpha Values)
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total Correlation
Cronbach's
Alpha if Item
Deleted
MEDIATOR1 119.6424 145.618 .312 .838
MEDIATOR2 119.3113 145.602 .373 .836
MEDIATOR3 119.2914 143.835 .481 .834
MEDIATOR4 119.6623 149.838 .204 .841
Data analysis using SPSS 15
MEDIATOR5 119.3377 145.398 .406 .836
MEDIATOR6 119.3377 143.865 .451 .834
MEDIATOR7 119.2119 147.461 .341 .838
MEDIATOR8 119.5960 147.429 .329 .838
MEDIATOR9 120.6689 149.156 .216 .841
MEDIATOR10 118.4834 139.238 .242 .850
MEDIATOR11 119.1258 145.737 .142 .851
RP1 119.4437 143.342 .448 .834
RP2 119.4636 144.397 .404 .835
RP3 119.4702 143.304 .442 .834
RP4 119.4967 146.265 .305 .838
TRU1 119.3642 144.966 .408 .836
TRU2 119.4570 142.903 .479 .833
TRU3 119.3775 146.943 .330 .838
TRU4 119.4238 146.072 .369 .837
KE1 119.3046 143.507 .494 .833
KE2 119.4834 146.545 .314 .838
KE3 119.3179 146.885 .343 .837
KE4 119.2185 146.799 .361 .837
KE5 119.3510 144.709 .415 .835
GROWTH1 119.2450 145.186 .391 .836
GROWTH2 119.3510 147.523 .284 .839
GROWTH3 119.2583 145.566 .394 .836
GROWTH4 119.2649 146.289 .359 .837
GROWTH5 119.2583 146.433 .362 .837
COMPETITIVE1 119.3377 144.265 .431 .835
COMPETITIVE2 119.3510 143.963 .461 .834
COMPETITIVE3 119.2781 144.429 .455 .834
Table 17
As can be observed from item total statistics table above, all items have Cronbach alpha values
well above 0.7. All of them are 0.83 and above. This is therefore an indication that no item
should be removed from the sample.
5. Multiple linear regression
MEDIATOR5 119.3377 145.398 .406 .836
MEDIATOR6 119.3377 143.865 .451 .834
MEDIATOR7 119.2119 147.461 .341 .838
MEDIATOR8 119.5960 147.429 .329 .838
MEDIATOR9 120.6689 149.156 .216 .841
MEDIATOR10 118.4834 139.238 .242 .850
MEDIATOR11 119.1258 145.737 .142 .851
RP1 119.4437 143.342 .448 .834
RP2 119.4636 144.397 .404 .835
RP3 119.4702 143.304 .442 .834
RP4 119.4967 146.265 .305 .838
TRU1 119.3642 144.966 .408 .836
TRU2 119.4570 142.903 .479 .833
TRU3 119.3775 146.943 .330 .838
TRU4 119.4238 146.072 .369 .837
KE1 119.3046 143.507 .494 .833
KE2 119.4834 146.545 .314 .838
KE3 119.3179 146.885 .343 .837
KE4 119.2185 146.799 .361 .837
KE5 119.3510 144.709 .415 .835
GROWTH1 119.2450 145.186 .391 .836
GROWTH2 119.3510 147.523 .284 .839
GROWTH3 119.2583 145.566 .394 .836
GROWTH4 119.2649 146.289 .359 .837
GROWTH5 119.2583 146.433 .362 .837
COMPETITIVE1 119.3377 144.265 .431 .835
COMPETITIVE2 119.3510 143.963 .461 .834
COMPETITIVE3 119.2781 144.429 .455 .834
Table 17
As can be observed from item total statistics table above, all items have Cronbach alpha values
well above 0.7. All of them are 0.83 and above. This is therefore an indication that no item
should be removed from the sample.
5. Multiple linear regression
Data analysis using SPSS 16
Multiple linear regression between independent variables; research performance
(questions from 22 to 25) + Transfer to research universities (questions from
26 to 29).
Dependent variables; Knowledge economy (question from 30 to 34) +
growth (questions 35 to 39) + competitive (questions 40 to 42)
Before performing the regression analysis, the values of the factors were averaged so as to come
up with one value. The factors were as mentioned in the previous paragraph. In order to ensure
all assumptions of multiple regression are observed, the tests for normality, multicollinearity,
independent errors, homoscedasticity and linearity were conducted (Miles, 2004). A scatter plot
showed a linear relationship between explanatory and the dependent variables.
Tables of multiple linear regression results
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 RP1, TRU1b . Enter
a. Dependent Variable: GROWTH1
b. All requested variables entered.
Table 18
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .200a .040 .027 .85974
a. Predictors: (Constant), RP1, TRU1
b. Dependent Variable: GROWTH1
Table 19
ANOVAa
Multiple linear regression between independent variables; research performance
(questions from 22 to 25) + Transfer to research universities (questions from
26 to 29).
Dependent variables; Knowledge economy (question from 30 to 34) +
growth (questions 35 to 39) + competitive (questions 40 to 42)
Before performing the regression analysis, the values of the factors were averaged so as to come
up with one value. The factors were as mentioned in the previous paragraph. In order to ensure
all assumptions of multiple regression are observed, the tests for normality, multicollinearity,
independent errors, homoscedasticity and linearity were conducted (Miles, 2004). A scatter plot
showed a linear relationship between explanatory and the dependent variables.
Tables of multiple linear regression results
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 RP1, TRU1b . Enter
a. Dependent Variable: GROWTH1
b. All requested variables entered.
Table 18
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .200a .040 .027 .85974
a. Predictors: (Constant), RP1, TRU1
b. Dependent Variable: GROWTH1
Table 19
ANOVAa
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Data analysis using SPSS 17
Model Sum of Squares df Mean Square F Sig.
1
Regression 4.580 2 2.290 3.098 .048b
Residual 109.394 148 .739
Total 113.974 150
a. Dependent Variable: GROWTH1
b. Predictors: (Constant), RP1, TRU1
Table 20
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
(Constan
t)
3.038 .389 7.809 .000 2.270 3.807
TRU1 .153 .084 .151 1.822 .071 -.013 .319 .943 1.061
RP1 .094 .078 .100 1.210 .228 -.060 .248 .943 1.061
a. Dependent Variable: GROWTH1
Table 21
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index Variance Proportions
(Constant) TRU1 RP1
1
1 2.939 1.000 .00 .01 .01
2 .040 8.600 .01 .45 .77
3 .021 11.735 .98 .54 .22
a. Dependent Variable: GROWTH1
Table 22
Model Sum of Squares df Mean Square F Sig.
