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Influence of Social Media Usage, Academic Performance and Working Memory among Undergraduate Students
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06 February 2024
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09 February 2024
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Abstract
Abstract: Social media has become a vital tool in ensuring productivity among Undergraduates, as studies have shown an increased number of users globally. This study assessed the impact of social media usage on undergraduates’ academic performance and working memory. Participants from three different universities such as the University of Georgia (UG), Eastern European University (EEU), and Batumi State University (BSU) completed this study using online google forms. UG and EEU are in the capital city, and BSU is in Batumi City. The study questionnaires were approved by the institutional review board before the data collection. Descriptive analyses were assessed for all study populations, characteristics, frequencies, percentages, means, and standard deviations using Statistical Package for Social Science (SPSS Inc., Chicago, IL, version 25.0 for Windows) software. 41.3% were male students, as most of the participants were foreign students living and studying in Georgia. Male students had the risk of ‘conflict’ with families and friends because of social media. Female students are at increased risk of using social media as an ‘escape’ from negative feelings. 89.7% of students that perform physical activities, considering their academic workload had the risk of preoccupation complaints. 87.9% felt dissatisfied regularly because they wanted to spend more time on social media. Utilization of social media differed based on the characteristics of the participants such as age, gender, and geography. We observed the increase in prevalence of social media addiction, its effect on academic performance and working memory among undergraduate students.
Keywords:
Subject: Medicine and Pharmacology - Psychiatry and Mental Health
1. Background
Social media has become a vital tool in ensuring productivity among Undergraduates, as studies have shown an increased number of users globally [1,2]. It can be a website or an application that students utilize to interact with peers [3], share ideas [4], and become popular [5]. It serves different purposes that aided online studies during the Covid-19 pandemic [6], as social events were restricted. Most students have social media accounts like Facebook having, 2.9 billion monthly active users [7]. Students have considered it helpful as it can help to maintain long-distance friendships and family relationships [8].
Meanwhile, students obtain essential benefits such as scholarships [9] and educative events [10,11] from some social media platforms. Considering benefits derived from Facebook, YouTube, WhatsApp, Instagram, Snapchat, Twitter, Google+, Pinterest, forums, etc., however, would be time-consuming. Most students tend to stay online for long hours [12,13] as some dedicate short hours to studies which could negatively affect their preparation for quizzes, exams, and academic performance [14].
Studies have observed that social media could influence memory processes [15,16]. Working memory has remained the essential tool required for academic activities among students [17]. Students utilize its availability to resolve issues and understand their study directives [18,19]. The process of how social media could influence working memory has not been well understood. Studies have shown that social media could have a positive effect [4,20,21] that can help achieve educative and scientific [14] activities to improve academic performance. Only a few students utilize social media to exploit educational purposes [22]. In as much as there could be helpful events that can boost the knowledge and skills of a student, there has not been a significant association between social media usage and student GPA [5,23,24]. Furthermore, most of these studies were not related to students working memory. Though good academic performance does not necessarily signify good memory, academic failure can be observed among students with memory issues [25].
The study objective was to examine the influence of social media on undergraduates working memory and academic performance. The students were from three Universities: two from Tbilisi (the capital city) and one from Batumi. We assessed the association between the harmful use of social media on gender differences, physical activities, academic performance, and working memory. There are limited studies on this directive, and this study has helped in finding the relationship regarding such measures. This study would help the students to have a broad understanding of the wide-growing influence of social media in their careers.
Methodology
2.1. Study Design
This cross-sectional study was designed to examine the effect of social media on the working memory of undergraduate students from three different Universities in Georgia.
2.2. Data Collection
722 participants from three different universities completed this study. The collection survey form was distributed among Undergraduate students from the University of Georgia (UG), Eastern European University (EEU), and Batumi State University (BSU) through google forms. UG and EEU are in the capital city, and BSU is in Batumi City. Students from these universities were represented as they engaged each participant from 14th June to 2nd July 2023.
2.3. Participants
The study involved males and females above 18 years, Georgian and International students living and studying in Georgia. There was no reward to the participants as the responses were anonymous.
