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 socialnetworking 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 socialnetworking among males and females.
H4: There are no significant differences between the major real and potential risks and opportunities via the use socialnetworking 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 ttest was used. The oneway ANOVA was employed to determine the perceived real and potential risks of using social networking among respondents across the age groups 1822, 2232, 3242, and those between 42 and 52 years of age.
Correlation analysis was also used to ascertain whether a relationship exists between the use of socialnetworking 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 1822 years of age comprised 26% of all respondents, while persons between 2232 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 4252 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 preuniversity 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 email. 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 email 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 emails.
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 

KaiserMeyerOlkin Measure of Sampling Adequacy. 
.809 

Bartlett’s Test of Sphericity 
Approx. ChiSquare 
1.114E3 
df 
300 

Sig. 
.000 
Total Variance Explained 

Component 
Initial Eigenvalues 
Extraction Sums of Squared Loadings 
Rotation Sums of Squared Loadings^{a} 

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 Matrix^{a} 


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 uptodate 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 KaiserMeyerOlkin 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 intercrossing relations with peers, such as artistic talents and sports (0.008).
Factors on Negative Impacts of Social Networking:
KMO and Bartlett’s Test 

KaiserMeyerOlkin Measure of Sampling Adequacy. 
.837 

Bartlett’s Test of Sphericity 
Approx. ChiSquare 
1.798E3 
df 
435 

Sig. 
.000 
Total Variance Explained 

Component 
Initial Eigenvalues 
Extraction Sums of Squared Loadings 
Rotation Sums of Squared Loadings^{a} 

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 Matrix^{a} 


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 KaiserMeyerOlkin 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 

KaiserMeyerOlkin Measure of Sampling Adequacy. 
.783 

Bartlett’s Test of Sphericity 
Approx. ChiSquare 
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 KaiserMeyerOlkin 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 hardtoworkwith 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 

KaiserMeyerOlkin Measure of Sampling Adequacy. 
.796 

Bartlett’s Test of Sphericity 
Approx. ChiSquare 
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 KaiserMeyerOlkin 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 

KaiserMeyerOlkin Measure of Sampling Adequacy. 
.829 

Bartlett’s Test of Sphericity 
Approx. ChiSquare 
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 KaiserMeyerOlkin 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 

KaiserMeyerOlkin Measure of Sampling Adequacy. 
.672 

Bartlett’s Test of Sphericity 
Approx. ChiSquare 
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 KaiserMeyerOlkin 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 singulartype 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 socialnetworking 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. (2tailed) 

.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. (2tailed) 
.001 

.551 

N 
100 
100 
100 

Positive Effects 
Pearson Correlation 
.116 
.060 
1 
Sig. (2tailed) 
.250 
.551 


N 
100 
100 
100 

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


The Pearson correlation coefficient between the hours an individual spends on social networks without taking a moment on their email and positive effects on social networking is 0.116, with a pvalue 0.250. The Pearson correlation coefficient for the hours an individual spends networking via their email is 0.060, with pvalue 0.551. Both coefficients have significance levels greater than the pvalue 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 socialnetworking 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 wellbeing 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 socialnetworking 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. (2tailed) 

.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. (2tailed) 
.001 

.606 

N 
100 
100 
100 

Negative Effects 
Pearson Correlation 
.072 
.052 
1 
Sig. (2tailed) 
.479 
.606 


N 
100 
100 
100 

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


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 pvalue 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 nonemail socialnetworking 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 email is 0.052. This points to a weak, inverse relationship, suggesting people’s perceptions are that emailnetworking has a positive effect on their security and other developmental aspects. However, with a pvalue 0.606, this relationship is not statistically significant. Conclusively, a significant relationship between the use of emailbased socialnetworking and the notion of insecurity and negative impacts on social, physical, and cognition aspects does not exist.
TTests:
Ttests were used to ascertain whether there existed significant differences between the major, real and potential risks and opportunities via the use socialnetworking 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 
ttest for Equality of Means 



F 
Sig. 
t 
df 
Sig. (2tailed) 
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 tvalue for factors of social networking that negatively affect cognitive development (0.718) had a pvalue (0.474); for factors of social networking that negatively affect social development t = 1.658 with a pvalue (0.101); for factors of social networking that negatively affec physical development, t = 0.255, and pvalue (0.051) and for security concerns, t = 1.064, with a pvalue 0.290.
All four factors have pvalues 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 agegroups’ 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 pvalues for the Fstatistics 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 pvalue (0.130) for the Fratio (1.926) is greater than the level of significance for the test. Therefore, it was concluded that there did not exist agebased differences between the interage 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 nonfactor 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 preidentified 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 illmotivated 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 interregional 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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