1
Regression 4.580 2 2.290 3.098 .048b
Residual 109.394 148 .739
Total 113.974 150
a. Dependent Variable: GROWTH1
b. Predictors: (Constant), RP1, TRU1
Table 20
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
(Constan
t)
3.038 .389 7.809 .000 2.270 3.807
TRU1 .153 .084 .151 1.822 .071 -.013 .319 .943 1.061
RP1 .094 .078 .100 1.210 .228 -.060 .248 .943 1.061
a. Dependent Variable: GROWTH1
Table 21
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index Variance Proportions
(Constant) TRU1 RP1
1
1 2.939 1.000 .00 .01 .01
2 .040 8.600 .01 .45 .77
3 .021 11.735 .98 .54 .22
a. Dependent Variable: GROWTH1
Table 22
Data analysis using SPSS 18
Figure 3
Figure 3
Data analysis using SPSS 19
Figure 4
According to regression results in table 22 above, the equation will be as below;
(K nowledge E conomy +Growth+competitive)=0.153 ( T ransfer¿ R esearchU niversities ) +0.94 ( R esearch P erfor
The value of R2 for this equation as can be obtained from table 19 is 0.04. Statistically, this is an
indication that the explanatory variables can only explain 4% of the variation that occurs in the
dependent variable. The p-value for the model above is 0.048 which is equal to the level of
significance which is 0.05. This means that the model is not significant at 95% confidence level
(Warnbrod, 2001). From the scatter plot in figure 3 above, it can be concluded that the data was
Figure 4
According to regression results in table 22 above, the equation will be as below;
(K nowledge E conomy +Growth+competitive)=0.153 ( T ransfer¿ R esearchU niversities ) +0.94 ( R esearch P erfor
The value of R2 for this equation as can be obtained from table 19 is 0.04. Statistically, this is an
indication that the explanatory variables can only explain 4% of the variation that occurs in the
dependent variable. The p-value for the model above is 0.048 which is equal to the level of
significance which is 0.05. This means that the model is not significant at 95% confidence level
(Warnbrod, 2001). From the scatter plot in figure 3 above, it can be concluded that the data was
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Data analysis using SPSS 20
normally distributed. To add on, there was no presence of outliers. The residual plot from figure
4 above also shows that variables of the question meet the assumption of normality of the
multiple regression since the data points are evenly distributed above and below the diagonal
line. The plot also attests to linearity due to the fact that the data points follow a linear pattern. At
95% confidence, results show that the value of the slope of the model lies between 2.27 and
3.807. This means that in case the values of independent values will be, the value of the
dependent variable in this regression model will be between 2.27 and 3.807.
Hypothesis test 1
H0: Research environment factors will have a significant positive effect on
research performance.
Versus
H1: Research environment factors will not have a significant positive effect
on research performance.
Results table
Correlations
RP1 KE1
RP1
Pearson Correlation 1 .262**
Sig. (2-tailed) .001
N 151 151
KE1
Pearson Correlation .262** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 23
normally distributed. To add on, there was no presence of outliers. The residual plot from figure
4 above also shows that variables of the question meet the assumption of normality of the
multiple regression since the data points are evenly distributed above and below the diagonal
line. The plot also attests to linearity due to the fact that the data points follow a linear pattern. At
95% confidence, results show that the value of the slope of the model lies between 2.27 and
3.807. This means that in case the values of independent values will be, the value of the
dependent variable in this regression model will be between 2.27 and 3.807.
Hypothesis test 1
H0: Research environment factors will have a significant positive effect on
research performance.
Versus
H1: Research environment factors will not have a significant positive effect
on research performance.
Results table
Correlations
RP1 KE1
RP1
Pearson Correlation 1 .262**
Sig. (2-tailed) .001
N 151 151
KE1
Pearson Correlation .262** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 23
Data analysis using SPSS 21
The result of the hypothesis above shows a Pearson correlation coefficient of .26. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
research environment factors will have a significant positive effect on
research performance.
Hypothesis test 2
H0: Following the dynamic role of university will lead to a significant positive
impact on transferring the universities to a higher level as research
universities.
Versus
H1: Following the dynamic role of university will not lead to a significant
positive impact on transferring the universities to a higher level as research
universities.
Results table
Correlations
TRU1 MEDIATOR1
TRU1
Pearson Correlation 1 .215**
Sig. (2-tailed) .008
N 151 151
MEDIATOR1
Pearson Correlation .215** 1
Sig. (2-tailed) .008
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 24
The result of the hypothesis above shows a Pearson correlation coefficient of .26. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
research environment factors will have a significant positive effect on
research performance.
Hypothesis test 2
H0: Following the dynamic role of university will lead to a significant positive
impact on transferring the universities to a higher level as research
universities.
Versus
H1: Following the dynamic role of university will not lead to a significant
positive impact on transferring the universities to a higher level as research
universities.
Results table
Correlations
TRU1 MEDIATOR1
TRU1
Pearson Correlation 1 .215**
Sig. (2-tailed) .008
N 151 151
MEDIATOR1
Pearson Correlation .215** 1
Sig. (2-tailed) .008
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 24
Data analysis using SPSS 22
The result of the hypothesis above shows a Pearson correlation coefficient of .21. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
dynamic role of university will lead to a significant positive impact on
transferring the universities to a higher level as research universities.
Hypothesis test 3
H0: The positive influence of research environment factors on research
performance is negatively moderated by investment in R&D. Versus
H1: The positive influence of research environment factors on research
performance is not negatively moderated by investment in R&D.
Results table
Correlations
MEDIATOR1 RP1
MEDIATOR1
Pearson Correlation 1 .257**
Sig. (2-tailed) .001
N 151 151
RP1
Pearson Correlation .257** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 25
The result of the hypothesis above shows a Pearson correlation coefficient of .28. This value is
positive. The decision therefore is to reject the null hypothesis hence we conclude that at 0.05,
the positive influence of research environment factors on research
performance is not negatively moderated by investment in R&D.
Hypothesis test 4
The result of the hypothesis above shows a Pearson correlation coefficient of .21. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
dynamic role of university will lead to a significant positive impact on
transferring the universities to a higher level as research universities.
Hypothesis test 3
H0: The positive influence of research environment factors on research
performance is negatively moderated by investment in R&D. Versus
H1: The positive influence of research environment factors on research
performance is not negatively moderated by investment in R&D.
Results table
Correlations
MEDIATOR1 RP1
MEDIATOR1
Pearson Correlation 1 .257**
Sig. (2-tailed) .001
N 151 151
RP1
Pearson Correlation .257** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 25
The result of the hypothesis above shows a Pearson correlation coefficient of .28. This value is
positive. The decision therefore is to reject the null hypothesis hence we conclude that at 0.05,
the positive influence of research environment factors on research
performance is not negatively moderated by investment in R&D.
