Chapter 6

 

 

 

 

 

 

 

 

 

 

 

 

Introduction

100 Saudi adults were sampled to establish the interrelationship between demographic factors and the negative and positive aspects of social networking. The youngest adults admitted into the study sample were 18 years while the eldest participants were 52 years of age. It was found that the average amount of time spent on social networks daily did not have a significant linear relationship with positive aspects of social networking. Further, there were no significant differences between the average response scores for males and female respondents in the sample regarding their opinions on the positive and negative aspects of social networking. Likewise, there did not exist significant statistical differences between the respondents’ opinions on both positive and negative aspects of social networking.

As stated, this research was conducted to establish the relationships between demographic features of a sample of Saudi Arabia’s population and positive and negative factors of social networking. The demographic factors sought included age, gender, education levels, current employment status and job type, courses studied, and the highest levels of education attained. The positive aspects of social networking included socialising, acquisition of friends, enhanced research capabilities and faster completion of studies, among others. The factors with negative effects were broadly classified into three categories, namely; cognitive, social and physical development.

The Applied Tests

            A sample of 100 Saudi Arabian residents between the age 18 and 52 years were randomly selected for the study. The group as a whole represents a technologically aware generation that has been receptive to social networking. The Social Package for Social Sciences (SPSS) was used to analyze the data.

Statistic

Value

Min Value

1

Max Value

2

Mean

1.59

Variance

0.24

Standard Deviation

0.49

Total Responses

100

 

The following hypotheses were posed for the analysis:

H1: There is no relationship between the use of social networking and the notion of sustainability awareness among students in Saudi Arabia.

H2: There is no relationship between social-networking and the development of a professional attitude among students in Saudi Arabia.

H3: There are no significant differences between the major real and potential risks and opportunities via the use of social-networking among males and females.

H4: There are no significant differences between the major real and potential risks and opportunities via the use social-networking among respondents of different ages.

Furthermore, the study explains the positive and negative effects of social networking usage by students in Saudi Arabia. Factor analysis was used to determine the variability of the positive and negative factors of social networking identified. Descriptive statistics were employed to display the frequencies of population features such as age, academic levels, current employment and gender. Through the descriptive statistics, the underlying sampling features were made clearer. These include the percentages of males and females per course studied.

To determine the differences between male and females’ perceived real and potential risks of using social networking, the independent samples t-test was used. The one-way ANOVA was employed to determine the perceived real and potential risks of using social networking among respondents across the age groups 18-22, 22-32, 32-42, and those between 42 and 52 years of age.

Correlation analysis was also used to ascertain whether a relationship exists between the use of social-networking and the notion of sustainability awareness among  students in Saudi Arabia. It was also used to determine whether there exists a relationship between social networking and the development of a professional attitude among students.

 

 

Data Analysis and Discussion

Participants (Descriptive Statistics):

Out of the 100 respondents whose responses were selected to form the survey data, 41.0% were male, while females comprised 59.0%.

 

 

Youths in the age bracket 18-22 years of age comprised 26% of all respondents, while persons between 22-32 years made up 32% of the entire group. Respondents between the ages 32 and 42 accounted for 22% of the entire sample, which is slightly higher than the 20%  represented by respondents between 42-52 years – the highest age at which individuals were admitted into the survey.

 

 

            Despite the fact that most respondents did not provide reliable information about their current jobs (75%), the remaining 25% listed their occupations as follows:  students (29%), teachers (24%), web developers (2%), doctors (3%), the jobless (12%), technicians (2%), teaching assistants (2%) and student advisors (2%). Other careers accounted for 24% of all responses from the group.

 

            Regarding the areas of specialization, those who took art and design as their main course after high school comprised the least number of respondents (1%), slightly less than those who took business law (2%), information technology specialists (3%) and specialists in economics and finance and computer science with (4%) for each. Accounting specialists accounted for 5%, while those who took health sciences comprised 7%. Information systems and management specialists made up 9% each, while science and engineering was represented at 10%. Humanities formed the single identifiable largest group (20%), just 6% shy of specialists whose major courses were not included in the survey.

            Respondents who had attained a bachelor’s degree as their highest level of education comprised the majority at 49%, more than twice their closest comparable group of higher secondary and pre-university achievers at 22%. Diploma holders were as many as those who have master’s degree holders (12%). Post graduate diploma holders made up only 4% while individuals with a professional certificate as their highest education level made up just 1%.

