Social Media and the Mental Health of Users in Sub-Saharan Africa

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The research hypotheses were; H 1 : Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Saharan Africa, and H 2 : External Locus of Control positively affects the mental health of social media users in Sub-Saharan Africa. A cross-sectional study approach was applied with quantitative research methods. Data were collected using questionnaires distributed to a sample of 450 social media users from three African countries including Cameroon, Uganda and Nigeria. Each country had a sample of 150 for the study. Findings revealed a negative significant relationship between Outcome Expectations and External Locus of Control, implying that H 1 was not supported. On the other hand, there was a positive significant relationship between External Locus of Control on mental health, implying that H 2 was supported. Since Outcome Expectations were found to positively affect External Locus of Control of social media users, it is important for social media platforms to be designed in such a way that they will make its users better and more acceptable people in society. Further, we recommend online community education, sensitization and policing to help educate the careless learners on the dangers of learning bad behaviors that negatively affect their mental health. In terms of policy, governments should enact laws that force social media developers to use local content. Using local content will ensure that only appropriate information is consumed by citizens via social media. Outcome Expectations External Locus of Control Mental health Social media Introduction The United Nations Statistics Division defines Sub-Sahara Africa as all of Africa excluding North Africa and Sudan (UN, 2013). It includes all African countries except Egypt, Algeria, Libya, Morocco, Sudan, and Tunisia. The region has a total of 52 countries with a population of about 943m people, 234m of these people use the Internet. The region has an internet penetration rate 25% of the population (Internet World Statistics, 2015). A total of 68,968,500 people representing 7.3% use Facebook. Although internet penetration is still low compared to the global average of 46% it is on the upward trend having shifted from 0.4% in the year 2000 to 7.3% in the year 2015 (Internet World Statistics, 2015). The region is forecasted to be among the fastest growing economies in the world, given its immense socio-economic opportunities and potential. Mobile penetration is currently standing at 34% of the population (GSMA Intelligence, 2014). Most of the mobile phones are being used to access social media. By the year 2020, a total of 504m people will have mobile phones in Sub-Saharan Africa, representing a penetration rate of 49% (GSMA Intelligence, 2014). Although application specific statistics are scanty, except for Facebook, most users of SM in Sub-Saharan Africa are on Facebook, followed by WhatsApp, Twitter, Instagram, YouTube and Skype. According to Statistica (2014b), South Africa has the highest WhatsApp adoption rate standing 78%. The study is hoped to make a contribution to body of knowledge by identifying the factors responsible for causing mental health changes of social media users. To achieve this, the study presents insights on the relationship between outcome expectations, external locus of control and mental health of social media users in Sub-Saharan Africa. Findings on these relationships will help researchers to know where to direct their scholarly efforts when studying social media and mental health. The Objective of this study was to examine the effect of Outcome Expectations and External Locus of Control on the mental health of social media users in Sub-Sahara Africa. The case of Bandura’s Social Learning Theory Introduced by Bandura (1965), the Social Learning Theory (SLT) which was later modified and renamed the Social Cognitive Theory by Bandura (1986) explains how human beings adopt behaviors when exposed to certain conditions. Bandura (1965) conducted experiments on kids and observed that children behaviors changed through a process of replication, retention and mimicking of their role models. Bandura’s Social learning theory was as a response to the limitations of behaviorism theories in addressing how humans leaned new behavior. The behaviorism theories assumed that human behavior could be learned and tested in a controlled environment – laboratories. Further, the behaviorism theories at the time failed to show how people responded to new situations. The third limitation was that the behaviorism theories could only explain direct learning - the form of learning that takes place in a classroom. They ignored other forms of learning. Bandura argued that learning could be delayed and could happen over a period of time through observation and mimicking of role models. Further, Bandura argues that learning influences behavioral change over a period of time (Bandura, 1986). According to Bandura (1986), learning can take place through a process called reinforcement. There are three forms of reinforcement proposed by Bandura including; 1) direct reinforcement - which is directly caused by the learner himself, 2) vicarious reinforcement - one that happens due to observation of a role model’s behavior. Vicarious reinforcement is caused by the role model, and 3) self-reinforcement - which manifests in the form of satisfaction and dissatisfaction arising from one’s good or poor performance. Bandura (1986) argues that the most influential aspect of learning is by seeing and experiencing actions of other people. The social learning theory can be used in studying the SM aspect in trying to understand how the participants on a given SM tool learn how to treat and manage their aliments through sharing experiences. More experienced or former patients of a similar disease can act as role models to newer, younger and inexperienced patients in the learning process. However, the social learning theory has its limitations in applicability, especially in this kind of research as it requires the physical interaction of participants for learning to take place. The theory also encompasses more of behavioral change than information sharing and the role played by technology in causing change. This far, we argue that SM’s main purpose is foster information sharing. We also argue that behavioral change on SM is just a symptomatic result of the core purpose of information sharing. In addition, Bandura’s (1965) social learning theory is only relevant if the role model being observed is “appropriate, relevant and similar to the observer” (Bandura et al. 1963). Considering the fact that most actors on SM are of diverse attributes such as backgrounds, cultures and educational levels among others, the social learning theory may actually have no impact on influencing behavioral change. Outcome Expectations Blalock et al. (2016) define Outcome Expectations as the "likelihood and value of the consequences of behavioral choices”. If the Outcome Expectations are positive, an individual will be attracted to the behavior. However, if the Outcome Expectations are negative, such as rejection, mistrust or punishment, the subject will not be attracted to the new behavior (Buck, 2010; Bandura, 2000; Bandura 1986). Blalock et al. (2016) expounds that an individual will be willing to reveal his HIV status if he expects a positive outcome. However, he will not reveal such a status if the expected outcome is negative. In Rotter’s SLT, Expectancy is used in place of Outcome Expectations. The only difference is that Rotter’s Expectancy construct is based on chance and is probabilistic. Expectancy is either high or low (Rotter, 1966), whereas Outcome Expectations in the SCT can be negative or positive (Blalock et al. , 2016; Bandura, 1986). Rotter uses Expectancy to show the probability that an act will result to a given behavioral outcome, while Bandura uses Outcome Expectations to show the nature and impact of behavioral outcomes (either negative or positive) given that an individual imitates a behavioral action (Bandura, 1986; Rotter, 1966). Research design and sampling This was a cross-sectional study that applied quantitative methods of research. Data were collected using questionnaires distributed to a sample of 450 social media users from three African countries including Cameroon, Uganda and Nigeria. Each country had a sample of 150 for the study. Study variables BYU (2016) defines a variable as “a measurable characteristic that varies. It may change from group to group, person to person, or even within one person over time.” It is a theoretically measurable thing that can have a dynamic value (Kaur, 2013). Variables are used to explain differences in things and what causes those differences. According to ORI (2016), the changes in variables are as a result of some force that may be from within the variable itself or another source. This study utilizes three different types of research variables namely, dependent, independent and control variables elucidated below. A dependent variable is one that is affected by a change in the independent variable (s) (BYU, 2016). Dependent variables in this study include External Locus of Control and Mental health. On the other hand, BYU (2016) argues that an independent variable is one whose change affects the dependent variable. It is within the researcher’s control. The independent variable in this study is Outcome Expectations. In addition to the above, this study also had control variables. According to BYU (2016), a control variable is one that can be silenced or ignored by the researcher for the interest of other more important variables. Demographic attributes such as age, gender and education in this study that treated tested as control variables Measurement and operationalization of variables Outcome Expectations is the likelihood and value of the consequences of behavioral choices. The variable was measured by literature from Blalock et al. (2016); Buck (2010); Bandura (2000); Bandura (1986); Rotter (1966). This variable investigated whether using social media on health related matters made respondents better people, using social media on health related matters made respondents more acceptable and trustworthy, amongst their peers, friends and family among others. On the other hand, External Locus of Control is used to refer to an individual’s locus of control or state of being where one is unable to controls the consequences of his / her behavior. Consistent with Boundless (2016) and Rotter (1966), External Locus of Control variable was employed to examine whether respondents were not in control of the consequences, achieve less, had low morale to learn, did not maintain good relations, considered themselves lucky, were not responsible for the bad things that happen to them, did not think about the consequences of their actions before doing them, and if the respondents were unable to help themselves when faced with challenging situations while using social media. Finally, the variable Mental health is used to refer to learned action, skills, practices an individual does via social media that influence his/her psychological wellbeing. It was measured by Bandura (1986); Blalock et al. (2016); Winett et al. (1999); Bandura (1990); Blalock et al. (2016); Kane (2004). This variable was structured into four constructs namely; Skills, Practice, Observational learning, and Moral degeneration. Skills construct was used to study whether respondents learned new health related skills by using social media. It also investigated whether respondents were able to treat diseases, manage chronic diseases and also whether they learned how to look after patients using information obtained via social media. The construct of Practice was used to study whether respondents learned new health related behavioral practices through observing role models and training themselves via social media. Observational learning construct on the other hand was used to establish whether respondents learned new health related behaviors by observing other influential people in society such as celebrities, political leaders, elders, religious do them. Lastly, Moral degeneration construct was used to study whether respondents learned new health related behaviors that decayed their morals by using social media. It investigated whether respondents learned how to and actually smoked, used drugs, drunk alcohol, consumed pornography, became gay, and had sex with multiple partners because of the information they consumed over time via social media. Research hypotheses The research hypotheses of this study are given below: H 1 : Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa. H 2 : External Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa. Content Validity In addition to the above, content validity index was run on each variable to establish how well each variable measured what is was intended to measure. The CVI for each variable is given in Table 1 below: Table 1: Variable Content Validity Index Variable No of items CVI Outcome Expectations 7 0.795 External Locus of Control 8 0.707 Mental health 25 0.778 Results in Table 1 above show that all variables met the minimum CVI of 0.6; hence the questionnaire was valid for the study. The individual variables CVI scores are given as follows; Outcome Expectations (CVI=0.795); External Locus of Control (CVI=0.707); Mental health (CVI=0.778). This indicates that the instrument was valid (Nunnally, 1978). Construct validity, Convergent validity and Discriminant validity Using Exploratory Factor Analysis (EFA), we researcher tested for commonalities. Commonalities for all variables were greater than 0.4, indicating that items were measuring the same variable (Costello & Osborne, 2005). Further, Kaiser-Meyer-Olkin (KMO) obtained was greater than 0.7 for all variables, indicating that the sample was adequate. According to Tabachnick and Fidell, (2001) a KMO above 0.5 is appropriate. The Total Variance Explained was greater than 0.7 indicating that the items and constructs largely explained the variables. Further still, the Rotated Component Matrix Factor Loadings were greater than 0.5 and items were distributed independently into different constructs. This meant that there was discriminant validity within each variable (Campell & Fiske, 1959), and also convergent validity within each construct. Quantitative data analysis methods Quantitative data analysis is the process of constructively summarizing, classifying, measuring, categorizing, tabulating, counting and interpreting numerical data. It is aimed at describing an event or a situation by trying to answer questions about it. It helps to answer the “how”, “why”, and “when” questions (Abeyasekera, 2016) and is done on numerical data (Aliaga & Gunderson, 2000). The various types of quantitative data analysis are; descriptive analysis, factor analysis, correlation analysis, regression analysis, and Structural Equation Modeling among others. Descriptive analysis methods aim to illustrate the object being analyzed. They include percentages, means and frequencies (Abeyasekera, 2016). While correlation analysis is used to examine the relationship between variables and regression analysis is used to determine the predicting power of the independent variable on the dependent variable. They help in measuring associations between two variables (Grosshans & Chelimsky, 1992). On the other hand, Exploratory Factor Analysis (EFA) is a statistical technique use to analyze and determine a set of interrelated observed variables or factors that measure a given latent variable (Suhr, 2017). It can also be used to test the data against hypothetical variable structures and establish their suitability, although the outcome of EFA is noncommittal to such structures (Child, 1990). Further, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are