Hypothesis test 4
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Data analysis using SPSS 23
H0: The positive influence of dynamic role of university on research
performance is negatively moderated by investment in R&D.
Versus
H1: The positive influence of dynamic role of university on research
performance is not negatively moderated by investment in R&D.
Results table
Correlations
MEDIATOR1 GROWTH1
MEDIATOR1
Pearson Correlation 1 .085
Sig. (2-tailed) .297
N 151 151
GROWTH1
Pearson Correlation .085 1
Sig. (2-tailed) .297
N 151 151
Table 26
The result of the hypothesis above shows a Pearson correlation coefficient of .09. This value is
positive. The decision therefore is to reject the null hypothesis hence we conclude that at 0.05,
the positive influence of dynamic role of university on research performance
is not negatively moderated by investment in R&D.
Hypothesis test 5
H0: Research performance is positively moderated by the impacts of the
relationship between dynamic role of universities and transfer to research
universities.
Versus
H0: The positive influence of dynamic role of university on research
performance is negatively moderated by investment in R&D.
Versus
H1: The positive influence of dynamic role of university on research
performance is not negatively moderated by investment in R&D.
Results table
Correlations
MEDIATOR1 GROWTH1
MEDIATOR1
Pearson Correlation 1 .085
Sig. (2-tailed) .297
N 151 151
GROWTH1
Pearson Correlation .085 1
Sig. (2-tailed) .297
N 151 151
Table 26
The result of the hypothesis above shows a Pearson correlation coefficient of .09. This value is
positive. The decision therefore is to reject the null hypothesis hence we conclude that at 0.05,
the positive influence of dynamic role of university on research performance
is not negatively moderated by investment in R&D.
Hypothesis test 5
H0: Research performance is positively moderated by the impacts of the
relationship between dynamic role of universities and transfer to research
universities.
Versus
Data analysis using SPSS 24
H1: Research performance is not positively moderated by the impacts of the
relationship between dynamic role of universities and transfer to research
universities.
Results table
Correlations
MEDIATOR1 RP2
MEDIATOR1
Pearson Correlation 1 .229**
Sig. (2-tailed) .005
N 151 151
RP2
Pearson Correlation .229** 1
Sig. (2-tailed) .005
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 27
The result of the hypothesis above shows a Pearson correlation coefficient of .229. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
research performance is positively moderated by the impacts of the
relationship between dynamic role of universities and transfer to research
universities.
Hypothesis test 6
H0: Transfer to research universities will positively impacts the relationship
between the dynamic role of universities and the transfer to research
universities
H1: Research performance is not positively moderated by the impacts of the
relationship between dynamic role of universities and transfer to research
universities.
Results table
Correlations
MEDIATOR1 RP2
MEDIATOR1
Pearson Correlation 1 .229**
Sig. (2-tailed) .005
N 151 151
RP2
Pearson Correlation .229** 1
Sig. (2-tailed) .005
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 27
The result of the hypothesis above shows a Pearson correlation coefficient of .229. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
research performance is positively moderated by the impacts of the
relationship between dynamic role of universities and transfer to research
universities.
Hypothesis test 6
H0: Transfer to research universities will positively impacts the relationship
between the dynamic role of universities and the transfer to research
universities
Data analysis using SPSS 25
Versus
H1: Transfer to research universities will not positively impacts the
relationship between the dynamic role of universities and the transfer to
research universities
Results table
Correlations
RP2 TRU3
RP2
Pearson Correlation 1 .114
Sig. (2-tailed) .163
N 151 151
TRU3
Pearson Correlation .114 1
Sig. (2-tailed) .163
N 151 151
Table 28
The result of the hypothesis above shows a Pearson correlation coefficient of .114. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
transfer to research universities will positively impacts the relationship
between the dynamic role of universities and the transfer to research
universities.
Hypothesis test 7
Versus
H1: Transfer to research universities will not positively impacts the
relationship between the dynamic role of universities and the transfer to
research universities
Results table
Correlations
RP2 TRU3
RP2
Pearson Correlation 1 .114
Sig. (2-tailed) .163
N 151 151
TRU3
Pearson Correlation .114 1
Sig. (2-tailed) .163
N 151 151
Table 28
The result of the hypothesis above shows a Pearson correlation coefficient of .114. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
transfer to research universities will positively impacts the relationship
between the dynamic role of universities and the transfer to research
universities.
Hypothesis test 7
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Data analysis using SPSS 26
H0: The higher level of knowledge economy in UAE as a mediator variable will
positively impact a higher chance of competitive in there market economy.
Versus
H1: The higher level of knowledge economy in UAE as a mediator variable will
not positively impact a higher chance of competitive in there market
economy.
Results table
Correlations
KE1 COMPETITIVE1
KE1
Pearson Correlation 1 .268**
Sig. (2-tailed) .001
N 151 151
COMPETITIVE1
Pearson Correlation .268** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 29
The result of the hypothesis above shows a Pearson correlation coefficient of .29. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
the higher level of knowledge economy in UAE as a mediator variable will
positively impact a higher chance of competitive in there market economy.
Hypothesis test 8
H0: The higher level of knowledge economy in UAE as a mediator variable will
positively impact a higher chance of competitive in there market economy.
Versus
H1: The higher level of knowledge economy in UAE as a mediator variable will
not positively impact a higher chance of competitive in there market
economy.
Results table
Correlations
KE1 COMPETITIVE1
KE1
Pearson Correlation 1 .268**
Sig. (2-tailed) .001
N 151 151
COMPETITIVE1
Pearson Correlation .268** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 29
The result of the hypothesis above shows a Pearson correlation coefficient of .29. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
the higher level of knowledge economy in UAE as a mediator variable will
positively impact a higher chance of competitive in there market economy.
Hypothesis test 8
Data analysis using SPSS 27
H0: The higher level of knowledge economy in UAE as a mediator variable will
positively impact higher chances of economic growth in there market
economy.
Versus
H1: The higher level of knowledge economy in UAE as a mediator variable will
not positively impact higher chances of economic growth in there market
economy.
Results table
Correlations
KE1 GROWTH2
KE1
Pearson Correlation 1 .219**
Sig. (2-tailed) .007
N 151 151
GROWTH2
Pearson Correlation .219** 1
Sig. (2-tailed) .007
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 30
The result of the hypothesis above shows a Pearson correlation coefficient of .219. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
the higher level of knowledge economy in UAE as a mediator variable will
positively impact higher chances of economic growth in there market
economy.
H0: The higher level of knowledge economy in UAE as a mediator variable will
positively impact higher chances of economic growth in there market
economy.