 

             It was found that the majority of respondents spent between 1 and 5 hours on average on other social networking activities apart from email (52%), a percentage higher than those who spent less than one hour daily (22%) and the group that admitted to spending between 5 to 10 hours on social networking with the exclusion of their e-mail. Groups in which respondents spent between 10 and 20 hours and those who spent over 20 hours daily on social networking comprised 3% and 2% respectively.

 

 

 

            Compared to the amount of time that respondents spent on networking using their e-mail features, those who spend less than one hour daily make up for a vast majority (89%), followed by those who spend between 1 and 5 hours (5%), 5 to 10 hours (3%), and 10 to 20 hours (2%). Only 1% spent more than 20 hours daily perusing the features of their e-mails.

 

 

In total, 50% of the respondents stated that they go online to look at their email, 15% to play games, 41% for study, 39% to work, 48% to shop online and 46% to chat with friends. Furthermore, 36% research  their hobbies online, 35% bank online, 22% purchase goods and services online, 7% buy stocks and make business investments online, 26% research travel information and make reservations online and 13% had other ways of using the internet.

 

 


Reliability Test:

Positives

 

Reliability Statistics

Cronbach’s Alpha

N of Items

.897

25

 

 

There were 25 items evaluated for consistency among the positive effects of social networking in Saudi Arabia. From the results above, it is seen that the model composed of positive attributes is highly consistent (Cronbach’s alpha = 0.897).

Negatives

 

Reliability Statistics

Cronbach’s Alpha

N of Items

.937

30

 

30 components were evaluated to establish the reliability of the negative aspects of social networking chosen for the survey. With a Cronbach’s alpha 0.937, the model comprising the negative aspects was found to be extremely highly consistent.  

From the two results, that is, for the negative and positive effects of social networking in Saudi Arabia, the choice of aspects of each main effect (positive and negative) was remarkably good.  
Factor Analysis:

Confirmatory factor analysis was used to estimate the variation of the positive effects of social networking and that of the negative effects of social networking in the Kingdom of Saudi Arabia. The Scree plot below was obtained for loadings of positive factors of social networking.

Factors on Positive Impacts of Social Networking

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.809

Bartlett’s Test of Sphericity

Approx. Chi-Square

1.114E3

df

300

Sig.

.000

 

 

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadingsa

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

1

7.738

30.953

30.953

7.738

30.953

30.953

4.634

2

2.064

8.256

39.209

2.064

8.256

39.209

5.010

3

1.903

7.612

46.821

1.903

7.612

46.821

2.561

4

1.731

6.924

53.745

1.731

6.924

53.745

4.510

5

1.350

5.401

59.146

1.350

5.401

59.146

3.535

6

1.213

4.853

63.999

1.213

4.853

63.999

1.432

7

.951

3.803

67.801

 

 

 

 

8

.925

3.700

71.501

 

 

 

 

9

.842

3.368

74.869

 

 

 

 

10

.714

2.857

77.726

 

 

 

 

11

.664

2.656

80.382

 

 

 

 

12

.608

2.431

82.813

 

 

 

 

13

.552

2.207

85.020

 

 

 

 

14

.473

1.892

86.912

 

 

 

 

15

.433

1.733

88.646

 

 

 

 

16

.412

1.649

90.295

 

 

 

 

17

.380

1.518

91.813

 

 

 

 

18

.367

1.469

93.281

 

 

 

 

19

.333

1.333

94.614

 

 

 

 

20

.304

1.217

95.831

 

 

 

 

21

.279

1.118

96.949

 

 

 

 

22

.232

.930

97.879

 

 

 

 

23

.224

.896

98.775

 

 

 

 

24

.166

.664

99.440

 

 

 

 

25

.140

.560

100.000

 

 

 

 

Extraction Method: Principal Component Analysis.

 

 

 

 

a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.

 

 

 

 

 

 

Pattern Matrixa

 

Component

 

1

2

3

4

5

6

Positive effects of Internet Under thissection, the r…-Learn new information and knowledge

.631

-.117

-.133

.144

-.023

-.047

Positive effects of Internet Under thissection, the r…-Gain up-to-date information

.777

-.016

.077

.102

.016

-.111

Positive effects of Internet Under thissection, the r…-Be more aware of global issues/local issues

.843

-.015

-.035

-.076

-.020

.179

Positive effects of Internet Under thissection, the r…-To remember facts/aspects of the past

.630

-.006

.114

.027

-.037

.101

Positive effects of Internet Under thissection, the r…-Communicate with my peers frequently