used to analyze measurement variables. Structural Equation Modeling (SEM) software is used to perform confirmatory factors analysis as well as latent growth modeling (Hox, 2016). This study used all the quantitative data analysis methods described above to analyze data and test hypotheses under investigation. For example, descriptive statistics were used to analyze background information, while correlation and regression analysis methods were used to analyze the relationships and strength of the relationship between variables. Further, confirmatory analysis methods and modeling were implemented using Structural Equation Modeling methods. Findings Exploratory Factor analysis EFA is done through a process called factor analysis and also component analysis which is used to reduce a given set of observed variables or factors a reasonable level that best explains latent variable(s) (Spearman, 1904). Some of the important tests conducted during EFA are the Kaiser-Meyer-Olkin Measure of Sampling Adequacy test (KMO), Bartlett's Test of Sphericity (Approx. Chi-Square, Df. Sig.), Communalities, and Principal Component Analysis. KMO is used to test for sample adequacy and should be above 0.5 for the sample to be adequate (Tabachnick & Fidell, 2001; Yong & Pearce, 2013). On the other hand, Bartlett's Test of Sphericity (Approx. Chi-Square, D.f., Sig.) which is used to test for homogeneity of samples. It ensures that there is similarity in the variances of a group of samples (Bartlett, 1937). Communalities show the variance in a latent variable that is explained by a given observed variable (Costello & Osborne, 2005). The higher the communality, the better that observed variable explains its latent variable (Hatcher, 1994), however, a communality of 0.4 and above is generally considered to be good (Costello & Osborne, 2005). Further, Principal Component Analysis is used to orthogonally transform a set of related observed variables in groups of factors, also known as components (Jérôme, 2014; François, Sébastien & Jérôme, 2009; Jolliffe, 2002). Data were analyzed using exploratory factor analysis with Extraction Method of Principal Component Analysis and Rotation Method of Varimax with Kaiser Normalization in order to extract the most important factors that measured the study variables. Factors with Eigen values >1 and factor loadings >0.5 were retained in the commonality and rotated component matrix. This validated the questionnaire in terms of convergent validity and discriminant validity (Campell & Fiske, 1959). For convergent validity, determinant with sig.>0.00, commonalities loadings >0.5 indicated convergence of items in measuring the same variable. For discriminant validity, Rotated Component Matrix distinct factors with loadings of above 0.5 indicated discrimination of factors from each other. In this study, factor analysis was performed on all latent variables as presented in the following section. EFA for Outcome Expectations A total of 7 items were listed to measure Outcome Expectations. Item correlation matrix produced a Determinant = .013 meaning that all items converged and were related in measuring Outcome Expectations. The KMO was used to measure sampling adequacy. A KMO =.809 meant that the study sample was adequate. On the other hand, Bartlett's Test of Sphericity was used to measure the significance of the sample. Bartlett's Test of Sphericity Approx. Chi-Square = 2085.746, D.F. =10, Sig=.000 meant that the sample was significant. Table 2 presents KMO and Bartlett's Test results for Outcome Expectations. Table 2: KMO and Bartlett's Test for Outcome Expectations Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .809 Bartlett's Test of Sphericity Approx. Chi-Square 2085.746 D.f. 10 Sig. .000 Communalities test for Outcome Expectations In addition the above descriptive, Communalities and determinant tests were used to examine convergent validity of Outcome Expectations as seen in Table 3. Table 3: Communalities for Outcome Expectations Initial Extraction Using social media on health related matters makes me a better person 1.000 .872 Using social media on health related matters makes me more acceptable amongst my peers 1.000 .913 My peers will trust me if I use social media on health related matters 1.000 .638 I will not be rejected by my peers if I use social media on health related matters 1.000 .718 I will not be punished by my family if I use social media on health related matters 1.000 .871 Average communality 0.802 Results in Table 3 above reveal that all the items measured Outcome Expectations since they all have factor loadings above 0.40 and determinant of .013. Hence convergent validity was achieved on Outcome Expectations. Rotated Component Matrix for Outcome Expectations Data were analyzed using Principal Component Analysis extraction methods with Varimax with Kaiser Normalization rotation method in order to identify the items that most explained Outcome Expectations. The results are presented in Table 4. Table 4: Component Matrix for Outcome Expectations Component Outcome Expectations Using social media on health related matters makes me more acceptable amongst my peers .956 Using social media on health related matters makes me a better person .934 I will not be punished by my family if I use social media on health related matters .933 I will not be rejected by my peers if I use social media on health related matters .847 My peers will trust me if I use social media on health related matters .799 Eigen Value 4.012 Total variance 80.237 Percentage Total Variance 80.237 Rotated Component Matrix results in Table 4 show that 5 factors explain Outcome Expectations with (Eigen Value = 4.012, Total Variance = 80.237, Percentage Total Variance = 80.237). these are; Using social media on health related matters makes me more acceptable amongst my peers (Factor loading=.956); Using social media on health related matters makes me a better person (Factor loading=.934); I will not be punished by my family if I use social media on health related matters (Factor loading=.933); I will not be rejected by my peers if I use social media on health related matters (Factor loading=.847); My peers will trust me if I use social media on health related matters (Factor loading=.799). EFA for External Locus of Control Similarly, data were collected and analyzed on a total of 8 items listed under External Locus of Control. Item correlation matrix for External Locus of Control produced a Determinant = .035, meaning that all items converged and were related in measuring the variable. The KMO was used to measure sampling adequacy for this variable. A KMO = .754 was obtained, meaning that the study sample was adequate. On the other hand, Bartlett's Test of Sphericity was used to measure the significance of the sample. Bartlett's Test of Sphericity Approx. Chi-Square = 1190.201, D.F. =10, Sig=.000 meant that the sample was significant. Table 5 presents KMO and Bartlett's Test results for Cognitive Factors. Table 5: KMO and Bartlett’s Test for External Locus of Control Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .754 Bartlett's Test of Sphericity Approx. Chi-Square 1190.201 D.f. 10 Sig. .000 Communalities test for External Locus of Control Further, Communalities and determinant tests were used to examine convergent validity of items under External Locus of Control. Table 6 presents the results. Table 6: Communalities for External Locus of Control Initial Extraction I am not in control of the consequences of my actions while using social media 1.000 .707 I achieve less by using social media 1.000 .803 I have low morale to learn new things on social media 1.000 .721 I consider myself lucky to be using social media 1.000 .559 I am not responsible for the bad things that happen to me while using social media 1.000 .643 Average communality 0.687 Results in Table 6 reveal that all the items measured External Locus of Control since they all have factor loadings above 0.40 and determinant of .035. This means that convergent validity was achieved on the variable. Component Matrix for External Locus of Control Data were analyzed using Principal Component Analysis extraction methods with Varimax with Kaiser Normalization rotation method in order to identify the items that most explained Internal Locus of Control. The results are presented in Table 7. Table 7: Component External Locus of Control Component External Locus of Control I do not maintain good relations on social media .998 I am unable to help myself when faced with challenging situations on social media even if I possess the ability to do so .348 I do not think about the consequences of my actions before doing them on social media .223 I am not responsible for the bad things that happen to me while using social media .192 I consider myself lucky to be using social media .130 Eigen Value 22.491 Total variance 70.755 Percentage Total Variance 70.755 Results in Table 7 show that the most important factors explaining External Locus of Control are; I do not maintain good relations on social media (Factor loading =.998), I am unable to help myself when faced with challenging situations on social media even if I possess the ability to do so (Factor loading =.348), I do not think about the consequences of my actions before doing them on social media (Factor loading =.223), I am not responsible for the bad things that happen to me while using social media (Factor loading =.192), I consider myself lucky to be using social media (Factor loading =.130). EFA for Mental health A total of 25 items grouped in four constructs including skills, practice, observational learning and moral degeneration were listed to measure mental health. Item correlation matrix produced a Determinant =6.806E-011 meaning that all items converged and were related in measuring Mental health. The KMO was used to measure sampling adequacy. A KMO =.867 meant that the study sample was adequate. On the other hand, Bartlett's Test of Sphericity was used to measure the significance of the sample. Bartlett's Test of Sphericity Approx. Chi-Square = 8197.645, D.F. =153, Sig=.000 meant that the sample was significant. Table 8 presents KMO and Bartlett's Test results for mental health. Table 8: KMO and Bartlett's Test for Mental health Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .867 Bartlett's Test of Sphericity Approx. Chi-Square 8197.645 D.f. 153 Sig. .000 Communalities test for mental health In addition the above descriptive, Communalities and determinant tests were used to examine convergent validity of mental health as seen in Table 9. Table 9: Communalities for Mental health Initial Extraction I have acquired health skills via social media 1.000 .819 I have learned how to treat diseases via social media 1.000 .721 I have the desire to do the health issues I see other influential people in society doing via social media 1.000 .802 I train myself on doing the health related things that I see and like on social media 1.000 .847 I try to do the health issues as I am told to do via social media 1.000 .805 I seek sexual pleasures via social media 1.000 .767 I have learned how to smoke by observing other people’s smoking images or videos via social media 1.000 .856 I have learned how to consume alcohol by observing other people’s images or videos drinking it via social media 1.000 .787 I have learned how to access sexual partners using social media because observing other people doing it 1.000 .585 I have learned how to make money by giving sexual pleasures via social media through observing others 1.000 .831 I smoke because of the information I have consumed over time via social media 1.000 .831 I use drugs because of the information I have consumed over time via social media 1.000 .826 I drink alcohol because of the information I have consumed over time via social media 1.000 .744 I use pornography because of the information I have consumed over time via social media 1.000 .643 I am gay because of the information I have consumed over time via social media 1.000 .869 I have multiple sex partners because of the information I consume via social media 1.000 .966 I know of someone who obtained sex via social media 1.000 .818 I know of someone who engages in commercial sex via social media 1.000 .967 Average communality 0.805 Results in Table 9 above reveal that all the items measured mental health since they all have factor loadings above 0.40 and determinant of 6.806E-011. Hence convergent validity was achieved on mental health. Rotated Component Matrix for Mental health Rotated Component Matrix shows that all the four components explained Mental health namely; observational learning (Percentage Total Variance=37.207), Moral Degeneration (Percentage Total Variance=55.107), Practice (Percentage Total Variance=70.440) and Skills (Percentage Total Variance=80.477). Hence discriminant validity was achieved. Table 10 presents the results. Table 10: Rotated Component Matrix for Mental health Component Observational learning Moral degeneration Practice Skills I have learned how to smoke by observing other people’s smoking images or videos via social media .869 I use drugs because of the information I have consumed over time via social media .865 I have learned how to make money by giving sexual pleasures via social media through observing others .836 I smoke because of the information I have consumed over time via social media .829 I have learned how to consume alcohol by observing other people’s images or videos drinking it via social media .815 I seek sexual pleasures via social media .739 I use pornography because of the information I have consumed over time via social media .710 I drink alcohol because of the information I have consumed over time via social media .704 I know of someone who obtained sex via social media .692 I have learned how to access sexual partners using social media because observing other people doing it .632 I know of someone who engages in commercial sex via social media .886 I have multiple sex partners because of the information I consume via social media .884 I am gay because of the information I have consumed over time via social media .721 I train myself on doing the health related things that I see and like on social media .852 I have the desire to do the health issues I see other influential people in society doing via social media .832 I try to do the health issues as I am told to do via social media .788 I have acquired health skills via social media .901 I have learned how to treat diseases via social media .820 Eigen Value 6.697 3.222 2.760 1.807 Total variance 37.207 17.900 15.333 10.037 Percentage Total Variance 37.207 55.107 70.440 80.477 The effect of Outcome Expectations and External Locus of Control on the mental health of social media users in Sub-Sahara Africa Multiple Hierarchical Regression analysis was used to determine the predicting power of outcome expectation and External Locus of Control on health. Gender, Age, Level of education, Marital Status, and Country of Residence were treated as extraneous or control variables. Table 11 presents the results. Table 11: Regression for Mental health Model 1 Model 2 Model 3 Variable B Beta B Beta B Beta (Constant) 4.037** 3.16** 1.70** Gender -0.33** -0.22** -0.44** -0.30** -0.30** -0.20** Age 0.02 0.02 0.11* 0.11* 0.09 0.09 Education 0.03 0.08 0.06* 0.16* 0.03 0.08 Marital Status 0.02 0.02 0.04 0.03 -0.04 -0.03 Country of Residence -0.08 -0.07 -0.05 -0.04 -0.18** -0.18** Outcome expectation 0.21** 0.39** 0.25** 0.47** External Locus of Control 0.43** 0.46** R .240 .434 .594 R 2 .058 .188 .352 Adj R 2 .044 .175 .339 R 2 Change .058 .131 .164 F Change 4.303 56.560 88.619 Sig. F .001 .000 .000 F 4.303 13.579 27.204 Sig. .001 .000 .000 **.Significant at 0.01 *. Significant at 0.05 As seen in Table 11, results in model 1 show that Control variables including Gender, Age, Education, Marital status, and Country of residence predict 4.4% of Mental health (Adj R 2 =0.044). The relationship between Gender and Mental health is significant (Beta=-0.22**, P<.01). The relationship between Age and Mental health is not significant (Beta=0.02). The relationship between level of education and Mental health is not significant (Beta=0.08). The relationship between, Marital Status and Mental health is not significant (Beta=0.02). The relationship between Country of Residence and Mental health is not significant (Beta=-0.07). Results in model 2 reveal that control variables together with Outcome Expectations predict 17.5% of Mental health (Adj R 2 =.175) while Outcome Expectation alone predicts 13.1% of Mental health (R 2 Change = .131). Further, the relationship between Outcome Expectation and Mental health is significant at 99% confidence level (Beta=0.39**). Results in model 3 reveal that control variables, Outcome expectation and External Locus of Control combined predict 33.9% of Mental health (Adj R 2 =.339). However, External Locus of Control alone predicts 16.4% of Mental health (R 2 Change=.164). The results also show that External Locus of Control has a significant relationship with Mental health at 99% confidence level (Beta=0.46**). Given the above, we see that Outcome Expectations and External Locus of Control together with control variables contributed 33.9% of the changes in mental health of social media users in Sub-Sahara Africa. Confirmatory analysis Table 12: Social media and mental health Model Fit Summary X 2 DF P X 2 /DF GFI AGFI NFI RFI IFI TLI CFI RMSEA 8.653 6 .194 1.442 .993 .968 .983 .940 .995 .981 .994 .035 Estimate S.E. C.R. Beta P Hypothesis External Locus of Control <--- Outcome Expectations -.230 .031 -7.511 -.363 *** H1 -rejected Mental health <--- External Locus of Control .411 .049 8.336 .385 *** H2 –accepted Testing of research hypotheses using SEM H1: Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa . Results in Table 12 reveal that the relationship between Outcome Expectations and External Locus of Control was significant and negative at 1% level of significance (Beta=-.363, P<0.001). This implies that an increase in the Outcome Expectations of social media users will reduce their External Locus of Control. In other words, if the expected outcome from learning new behaviors that affect mental health via social media are high then the reliance on others to learn the behavior reduces. This relationship could probably be attributed to the confidential nature of health related information which most people do not want to share easily via social media. Therefore H1 that stated that Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa was not supported. H2: External Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa . As seen in Table 12, the relationship between External Locus of Control and Mental health was also found to be positive and significant at 99% confidence level (Beta=.385, P<0.001). This indicated that there is a high certainty of the existence of a relationship between External Locus of Control and Mental health. More to the relationship between External Locus of Control and Mental health, it suffices to mention that, social media users who are highly influenced by external factors such as social influence from friends and family are more likely to learn new behaviors that affect mental health from social media platforms. This finding is in support of the hypothesis H2 that External Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa. Discussion of findings Outcome Expectations and External Locus of Control of social media users Hypothesis H1 stated that Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa. However, correlation and Multiple Hierarchical Regressions results revealed that a negative significant relationship existed between Outcome Expectations and External Locus of Control. The SEM results also revealed a significant but negative effect of Outcome Expectations on the External Locus of Control of social media users. Both results rejected H1. The above findings disagree with literature. The literature had suggest that increasing Outcome Expectations such as the benefits in terms of becoming a better person, becoming more acceptable to others, becoming more trustworthy among peers (Blalock et al. 2016; Buck, 2010; Bandura, 2000) increased External Locus of Control. This made farmers unaccountable of their decisions, having low morale, achieving less, feeling lucky about their achievements and being unable to help themselves (Boundless, 2016; Rotter, 1966). This finding is useful to the study in the sense that, even if social media users anticipate several benefits from using the technology, they remain in control of and responsible for their actions. Social media users who expect high benefits in terms learning new useful health related behaviors do not blame others for their shortcomings. Even if they eventually fail to yield any benefits, they will not blame it others but themselves. Low External Locus of Control is desirable given the nature of information and behaviors to be learned via. Most people in Sub-Saharan Africa consider health matters private due to socio-culture constraints such as stigmatization. For example a person suffering from a give disease such as tuberculosis, drug abuse, HIV/AIDS among will not wish for information about this ailment to reach their communities, and even worse, the online community via social media. Such information is held with utmost privacy. This leads them to access health related information via social secretly. External Locus of Control and Mental health Correlation and regression findings revealed that External Locus of Control positively influenced mental health. Similarly, the SEM results indicated that External Locus of Control significantly predicted mental health of social media users in Sub-Saharan Africa. The finding infer that social media users who are willing to rely on others for their learning needs have high chances of learning new health behaviors that affect their mental health. According to Rotter (1966) social learning theory, individuals with high External Locus of Control rely mainly on others for achieving their goals. They also attribute their failures to others. They are more outgoing, friendly and free with information sharing. The current finding indicates that such individuals are more likely to learn new behaviors that affect their mental health via social media. The social cognitive theory (Bandura, 1990) suggests that through observational learning, individual who rely on others – role models, can easily learn new behaviors by observing them convey such information through actions. This enables them to learn new skills, practices, but often times, their morals are affected. For example if a youth has a high level of External Locus of Control, he/she is likely to trust and rely on the information posted by someone influential in their community. This, in the long run affects him or her behavior. For the case of practice, if an individual consumes information about exercising for physical fitness and health-wellbeing, the individual is likely to start physical exercises in the hope that they probably cut weight or reduce their blood pressure. In the long run, this becomes a routine practice, thereby changing the individual’s mental health. For the case of moral degeneration, assuming the role model shares information about pornography or drug abuse, an individual with high External Locus of Control will trust and rely on such information for their sexual and psychological wellbeing. Hence, they will begin practicing what they have observed from the role model (Blalock et al. 2016; Kane, 2004; Bandura, 1990). These two scenarios point the effect that social media may influence an individual with a high external locus of in a positive as well as negative way. Conclusion and recommendations The study sought to investigate the effect of Outcome Expectations and External Locus of Control on the mental health of social media users in Sub-Sahara Africa. This was accomplished through two hypotheses - H1 and H2. H1 stated that Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa, while H2 stated that External Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa. The study findings on H1 revealed a negative significant relationship between Outcome Expectations and External Locus of Control – meaning that individuals with high Outcome Expectations had low External Locus of Control. On the other hand, H2 findings revealed a positive significant relationship between External Locus of Control and Mental health- implying that individuals with External Locus of Control were likely to learn new behaviors that affect mental health. Given the above findings, we conclude that both Outcome Expectations and External Locus of Control significantly contributed to the changes in mental health change of social media users in Sub-Saharan Africa. Whereas the contribution of External Locus of Control was positive, Outcome Expectations made a negative contribution. Implications to practice Drawing from the findings, we make some recommendations relevant to practice in health and social media. Recommendations under this section are aimed at the individuals and institutions that develop, run, monitor and use social media platforms. They may include health service providers, parents, counselors, teachers, marriage doctors, bloggers, social media users, social media content developers among others. It hoped that once they adopt these recommendations, they will be able to self-regulate their actions while using social media, access, share and consume health related information in a selective manner, as well as know what is required for one to learn only good behaviors for good mental health that will positively change in their lives. Since Outcome Expectations were found to positively affect External Locus of Control of social media users, it is important for social media platforms to be designed in such a way that they will make its users better and more acceptable people in society. To achieve this, social media developers should be mindful of the difference in cultures of its users and preferably provide content which promotes local cultures. This way, individuals using social media will not be rejected by their communities. Most rejections come in when social media users consume information that alters their mental health contrally to what is generally known and acceptable by their communities. For example, whereas smoking maybe prestigious in one community, it is taboo in another. Therefore if social media promotes content on smoking in a community that detests the act, if one in that community begins smoking, they will be rejected due to the newly learned behavior. However, if such content is promoted in a community where smoking is generally acceptable, individual will not be rejected for learning how to smoke and eventually starting to smoke. Further, since the results revealed a positive significant relationship between External Locus of Control and Mental health, it implies that social media users relied mainly on online communities for health problem solving. Moreover, they did not take responsibility of the consequences of their actions while using social media. Further, social media users cared less in creating and maintaining good relations on social media. This finding points to a notion that social media users were irresponsible, lazy and careless learners. Such individuals were likely to learn behaviors that negatively affect their mental health such as sexting, drug abuse, among other. Therefore, it is on this basis the study recommends online community education, sensitization and policing. This would help educate the careless learners on the dangers of learning bad mental health behaviors. For example sensitization programs showing images of bedridden patients of sexually transmitted diseases, or diseases caused by smoking giving their experiences to scare the would be learning of such behaviors. Another example is sharing the lungs of smokers compared to the lungs of nonsmokers. In terms of policing, parents, teachers, and elders in the community can take keen interest in monitoring the activities of their young one online. Where for example, a young one is found on wrong online communities such as porn websites, or online dating sites, such individuals should be reprimanded by the relevant authorities in the community. This would scare away young people with high External Locus of Control from learning negative behaviors that deteriorate mental health. Recommendations to policy Recommendations to policy makers target government institutions charged with authority to formulate national and regional policies on social media, health and behavioral change. These institutions include parliament, senate, local governments, ministries of Information Communication Technology, Gender and Culture, Education, Health, Trade, Youth affairs and their agencies in respective countries. In respect to Outcome Expectations, parliaments of affected countries should enact laws that force social media developers to use local content. Using local content will ensure that only appropriate information is consumed by citizens via social media. Content from foreign sources especially the developed world should be discouraged because most of the content is contaminated with wrong messages. For example, many times, information on drug abuse, pornography, sexting and nudity, violence comes from social media contents developed outside Africa. Whereas such information has become in norm in western countries, it is considered taboo in many African communities to watch pornographic materials, have online sexual partners, smoke, especially among the youths. Once this is implemented, it is very likely that people will be encouraged to join social media platforms for purposes of learning new useful behaviors for good mental health. Relevant institutions such as ministries and communication commissions of African countries should then transform the enacted laws into policy that should be implemented by all media houses. Media houses found circulating information that is harmful to health of users should be penalized accordingly. Similarly, media houses that adhere to the policy framework should be encouraged and where possible can be rewarded through public recognition and / or awards. As was already observed, External Locus of Control created a social media user group that was so reliant on the online communities as the main source of solutions to their health related problems. It was also observed that this category of users was careless, irresponsible and minded less about the consequences of their actions while using social media. Therefore, they were likely to share harmful information to the health of other users. They were also likely to access health related information randomly without regard to the sources and motive behind such information. The end results would be negative mental health exhibited inform of drug abuse, homosexuality, pornography and nudity, sexting among others. Given the above, we recommend that ministries of education, youth, gender and culture come up with online educational programs which could be incorporated in the mainstream education system. The purpose of this curriculum will be to educated young people in schools, churches, mosques and other avenues about the dangers of reckless consumption of online health related materials such as pornography to their health. The young people should know that not all that comes from developed countries is good. Therefore they should not embrace foreign ideals in their way they handle their health related problems. It is also hoped that health education programs once incorporated in schools, churches and mosques will help improve the knowledge of social media users. This, coupled with strong cultural and religious beliefs will help social media users