2.4. Informed Consent
Instructions and information on data privacy were available in the consent form and questionnaire to allow each participant to read through them before completing the study. The ethics approval for this study was provided by the Bioethics council of the University of Georgia.
2.5. Evaluation Tools and Techniques
The questionnaire consisted of sociodemographic characteristics (e.g., age, gender, and institution) and three sections; social media disorder scale (SMD), academic performance scale (APS), and working memory (WM).
SMD scale was developed based on nine DSM-5 criteria for internet gaming disorder which had a binary variable of yes/no responses with nine questions regarding the social media experience (e.g., Facebook, Twitter, Instagram, WhatsApp, Snap Chat, Pinterest, TikTok, Google+, weblogs, and forums) over the past year [26]. Questions on preoccupation, tolerance, withdrawal, persistence, displacement, problems, deception, escape, and conflict were included in the measure which are elements of behavioral addictions [27,28,29,30,31]. Based on DSM-5, an individual with five or more of the nine criteria for 12 months qualifies for internet addiction. [26,27].
APS comprises 8- questions with a 5-point Likert scale covering questions relating to students’ academic performance. Students were scored based as follows; strongly agree (5), agree (4), neutral (3), disagree (2), and strongly disagree (1). The criteria for rating the performance were excellent (33-40), good (25-32), moderate (17-24), poor (9-16), and failing (0-8).
WM has 30 questions divided into storage domain questions, attention domain, and executive domain [32]. Each subcategory involved 10- questions presented randomly as storage domain questions representing the short-term storage ability of the participants, text comprehension, and mental articulation. The attention domain included questions on distractions and multitasking. The executive domain involved questions on the ability to make plans and decisions. Each question was rated on a 5-point Likert scale, from 4 (severe problems in daily life) to 0 (no problem). The maximal score for each domain was 40, and the total score was out of 120, as higher scores indicate more complaints [33].
2.6. Statistical Analysis
The data was analyzed using Statistical Package for Social Science (SPSS Inc., Chicago, IL, version 25.0 for Windows) software. Descriptive analyses were assessed for all study populations, characteristics, frequencies, percentages, means, and standard deviations. We analyzed the comparison between gender difference, nationality, and Universities, and statistically significant at p<0.05. Before assessing the association between variables and multivariate analysis, APS and WM scores were grouped into dichotomous responses (Good/Needs improvement). The students that had cutoff < 24 were in ALS performance and > 25 WM subcategories, ‘’needs improvement’’. The odds ratio (OR) and prevalence evaluation at 95% confidence intervals (95% Cl) were presented.
3.0. Results3.1. Students’ Demographic Distribution
58.7% were female students (Figure 1), the mean age was 21.94 (SD ± 2.8), and most of the participants were international students (Figure 2) from all three universities (BSU, UG, and EEU, Figure 3). In Table 1, the distribution of social media usage disorder among the students was observed higher in certain categories such as preoccupation (41.6%), persistence (60.9%), displacement (44.3%), and escape (67.6%). In the Supplementary Table S1, 16.2% of students needed an improvement in their academic performance. The students that had higher scores (>25) in the storage domain (4.4%), attention domain (6.2%), and executive domain (4.4%) had complaints (Table S1).
From Table 2, the student’s demographic analysis based on cities (Tbilisi and Batumi) showed that more students from Tbilisi had persistence (59.8%), escape (69%) complaints, and students from Batumi had more preoccupation (43%), persistence (62.5%) and escape (65.7%). Table S2, showed that 14.5% and 18.4% of students in Tbilisi and Batumi needed an improvement in their academic performance. Few students from WM subcategories had complaints. From Table S2, 4.1% and 4.9% of students from Tbilisi and Batumi respectively, had short term storage domain, as 6.1% ( Batumi) and 6.3% (Tbilisi) required more action in the short-term.