Versus
H1: The higher level of knowledge economy in UAE as a mediator variable will
not positively impact higher chances of economic growth in there market
economy.
Results table
Correlations
KE1 GROWTH2
KE1
Pearson Correlation 1 .219**
Sig. (2-tailed) .007
N 151 151
GROWTH2
Pearson Correlation .219** 1
Sig. (2-tailed) .007
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 30
The result of the hypothesis above shows a Pearson correlation coefficient of .219. This value is
positive. The decision therefore is to accept the null hypothesis hence we conclude that at 0.05,
the higher level of knowledge economy in UAE as a mediator variable will
positively impact higher chances of economic growth in there market
economy.
Data analysis using SPSS 28
Discussion
This research study has revealed that there could be more females than males working in the
private sector. This could be true if the statistics obtained is anything to go by. It was found that
the percentage of females who took part in the survey was over 70%. The research could not
attribute this wide difference to anything except that there are more females than males in the
private sector. When it comes to marital status, there majority of the employees in the private
sector are married. This can be attributed to the fact that employed individuals have got the
financial capacity to raise families. In addition to this, it was also found that majority of the
respondents were either holders of bachelor’s or doctorate degrees. This partly explains why
most of them are employed. Statistics has shown that over 86% are employed. In any learned
society, it is not surprising to have such a scenario where the rate of employment is very high.
Discussion
This research study has revealed that there could be more females than males working in the
private sector. This could be true if the statistics obtained is anything to go by. It was found that
the percentage of females who took part in the survey was over 70%. The research could not
attribute this wide difference to anything except that there are more females than males in the
private sector. When it comes to marital status, there majority of the employees in the private
sector are married. This can be attributed to the fact that employed individuals have got the
financial capacity to raise families. In addition to this, it was also found that majority of the
respondents were either holders of bachelor’s or doctorate degrees. This partly explains why
most of them are employed. Statistics has shown that over 86% are employed. In any learned
society, it is not surprising to have such a scenario where the rate of employment is very high.
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Data analysis using SPSS 29
Higher education comes with skills that are relevant for the job market hence this is a pointer to
any government or a society to channel more resources in the education sector as it is one of the
ways to increase employability. Many are working in the field of business compared to other
sectors. This also tends to explain the high rate of employment since businesses are synonymous
with job creation. It has been found that in many economies where there are high levels of
unemployment, many learned people usually wait for employment in government or the private
sector rather venturing into entrepreneurship. Majority of the individuals who are employed
research shows that they occupy top management position. This could be because of high levels
of literacy among the respondents. The study discovered an interesting scenario; that there was
no correlation between gender and field of work. Any gender worked in any sector. On
regression analysis where the research sought to establish whether transfer to research
universities and research performance has effect on knowledge economy, growth and
competitiveness and It was found that transfer to research universities and research performance
has very little effect on knowledge economy, growth and competitiveness.
Higher education comes with skills that are relevant for the job market hence this is a pointer to
any government or a society to channel more resources in the education sector as it is one of the
ways to increase employability. Many are working in the field of business compared to other
sectors. This also tends to explain the high rate of employment since businesses are synonymous
with job creation. It has been found that in many economies where there are high levels of
unemployment, many learned people usually wait for employment in government or the private
sector rather venturing into entrepreneurship. Majority of the individuals who are employed
research shows that they occupy top management position. This could be because of high levels
of literacy among the respondents. The study discovered an interesting scenario; that there was
no correlation between gender and field of work. Any gender worked in any sector. On
regression analysis where the research sought to establish whether transfer to research
universities and research performance has effect on knowledge economy, growth and
competitiveness and It was found that transfer to research universities and research performance
has very little effect on knowledge economy, growth and competitiveness.
Data analysis using SPSS 30
Appendix 1
Count Column N %
GENDER FEMALE 106 70.2%
MALE 45 29.8%
Total 151 100.0%
Table 1
Count Column N %
NATIONALIT
Y
AUSTRALIA 4 2.6%
CANADA 3 2.0%
CHINA 2 1.3%
EGYPT 5 3.3%
FRANCE 2 1.3%
Appendix 1
Count Column N %
GENDER FEMALE 106 70.2%
MALE 45 29.8%
Total 151 100.0%
Table 1
Count Column N %
NATIONALIT
Y
AUSTRALIA 4 2.6%
CANADA 3 2.0%
CHINA 2 1.3%
EGYPT 5 3.3%
FRANCE 2 1.3%
Data analysis using SPSS 31
INDIA 6 4.0%
IRAQ 4 2.6%
IRELAND 4 2.6%
JAPAN 1 0.7%
LEBANON 4 2.6%
MOROCCO 4 2.6%
OMAN 1 0.7%
PALESTINIA
N
6 4.0%
SWEDEN 5 3.3%
SYRIAN 5 3.3%
TUNISIA 4 2.6%
UAE 80 53.0%