to sources health related information from rightful channels. Once this is done well, the chances of learning positive mental health behaviors among the youth will increase. It is also important for government to enact laws and policies that prohibit child abuse pornography, prostitution, bestiality in all forms of media including social media, children games, television programs, churches, mosques, schools among other avenues. This is because, in recent times health related information that can be learnt and gradually transform somebody’s behaviors is diffused through different media and channels. Some of these acts in recent days have been found to occur even in schools and places of worship. Therefore restricting such information via social alone may not yield the best results. A more holistic approach to eradicating immorality and moral degeneration should be adopted. Individuals who are found circulation harmful information via social media and those found inducting children in acts of immorality, upon conviction should be punished severely in order to discourage others from doing it. Declarations Consent to Participate declaration Participation in the study was on a voluntary basis. Participants were requested to agree to an informed consent form before participating in the study. Approval Committee or the Internal Review Board The research proposal and instruments were approved by the Doctoral Committee of the ICT University before implementation. Funding Declaration This research was funded by Makerere University Business School under the Ph.D. fellowship program. Human Ethics and Consent to Participate declaration Personal data were not collected and participation was on a voluntary basis. Participants were requested to agree to an informed consent form before participating in the study. Data availability declaration The primary data for this study is available in SPSS. Author Contribution Kituyi is the sole author of this article. References Abeyasekera S. (2016) Quantitative analysis approaches to qualitative data: why, when and how, University of Reading. Retrieved on 27 th march 2016 from: http://www.reading.ac.uk/ssc/resources/Docs/Quantitative_analysis_approaches_to_qualitative_data.pdf Blalock S. J., Bone L., Brewer N. T., Butterfoss F. D., Champion V. L., Epstein R. E.,… (2016) health behavior and Health education: Theory, Research and Practice. K aren G., Rimer B. K., Viswanath K. (Ed.), Perelman School of Medicine, University of Pennsylvania, Retrieved on March 3 rd 2016 from: http://www.med.upenn.edu/hbhe4 Bandura, A. (1990). Mechanisms of moral disengagement. In W. Reich (Ed.), Origins of terrorism: Psychologies, ideologies, theologies, states of mind (pp. 161-191). Cambridge: Cambridge University Press . Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (2000) Health promotion , New York: Norton. Bartlett, M. S. (1937) Properties of sufficiency and statistical tests. Proceedings of the Royal Statistical Society, Series A 160, 268–282 Buck M. (2010) Dysfunctional Behaviours from the Social – Cognitive Learning Theory Perspective, Health Education: International Experiences BYU (2016) Variables, Brigham Young University. Retrieved on 26 th march from: http://linguistics.byu.edu/faculty/henrichsenl/ResearchMethods/RM_2_14.html Campell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait- multi method matrix, Psychological Bulletin , 56, 81-105 Child, D. (1990) The essentials of factor analysis, second edition. London: Cassel Educational Limited François, H, Sébastien L, and Jérôme, P. (2009) Exploratory Multivariate Analysis by Example Using R. Chapman & Hall/CRC The R Series, London. 224p. Grosshans W. and Chelimsky E. (1992) Quantitative data analysis: an introduction: Report to the program evaluation and methodology division, Unites States General Office GSMA Intelligence (2014) The mobile economy: Sub Sahara Africa 2014, GSMA Intelligence, Retrieved on 26 th March 2016 from: http://www.gsmamobileeconomyafrica.com/GSMA_ME_SubSaharanAfrica_Web_Singles.pdf Hatcher, L. (1994) A step-by-step approach to using the SAS® System for factor analysis and Structural Equation Modeling. Cary, NC: SAS Institute Inc . Hox J. J. (2016) An introduction to Structural Equation Modeling, Family science review, 11,354,-373. Retrieved on 27 th march 2016 from: http://joophox.net/publist/semfamre.pdf Internet World Statistics (2015) Africa 2015 population and internet users statistics for 2015, Internet World Statistics , Retrieved on 26 March, 2016 from: http://www.internetworldstats.com/stats1.htm Jérôme P. (2014) Multiple Factor Analysis by Example Using R. Chapman & Hall/CRC The R Series London 272 p Jolliffe, I.T. (2002). Principal Component Analysis, second edition, Springer . Kane RL, Johnson PE, Town RJ, Butler M. A, (2004) Structured Review of the Effect of Economic Incentives on Consumers ‘Preventive Behavior, American Journal of Preventive Medicine 27:4, 327-352 Kaur SP (2013) Variables in research, Indian Journal of Research and Reports in Medical Sciences, VOL-3, No.4 pg 36-38 Langlois M, Petosa R, Hallam J. (1999) Why do effective smoking prevention programs work? Student changes in social cognitive theory constructs, Journal of School Health 69(8), 326-331. Nunnally, J. C. (1978). Psychometric theory (2nd ed.), New York, NY: McGraw-Hill ORI (2016) Elements of Research, San Diego State University retrieved on 7 th March 2016 at https://ori.hhs.gov/education/products/sdsu/variables.htm Suhr, D. D. (2017) Exploratory or Confirmatory Factor Analysis? Sas , Retrieved on March 20 th 2017 from http://www2.sas.com/proceedings/sugi31/200-31.pdf Tabachnick & L. S. Fidell, Ullman, J. B. (2001) Structural Equation Modeling. In B. G. (2001) Using Multivariate Statistics (4th ed& pp 653- 771). Heights, MA: Allyn & Bacon. UN (2013) Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings, The United Nations Statistics Division, Retrieved on 26 th March, 2016 from: http://unstats.un.org/unsd/methods/m49/m49regin.htm Winett R. A., Anderson E. S., Whiteley J. A., Wojcik J. R., Rovniak L. S., Graves K. D., Galper D. I., Winett S. G. (1999) Church-based health behavior programs: Using Social Cognitive Theory to formulate interventions for at-risk populations, Applied & Preventive Psychology 8:129-142. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 16 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Submission checks completed at journal 23 Mar, 2024 First submitted to journal 17 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4117150","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283056287,"identity":"8ea1ade2-cdef-48cf-ba30-b7c2158088f2","order_by":0,"name":"Geoffrey Mayoka Kituyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACCWYwlcDAD6YKSNEi2QCiDIjRwgDVYnAARBOjRbKdO/EzT0WavPH51YkfHhgwyPOLHcCvRZqZd7M0z5kcw2033m6WADrMcObsBPxa5Jh5N0jztlUwbrtxdgNIS4LBbcJaNv8GarHfPOPs5h9EaQE6bBvQlpzEDfy924izRbKZd5vlnDNpyTNu8G6zSDCQIOwXifNnN994U5Fs299/dvPNHxU28vzSBLSAABMPWDNYpQRh5SDA+ANE8h8gTvUoGAWjYBSMPAAAcvVCaI/qCV0AAAAASUVORK5CYII=","orcid":"","institution":"Makerere University Business School","correspondingAuthor":true,"prefix":"","firstName":"Geoffrey","middleName":"Mayoka","lastName":"Kituyi","suffix":""}],"badges":[],"createdAt":"2024-03-17 13:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4117150/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4117150/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-20707-2","type":"published","date":"2024-11-18T15:57:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69835175,"identity":"a32b84a1-69ba-4e03-ba51-5cb00b7dbbaf","added_by":"auto","created_at":"2024-11-25 16:12:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1088173,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4117150/v1/ff79084d-83f2-45e0-92e6-390e2071ee66.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Media and the Mental Health of Users in Sub-Saharan Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe United Nations Statistics Division defines Sub-Sahara Africa as all of Africa excluding North Africa and Sudan (UN, 2013). It includes all African countries except Egypt, Algeria, Libya, Morocco, Sudan, and Tunisia. \u0026nbsp;The region has a total of 52 countries with a population of about 943m people, 234m of these people use the Internet. The region has an internet penetration rate 25% of the population (Internet World Statistics, 2015). A total of 68,968,500 people representing 7.3% use Facebook. Although internet penetration is still low compared to the global average of 46% it is on the upward trend having shifted from 0.4% in the year 2000 to 7.3% in the year 2015 (Internet World Statistics, 2015). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe region is forecasted to be among the fastest growing economies in the world, given its immense socio-economic opportunities and potential. Mobile penetration is currently standing at 34% of the population (GSMA Intelligence, 2014). Most of the mobile phones are being used to access social media. By the year 2020, a total of 504m people will have mobile phones in Sub-Saharan Africa, representing a penetration rate of 49% (GSMA Intelligence, 2014). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough application specific statistics are scanty, except for Facebook, most users of SM in Sub-Saharan Africa are on Facebook, followed by WhatsApp, Twitter, Instagram, YouTube and Skype. According to Statistica (2014b), South Africa has the highest WhatsApp adoption rate standing 78%. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study is hoped to make a contribution to body of knowledge by identifying the factors responsible for causing mental health changes of social media users. To achieve this, the study presents insights on the relationship between outcome expectations, external locus of control and mental health of social media users in Sub-Saharan Africa. Findings on these relationships will help researchers to know where to direct their scholarly efforts when studying social media and mental health. The Objective of this study was to examine the effect of Outcome Expectations and External Locus of Control on the mental health of social media users in Sub-Sahara Africa.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eThe case of Bandura\u0026rsquo;s Social Learning Theory\u003c/h3\u003e\n\u003cp\u003eIntroduced by Bandura (1965), the Social Learning Theory (SLT) which was later modified and renamed the Social Cognitive Theory by Bandura (1986) explains how human beings adopt behaviors when exposed to certain conditions. Bandura (1965) conducted experiments on kids and observed that children behaviors changed through a process of replication, retention and mimicking of their role models. Bandura\u0026rsquo;s Social learning theory was as a response to the limitations of behaviorism theories in addressing how humans leaned new behavior. The behaviorism theories assumed that human behavior could be learned and tested in a controlled environment \u0026ndash; laboratories. Further, the behaviorism theories at the time failed to show how people responded to new situations. The third limitation was that the behaviorism theories could only explain direct learning - the form of learning that takes place in a classroom. They ignored other forms of learning. Bandura argued that learning could be delayed and could happen over a period of time through observation and mimicking of role models. Further, Bandura argues that learning influences behavioral change over a period of time (Bandura, 1986). According to Bandura (1986), learning can take place through a process called reinforcement. There are three forms of reinforcement proposed by Bandura including; 1) direct reinforcement - which is directly caused by the learner himself, 2) vicarious reinforcement - one that happens due to observation of a role model\u0026rsquo;s behavior. \u0026nbsp;Vicarious reinforcement is caused by the role model, and 3) self-reinforcement - which manifests in the form of satisfaction and dissatisfaction arising from one\u0026rsquo;s good or poor performance. Bandura (1986) argues that the most influential aspect of learning is by seeing and experiencing actions of other people. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe social learning theory can be used in studying the SM aspect in trying to understand how the participants on a given SM tool learn how to treat and manage their aliments through sharing experiences. More experienced or former patients of a similar disease can act as role models to newer, younger and inexperienced patients in the learning process. However, the social learning theory has its limitations in applicability, especially in this kind of research as it requires the physical interaction of participants for learning to take place. The theory also encompasses more of behavioral change than information sharing and the role played by technology in causing change. This far, we argue that SM\u0026rsquo;s main purpose is foster information sharing. We also argue that behavioral change on SM is just a symptomatic result of the core purpose of information sharing. In addition, Bandura\u0026rsquo;s (1965) social learning theory is only relevant if the role model being observed is \u0026ldquo;appropriate, relevant and similar to the observer\u0026rdquo; (Bandura \u003cem\u003eet al.\u003c/em\u003e 1963). Considering the fact that most actors on SM are of diverse attributes such as backgrounds, cultures and educational levels among others, the social learning theory may actually have no impact on influencing behavioral change.\u003c/p\u003e"},{"header":"Outcome Expectations","content":"\u003cp\u003eBlalock \u003cem\u003eet al.\u003c/em\u003e (2016) define Outcome Expectations as the \u0026quot;likelihood and value of the consequences of behavioral choices\u0026rdquo;. If the Outcome Expectations are positive, an individual will be attracted to the behavior. However, if the Outcome Expectations are negative, such as rejection, mistrust or punishment, the subject will not be attracted to the new behavior (Buck, 2010; Bandura, 2000; Bandura 1986). Blalock \u003cem\u003eet al.\u003c/em\u003e (2016) expounds that an individual will be willing to reveal his HIV status if he expects a positive outcome. However, he will not reveal such a status if the expected outcome is negative.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Rotter\u0026rsquo;s SLT, Expectancy is used in place of Outcome Expectations. The only difference is that Rotter\u0026rsquo;s Expectancy construct is based on chance and is probabilistic. Expectancy is either high or low (Rotter, 1966), whereas Outcome Expectations in the SCT can be negative or positive (Blalock \u003cem\u003eet al.\u003c/em\u003e, 2016; Bandura, 1986). Rotter uses Expectancy to show the probability that an act will result to a given behavioral outcome, while Bandura uses Outcome Expectations to show the nature and impact of behavioral outcomes (either negative or positive) given that an individual imitates a behavioral action (Bandura, 1986; Rotter, 1966). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch design and sampling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a cross-sectional study that applied quantitative methods of research. Data were collected using questionnaires distributed to a sample of 450 social media users from three African countries including Cameroon, Uganda and Nigeria. Each country had a sample of 150 for the study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy variables\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBYU (2016) defines a variable as \u0026ldquo;a measurable characteristic that varies. It may change from group to group, person to person, or even within one person over time.