.435

.093

.067

.172

-.458

-.212

Positive effects of Internet Under thissection, the r…-Collaborate with my peers frequently

.166

.104

.175

.382

-.520

-.108

Positive effects of Internet Under thissection, the r…-Communicate with my peers from different universities

-.057

-.055

-.087

.022

-.887

.066

Positive effects of Internet Under thissection, the r…-Communicate with my different communities

.145

-.302

-.009

-.107

-.679

.059

Positive effects of Internet Under thissection, the r…-Develop intercrossing relationships with my peers (i.e. Artistic talents, sport and common interests)

.428

-.013

.191

.081

-.328

-.019

Positive effects of Internet Under thissection, the r…-Study independently

-.060

.104

-.012

.735

-.234

-.082

Positive effects of Internet Under thissection, the r…-Overcome study stress

.261

-.217

-.142

.436

.109

.300

Positive effects of Internet Under thissection, the r…-Complete my study more quickly

-.019

-.044

.198

.782

.145

.086

Positive effects of Internet Under thissection, the r…-Understand and solve study problems easily

.107

-.112

-.184

.763

-.046

-.024

Positive effects of Internet Under thissection, the r…-Scrutinize my research study more easily

.170

-.171

-.017

.740

.074

-.040

Positive effects of Internet Under thissection, the r…-Develop my personal and communication skills

.089

-.818

-.181

.021

.015

-.161

Positive effects of Internet Under thissection, the r…-Concentrate more on my reading and writing skills

-.006

-.715

-.133

-.009

-.192

.054

Positive effects of Internet Under thissection, the r…-To prepare  my professional attitude toward study and work

.172

-.687

-.009

.017

.075

.053

Positive effects of Internet Under thissection, the r…-Be more sustainable person

-.122

-.717

-.017

.132

-.159

.256

Positive effects of Internet Under thissection, the r…-Provide reliable and scalable services

.006

-.642

.190

.015

-.004

-.100

Positive effects of Internet Under thissection, the r…-Become more “Greener” in my activities

-.054

-.523

.239

.177

-.039

.050

Positive effects of Internet Under thissection, the r…-Reduce carbon footprint in my activities

-.271

-.144

.536

.195

-.132

.356

Positive effects of Internet Under thissection, the r…-Acquire  new acquaintances – work related

.111

-.428

.496

.066

.058

-.526

Positive effects of Internet Under thissection, the r…-Acquire new acquaintances –  friendship relationship

.192

-.200

.571

-.195

-.278

-.017

Positive effects of Internet Under thissection, the r…-Acquire new acquaintances –  romance relationship

.103

.170

.835

.028

.104

.079

Positive effects of Internet Under thissection, the r…-Do whatever I want, say whatever I want, and be whoever I want

.213

-.035

.229

-.026

-.015

.779

Extraction Method: Principal Component Analysis.

 Rotation Method: Oblimin with Kaiser Normalization.

 

 

 

a. Rotation converged in 12 iterations.

 

 

 

 

 

 

 

            The Kaiser-Meyer-Olkin measure of sampling adequacy (0.809) was large enough to certify the adequacy of the sample. All factors that could not load as much variance as themselves were eliminated from the model. On this basis, it was observed that only 6 components could be retained, therefore dropping the remaining 19 factors off the model.

From the Principal Component Analysis, it was observed that the highest correlation existed between factor number one and the ability to scrutinize research study more easily (0.692). The least correlation was observed between factor number six and the development of inter-crossing relations with peers, such as artistic talents and sports (0.008).

Factors on Negative Impacts of Social Networking:

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.837

Bartlett’s Test of Sphericity

Approx. Chi-Square

1.798E3

df

435

Sig.

.000

 

 

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadingsa

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

1

10.755

35.851

35.851

10.755

35.851

35.851

5.001

2

2.474

8.246

44.097

2.474

8.246

44.097

5.840

3

2.047

6.823

50.920

2.047

6.823

50.920

4.065

4

1.491

4.972

55.892

1.491

4.972

55.892

4.625

5

1.328

4.428

60.319

1.328

4.428

60.319

2.533

6

1.136

3.787

64.107

1.136

3.787

64.107

2.296

7

1.090

3.633

67.740

1.090

3.633

67.740

4.541

8

1.013

3.375

71.115

1.013

3.375

71.115

6.151

9

.833

2.776

73.891

 

 

 

 

10

.824

2.746

76.637

 

 