UK 5 3.3%
USA 6 4.0%
Total 151 100.0%
Table 2
Count Column N
%
AGE 20-29 32 21.2%
30-39 52 34.4%
40-49 32 21.2%
MORE THAN
50
35 23.2%
Total 151 100.0%
Table 3
Count Column N %
MARITAL_STATUS MARRIED 79 52.3%
SINGLE 67 44.4%
DIVORCED 5 3.3%
Total 151 100.0%
Table 4
INDIA 6 4.0%
IRAQ 4 2.6%
IRELAND 4 2.6%
JAPAN 1 0.7%
LEBANON 4 2.6%
MOROCCO 4 2.6%
OMAN 1 0.7%
PALESTINIA
N
6 4.0%
SWEDEN 5 3.3%
SYRIAN 5 3.3%
TUNISIA 4 2.6%
UAE 80 53.0%
UK 5 3.3%
USA 6 4.0%
Total 151 100.0%
Table 2
Count Column N
%
AGE 20-29 32 21.2%
30-39 52 34.4%
40-49 32 21.2%
MORE THAN
50
35 23.2%
Total 151 100.0%
Table 3
Count Column N %
MARITAL_STATUS MARRIED 79 52.3%
SINGLE 67 44.4%
DIVORCED 5 3.3%
Total 151 100.0%
Table 4
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Data analysis using SPSS 32
Count Column N %
EDUCATIONAL_LEVE
L
BACHELORS 56 37.1%
DOCTORATE 61 40.4%
HIGH DIPLOMA 6 4.0%
HIGH SCHOOL 6 4.0%
MASTERS
DEGREE
22 14.6%
Total 151 100.0%
Table 5
Count Column N %
ORGANIZATION_TYPE PUBLIC/GOVERNMENT 132 87.4%
SEMI-GOVERNMENT 14 9.3%
PRIVATE 2 1.3%
OTHER 3 2.0%
Total 151 100.0%
Table 6
Count Column N %
CURRENTLY_YOU_ARE EMPLOYED 131 86.8%
STUDENT 15 9.9%
UNEMPLOYED 5 3.3%
Total 151 100.0%
Table 7
Count Column N
%
FIELD_OF_WORK BUSINESS 45 29.8%
EDUCATION 26 17.2%
ENGINEERING 14 9.3%
INFORMATION
TECHNOLOGY
14 9.3%
MEDICINE & HEALTH
SCIENCES
13 8.6%
Count Column N %
EDUCATIONAL_LEVE
L
BACHELORS 56 37.1%
DOCTORATE 61 40.4%
HIGH DIPLOMA 6 4.0%
HIGH SCHOOL 6 4.0%
MASTERS
DEGREE
22 14.6%
Total 151 100.0%
Table 5
Count Column N %
ORGANIZATION_TYPE PUBLIC/GOVERNMENT 132 87.4%
SEMI-GOVERNMENT 14 9.3%
PRIVATE 2 1.3%
OTHER 3 2.0%
Total 151 100.0%
Table 6
Count Column N %
CURRENTLY_YOU_ARE EMPLOYED 131 86.8%
STUDENT 15 9.9%
UNEMPLOYED 5 3.3%
Total 151 100.0%
Table 7
Count Column N
%
FIELD_OF_WORK BUSINESS 45 29.8%
EDUCATION 26 17.2%
ENGINEERING 14 9.3%
INFORMATION
TECHNOLOGY
14 9.3%
MEDICINE & HEALTH
SCIENCES
13 8.6%
Data analysis using SPSS 33
AGRICULTURE 4 2.6%
COMMUNICATION/
PUBLIC RELATIONS
8 5.3%
LEGAL 2 1.3%
PILOT/AIRFORCE 1 0.7%
OTHER 24 15.9%
Total 151 100.0%
Table 8
Count Column N %
JOB_LEVEL JUNIOR EMPLOYEE 13 8.6%
LOWER
MANAGEMENT
3 2.0%
MIDDLE
MANAGEMENT
30 19.9%
SENIOR EMPLOYEE 30 19.9%
TOP MANAGEMENT 59 39.1%
UNEMPLOYED 10 6.6%
OTHER 6 4.0%
Total 151 100.0%
Table 9
Count Column N %
WORKING_EXPERIENCE 1 YEAR OR LESS 5 3.3%
2 TO 6 YEARS 25 16.6%
7 TO 11 YEARS 36 23.8%
AGRICULTURE 4 2.6%
COMMUNICATION/
PUBLIC RELATIONS
8 5.3%
LEGAL 2 1.3%
PILOT/AIRFORCE 1 0.7%
OTHER 24 15.9%
Total 151 100.0%
Table 8
Count Column N %
JOB_LEVEL JUNIOR EMPLOYEE 13 8.6%
LOWER
MANAGEMENT
3 2.0%
MIDDLE
MANAGEMENT
30 19.9%
SENIOR EMPLOYEE 30 19.9%
TOP MANAGEMENT 59 39.1%
UNEMPLOYED 10 6.6%
OTHER 6 4.0%
Total 151 100.0%
Table 9
Count Column N %
WORKING_EXPERIENCE 1 YEAR OR LESS 5 3.3%
2 TO 6 YEARS 25 16.6%
7 TO 11 YEARS 36 23.8%
Data analysis using SPSS 34
12 TO 16 YEARS 46 30.5%
MORE THAN 17
YEARS
27 17.9%
UNEMPLOYED 12 7.9%
Total 151 100.0%
Table 10
Table of summary statistics
Statistics
WORKING_EXPERIENCE
N Valid 151
Missing 0
Mean 3.6689
Median 4.0000
Mode 4.00
Std. Deviation 1.26344
Table 11
12 TO 16 YEARS 46 30.5%
MORE THAN 17
YEARS
27 17.9%
UNEMPLOYED 12 7.9%
Total 151 100.0%
Table 10
Table of summary statistics
Statistics
WORKING_EXPERIENCE
N Valid 151
Missing 0
Mean 3.6689
Median 4.0000
Mode 4.00
Std. Deviation 1.26344
Table 11
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Data analysis using SPSS 35
Figure 1
Correlations
GENDER FIELD_OF_WO
RK
GENDER
Pearson Correlation 1 .003
Sig. (2-tailed) .969
N 151 151
FIELD_OF_WORK
Pearson Correlation .003 1
Sig. (2-tailed) .969
N 151 151
Table 12
Figure 1
Correlations
GENDER FIELD_OF_WO
RK
GENDER
Pearson Correlation 1 .003
Sig. (2-tailed) .969
N 151 151
FIELD_OF_WORK
Pearson Correlation .003 1
Sig. (2-tailed) .969
N 151 151
Table 12
Data analysis using SPSS 36
Table of reliability test results
Reliability Statistics
Cronbach's
Alpha
N of Items
.841 32
Table 13
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733
Bartlett's Test of Sphericity Approx. Chi-Square 1538.19
2
df 496
Sig. .000
Table 14
Total variance explained table
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 6.238 19.494 19.494 6.238 19.494 19.494
2 2.856 8.924 28.418 2.856 8.924 28.418
3 1.849 5.777 34.195 1.849 5.777 34.195
4 1.649 5.153 39.348 1.649 5.153 39.348
5 1.566 4.895 44.243 1.566 4.895 44.243
6 1.439 4.496 48.738 1.439 4.496 48.738
7 1.342 4.194 52.932 1.342 4.194 52.932
8 1.266 3.957 56.890 1.266 3.957 56.890
9 1.103 3.448 60.338 1.103 3.448 60.338
10 1.090 3.406 63.744 1.090 3.406 63.744
11 .976 3.050 66.794
12 .940 2.936 69.730
13 .856 2.675 72.406
14 .814 2.543 74.949
Table of reliability test results
Reliability Statistics
Cronbach's
Alpha
N of Items
.841 32
Table 13
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733
Bartlett's Test of Sphericity Approx. Chi-Square 1538.19
2
df 496
Sig. .000
Table 14
Total variance explained table
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 6.238 19.494 19.494 6.238 19.494 19.494
2 2.856 8.924 28.418 2.856 8.924 28.418
3 1.849 5.777 34.195 1.849 5.777 34.195
4 1.649 5.153 39.348 1.649 5.153 39.348
5 1.566 4.895 44.243 1.566 4.895 44.243
6 1.439 4.496 48.738 1.439 4.496 48.738
7 1.342 4.194 52.932 1.342 4.194 52.932
8 1.266 3.957 56.890 1.266 3.957 56.890
9 1.103 3.448 60.338 1.103 3.448 60.338
10 1.090 3.406 63.744 1.090 3.406 63.744
11 .976 3.050 66.794
12 .940 2.936 69.730
13 .856 2.675 72.406
14 .814 2.543 74.949
Data analysis using SPSS 37
15 .770 2.407 77.355
16 .733 2.290 79.646
17 .725 2.267 81.913
18 .646 2.018 83.931
19 .586 1.833 85.764
20 .559 1.746 87.510
21 .495 1.548 89.057
22 .453 1.416 90.473
23 .431 1.348 91.820
24 .414 1.292 93.113
25 .355 1.108 94.221
26 .345 1.077 95.298
27 .311 .972 96.271
28 .288 .901 97.171
29 .268 .836 98.007
30 .255 .797 98.804
31 .210 .657 99.461
32 .173 .539 100.000
Extraction Method: Principal Component Analysis.