\u0026rdquo; It is a theoretically measurable thing that can have a dynamic value (Kaur, 2013). Variables are used to explain differences in things and what causes those differences. According to ORI (2016), the changes in variables are as a result of some force that may be from within the variable itself or another source. This study utilizes three different types of research variables namely, dependent, independent and control variables elucidated below. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA dependent variable is one that is affected by a change in the independent variable (s) (BYU, 2016). Dependent variables in this study include External Locus of Control and Mental health. On the other hand, BYU (2016) argues that an independent variable is one whose change affects the dependent variable. It is within the researcher\u0026rsquo;s control. The independent variable in this study is Outcome Expectations.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the above, this study also had control variables. According to BYU (2016), a control variable is one that can be silenced or ignored by the researcher for the interest of other more important variables. Demographic attributes such as age, gender and education in this study that treated tested as control variables \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeasurement and operationalization of variables\u003c/p\u003e\n\u003cp\u003eOutcome Expectations is the likelihood and value of the consequences of behavioral choices. The variable was measured by literature from Blalock et al. (2016); Buck (2010); Bandura (2000); Bandura (1986); Rotter (1966). \u0026nbsp;This variable investigated whether using social media on health related matters made respondents better people, using social media on health related matters made respondents more acceptable and trustworthy, amongst their peers, friends and family among others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, External Locus of Control is used to refer to an individual\u0026rsquo;s locus of control or state of being where one is unable to controls the consequences of his / her behavior. Consistent with Boundless (2016) and Rotter (1966), External Locus of Control variable was employed to examine whether respondents were not in control of the consequences, achieve less, had low morale to learn, did not maintain good relations, considered themselves lucky, were not responsible for the bad things that happen to them, did not think about the consequences of their actions before doing them, and if the respondents were unable to help themselves when faced with challenging situations while using social media.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the variable Mental health is used to refer to learned action, skills, practices an individual does via social media that influence his/her psychological wellbeing. It was measured by Bandura (1986); Blalock et al. (2016); Winett et al. (1999); Bandura (1990); Blalock et al. (2016); Kane (2004). This variable was structured into four constructs namely; Skills, Practice, Observational learning, and Moral degeneration. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSkills construct was used to study whether respondents learned new health related skills by using social media. It also investigated whether respondents were able to treat diseases, manage chronic diseases and also whether they learned how to look after patients using information obtained via social media.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe construct of Practice was used to study whether respondents learned new health related behavioral practices through observing role models and training themselves via social media. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObservational learning construct on the other hand was used to establish whether respondents learned new health related behaviors by observing other influential people in society such as celebrities, political leaders, elders, religious do them. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLastly, Moral degeneration construct was used to study whether respondents learned new health related behaviors that decayed their morals by using social media. It investigated whether respondents learned how to and actually smoked, used drugs, drunk alcohol, consumed pornography, became gay, and had sex with multiple partners because of the information they consumed over time via social media.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch hypotheses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe research hypotheses of this study are given below:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003csub\u003e1\u003c/sub\u003e:\u003c/strong\u003e Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003csub\u003e2\u003c/sub\u003e:\u003c/strong\u003e External Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContent Validity\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the above, content validity index was run on each variable to establish how well each variable measured what is was intended to measure. The CVI for each variable is given in Table 1 below:\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1: Variable Content Validity Index\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"347\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.75504322766571%\" valign=\"top\"\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.818443804034583%\" valign=\"top\"\u003e\u003cstrong\u003eNo of items\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.42651296829971%\" valign=\"top\"\u003e\u003cstrong\u003eCVI\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.75504322766571%\" valign=\"top\"\u003eOutcome Expectations\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.818443804034583%\" valign=\"top\"\u003e7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.42651296829971%\" valign=\"top\"\u003e0.795\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.75504322766571%\" valign=\"top\"\u003eExternal Locus of Control\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.818443804034583%\" valign=\"top\"\u003e8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.42651296829971%\" valign=\"top\"\u003e0.707\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.75504322766571%\" valign=\"top\"\u003eMental health\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.818443804034583%\" valign=\"top\"\u003e25\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.42651296829971%\" valign=\"top\"\u003e0.778\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults in Table 1 above show that all variables met the minimum CVI of 0.6; hence the questionnaire was valid for the study. The individual variables CVI scores are given as follows; Outcome Expectations (CVI=0.795); External Locus of Control (CVI=0.707); Mental health (CVI=0.778). This indicates that the instrument was valid (Nunnally, 1978).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConstruct validity, Convergent validity and Discriminant validity\u003c/p\u003e\n\u003cp\u003eUsing Exploratory Factor Analysis (EFA), we researcher tested for commonalities. Commonalities for all variables were greater than 0.4, indicating that items were measuring the same variable (Costello \u0026amp; Osborne, 2005). Further, Kaiser-Meyer-Olkin (KMO) obtained was greater than 0.7 for all variables, indicating that the sample was adequate. According to Tabachnick and Fidell, (2001) a KMO above 0.5 is appropriate. The Total Variance Explained was greater than 0.7 indicating that the items and constructs largely explained the variables. Further still, the Rotated Component Matrix Factor Loadings were greater than 0.5 and items were distributed independently into different constructs. This meant that there was discriminant validity within each variable (Campell \u0026amp; Fiske, 1959), and also convergent validity within each construct. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuantitative data analysis methods\u003c/p\u003e\n\u003cp\u003eQuantitative data analysis is the process of constructively summarizing, classifying, measuring, categorizing, tabulating, counting and interpreting numerical data. \u0026nbsp;It is aimed at describing an event or a situation by trying to answer questions about it. It helps to answer the \u0026ldquo;how\u0026rdquo;, \u0026ldquo;why\u0026rdquo;, and \u0026ldquo;when\u0026rdquo; questions (Abeyasekera, 2016) and is done on numerical data (Aliaga \u0026amp; Gunderson, 2000). The various types of quantitative data analysis are; descriptive analysis, factor analysis, correlation analysis, regression analysis, and Structural Equation Modeling among others. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive analysis methods aim to illustrate the object being analyzed. They include percentages, means and frequencies (Abeyasekera, 2016). While correlation analysis is used to examine the relationship between variables and regression analysis is used to determine the predicting power of the independent variable on the dependent variable. They help in measuring associations between two variables (Grosshans \u0026amp; Chelimsky, 1992). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, Exploratory Factor Analysis (EFA) is a statistical technique use to analyze and determine a set of interrelated observed variables or factors that measure a given latent variable (Suhr, 2017). It can also be used to test the data against hypothetical variable structures and establish their suitability, although the outcome of EFA is noncommittal to such structures (Child, 1990).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are used to analyze measurement variables. Structural Equation Modeling (SEM) software is used to perform confirmatory factors analysis as well as latent growth modeling (Hox, 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study used all the quantitative data analysis methods described above to analyze data and test hypotheses under investigation. For example, descriptive statistics were used to analyze background information, while correlation and regression analysis methods were used to analyze the relationships and strength of the relationship between variables. Further, confirmatory analysis methods and modeling were implemented using Structural Equation Modeling methods.\u0026nbsp;\u003c/p\u003e"},{"header":"Findings ","content":"\u003ch2\u003eExploratory Factor analysis\u003c/h2\u003e\n\u003cp\u003eEFA is done through a process called factor analysis and also component analysis which is used to reduce a given set of observed variables or factors a reasonable level that best explains latent variable(s) (Spearman, 1904). Some of the important tests conducted during EFA are the Kaiser-Meyer-Olkin Measure of Sampling Adequacy test (KMO), Bartlett\u0026apos;s Test of Sphericity (Approx. Chi-Square, Df. Sig.), Communalities, and Principal Component Analysis. KMO is used to test for sample adequacy and should be above 0.5 for the sample to be adequate (Tabachnick \u0026amp; Fidell, 2001; Yong \u0026amp; Pearce, 2013). On the other hand, Bartlett\u0026apos;s Test of Sphericity (Approx. Chi-Square, D.f., Sig.) which is used to test for homogeneity of samples. It ensures that there is similarity in the variances of a group of samples (Bartlett, 1937). Communalities show the variance in a latent variable that is explained by a given observed variable (Costello \u0026amp; Osborne, 2005). The higher the communality, the better that observed variable explains its latent variable (Hatcher, 1994), however, a communality of 0.4 and above is generally considered to be good (Costello \u0026amp; Osborne, 2005). Further, Principal Component Analysis is used to orthogonally transform a set of related observed variables in groups of factors, also known as components (J\u0026eacute;r\u0026ocirc;me, 2014; Fran\u0026ccedil;ois, S\u0026eacute;bastien \u0026amp; J\u0026eacute;r\u0026ocirc;me, 2009; Jolliffe, 2002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData were analyzed using exploratory factor analysis with Extraction Method of Principal Component Analysis and Rotation Method of Varimax with Kaiser Normalization in order to extract the most important factors that measured the study variables. Factors with Eigen values \u0026gt;1 and factor loadings \u0026gt;0.5 were retained in the commonality and rotated component matrix. \u0026nbsp;This validated the questionnaire in terms of convergent validity and discriminant validity (Campell \u0026amp; Fiske, 1959).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor convergent validity, determinant with sig.\u0026gt;0.00, commonalities loadings \u0026gt;0.5 indicated convergence of items in measuring the same variable. For discriminant validity, Rotated Component Matrix distinct factors with loadings of above 0.5 indicated discrimination of factors from each other. In this study, factor analysis was performed on all latent variables as presented in the following section.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eEFA for Outcome Expectations\u003c/h3\u003e\n\u003cp\u003eA total of 7 items were listed to measure Outcome Expectations. Item correlation matrix produced a\u0026nbsp;Determinant = .013 meaning that all items converged and were related in measuring\u0026nbsp;Outcome Expectations. The KMO was used to measure sampling adequacy. A KMO =.809 meant that the study sample was adequate. On the other hand, Bartlett\u0026apos;s Test of Sphericity was used to measure the significance of the sample. Bartlett\u0026apos;s Test of Sphericity Approx. Chi-Square = 2085.746, D.F. =10, Sig=.000 meant that the sample was significant. Table 2 presents KMO and Bartlett\u0026apos;s Test results for\u0026nbsp;Outcome Expectations.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: KMO and Bartlett\u0026apos;s Test for Outcome Expectations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBartlett\u0026apos;s Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eApprox. Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2085.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD.f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCommunalities test for Outcome Expectations\u003c/h3\u003e\n\u003cp\u003eIn addition the above descriptive, Communalities and determinant tests were used to examine convergent validity of Outcome Expectations as seen in Table 3.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3: Communalities for Outcome Expectations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInitial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsing social media on health related matters makes me a better person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsing social media on health related matters makes me more acceptable \u0026nbsp;amongst my peers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy peers will trust me if I use social media on health related matters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI will not be rejected by my peers if I use social media on health related matters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI will not be punished by my family if I use social media on health related matters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage communality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.802\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults in Table 3 above reveal that all the items measured Outcome Expectations since they all have factor loadings above 0.40 and determinant of .013. Hence convergent validity was achieved on Outcome Expectations.\u003c/p\u003e\n\u003ch3\u003eRotated Component Matrix for Outcome Expectations\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eData were analyzed using Principal Component Analysis extraction methods with Varimax with Kaiser Normalization rotation method in order to identify the items that most explained Outcome Expectations. The results are presented in Table 4. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Component Matrix for Outcome Expectations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome Expectations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsing social media on health related matters makes me more acceptable \u0026nbsp;amongst my peers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsing social media on health related matters makes me a better person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI will not be punished by my family if I use social media on health related matters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI will not be rejected by my peers if I use social media on health related matters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy peers will trust me if I use social media on health related matters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigen Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal variance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.237\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage Total Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.237\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRotated Component Matrix results in Table 4 show that 5 factors explain Outcome Expectations with (Eigen Value = 4.012, Total Variance = 80.237, Percentage Total Variance = 80.237). these are; Using social media on health related matters makes me more acceptable \u0026nbsp;amongst my peers (Factor loading=.956); Using social media on health related matters makes me a better person (Factor loading=.934); I will not be punished by my family if I use social media on health related matters (Factor loading=.933); I will not be rejected by my peers if I use social media on health related matters (Factor loading=.847); My peers will trust me if I use social media on health related matters (Factor loading=.799).\u003c/p\u003e\n\u003ch3\u003eEFA for External Locus of Control\u003c/h3\u003e\n\u003cp\u003eSimilarly, data were collected and analyzed on a total of 8 items listed under External Locus of Control. Item correlation matrix for External Locus of Control produced a\u0026nbsp;Determinant = .035, meaning that all items converged and were related in measuring the variable. The KMO was used to measure sampling adequacy for this variable. A KMO = .754 was obtained, meaning that the study sample was adequate. On the other hand, Bartlett\u0026apos;s Test of Sphericity was used to measure the significance of the sample. Bartlett\u0026apos;s Test of Sphericity Approx. Chi-Square = 1190.201, D.F. =10, Sig=.000 meant that the sample was significant. Table 5 presents KMO and Bartlett\u0026apos;s Test results for Cognitive Factors.\u003c/p\u003e\n\u003cp\u003eTable 5: KMO and Bartlett\u0026rsquo;s Test for External Locus of Control\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBartlett\u0026apos;s Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eApprox. Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1190.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD.f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eCommunalities test for External Locus of Control\u003c/h3\u003e\n\u003cp\u003eFurther, Communalities and determinant tests were used to examine convergent validity of items under External Locus of Control. Table 6 presents the results.\u003c/p\u003e\n\u003cp\u003eTable 6: Communalities for External Locus of Control\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI am not in control of the consequences of my actions while using social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI achieve less by using social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have low morale to learn new things on social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI consider myself lucky to be using social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI am not responsible for the bad things that happen to me while using social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage communality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.687\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults in Table 6 reveal that all the items measured External Locus of Control since they all have factor loadings above 0.40 and determinant of .035. This means that convergent validity was achieved on the variable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eComponent Matrix for External Locus of Control\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eData were analyzed using\u0026nbsp;Principal Component Analysis extraction methods with Varimax with Kaiser Normalization rotation method in order to identify the items that most explained Internal Locus of Control. The results are presented in Table 7.\u003c/p\u003e\n\u003cp\u003eTable 7: Component External Locus of Control\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternal Locus of Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI do not maintain good relations on social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI am unable to help myself when faced with challenging situations on social media even if I possess the ability to do so\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI do not think about the consequences of my actions before doing them on social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI am not responsible for the bad things that happen to me while using social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI consider myself lucky to be using social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigen Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.491\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal variance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e70.755\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage Total Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e70.755\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults in Table 7 show that the most important factors explaining External Locus of Control are; I do not maintain good relations on social media (Factor loading =.998), I am unable to help myself when faced with challenging situations on social media even if I possess the ability to do so (Factor loading =.348), I do not think about the consequences of my actions before doing them on social media (Factor loading =.223), I am not responsible for the bad things that happen to me while using social media (Factor loading =.192), I consider myself lucky to be using social media (Factor loading =.130).\u003c/p\u003e\n\u003ch3\u003eEFA for Mental health\u003c/h3\u003e\n\u003cp\u003eA total of 25 items grouped in four constructs including skills, practice, observational learning and moral degeneration were listed to measure mental health. Item correlation matrix produced a\u0026nbsp;Determinant =6.806E-011 meaning that all items converged and were related in measuring\u0026nbsp;Mental health. The KMO was used to measure sampling adequacy. A KMO =.867 meant that the study sample was adequate. On the other hand, Bartlett\u0026apos;s Test of Sphericity was used to measure the significance of the sample. Bartlett\u0026apos;s Test of Sphericity Approx. Chi-Square = 8197.645, D.F. =153, Sig=.000 meant that the sample was significant. Table 8 presents KMO and Bartlett\u0026apos;s Test results for\u0026nbsp;mental health.\u003c/p\u003e\n\u003cp\u003eTable 8: KMO and Bartlett\u0026apos;s Test for Mental health\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"477\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.58995815899581%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eKaiser-Meyer-Olkin Measure of Sampling Adequacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.410041841004183%\" valign=\"top\"\u003e\n \u003cp\u003e.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.928721174004195%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBartlett\u0026apos;s Test of Sphericity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.62264150943396%\" valign=\"top\"\u003e\n \u003cp\u003eApprox. Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\" valign=\"top\"\u003e\n \u003cp\u003e8197.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.23104693140795%\" valign=\"top\"\u003e\n \u003cp\u003eD.f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"68.23104693140795%\" valign=\"top\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.768953068592058%\" valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eCommunalities test for mental health\u003c/h3\u003e\n\u003cp\u003eIn addition the above descriptive, Communalities and determinant tests were used to examine convergent validity of mental health as seen in Table 9.\u003c/p\u003e\n\u003cp\u003eTable 9: Communalities for Mental health\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInitial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have acquired health skills via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to treat diseases via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have the desire to do the health issues I see other influential people in society doing via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI train myself on doing the health related things that I see and like on social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI try to do the health issues as I am told to do via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI seek sexual pleasures via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to smoke by observing other people\u0026rsquo;s smoking images or videos via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to consume alcohol by observing other people\u0026rsquo;s images or videos drinking it via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to access sexual partners using social media because observing other people doing it\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to make money by giving sexual pleasures via social media through observing others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI smoke because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI use drugs because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI drink alcohol because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI use pornography because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI am gay because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have multiple sex partners because of the information I consume via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI know of someone who obtained sex via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI know of someone who engages in commercial sex via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage communality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.805\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults in Table 9 above reveal that all the items measured mental health since they all have factor loadings above 0.40 and determinant of 6.806E-011. Hence convergent validity was achieved on mental health.\u003c/p\u003e\n\u003ch3\u003eRotated Component Matrix for Mental health\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eRotated Component Matrix shows that all the four components explained\u0026nbsp;Mental health\u0026nbsp;namely; observational learning (Percentage Total Variance=37.207), Moral Degeneration (Percentage Total Variance=55.107), Practice (Percentage Total Variance=70.440) and Skills (Percentage Total Variance=80.477). Hence discriminant validity was achieved. Table 10 presents the results.\u003c/p\u003e\n\u003cp\u003eTable 10: Rotated Component Matrix for Mental health\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservational learning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoral degeneration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePractice\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkills\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to smoke by observing other people\u0026rsquo;s smoking images or videos via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI use drugs because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to make money by giving sexual pleasures via social media through observing others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI smoke because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to consume alcohol by observing other people\u0026rsquo;s images or videos drinking it via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI seek sexual pleasures via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI use pornography because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI drink alcohol because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI know of someone who obtained sex via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to access sexual partners using social media because observing other people doing it\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI know of someone who engages in commercial sex via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have multiple sex partners because of the information I consume via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI am gay because of the information I have consumed over time via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI train myself on doing the health related things that I see and like on social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have the desire to do the health issues I see other influential people in society doing via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI try to do the health issues as I am told to do via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have acquired health skills via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI have learned how to treat diseases via social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigen Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.697\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.222\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.760\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.807\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal variance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.207\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.333\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage Total Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.207\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e55.107\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e70.440\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.477\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eThe effect of Outcome Expectations and External Locus of Control on the mental health of social media users in Sub-Sahara Africa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple Hierarchical Regression analysis was used to determine the predicting power of outcome expectation and External Locus of Control on health. Gender, Age, Level of education, Marital Status, and Country of Residence were treated as extraneous or control variables. Table 11 presents the results.\u003c/p\u003e\n\u003cp\u003eTable 11: Regression\u0026nbsp;for Mental health\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e4.037**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e3.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e1.70**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e-0.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.22**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.20**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003eCountry of Residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003eOutcome expectation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.21**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.44871794871795%\" valign=\"top\"\u003e\n \u003cp\u003eExternal Locus of Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.85897435897436%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\" valign=\"top\"\u003e\n \u003cp\u003e0.