 

 

11

.693

2.311

78.948

 

 

 

 

12

.684

2.279

81.228

 

 

 

 

13

.621

2.071

83.298

 

 

 

 

14

.550

1.835

85.133

 

 

 

 

15

.521

1.737

86.870

 

 

 

 

16

.488

1.625

88.495

 

 

 

 

17

.425

1.415

89.910

 

 

 

 

18

.390

1.300

91.210

 

 

 

 

19

.387

1.289

92.499

 

 

 

 

20

.361

1.203

93.702

 

 

 

 

21

.343

1.142

94.844

 

 

 

 

22

.282

.940

95.784

 

 

 

 

23

.267

.889

96.673

 

 

 

 

24

.223

.744

97.417

 

 

 

 

25

.208

.692

98.109

 

 

 

 

26

.171

.569

98.678

 

 

 

 

27

.135

.451

99.129

 

 

 

 

28

.126

.421

99.550

 

 

 

 

29

.073

.244

99.795

 

 

 

 

30

.062

.205

100.000

 

 

 

 

Extraction Method: Principal Component Analysis.

 

 

 

 

a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.

 

 

 

Pattern Matrixa

 

Component

 

1

2

3

4

5

6

7

8

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from concentrating more on writing and reading skills

-.012

.185

.785

-.049

-.035

.248

-.062

-.171

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from remembering the fundamental knowledge and skills

-.039

.123

.795

.088

.035

-.018

.011

-.024

Under this section, the researchers will examine how students will use the social networking in…-Scatters my attention

.401

-.137

.543

.087

-.090

-.221

.194

.041

Under this section, the researchers will examine how students will use the social networking in…-Decreases my grammar and proofreading skills

.105

.103

.415

.081

.332

-.350

-.089

-.006

Under this section, the researchers will examine how students will use the social networking in…-Decreases my deep thinking

.133

.264

.443

-.143

.110

-.386

-.067

-.150

Under this section, the researchers will examine how students will use the social networking in…-Distracts me easily

.811

.004

.113

.010

-.046

-.037

.003

.051

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from participating in social activities

.063

-.147

.159

.099

.622

-.122

.234

-.158

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from completing my work/study on time

.663

.075

-.084

-.017

.447

-.061

.026

-.008

Under this section, the researchers will examine how students will use the social networking in…-Makes me sick and unhealthy

.174

.414

.001

-.070

.552

.047

-.058

-.110

Under this section, the researchers will examine how students will use the social networking in…-Bores me

.011

.715

.027

-.005

.004

-.205

.012

.022

Under this section, the researchers will examine how students will use the social networking in…-Stresses me

.096

.728

.106

.054

-.194

-.126

.154

.029

Under this section, the researchers will examine how students will use the social networking in…-Depresses me

.015

.859

.062

.078

.012

.121

.063

.099

Under this section, the researchers will examine how students will use the social networking in…-Makes me feel lonely

-.075

.698

.119

.149

.106

.094

.053

.010

Under this section, the researchers will examine how students will use the social networking in…-Makes me lazy

.246

.222

-.127

-.090

-.143

.021

.621

-.229

Under this section, the researchers will examine how students will use the social networking in…-Makes me addict

-.038

.076

.001

-.041

.069

-.046

.868

-.054

Under this section, the researchers will examine how students will use the social networking in…-Makes me more gambler

-.262

.054

.403

.039

.359

.035

.436

-.042

Under this section, the researchers will examine how students will use the social networking in…-Makes me insecure to release my personal details from the theft of personal information

.110

-.028

.199

-.054

-.119

-.175

.007

-.701

Under this section, the researchers will examine how students will use the social networking in…-Makes me receive an immoral images and information from unscrupulous people and it is difficult to act against them at present

.097

.202

.070

-.015

-.215

-.344

.111

-.389

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from having face to face contact with my family

.041

.170

-.163

.202

.250

-.520

.218

-.097

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from having face to face contact with my friends

-.062

.149

-.089

.500

.048

-.379

.149

-.124

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from participating in physical activities

.447

-.064

-.024

.152

-.002

-.263

.275

-.176

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from shopping in stores

.221

.040

-.018

.560

.338

.110

.062

.049

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from watching television

.241

.093

.092

.526

-.232

.266

.322

.136

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from reading the newspapers

-.073

-.001

.061

.796

.043

.037

-.035

-.152

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from talking on the phone/mobile

.020

.176

.012

.687

-.148

-.191

-.189

-.160

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from completing my work on time