Table 15
15 .770 2.407 77.355
16 .733 2.290 79.646
17 .725 2.267 81.913
18 .646 2.018 83.931
19 .586 1.833 85.764
20 .559 1.746 87.510
21 .495 1.548 89.057
22 .453 1.416 90.473
23 .431 1.348 91.820
24 .414 1.292 93.113
25 .355 1.108 94.221
26 .345 1.077 95.298
27 .311 .972 96.271
28 .288 .901 97.171
29 .268 .836 98.007
30 .255 .797 98.804
31 .210 .657 99.461
32 .173 .539 100.000
Extraction Method: Principal Component Analysis.
Table 15
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Data analysis using SPSS 38
Scree plot
Figure 2
Component Matrixa
Component
1 2 3 4 5 6 7 8 9 10
MEDIATOR1 .391 -.537 -.050 -.031 -.012 -.222 .173 -.209 -.241 .120
MEDIATOR2 .435 -.272 .264 -.367 -.171 .101 .129 -.190 -.072 .133
MEDIATOR3 .560 -.236 .061 -.353 .038 -.167 .211 .008 -.105 -.131
MEDIATOR4 .221 .130 .422 .171 .350 .305 -.193 .113 .024 -.120
MEDIATOR5 .468 -.410 -.200 .082 .131 .373 -.073 -.022 .015 .015
MEDIATOR6 .535 -.433 .182 -.117 -.088 -.246 .030 .025 -.256 -.116
MEDIATOR7 .400 .068 .029 .123 -.058 .372 -.288 .269 -.156 .398
MEDIATOR8 .387 -.397 .147 .112 .209 -.129 -.091 .261 .010 -.284
MEDIATOR9 .241 .127 .250 -.004 .204 -.032 .002 -.178 .462 .105
Scree plot
Figure 2
Component Matrixa
Component
1 2 3 4 5 6 7 8 9 10
MEDIATOR1 .391 -.537 -.050 -.031 -.012 -.222 .173 -.209 -.241 .120
MEDIATOR2 .435 -.272 .264 -.367 -.171 .101 .129 -.190 -.072 .133
MEDIATOR3 .560 -.236 .061 -.353 .038 -.167 .211 .008 -.105 -.131
MEDIATOR4 .221 .130 .422 .171 .350 .305 -.193 .113 .024 -.120
MEDIATOR5 .468 -.410 -.200 .082 .131 .373 -.073 -.022 .015 .015
MEDIATOR6 .535 -.433 .182 -.117 -.088 -.246 .030 .025 -.256 -.116
MEDIATOR7 .400 .068 .029 .123 -.058 .372 -.288 .269 -.156 .398
MEDIATOR8 .387 -.397 .147 .112 .209 -.129 -.091 .261 .010 -.284
MEDIATOR9 .241 .127 .250 -.004 .204 -.032 .002 -.178 .462 .105
Data analysis using SPSS 39
MEDIATOR10 .274 -.125 -.054 -.078 .112 .371 .350 -.003 .420 .205
MEDIATOR11 .161 .127 .126 .115 .104 -.029 .441 .657 .130 .045
RP1 .517 -.096 .192 -.427 -.112 -.034 -.222 .033 .075 .057
RP2 .481 -.291 -.141 .195 .196 -.063 -.112 .081 .016 .270
RP3 .508 -.263 -.184 .140 .052 -.311 -.163 .099 .250 .336
RP4 .354 -.264 .431 -.266 -.006 .118 -.125 -.118 .204 .076
TRU1 .509 -.002 -.122 .135 .295 -.343 -.407 .011 -.083 .012
TRU2 .544 -.019 .249 .218 .088 -.010 -.168 .036 .151 -.321
TRU3 .374 .080 .357 .389 .008 .075 .153 -.460 -.112 .079
TRU4 .439 .228 .046 .531 .197 .107 .034 -.228 -.264 -.079
KE1 .552 .062 -.083 -.290 .274 .297 .185 .132 -.369 .054
KE2 .397 -.140 -.172 .132 -.510 .161 -.121 -.250 .264 -.266
KE3 .429 .353 -.385 -.334 .233 .136 -.076 -.232 .064 -.148
KE4 .437 .373 -.366 -.178 .396 -.017 -.046 -.184 .063 .041
KE5 .500 .407 -.060 -.316 -.119 .058 -.098 .198 -.067 -.267
GROWTH1 .455 .390 -.003 .026 -.134 -.331 .190 -.071 .138 .197
GROWTH2 .319 .543 .414 .020 -.051 .091 .143 -.033 -.088 -.070
GROWTH3 .482 .512 -.153 .083 -.274 -.247 -.058 .036 -.103 .208
GROWTH4 .414 .425 .443 -.018 -.290 -.181 -.080 .102 .012 .107
GROWTH5 .435 .113 -.277 .080 -.466 .367 -.091 .076 -.130 .130
COMPETITIVE1 .480 -.075 -.153 .219 .019 -.050 .570 -.068 -.015 -.043
COMPETITIVE2 .537 -.334 -.189 .273 -.360 .097 .022 .185 .113 -.239
COMPETITIVE3 .529 .302 -.292 .001 .035 -.135 .103 .064 .155 -.256
Extraction Method: Principal Component Analysis.
a. 10 components extracted.