In Table 3, students from UG had more complaints about persistence (58.7%), displacement (48.7%), and escape (67.9%), students from EEU had more complaints about preoccupation (44.2%), persistence (62.8%) and escape (73.5%), and students from BSU had more complaints on preoccupation (43%), persistence (62.5%), displacement (41.4), and escape (65.7). In Table S3, 16% (UG students), 18.4% (BSU students) and 10.6% (EEU students) complained of moderate to poor academic performance. As 7.3% (UG students), and 6.1 (BSU students) had more complaints with attention domain, 3.5% (EEU) had the same complaints in all WM subcategories.
3.2. Association between Social Media Usage Disorder, Gender Differences, and Physical Activities
From Table 4, 35.9% of male students and 64.1% of female students are at increased risk of using social media as an escape from negative feelings (OR 0.50; χ2 (18.206), p= 0.000, 95% Cl[0.368-0.692]). 51.6% of male students and 48.4% of female students had the risk of conflict with families and friends because of social media (OR 1.65; χ2 (6.507), p= 0.011, 95% Cl[1.122-2.452] Table 4).
As illustrated in Table 5, 89.7% that perform physical activities, considering their academic workload had the risk of preoccupation complaints (OR 1.91); χ2 (5.379), p= 0.020, 95% Cl[1.097-3.329]). 87.9% felt dissatisfied regularly because they wanted to spend more time on social media (OR 2.23; χ2 (8.361), p= 0.004, 95% Cl[1.281-3.908] Table 5). As 87.8% of students intended to neglect other activities because of social media (OR 3.34; χ2 (17.054), p= 0.000, 95% Cl[1.834-6.112] Table 5).
3.3. Association between Social Media Usage Disorder, Academic Performance, and Working Memory
In Table 6, 80.3% of students that had good academic performance are at risk of neglecting activities such as hobbies, sports, and class assignments because of social media (OR 0.63; χ2 (5.133), p= 0.023, 95% Cl[0.425-0.942]). Most of the students with good academic performance had the risk of having arguments with friends because of social media usage (90%, OR 1.13; χ2 (5.368), p= 0.021, 95% Cl[1.099-3.470] Table 6).
Table 7 presented that 94% of students with good working memory had the risk of withdrawal complaints (OR 0.34; χ2 (6.865a), p= 0.009, 95% Cl[0.154-0.793]). As 93.4% of having conflicts with parents, siblings, and partners as a result of social media.
3.4. Multivariate Analyses Predicting Academic Performance among the Students
The multicollinearity in social media addiction was evaluated following that all independent variables did not strongly correlate with the dependent. In the final model, out of the fourteen variables represented, physical activities, displacement, and problems were significant correlates of academic performance (Table 8). The strongest correlate was physical activities which had an adjusted odd ratio (aOR) of 2.18 times. Students that complained of neglecting other activities for the purpose of social media are likely to have poor academic performance (aOR1.59) which is the same with students that had arguments with others because of social media usage (aOR 0.47).
4. Discussion
Our studies presented the prevalence of social media addiction and its effect on academic performance and working memory among undergraduate students. Utilization of social media differed based on the characteristics of the participants such as age, gender, and geography [34]. Meng et al, [35] reported an increasing trend in global social media addiction that can be observed among the participants in this study, as most of the students were international students. The influence of social media on individuals and society has been significant; it has affected both genders, shaping their perspectives, behaviors, and interactions within society [36]. Social media has provided a platform to express emotions, encourage positivity, expose unrealistic beauty standards, and foster feelings of inadequacy, and dominant behavior [3,5,37]. These could result in feelings of comparison, low self-esteem, jealousy, and anxiety [38], suggested to have an influence on social media addiction in this study.