.605

.165

-.059

.141

.027

.153

.056

-.311

Under this section, the researchers will examine how students will use the social networking in…-Prevents me from completing my study on time

.513

.235

-.007

.109

.109

.307

.075

-.382

Under this section, the researchers will examine how students will use the social networking in…-Increase privacy concerns

-.030

.071

.015

-.025

.186

.051

.041

-.821

Under this section, the researchers will examine how students will use the social networking in…-Increase security concerns

-.098

-.148

.009

.138

.061

-.034

.089

-.892

Under this section, the researchers will examine how students will use the social networking in…-Increase intellectual property concerns

.036

-.086

-.014

.136

-.034

.092

.012

-.809

Extraction Method: Principal Component Analysis.

 Rotation Method: Oblimin with Kaiser Normalization.

 

 

 

 

 

a. Rotation converged in 24 iterations.

 

 

 

 

 

 

 

 

            By examining the Kaiser-Meyer-Olkin measure of sampling adequacy (0.837), it was concluded that the sampling adequacy of the test components was fulfilled. The criterion used for factor elimination was whether a factor could load as much variance as itself. On this basis, it is seen that only 8 of the 30 factors could load at least as much variance as their sizes. Therefore, 22 of the factors were eligible for elimination before further tests were to be carried out. This confirms that some factors among those examined are not very useful to the model.

            Using the Principal Component Analysis, it was observed that the highest correlation existed between factor one and the learners’ failing to complete their studies on time (0.770). The least correlation observed existed between factor three and the respondents’ ability to meet and have time with their families (0.001).

Cognitive Development:

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.783

Bartlett’s Test of Sphericity

Approx. Chi-Square

188.545

df

15

Sig.

.000

 

 

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3.064

51.072

51.072

3.064

51.072

51.072

2

.951

15.846

66.918

 

 

 

3

.750

12.493

79.411

 

 

 

4

.530

8.830

88.241

 

 

 

5

.405

6.758

94.999

 

 

 

6

.300

5.001

100.000

 

 

 

Extraction Method: Principal Component Analysis.

 

 

 

 

 

 

The Kaiser-Meyer-Olkin measure of sampling adequacy (0.783) points to the fact that the sample was adequate and that the correlation matrix formed by correlating the factors against each other did not yield the hard-to-work-with singular matrix since the chi square statistic is statistically significant (Chi Square = 188.545, df = 15, level of significance < 0.001). From both the variance table and the Scree plot it was found that only one factor could be formed from the initial factors. This indicates that there was high correlation among all the factors in the test. The model also shows that interference of social networking with remembrance of fundamental  knowledge and skills was quite high, recording the highest squared correlation coefficient with other factors (0.802) while distraction was least affected (-0.048).

Social Development:

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.796

Bartlett’s Test of Sphericity

Approx. Chi-Square

474.509

df

66

Sig.

.000

 

 

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

4.840

40.330

40.330

4.840

40.330

40.330

2.913

24.277

24.277

2

1.455

12.127

52.457

1.455

12.127

52.457

2.264

18.864

43.141

3

1.097

9.145

61.602

1.097

9.145

61.602

2.215

18.461

61.602

4

.858

7.149

68.751

 

 

 

 

 

 

5

.755

6.293

75.044

 

 

 

 

 

 

6

.705

5.877

80.921

 

 

 

 

 

 

7

.621

5.178

86.099

 

 

 

 

 

 

8

.479

3.993

90.092

 

 

 

 

 

 

9

.473

3.946

94.038

 

 

 

 

 

 

10

.274

2.285

96.323

 

 

 

 

 

 

11

.255

2.128

98.451

 

 

 

 

 

 

12

.186

1.549

100.000

 

 

 

 

 

 

Extraction Method: Principal Component Analysis.

 

 

 

 

 

 

 

 

The Kaiser-Meyer-Olkin measure of sampling adequacy (0.796) indicates that the sample was adequate and that the correlation matrix formed by correlating the factors against each other did not yield a singular matrix. The chi square statistic is statistically significant (Chi Square = 474, df = 66, level of significance < 0.001) at the 5% level of significance. From both the variance table and the Scree plot, it was found that three factors were formed from the initial factors. Therefore, going by correlation patterns, 3 distinct patterns were observed. The results also show that stress resulting from social networking was high, recording the highest squared correlation coefficient with other factors (0.760) while boredom rarely occurred (0.018).