Table 16
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total Correlation
Cronbach's
Alpha if Item
Deleted
MEDIATOR1 119.6424 145.618 .312 .838
MEDIATOR2 119.3113 145.602 .373 .836
MEDIATOR3 119.2914 143.835 .481 .834
MEDIATOR4 119.6623 149.838 .204 .841
MEDIATOR5 119.3377 145.398 .406 .836
MEDIATOR6 119.3377 143.865 .451 .834
MEDIATOR7 119.2119 147.461 .341 .838
MEDIATOR10 .274 -.125 -.054 -.078 .112 .371 .350 -.003 .420 .205
MEDIATOR11 .161 .127 .126 .115 .104 -.029 .441 .657 .130 .045
RP1 .517 -.096 .192 -.427 -.112 -.034 -.222 .033 .075 .057
RP2 .481 -.291 -.141 .195 .196 -.063 -.112 .081 .016 .270
RP3 .508 -.263 -.184 .140 .052 -.311 -.163 .099 .250 .336
RP4 .354 -.264 .431 -.266 -.006 .118 -.125 -.118 .204 .076
TRU1 .509 -.002 -.122 .135 .295 -.343 -.407 .011 -.083 .012
TRU2 .544 -.019 .249 .218 .088 -.010 -.168 .036 .151 -.321
TRU3 .374 .080 .357 .389 .008 .075 .153 -.460 -.112 .079
TRU4 .439 .228 .046 .531 .197 .107 .034 -.228 -.264 -.079
KE1 .552 .062 -.083 -.290 .274 .297 .185 .132 -.369 .054
KE2 .397 -.140 -.172 .132 -.510 .161 -.121 -.250 .264 -.266
KE3 .429 .353 -.385 -.334 .233 .136 -.076 -.232 .064 -.148
KE4 .437 .373 -.366 -.178 .396 -.017 -.046 -.184 .063 .041
KE5 .500 .407 -.060 -.316 -.119 .058 -.098 .198 -.067 -.267
GROWTH1 .455 .390 -.003 .026 -.134 -.331 .190 -.071 .138 .197
GROWTH2 .319 .543 .414 .020 -.051 .091 .143 -.033 -.088 -.070
GROWTH3 .482 .512 -.153 .083 -.274 -.247 -.058 .036 -.103 .208
GROWTH4 .414 .425 .443 -.018 -.290 -.181 -.080 .102 .012 .107
GROWTH5 .435 .113 -.277 .080 -.466 .367 -.091 .076 -.130 .130
COMPETITIVE1 .480 -.075 -.153 .219 .019 -.050 .570 -.068 -.015 -.043
COMPETITIVE2 .537 -.334 -.189 .273 -.360 .097 .022 .185 .113 -.239
COMPETITIVE3 .529 .302 -.292 .001 .035 -.135 .103 .064 .155 -.256
Extraction Method: Principal Component Analysis.
a. 10 components extracted.
Table 16
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total Correlation
Cronbach's
Alpha if Item
Deleted
MEDIATOR1 119.6424 145.618 .312 .838
MEDIATOR2 119.3113 145.602 .373 .836
MEDIATOR3 119.2914 143.835 .481 .834
MEDIATOR4 119.6623 149.838 .204 .841
MEDIATOR5 119.3377 145.398 .406 .836
MEDIATOR6 119.3377 143.865 .451 .834
MEDIATOR7 119.2119 147.461 .341 .838
Data analysis using SPSS 40
MEDIATOR8 119.5960 147.429 .329 .838
MEDIATOR9 120.6689 149.156 .216 .841
MEDIATOR10 118.4834 139.238 .242 .850
MEDIATOR11 119.1258 145.737 .142 .851
RP1 119.4437 143.342 .448 .834
RP2 119.4636 144.397 .404 .835
RP3 119.4702 143.304 .442 .834
RP4 119.4967 146.265 .305 .838
TRU1 119.3642 144.966 .408 .836
TRU2 119.4570 142.903 .479 .833
TRU3 119.3775 146.943 .330 .838
TRU4 119.4238 146.072 .369 .837
KE1 119.3046 143.507 .494 .833
KE2 119.4834 146.545 .314 .838
KE3 119.3179 146.885 .343 .837
KE4 119.2185 146.799 .361 .837
KE5 119.3510 144.709 .415 .835
GROWTH1 119.2450 145.186 .391 .836
GROWTH2 119.3510 147.523 .284 .839
GROWTH3 119.2583 145.566 .394 .836
GROWTH4 119.2649 146.289 .359 .837
GROWTH5 119.2583 146.433 .362 .837
COMPETITIVE1 119.3377 144.265 .431 .835
COMPETITIVE2 119.3510 143.963 .461 .834
COMPETITIVE3 119.2781 144.429 .455 .834
Table 17
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 RP1, TRU1b . Enter
a. Dependent Variable: GROWTH1
b. All requested variables entered.
Table 18
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .200a .040 .027 .85974
a. Predictors: (Constant), RP1, TRU1
MEDIATOR8 119.5960 147.429 .329 .838
MEDIATOR9 120.6689 149.156 .216 .841
MEDIATOR10 118.4834 139.238 .242 .850
MEDIATOR11 119.1258 145.737 .142 .851
RP1 119.4437 143.342 .448 .834
RP2 119.4636 144.397 .404 .835
RP3 119.4702 143.304 .442 .834
RP4 119.4967 146.265 .305 .838
TRU1 119.3642 144.966 .408 .836
TRU2 119.4570 142.903 .479 .833
TRU3 119.3775 146.943 .330 .838
TRU4 119.4238 146.072 .369 .837
KE1 119.3046 143.507 .494 .833
KE2 119.4834 146.545 .314 .838
KE3 119.3179 146.885 .343 .837
KE4 119.2185 146.799 .361 .837
KE5 119.3510 144.709 .415 .835
GROWTH1 119.2450 145.186 .391 .836
GROWTH2 119.3510 147.523 .284 .839
GROWTH3 119.2583 145.566 .394 .836
GROWTH4 119.2649 146.289 .359 .837
GROWTH5 119.2583 146.433 .362 .837
COMPETITIVE1 119.3377 144.265 .431 .835
COMPETITIVE2 119.3510 143.963 .461 .834
COMPETITIVE3 119.2781 144.429 .455 .834
Table 17
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 RP1, TRU1b . Enter
a. Dependent Variable: GROWTH1
b. All requested variables entered.