4.1. Gender Difference
We observed that ‘escape’ behavior was prevalent among female students. Considering certain societal expectations and pressures, that may lead to higher levels of stress and anxiety. Escape behaviors on social media can take many different forms, such as mindlessly browsing through feeds, playing video games online, watching YouTube videos, or attempting to attract attention and validation through likes and comments. Social media could be a coping mechanism, female students utilize to find solace [39]. Moreover, male students had more ‘conflict’ with families and loved ones towards social media as female students may express emotions to avoid conflict and maintain relationships [40]. Conversely, masculinity norms may discourage male students from engaging in activities perceived as distractions, leading to a low prevalence of escape behaviors among male participants [39,41,42]. Additionally, social media companies' algorithms that are designed to keep both genders engaged and encourage addiction could increase the level of conflicts by making people less receptive to reasoned debate and more entrenched in their positions. Conflicts might become worse by misinterpretations and aggressive exchanges brought on by the lack of face-to-face engagement on social media [43].
4.2. Physical Activities
Shimoga, et al [44] reported that students who spend less time on social media could be productive in performing physical activities as observed in this study. The students that did not perform physical activity had complaints of ‘preoccupation’, ‘tolerance’, and ‘displacement’. As students tend to spend more time posting their daily activities [45], it was reported in another study [46] that ‘preoccupation’ strengthened the time spent. Negative online interactions might cause people to become distracted, struggle with time management, and put off tasks. The addictive nature of social media platforms, peer pressure, escapism, quick gratification, and the fear of missing out are all likely causes of students' obsession with abusing social media [47]. Due to the lack of proper offline assistance, students may use social media as their main platform for social engagement. Stress, pressure, low self-esteem, a lack of coping mechanisms, peer pressure, anonymity, escapism, and attention-seeking are all likely causes of students' displacement behavior and social media abuse [48].
4.3. Academic Performance
Our study did not predict GPA regarding academic performance although students with good performance had ‘displacement’ and ‘problems’. It has been reported that social media could have a negative effect on academic performance because of less time spent on learning and class activities which can be observed among students that need to improve their academic performance [49]. It could be that the high prevalence of students who had complaints of neglecting other activities to entertain social media used it for educational activities [9]. Though the increased duration of social media use per day could cause a deterioration in students’ academic performance [50]. The modification of social networking sites has become more and more popular among students and is a major concern with its effects on studies [51].
4.4. Working Memory
Our results showed that social media addiction regarding its effect on working memory did not widely affect the students’ working memory. The withdrawal and conflict complaints were observed among the students’ working memory. This result shows that no direct impact of social media addiction on working memory, but there could be an effect on academic performance because of negligence in important activities. A similar study that investigated the correlation between working memory and social media also found that working memory is less likely to be affected by social media use in healthy patients [35]. In the same study [35], it showed signs that excessive exposure to social media usage in patients with mental disorders like depression can influence working memory which is beyond the scope of this study. It was observed in another study that memory was negatively affected by social media usage which correlated to the stressors exposed to the brain [52]. Though our study had a high prevalence of students with a good memory that struggled with complaints such as ‘withdrawal’ and ‘conflict’. We observed that some students have to work on improving their working memory.
4.5. Recommendation
To address the issue of social networking addiction, it is necessary to encourage digital empathy, inform people about the repercussions, and cultivate a safe online space with honest dialogue and encouragement for reporting abusive behavior [53]. Students may get restless, irritable, anxious, and crave being online again when trying to cut back on consumption. Students find it difficult to stop using social media because of this addictive cycle, which emphasizes the importance of encouraging sensible usage patterns and digital well-being [54]. There are possible effects on one's academic or professional career, concerns associated with online safety, and effects from social networking platforms. To reduce these hazards, it is important to encourage responsible behavior online, inform people of the repercussions, and put in place efficient reporting and moderation systems. It is imperative to encourage digital balance among students, teach them about responsible usage, encourage offline interactions, and increase their resilience so they can deal with challenges in real life. Students should establish boundaries, use digital moderation, and seek treatment for emotional difficulties. This will help people refocus their attention on worthwhile activities especially their studies [55].
Furthermore, it offers a virtual environment where people can divert their attention from pressing issues or momentarily disconnect from pressures. Social media is an accessible alternative for stress release or relaxation because it can be easily accessed on smartphones and other devices. Social media can be very beneficial if used carefully but can also be detrimental when mishandled.