 

Physical Development:

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.829

Bartlett’s Test of Sphericity

Approx. Chi-Square

383.688

df

36

Sig.

.000

 

 

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

4.373

48.586

48.586

4.373

48.586

48.586

2.926

32.514

32.514

2

1.022

11.353

59.939

1.022

11.353

59.939

2.468

27.424

59.939

3

.904

10.042

69.981

 

 

 

 

 

 

4

.664

7.381

77.362

 

 

 

 

 

 

5

.588

6.532

83.894

 

 

 

 

 

 

6

.489

5.435

89.329

 

 

 

 

 

 

7

.481

5.340

94.669

 

 

 

 

 

 

8

.338

3.759

98.428

 

 

 

 

 

 

9

.141

1.572

100.000

 

 

 

 

 

 

Extraction Method: Principal Component Analysis.

 

 

 

 

 

 

 

The Kaiser-Meyer-Olkin measure of sampling adequacy (0.829) indicates that the sample was adequate and that the correlation matrix formed by correlating the factors against each other did not yield a singular matrix. The chi square statistic is statistically significant (Chi Square = 383, df = 36, level of significance < 0.001) at the 5% level of significance. From both the variance table and the Scree plot, it was found that two factors were formed from the initial factors. Therefore, going by correlation patterns, three distinct patterns were observed. The results indicate that social networking was a great hindrance to completion of other intended work, recording the highest squared correlation coefficient with other factors (0.796) while interference with plans to shop in stores was rare (-0.048).  

 

 

 

Security Concerns:

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.672

Bartlett’s Test of Sphericity

Approx. Chi-Square

185.959

df

3

Sig.

.000

 

 

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

2.432

81.074

81.074

2.432

81.074

81.074

2

.425

14.170

95.244

 

 

 

3

.143

4.756

100.000

 

 

 

Extraction Method: Principal Component Analysis.

 

 

 

 

 

 

 

The Kaiser-Meyer-Olkin measure of sampling adequacy (0.672) points to the fact that the sample was inadequate and that the correlation matrix formed by correlating the factors against each other did not yield the singular-type matrix since the chi square statistic is statistically significant (Chi Square = 186, df = 3, level of significance < 0.001). Indeed, three components only is a small sample. From both the variance table and the Scree plot it was found that only one factor could be formed from the initial factors. This indicates that there was a high correlation among all the factors in the test, perhaps due to their small number. The model also shows that social networking significantly raised security concerns, recording the highest squared correlation coefficient with other factors (0.947) while concerns for security of intellectual properties least concerned the group (0.843).  
Correlation Analysis
:

Use of social-networking and the notion of sustainability awareness among the students 

The null hypothesis:

H0: There is no relationship between the use of social networking and the notion of sustainability awareness among the students in Saudi Arabia; was tested against the alternative hypothesis:

H1: There exists a relationship between the use social networking and the notion of sustainability awareness among the students in Saudi Arabia

Variables: Average amount of time spent on social networking daily and social networking functions.

 

The following table was developed using the SPSS:

Correlations

 

 

How many hours do you spend on the social networking daily, not including email? (Per day)

How many hours do you spend on the internet for email? (Per day)

Positive Effects

How many hours do you spend on the social networking daily, not including email? (Per day)

Pearson Correlation

1

.323**

.116

Sig. (2-tailed)

 

.001

.250

N

100

100

100

How many hours do you spend on the internet for email? (Per day)

Pearson Correlation

.323**

1

.060

Sig. (2-tailed)

.001

 

.551

N

100

100

100

Positive Effects

Pearson Correlation

.116

.060

1

Sig. (2-tailed)

.250

.551

 

N

100

100

100

**. Correlation is significant at the 0.01 level (2-tailed).

 

 

 

            The Pearson correlation coefficient between the hours an individual spends on social networks without taking a moment on their e-mail and positive effects on social networking is 0.116, with a p-value 0.250. The Pearson correlation coefficient for the hours an individual spends networking via their email is 0.060, with p-value 0.551. Both coefficients have significance levels greater than the p-value adopted for the test (0.05). Therefore, the null hypothesis failed to be rejected. It was concluded that there did not exist significant linear relationships between the time factor and the tendency to be content with aspects of positive effects of social networking.


Use of social-networking and the notion of insecurity and other negative effects among students 

Further, the relationship between the amounts of time spent on different modes of social networking daily, and individuals’ sense of insecurity and negative contribution to well-being was studied. The total of individual’s response scores for insecurity and negative effects resulting from social networking was computed and averaged.  