Table 18
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .200a .040 .027 .85974
a. Predictors: (Constant), RP1, TRU1
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Data analysis using SPSS 41
b. Dependent Variable: GROWTH1
Table 19
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 4.580 2 2.290 3.098 .048b
Residual 109.394 148 .739
Total 113.974 150
a. Dependent Variable: GROWTH1
b. Predictors: (Constant), RP1, TRU1
Table 20
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
(Constan
t)
3.038 .389 7.809 .000 2.270 3.807
TRU1 .153 .084 .151 1.822 .071 -.013 .319 .943 1.061
RP1 .094 .078 .100 1.210 .228 -.060 .248 .943 1.061
a. Dependent Variable: GROWTH1
Table 21
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index Variance Proportions
(Constant) TRU1 RP1
1
1 2.939 1.000 .00 .01 .01
2 .040 8.600 .01 .45 .77
3 .021 11.735 .98 .54 .22
a. Dependent Variable: GROWTH1
Table 22
b. Dependent Variable: GROWTH1
Table 19
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 4.580 2 2.290 3.098 .048b
Residual 109.394 148 .739
Total 113.974 150
a. Dependent Variable: GROWTH1
b. Predictors: (Constant), RP1, TRU1
Table 20
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
(Constan
t)
3.038 .389 7.809 .000 2.270 3.807
TRU1 .153 .084 .151 1.822 .071 -.013 .319 .943 1.061
RP1 .094 .078 .100 1.210 .228 -.060 .248 .943 1.061
a. Dependent Variable: GROWTH1
Table 21
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index Variance Proportions
(Constant) TRU1 RP1
1
1 2.939 1.000 .00 .01 .01
2 .040 8.600 .01 .45 .77
3 .021 11.735 .98 .54 .22
a. Dependent Variable: GROWTH1
Table 22
Data analysis using SPSS 42
Figure 3
Figure 3
Data analysis using SPSS 43
Figure 4
Correlations
RP1 KE1
RP1
Pearson Correlation 1 .262**
Sig. (2-tailed) .001
N 151 151
KE1 Pearson Correlation .262** 1
Figure 4
Correlations
RP1 KE1
RP1
Pearson Correlation 1 .262**
Sig. (2-tailed) .001
N 151 151
KE1 Pearson Correlation .262** 1
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Data analysis using SPSS 44
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 23
Correlations
TRU1 MEDIATOR1
TRU1
Pearson Correlation 1 .215**
Sig. (2-tailed) .008
N 151 151
MEDIATOR1
Pearson Correlation .215** 1
Sig. (2-tailed) .008
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 24
Correlations
MEDIATOR1 RP1
MEDIATOR1
Pearson Correlation 1 .257**
Sig. (2-tailed) .001
N 151 151
RP1
Pearson Correlation .257** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 25
Correlations
MEDIATOR1 GROWTH1
MEDIATOR1
Pearson Correlation 1 .085
Sig. (2-tailed) .297
N 151 151
GROWTH1 Pearson Correlation .085 1
Sig. (2-tailed) .297
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 23
Correlations
TRU1 MEDIATOR1
TRU1
Pearson Correlation 1 .215**
Sig. (2-tailed) .008
N 151 151
MEDIATOR1
Pearson Correlation .215** 1
Sig. (2-tailed) .008
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 24
Correlations
MEDIATOR1 RP1
MEDIATOR1
Pearson Correlation 1 .257**
Sig. (2-tailed) .001
N 151 151
RP1
Pearson Correlation .257** 1
Sig. (2-tailed) .001
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 25
Correlations
MEDIATOR1 GROWTH1
MEDIATOR1
Pearson Correlation 1 .085
Sig. (2-tailed) .297
N 151 151
GROWTH1 Pearson Correlation .085 1
Sig. (2-tailed) .297
Data analysis using SPSS 45
N 151 151
Table 26
Correlations
MEDIATOR1 RP2
MEDIATOR1
Pearson Correlation 1 .229**
Sig. (2-tailed) .005
N 151 151
RP2
Pearson Correlation .229** 1
Sig. (2-tailed) .005
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 27
Correlations
RP2 TRU3
RP2
Pearson Correlation 1 .114
Sig. (2-tailed) .163
N 151 151
TRU3
Pearson Correlation .114 1
Sig. (2-tailed) .163
N 151 151
Table 28
Correlations
KE1 COMPETITIVE1
KE1
Pearson Correlation 1 .268**
Sig. (2-tailed) .001
N 151 151
COMPETITIVE1 Pearson Correlation .268** 1
Sig. (2-tailed) .001
N 151 151
Table 26
Correlations
MEDIATOR1 RP2
MEDIATOR1
Pearson Correlation 1 .229**
Sig. (2-tailed) .005
N 151 151
RP2
Pearson Correlation .229** 1
Sig. (2-tailed) .005
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 27
Correlations
RP2 TRU3
RP2
Pearson Correlation 1 .114
Sig. (2-tailed) .163
N 151 151
TRU3
Pearson Correlation .114 1
Sig. (2-tailed) .163
N 151 151
Table 28
Correlations
KE1 COMPETITIVE1
KE1
Pearson Correlation 1 .268**
Sig. (2-tailed) .001
N 151 151
COMPETITIVE1 Pearson Correlation .268** 1
Sig. (2-tailed) .001
Data analysis using SPSS 46
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 29
Correlations
KE1 GROWTH2
KE1
Pearson Correlation 1 .219**
Sig. (2-tailed) .007
N 151 151
GROWTH2
Pearson Correlation .219** 1
Sig. (2-tailed) .007
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 30
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 29
Correlations
KE1 GROWTH2
KE1
Pearson Correlation 1 .219**
Sig. (2-tailed) .007
N 151 151
GROWTH2
Pearson Correlation .219** 1
Sig. (2-tailed) .007
N 151 151
**. Correlation is significant at the 0.01 level (2-tailed).
Table 30
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Data analysis using SPSS 47
References
Miles, M. B. (2004). Qualitative data analysis. Newburry Park: SAGE.
Morey, D. (2015). Robust Misinterpretation of Confidence intervals.
Pallant, J. F. (2009). A step by step guide to data analysis using SPSS: Survival model.
Porter, M. E. (2008). Competitive strategy technique for analyzing industries and competitors.
New York: Free Press.
Richler, J. (2012). Behaviour research methods.
Warnbrod, J. R. (2001). Conducting, interpreting and reporting quantitative research. New
Orleans: Pre-Session.
White, H. D. (2003). Author cocitation analysis and Pearson's r. Journal of American society for
information science and technology.
Winter, J. (2010). Practical assessment, research and evaluation. (Vol. 8).
References
Miles, M. B. (2004). Qualitative data analysis. Newburry Park: SAGE.
Morey, D. (2015). Robust Misinterpretation of Confidence intervals.
Pallant, J. F. (2009). A step by step guide to data analysis using SPSS: Survival model.
Porter, M. E. (2008). Competitive strategy technique for analyzing industries and competitors.
New York: Free Press.
Richler, J. (2012). Behaviour research methods.
Warnbrod, J. R. (2001). Conducting, interpreting and reporting quantitative research. New
Orleans: Pre-Session.
White, H. D. (2003). Author cocitation analysis and Pearson's r. Journal of American society for
information science and technology.
Winter, J. (2010). Practical assessment, research and evaluation. (Vol. 8).
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