Conclusion
Our study has identified significant predictors for academic performance which are physical activities, and complaints such as displacement, and problems. Students are expected to consider the attention directed towards social media which could have a huge impact on their study. As there could be a request for counsel from any student experiencing these complaints. This would help to build healthy memory among the students and prevent psychosocial effects which are not covered in this study.
Strength and Limitations
This study covered the significant population of students from three different universities in the two most populated cities. It involved mainly international students in these institutions in different years of study. The study extensively considered each of the nine DSM-5 criteria for behavioral addiction in comparison with factors that could be a major concern among the students. These nine DSM-5 criteria were a subjective scale in which we could not specifically note the response towards a particular social network such as Facebook or Instagram or YouTube. But we assumed that the students’ response covers all social networks which remains a limitation in our study. As two or more of the criteria showed significant differences, further detailed studies are recommended. We were not able to diagnose students who had addiction considering the large number of students involved in this study, but the responses of students should attract more attention to creating awareness regarding social media addiction.
Author Contributions
M. E. N: Original draft, conceptualization, Formal analysis, Data curation, Writing—review & editing. M. E. M. and C.P: Methodology, Investigation, Formal analysis, Writing—review & editing. C. R and V. N: Methodology, Data curation, Writing—review and editing. Z. N: Original draft, Conceptualization, Methodology, Formal analysis, Data curation, Writing—original draft, Writing—review and editing. All authors have read and agreed to the published version of the manuscript. .
Funding
This study received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Bioethics council of the University of Georgia (UGREC-31-23).
Informed consent: Comprehensive instruction was provided in the survey including a consent form in which the participant can discontinue the study at any time.
Data Availability Statement
The data that support our findings are available from the first author upon reasonable request.
Acknowledgments
The authors wish to thank all participants for the time dedicated to completing this study.
Conflicts of interest: The authors have no conflict of interest. .
The authors have no conflict of interest.
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- Skues, J., Williams, B., Oldmeadow, J., & Wise, L. (2016). The Effects of Boredom, Loneliness, and Distress Tolerance on Problem Internet Use Among University Students. International Journal of Mental Health and Addiction, 14(2), 167–180. [CrossRef]
- Chen, B. C., Chen, M. Y., Wu, Y. F., & Wu, Y. T. (2022). The Relationship of Social Media Addiction With Internet Use and Perceived Health: The Moderating Effects of Regular Exercise Intervention. Frontiers in Public Health, 10. [CrossRef]
- Throuvala, M. A., Griffiths, M. D., Rennoldson, M., & Kuss, D. J. (2021). Perceived challenges and online harms from social media use on a severity continuum: A qualitative psychological stakeholder perspective. International Journal of Environmental Research and Public Health, 18(6), 1–26. [CrossRef]
Figure 1.
Pie chart representation for gender difference among the students.
Figure 2.
Pie chart illustration of the subjects based on nationality.
Figure 3.
Pie chart illustration of the subjects based on universities.
Table 1.
Frequency distribution of students’ response on SMD.
| Social Media Usage Disorder | ||
|---|---|---|
| Preoccupation | Frequency (N) | Percent (%) |
| Yes | 300 | 41.6 |
| No | 422 | 58.4 |
| Tolerance | ||
| Yes | 206 | 28.5 |
| No | 516 | 71.5 |
| Withdrawal | ||
| Yes | 217 | 30.1 |
| No | 505 | 69.9 |
| Persistence | ||
| Yes | 440 | 60.9 |
| No | 282 | 39.1 |
| Displacement | ||
| Yes | 320 | 44.3 |
| No | 402 | 55.7 |
| Problems | ||
| Yes | 150 | 20.8 |
| No | 572 | 79.2 |
| Deception | ||
| Yes | 207 | 28.7 |
| No | 515 | 71.3 |
| Escape | ||
| Yes | 488 | 67.6 |
| No | 234 | 32.4 |
| Conflict | ||
| Yes | 122 | 16.9 |
| No | 600 | 83.1 |
Table 2.