To this effect, the following hypothesis was developed:

H0: There is no statistically significant relationship between the use of social networking and the notion of insecurity and negatives related to social, physical, and cognition aspects among students  in Saudi Arabia was tested against the alternative hypothesis:

H1: There exists a significant relationship between the use of social-networking and the notion of insecurity and negative effects on social, physical, and cognition aspects among students in Saudi Arabia.


Variables: Average amount of time spent on social networking daily and average negative impacts of social networking per individual.

Below is the table developed using the SPSS:

 

Correlations: Negative Effects versus time spent on Social Networking

 

 

 

How many hours do you spend on the social networking daily, not including email? (Per day)

How many hours do you spend on the internet for email? (Per day)

Negative Effects

How many hours do you spend on the social networking daily, not including email? (Per day)

Pearson Correlation

1

.323**

.072

Sig. (2-tailed)

 

.001

.479

N

100

100

100

How many hours do you spend on the internet for email? (Per day)

Pearson Correlation

.323**

1

-.052

Sig. (2-tailed)

.001

 

.606

N

100

100

100

Negative Effects

Pearson Correlation

.072

-.052

1

Sig. (2-tailed)

.479

.606

 

N

100

100

100

**. Correlation is significant at the 0.01 level (2-tailed).

 

 

 

            The correlation coefficient for the relationship between the overall negative effects of social networking and the number of hours on social networking sites, excluding individual’s emails, is 0.072. With a p-value 0.479, the null hypothesis fails to be rejected at the 5% level of significance. Therefore, it was established that there did not exist a significant relationship between the use of non-email social-networking and the notion of insecurity and negative impacts on social, physical, and cognition aspects.

           

 

The Pearson correlation between the negative effects of social networking and the amount of time an individual spends on using e-mail is -0.052. This points to a weak, inverse relationship, suggesting people’s perceptions are that email-networking has a positive effect on their security and other developmental aspects. However, with a p-value 0.606, this relationship is not statistically significant. Conclusively, a significant relationship between the use of email-based social-networking and the notion of insecurity and negative impacts on social, physical, and cognition aspects does not exist.

T-Tests:

T-tests were used to ascertain whether there existed significant differences between the major, real and potential risks and opportunities via the use social-networking among males and females. The following hypothesis was developed:

H0: There are no significant differences between the major real and potential risks and opportunities via the use of social networking among males and females.

H1: There exists a significant difference between the major real and potential risks and opportunities via the use of social networking among males and females.

 

 

Independent Samples Test

 

 

Levene’s Test for Equality of Variances

t-test for Equality of Means

 

 

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

 

 

Lower

Upper

Cognitive Development

Equal variances assumed

3.335

.071

-.718

98

.474

-.11623

.16182

-.43736

.20489

Equal variances not assumed

 

 

-.684

70.840

.496

-.11623

.16983

-.45487

.22241

Social Development

Equal variances assumed

7.180

.009

-1.658

98

.101

-.24407

.14724

-.53628

.04813

Equal variances not assumed

 

 

-1.554

65.954

.125

-.24407

.15704

-.55762

.06947

Physical Development

Equal variances assumed

1.311

.255

-1.975

98

.051

-.31799

.16099

-.63747

.00149

Equal variances not assumed

 

 

-1.921

77.374

.058

-.31799

.16553

-.64757

.01159

Security Concerns

Equal variances assumed

.173

.678

-1.064

98

.290

-.21786

.20468

-.62405

.18833

Equal variances not assumed

 

 

-1.051

82.288

.296

-.21786

.20726

-.63015

.19443

 

 

           

 

All tests were carried out assuming a 5% level of significance for the test statistic. Likewise, equal variances were assumed throughout. The t-value for factors of social networking that negatively affect cognitive development (-0.718) had a p-value (0.474); for factors of social networking that negatively affect social development t = -1.658 with a p-value (0.101); for factors of social networking that negatively affec physical development, t = 0.255, and p-value (0.051) and for security concerns, t = -1.064, with a p-value 0.290.

All four factors have p-values greater than 0.05 as the level of significance for the test. Therefore, the null hypothesis was not rejected for any of the factors. It was concluded that average response scores per gender (male and female) were not significantly different.


Analysis of Variance (ANOVA):

ANOVA was used to test whether there existed differences between the risks posed by the negative factors of social networking among members of specific age groups. The test was carried out at a 5% level of significance:

H0: There is no significant difference between the factors that pose risk to social networkers in Saudi Arabia based on age groups.

