# 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 bachelorâ€™s degree who were 56 and making 37.1% of the total.

Respondents having masterâ€™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 bachelorâ€™s degree who were 56 and making 37.1% of the total.

Respondents having masterâ€™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 Cronbachâ€™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 Cronbachâ€™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 Cronbachâ€™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 Cronbachâ€™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 ransferÂ¿ 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 ransferÂ¿ 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 bachelorâ€™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 bachelorâ€™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