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.240\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.434\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.594\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.058\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.188\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.352\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.175\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.339\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.058\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.131\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.164\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.303\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.560\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.619\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.303\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.579\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e27.204\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.54662379421222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.472668810289388%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.990353697749196%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.497592295345104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"69.5024077046549%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e**.Significant at 0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.497592295345104%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"69.5024077046549%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e*. Significant at 0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs seen in Table 11, results in model 1 show that Control variables including Gender, Age, Education, Marital status, and Country of residence predict 4.4% of Mental health (Adj R\u003csup\u003e2\u003c/sup\u003e =0.044). The relationship between Gender and Mental health is significant (Beta=-0.22**, P\u0026lt;.01). The relationship between Age and Mental health is not significant (Beta=0.02). The relationship between level of education and Mental health is not significant (Beta=0.08). The relationship between, Marital Status and Mental health is not significant (Beta=0.02). The relationship between Country of Residence and Mental health is not significant (Beta=-0.07).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults in model 2 reveal that control variables together with Outcome Expectations predict\u0026nbsp;17.5% of Mental health (Adj R\u003csup\u003e2\u003c/sup\u003e=.175) while Outcome Expectation alone predicts\u0026nbsp;13.1% of Mental health (R\u003csup\u003e2\u003c/sup\u003e Change = .131). Further, the relationship between Outcome Expectation and Mental health is significant at 99% confidence level (Beta=0.39**).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults in model 3 reveal that control variables, Outcome expectation and External Locus of Control combined predict\u0026nbsp;33.9% of Mental health (Adj R\u003csup\u003e2\u003c/sup\u003e=.339). However,\u0026nbsp;External Locus of Control alone predicts\u0026nbsp;16.4% of Mental health (R\u003csup\u003e2\u003c/sup\u003e Change=.164). The results also show that External Locus of Control has a significant relationship with Mental health at 99% confidence level (Beta=0.46**).\u003c/p\u003e\n\u003cp\u003eGiven the above, we see that Outcome Expectations and External Locus of Control together with control variables contributed 33.9% of the changes in mental health of social media users in Sub-Sahara Africa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmatory analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 12: Social media and mental health Model Fit Summary\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"761\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003csup\u003e2\u003c/sup\u003e/DF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAGFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTLI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRMSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.E.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.R.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eExternal Locus of Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eOutcome Expectations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eH1 -rejected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMental health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eExternal Locus of Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eH2 \u0026ndash;accepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eTesting of research hypotheses\u0026nbsp;using SEM\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e \u003cem\u003eOutcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eResults in Table 12 reveal that the relationship between Outcome Expectations and External Locus of Control was significant and negative at 1% level of significance (Beta=-.363, P\u0026lt;0.001). This implies that an increase in the Outcome Expectations of social media users will reduce their External Locus of Control. In other words, if the expected outcome from learning new behaviors that affect mental health via social media are high then the reliance on others to learn the behavior reduces. This relationship could probably be attributed to the confidential nature of health related information which most people do not want to share easily via social media. Therefore H1 that stated that Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa was not supported. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e \u003cem\u003eExternal Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs seen in Table 12, the relationship between External Locus of Control and Mental health was also found to be positive and significant at 99% confidence level (Beta=.385, P\u0026lt;0.001). This indicated that there is a high certainty of the existence of a relationship between External Locus of Control and Mental health. More to the relationship between External Locus of Control and Mental health, it suffices to mention that, social media users who are highly influenced by external factors such as social influence from friends and family are more likely to learn new behaviors that affect mental health from social media platforms. This finding is in support of the hypothesis H2 that External Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa.\u003c/p\u003e"},{"header":"Discussion of findings","content":"\u003ch3\u003eOutcome Expectations and External Locus of Control of social media users\u003c/h3\u003e\n\u003cp\u003eHypothesis H1 stated that Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa. However, correlation and Multiple Hierarchical Regressions results revealed that a negative significant relationship existed between Outcome Expectations and External Locus of Control. The SEM results also revealed a significant but negative effect of Outcome Expectations on the External Locus of Control of social media users. Both results rejected H1. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe above findings disagree with literature. The literature had suggest that increasing Outcome Expectations such as the benefits in terms of becoming a better person, becoming more acceptable \u0026nbsp;to others, becoming more trustworthy among peers (Blalock et al. 2016; Buck, 2010; Bandura, 2000) increased External Locus of Control. This made farmers unaccountable of their decisions, having low morale, achieving less, feeling lucky about their achievements and being unable to help themselves (Boundless, 2016; Rotter, 1966). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis finding is useful to the study in the sense that, even if social media users anticipate several benefits from using the technology, they remain in control of and responsible for their actions. Social media users who expect high benefits in terms learning new useful health related behaviors do not blame others for their shortcomings. Even if they eventually fail to yield any benefits, they will not blame it others but themselves. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLow External Locus of Control is desirable given the nature of information and behaviors to be learned via. Most people in Sub-Saharan Africa consider health matters private due to socio-culture constraints such as stigmatization. For example a person suffering from a give disease such as tuberculosis, drug abuse, HIV/AIDS among will not wish for information about this ailment to reach their communities, and even worse, the online community via social media. Such information is held with utmost privacy. This leads them to access health related information via social secretly. \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eExternal Locus of Control and Mental health\u003c/h3\u003e\n\u003cp\u003eCorrelation and regression findings revealed that External Locus of Control positively influenced mental health. Similarly, the SEM results indicated that External Locus of Control significantly predicted mental health of social media users in Sub-Saharan Africa. The finding infer that social media users who are willing to rely on others for their learning needs have high chances of learning new health behaviors that affect their mental health. According to Rotter (1966) social learning theory, individuals with high External Locus of Control rely mainly on others for achieving their goals. They also attribute their failures to others. \u0026nbsp;They are more outgoing, friendly and free with information sharing. The current finding indicates that such individuals are more likely to learn new behaviors that affect their mental health via social media. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe social cognitive theory (Bandura, 1990) suggests that through observational learning, individual who rely on others \u0026ndash; role models, can easily learn new behaviors by observing them convey such information through actions. This enables them to learn new skills, practices, but often times, their morals are affected. For example if a youth has a high level of External Locus of Control, he/she is likely to trust and rely on the information posted by someone influential in their community. This, in the long run affects him or her behavior. For the case of practice, if an individual consumes information about exercising for physical fitness and health-wellbeing, the individual is likely to start physical exercises in the hope that they probably cut weight or reduce their blood pressure. In the long run, this becomes a routine practice, thereby changing the individual\u0026rsquo;s mental health. For the case of moral degeneration, assuming the role model shares information about pornography or drug abuse, an individual with high External Locus of Control will trust and rely on such information for their sexual and psychological wellbeing. Hence, they will begin practicing what they have observed from the role model (Blalock et al. 2016; Kane, 2004; Bandura, 1990). These two scenarios point the effect that social media may influence an individual with a high external locus of in a positive as well as negative way.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion and recommendations","content":"\u003cp\u003eThe study sought to investigate the effect of Outcome Expectations and External Locus of Control on the mental health of social media users in Sub-Sahara Africa. This was accomplished through two hypotheses - H1 and H2. H1 stated that Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Sahara Africa, while H2 stated that External Locus of Control positively affects the mental health of social media users in Sub-Sahara Africa. The study findings on H1 revealed a negative significant relationship between Outcome Expectations and External Locus of Control \u0026ndash; meaning that individuals with high Outcome Expectations had low External Locus of Control. On the other hand, H2 findings revealed a positive significant relationship between External Locus of Control and Mental health- implying that individuals with External Locus of Control were likely to learn new behaviors that affect mental health.\u003c/p\u003e \u003cp\u003eGiven the above findings, we conclude that both Outcome Expectations and External Locus of Control significantly contributed to the changes in mental health change of social media users in Sub-Saharan Africa. Whereas the contribution of External Locus of Control was positive, Outcome Expectations made a negative contribution.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImplications to practice\u003c/h2\u003e \u003cp\u003eDrawing from the findings, we make some recommendations relevant to practice in health and social media. Recommendations under this section are aimed at the individuals and institutions that develop, run, monitor and use social media platforms. They may include health service providers, parents, counselors, teachers, marriage doctors, bloggers, social media users, social media content developers among others. It hoped that once they adopt these recommendations, they will be able to self-regulate their actions while using social media, access, share and consume health related information in a selective manner, as well as know what is required for one to learn only good behaviors for good mental health that will positively change in their lives.\u003c/p\u003e \u003cp\u003eSince Outcome Expectations were found to positively affect External Locus of Control of social media users, it is important for social media platforms to be designed in such a way that they will make its users better and more acceptable people in society. To achieve this, social media developers should be mindful of the difference in cultures of its users and preferably provide content which promotes local cultures. This way, individuals using social media will not be rejected by their communities. Most rejections come in when social media users consume information that alters their mental health contrally to what is generally known and acceptable by their communities. For example, whereas smoking maybe prestigious in one community, it is taboo in another. Therefore if social media promotes content on smoking in a community that detests the act, if one in that community begins smoking, they will be rejected due to the newly learned behavior. However, if such content is promoted in a community where smoking is generally acceptable, individual will not be rejected for learning how to smoke and eventually starting to smoke.\u003c/p\u003e \u003cp\u003eFurther, since the results revealed a positive significant relationship between External Locus of Control and Mental health, it implies that social media users relied mainly on online communities for health problem solving. Moreover, they did not take responsibility of the consequences of their actions while using social media. Further, social media users cared less in creating and maintaining good relations on social media. This finding points to a notion that social media users were irresponsible, lazy and careless learners. Such individuals were likely to learn behaviors that negatively affect their mental health such as sexting, drug abuse, among other.\u003c/p\u003e \u003cp\u003eTherefore, it is on this basis the study recommends online community education, sensitization and policing. This would help educate the careless learners on the dangers of learning bad mental health behaviors. For example sensitization programs showing images of bedridden patients of sexually transmitted diseases, or diseases caused by smoking giving their experiences to scare the would be learning of such behaviors. Another example is sharing the lungs of smokers compared to the lungs of nonsmokers.