H1: With respect to respondents’ ages, there is a significant difference between age-groups’ considerations of the factors that pose risk to social networkers in Saudi Arabia.

ANOVA

 

 

Sum of Squares

df

Mean Square

F

Sig.

Positive Effects

Between Groups

1.552

3

.517

1.926

.130

Within Groups

25.791

96

.269

 

 

Total

27.344

99

 

 

 

Cognitive Development

Between Groups

2.599

3

.866

1.391

.250

Within Groups

59.804

96

.623

 

 

Total

62.403

99

 

 

 

Social Development

Between Groups

1.117

3

.372

.691

.560

Within Groups

51.722

96

.539

 

 

Total

52.839

99

 

 

 

Physical Development

Between Groups

1.927

3

.642

.995

.398

Within Groups

61.960

96

.645

 

 

Total

63.888

99

 

 

 

Security Concerns

Between Groups

1.738

3

.579

.563

.641

Within Groups

98.728

96

1.028

 

 

Total

100.466

99

 

 

 

 

From the ANOVA table above, there does not exist significant differences between the factors of social networking that negatively affect cognitive development (F = 1.391, p = 0.130); factors of social networking that negatively affect social development (F = 0.691, p = 0.560); factors of social networking that negatively affect physical development (F = 0.995, p = 0.398); and factors of social networking that raise security concerns (F = 0.563, p = 0.641). Notably, all p-values for the F-statistics in the test were greater than the level of significance at which the study was undertaken. This led to the failure to reject the null hypothesis.

Using ANOVA, the research also tested for the existence of significant statistical differences between respondents’ favouring of the positive effects of social networking, again based on age groups. The following hypothesis was developed:

H0: There is no significant difference between the factors that bring positive effects to social networkers in Saudi Arabia based on age groups.

H1: With respect to respondents’ ages, there is a significant difference between the factors that cause positive effects to social networkers in Saudi Arabia.

            Again, based on the ANOVA table above, the null hypothesis failed to be rejected. It is observed that the p-value (0.130) for the F-ratio (1.926) is greater than the level of significance for the test. Therefore, it was concluded that there did not exist age-based differences between the inter-age responses of the respondents.

Discussion

            There is significant growth in the acceptance of online environments as legitimate, social platforms. However, it is perceived that the digital environment poses great risks for users. In this study, it is realized that social networkers in Saudi Arabia do not view online exposure as a significant threat to their well being on social, cognitive, physical and security grounds. Furthermore, regarding this study, it was difficult to establish any significant relationships between social networking and satisfaction among respondents.

            There is no indication that age is a significant factor for controlling the population’s perceptions. Equally, gender emerged as a non-factor in deciding the role played by negative aspects of social networking. This could point to the possibility of the sexes having equal exposure to devices and environments that promote social networking.

            It is possible that the pattern realized in the responses by the population sampled could be the result of low knowledge of the existence of high risks for those networking online.

 

Conclusion

            From the study, it was learnt that there did not exist significant statistical differences between the opinions of male and female respondents. For that reason, it was noted that gender was not a distinguishing factor of the opinions of Saudi residents regarding the positive and negative effects of social networking. Likewise, there did not exist observable statistical differences between responses given by respondents from varying age groups and the opinions of male and female respondents.

            Generally, demographic aspects used in the research failed to elicit substantive differences based on the pre-identified positive and negative aspects of social networking. This shows that the approaches of the entire population are roughly similar across the kingdom.

            There are many indications that the populations within the age bracket covered are largely undertaking similar activities, with learning emerging as the most conspicuous. This is confirmed by the higher correlation between the population’s (as implied by the tests carried out on this sample derived from the general population) demographic features and elements of education, mainly in the factor analysis. This may also suggest that the educational facilities are equally utilized by both males and females.

 

 

Recommendations

            With the rising risks of insecurity caused by online spying and stealing of confidential data by ill-motivated individuals, it is necessary that media campaigns be launched across the kingdom of Saudi Arabia to counter the lack of adequate sensitivity to risks and danger displayed by the results of this study.

            Comparative studies between the perceptions of inter-regional responses to the same questions asked in this survey would help to establish whether media information on security risks brought about by virtual interactions compare favourably across regions. This research could likely analyse how often the people get to view a security caution on the internet or other news sources.

 

 

 

 

 

 

 

 

 

 

 

 

References:

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