\u003c/p\u003e \u003cp\u003eIn terms of policing, parents, teachers, and elders in the community can take keen interest in monitoring the activities of their young one online. Where for example, a young one is found on wrong online communities such as porn websites, or online dating sites, such individuals should be reprimanded by the relevant authorities in the community. This would scare away young people with high External Locus of Control from learning negative behaviors that deteriorate mental health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations to policy\u003c/h2\u003e \u003cp\u003eRecommendations to policy makers target government institutions charged with authority to formulate national and regional policies on social media, health and behavioral change. These institutions include parliament, senate, local governments, ministries of Information Communication Technology, Gender and Culture, Education, Health, Trade, Youth affairs and their agencies in respective countries.\u003c/p\u003e \u003cp\u003eIn respect to Outcome Expectations, parliaments of affected countries should enact laws that force social media developers to use local content. Using local content will ensure that only appropriate information is consumed by citizens via social media. Content from foreign sources especially the developed world should be discouraged because most of the content is contaminated with wrong messages. For example, many times, information on drug abuse, pornography, sexting and nudity, violence comes from social media contents developed outside Africa. Whereas such information has become in norm in western countries, it is considered taboo in many African communities to watch pornographic materials, have online sexual partners, smoke, especially among the youths. Once this is implemented, it is very likely that people will be encouraged to join social media platforms for purposes of learning new useful behaviors for good mental health.\u003c/p\u003e \u003cp\u003eRelevant institutions such as ministries and communication commissions of African countries should then transform the enacted laws into policy that should be implemented by all media houses. Media houses found circulating information that is harmful to health of users should be penalized accordingly. Similarly, media houses that adhere to the policy framework should be encouraged and where possible can be rewarded through public recognition and / or awards.\u003c/p\u003e \u003cp\u003eAs was already observed, External Locus of Control created a social media user group that was so reliant on the online communities as the main source of solutions to their health related problems. It was also observed that this category of users was careless, irresponsible and minded less about the consequences of their actions while using social media. Therefore, they were likely to share harmful information to the health of other users. They were also likely to access health related information randomly without regard to the sources and motive behind such information. The end results would be negative mental health exhibited inform of drug abuse, homosexuality, pornography and nudity, sexting among others.\u003c/p\u003e \u003cp\u003eGiven the above, we recommend that ministries of education, youth, gender and culture come up with online educational programs which could be incorporated in the mainstream education system. The purpose of this curriculum will be to educated young people in schools, churches, mosques and other avenues about the dangers of reckless consumption of online health related materials such as pornography to their health. The young people should know that not all that comes from developed countries is good. Therefore they should not embrace foreign ideals in their way they handle their health related problems.\u003c/p\u003e \u003cp\u003eIt is also hoped that health education programs once incorporated in schools, churches and mosques will help improve the knowledge of social media users. This, coupled with strong cultural and religious beliefs will help social media users to sources health related information from rightful channels. Once this is done well, the chances of learning positive mental health behaviors among the youth will increase.\u003c/p\u003e \u003cp\u003eIt is also important for government to enact laws and policies that prohibit child abuse pornography, prostitution, bestiality in all forms of media including social media, children games, television programs, churches, mosques, schools among other avenues. This is because, in recent times health related information that can be learnt and gradually transform somebody\u0026rsquo;s behaviors is diffused through different media and channels. Some of these acts in recent days have been found to occur even in schools and places of worship. Therefore restricting such information via social alone may not yield the best results. A more holistic approach to eradicating immorality and moral degeneration should be adopted. Individuals who are found circulation harmful information via social media and those found inducting children in acts of immorality, upon conviction should be punished severely in order to discourage others from doing it.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipation in the study was on a voluntary basis. Participants were requested to agree to an informed consent form before participating in the study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval Committee or the Internal Review Board\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research proposal and instruments were approved by the Doctoral Committee of the ICT University before implementation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Makerere University Business School under the Ph.D. fellowship program.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePersonal data were not collected and participation was on a voluntary basis. Participants were requested to agree to an informed consent form before participating in the study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary data for this study is available in SPSS.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKituyi is the sole author of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbeyasekera S. (2016) Quantitative analysis approaches to qualitative data: why, when and how, \u003cem\u003eUniversity of Reading. \u003c/em\u003eRetrieved on 27\u003csup\u003eth\u003c/sup\u003e march 2016 from: http://www.reading.ac.uk/ssc/resources/Docs/Quantitative_analysis_approaches_to_qualitative_data.pdf \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBlalock S. J., Bone L., Brewer N. T., Butterfoss F. D., Champion V. L., Epstein R. E.,\u0026hellip;\u003c/strong\u003e\u003cstrong\u003e(2016) health behavior and Health education: Theory, Research and Practice. K\u003c/strong\u003earen G., Rimer B. K., Viswanath K. (Ed.), Perelman School of Medicine, University of Pennsylvania, Retrieved on March 3\u003csup\u003erd\u003c/sup\u003e 2016 from: http://www.med.upenn.edu/hbhe4\u003c/li\u003e\n\u003cli\u003eBandura, A. (1990). Mechanisms of moral disengagement. In W. Reich (Ed.), Origins of terrorism: Psychologies, ideologies, theologies, states of mind (pp. 161-191). \u003cem\u003eCambridge: Cambridge University Press\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. \u003c/li\u003e\n\u003cli\u003eBandura, A. (2000) Health promotion\u003cem\u003e, \u003c/em\u003e\u003cem\u003eNew York: Norton.\u003c/em\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eBartlett, M. S. (1937) Properties of sufficiency and statistical tests. \u003cem\u003eProceedings of the Royal \u003c/em\u003e\u003cem\u003e Statistical Society,\u003c/em\u003e Series A 160, 268\u0026ndash;282 \u003c/li\u003e\n\u003cli\u003eBuck M. (2010) Dysfunctional Behaviours from the Social \u0026ndash; Cognitive Learning Theory Perspective,\u003cem\u003e \u003c/em\u003e\u003cem\u003eHealth Education: International Experiences\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBYU (2016) Variables, \u003cem\u003eBrigham Young University. \u003c/em\u003eRetrieved on 26\u003csup\u003eth\u003c/sup\u003e march from: http://linguistics.byu.edu/faculty/henrichsenl/ResearchMethods/RM_2_14.html \u003cem\u003e \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eCampell, D. T., \u0026amp; Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait- multi method matrix,\u003cem\u003e Psychological Bulletin\u003c/em\u003e, 56, 81-105\u003c/li\u003e\n\u003cli\u003eChild, D. (1990) The essentials of factor analysis, second edition. \u003cem\u003eLondon: Cassel Educational\u003c/em\u003e \u003cem\u003eLimited\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eFran\u0026ccedil;ois, H, S\u0026eacute;bastien L, and J\u0026eacute;r\u0026ocirc;me, P. (2009) Exploratory Multivariate Analysis by Example Using R. \u003cem\u003eChapman \u0026amp; Hall/CRC The R Series, London.\u003c/em\u003e 224p. \u003c/li\u003e\n\u003cli\u003eGrosshans W. and Chelimsky E. (1992) Quantitative data analysis: an introduction: Report to the program evaluation and methodology division, \u003cem\u003eUnites States General Office\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eGSMA Intelligence (2014) The mobile economy: Sub Sahara Africa 2014, \u003cem\u003eGSMA Intelligence, \u003c/em\u003eRetrieved on 26\u003csup\u003eth\u003c/sup\u003e March 2016 from: http://www.gsmamobileeconomyafrica.com/GSMA_ME_SubSaharanAfrica_Web_Singles.pdf \u003c/li\u003e\n\u003cli\u003eHatcher, L. (1994) A step-by-step approach to using the SAS\u0026reg; System for factor analysis and Structural Equation Modeling. \u003cem\u003eCary, NC: SAS Institute Inc\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eHox J. J. (2016) An introduction to Structural Equation Modeling, \u003cem\u003eFamily science review, \u003c/em\u003e\u003cem\u003e \u003c/em\u003e11,354,-373. Retrieved on 27\u003csup\u003eth\u003c/sup\u003e march 2016 from: http://joophox.net/publist/semfamre.pdf \u003c/li\u003e\n\u003cli\u003eInternet World Statistics (2015) Africa 2015 population and internet users statistics for 2015,\u003cem\u003e \u003c/em\u003e\u003cem\u003eInternet World Statistics\u003c/em\u003e, Retrieved on 26 March, 2016 from: http://www.internetworldstats.com/stats1.htm\u003c/li\u003e\n\u003cli\u003eJ\u0026eacute;r\u0026ocirc;me P. (2014) Multiple Factor Analysis by Example Using R. \u003cem\u003eChapman \u0026amp; Hall/CRC The R \u003c/em\u003e\u003cem\u003e Series London\u003c/em\u003e 272 p\u003c/li\u003e\n\u003cli\u003eJolliffe, I.T. (2002). Principal Component Analysis, second edition, \u003cem\u003eSpringer\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eKane RL, Johnson PE, Town RJ, Butler M. A, (2004) Structured Review of the Effect of Economic Incentives on Consumers \u0026lsquo;Preventive Behavior, \u003cem\u003eAmerican Journal of Preventive Medicine\u003c/em\u003e 27:4, 327-352\u003c/li\u003e\n\u003cli\u003eKaur SP (2013) Variables in research, \u003cem\u003eIndian Journal of Research and Reports in Medical \u003c/em\u003e\u003cem\u003e Sciences,\u003c/em\u003e VOL-3, No.4 pg 36-38\u003c/li\u003e\n\u003cli\u003eLanglois M, Petosa R, Hallam J. (1999) Why do effective smoking prevention programs work? Student changes in social cognitive theory constructs,\u003cem\u003e Journal of School Health\u003c/em\u003e 69(8), 326-331.\u003c/li\u003e\n\u003cli\u003eNunnally, J. C. (1978). Psychometric theory (2nd ed.), \u003cem\u003eNew York, NY: McGraw-Hill \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eORI (2016) Elements of Research, \u003cem\u003eSan Diego State University retrieved on 7\u003csup\u003eth\u003c/sup\u003e March 2016 at \u003c/em\u003e\u003cem\u003e \u003c/em\u003ehttps://ori.hhs.gov/education/products/sdsu/variables.htm\u003c/li\u003e\n\u003cli\u003eSuhr, D. D. (2017) Exploratory or Confirmatory Factor Analysis?\u003cem\u003e Sas\u003c/em\u003e, Retrieved on March 20\u003csup\u003eth\u003c/sup\u003e 2017 from http://www2.sas.com/proceedings/sugi31/200-31.pdf \u003c/li\u003e\n\u003cli\u003eTabachnick \u0026amp; L. S. Fidell, Ullman, J. B. (2001) Structural Equation Modeling. In B. G. (2001) Using Multivariate Statistics (4th ed\u0026amp; pp 653- 771).\u003cem\u003e Heights, MA: Allyn \u0026amp; Bacon. \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eUN (2013) Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings, The United Nations Statistics Division, Retrieved on 26\u003csup\u003eth\u003c/sup\u003e March, 2016 from: http://unstats.un.org/unsd/methods/m49/m49regin.htm \u003c/li\u003e\n\u003cli\u003eWinett R. A., Anderson E. S., Whiteley J. A., Wojcik J. R., Rovniak L. S., Graves K. D., Galper D. I., Winett S. G. (1999) Church-based health behavior programs: Using Social Cognitive Theory to formulate interventions for at-risk populations, \u003cem\u003eApplied \u0026amp; Preventive Psychology \u003c/em\u003e8:129-142.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Outcome Expectations, External Locus of Control, Mental health, Social media","lastPublishedDoi":"10.21203/rs.3.rs-4117150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study applies social learning theories to understand how users of social media platforms learn new health behaviors that affect their mental health \u0026ndash; either negatively or positively. The research hypotheses were; H\u003csub\u003e1\u003c/sub\u003e: Outcome Expectations have a positive effect on the External Locus of Control of social media users in Sub-Saharan Africa, and H\u003csub\u003e2\u003c/sub\u003e: External Locus of Control positively affects the mental health of social media users in Sub-Saharan Africa. A cross-sectional study approach was applied with quantitative research methods. Data were collected using questionnaires distributed to a sample of 450 social media users from three African countries including Cameroon, Uganda and Nigeria. Each country had a sample of 150 for the study.\u003c/p\u003e \u003cp\u003eFindings revealed a negative significant relationship between Outcome Expectations and External Locus of Control, implying that H\u003csub\u003e1\u003c/sub\u003e was not supported. On the other hand, there was a positive significant relationship between External Locus of Control on mental health, implying that H\u003csub\u003e2\u003c/sub\u003e was supported.\u003c/p\u003e \u003cp\u003eSince Outcome Expectations were found to positively affect External Locus of Control of social media users, it is important for social media platforms to be designed in such a way that they will make its users better and more acceptable people in society. Further, we recommend online community education, sensitization and policing to help educate the careless learners on the dangers of learning bad behaviors that negatively affect their mental health. In terms of policy, governments should enact laws that force social media developers to use local content. Using local content will ensure that only appropriate information is consumed by citizens via social media.\u003c/p\u003e","manuscriptTitle":"Social Media and the Mental Health of Users in Sub-Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 13:54:05","doi":"10.21203/rs.3.rs-4117150/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-16T06:50:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T06:36:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-23T16:06:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-03-17T13:07:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac2ae76f-807d-40b2-bc1f-c5dc3b7ea506","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:06:07+00:00","versionOfRecord":{"articleIdentity":"rs-4117150","link":"https://doi.org/10.1186/s12889-024-20707-2","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-11-18 15:57:23","publishedOnDateReadable":"November 18th, 2024"},"versionCreatedAt":"2024-03-27 13:54:05","video":"","vorDoi":"10.1186/s12889-024-20707-2","vorDoiUrl":"https://doi.org/10.1186/s12889-024-20707-2","workflowStages":[]},"version":"v1","identity":"rs-4117150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4117150","identity":"rs-4117150","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}