Frequency distribution of students SMD based on Cities.
| Social Media Usage Disorder | ||||
|---|---|---|---|---|
| Tbilisi | Batumi | |||
| Preoccupation | N | % | N | % |
| Yes | 167 | 40.4 | 133 | 43 |
| No | 246 | 59.6 | 176 | 57 |
| Tolerance | ||||
| Yes | 119 | 28.8 | 87 | 28.2 |
| No | 294 | 71.2 | 222 | 71.8 |
| Withdrawal | ||||
| Yes | 123 | 29.8 | 94 | 30.4 |
| No | 290 | 70.2 | 215 | 69.6 |
| Persistence | ||||
| Yes | 247 | 59.8 | 193 | 62.5 |
| No | 166 | 40.2 | 116 | 37.5 |
| Displacement | ||||
| Yes | 192 | 46.5 | 128 | 41.4 |
| No | 221 | 53.5 | 181 | 58.6 |
| Problems | ||||
| Yes | 77 | 18.6 | 73 | 23.6 |
| No | 336 | 81.4 | 236 | 76.4 |
| Deception | ||||
| Yes | 111 | 26.9 | 96 | 31.1 |
| No | 302 | 73.1 | 213 | 68.9 |
| Escape | ||||
| Yes | 285 | 69 | 203 | 65.7 |
| No | 128 | 31 | 106 | 34.3 |
| Conflict | ||||
| Yes | 59 | 14.3 | 63 | 20.4 |
| No | 354 | 85.7 | 246 | 79.6 |
Table 3.
Frequency distribution of students SMD based on based on universities.
| UG students | BSU students | EEU students | ||||
| Preoccupation | N=300 | Percent (%) | N=309 | Percent (%) | N=119 | Percent (%) |
| Yes | 117 | 39 | 133 | 43 | 50 | 44.2 |
| No | 183 | 61 | 176 | 57 | 63 | 55.8 |
| Tolerance | ||||||
| Yes | 90 | 30 | 87 | 28.2 | 29 | 25.7 |
| No | 210 | 70 | 222 | 71.8 | 84 | 74.3 |
| Withdrawal | ||||||
| Yes | 85 | 28.3 | 94 | 30.4 | 38 | 33.6 |
| No | 215 | 71.7 | 215 | 69.6 | 75 | 66.4 |
| Persistence | ||||||
| Yes | 176 | 58.7 | 193 | 62.5 | 71 | 62.8 |
| No | 124 | 41.3 | 116 | 37.5 | 42 | 37.2 |
| Displacement | ||||||
| Yes | 149 | 49.7 | 128 | 41.4 | 43 | 38.1 |
| No | 151 | 50.3 | 181 | 58.6 | 70 | 61.9 |
| Problems | ||||||
| Yes | 51 | 17 | 73 | 23.6 | 26 | 23 |
| No | 249 | 83 | 236 | 76.4 | 87 | 77 |
| Deception | ||||||
| Yes | 77 | 25.7 | 96 | 31.1 | 34 | 30.1 |
| No | 223 | 74.3 | 213 | 68.9 | 79 | 69.9 |
| Escape | ||||||
| Yes | 202 | 67.3 | 203 | 65.7 | 83 | 73.5 |
| No | 98 | 32.7 | 106 | 34.3 | 30 | 26.5 |
| Conflict | ||||||
| Yes | 43 | 14.3 | 63 | 20.4 | 16 | 14.2 |
| No | 257 | 85.7 | 246 | 79.6 | 97 | 85.8 |
Table 4.
Comparison between gender differences and SMD.
| Gender | Yes | No | chi-square | P-value | OR | 95% Confidence Interval | |
| Preoccupation | Lower | Upper | |||||
| Male | 43.3% | 39.8% | .898a | 0.343 | 1.156 | 0.856 | 1.561 |
| Female | 56.7% | 60.2% | |||||
| Tolerance | |||||||
| Male | 38.3% | 42.4% | 1.017a | 0.313 | 0.844 | 0.606 | 1.174 |
| Female | 61.7% | 57.6% | |||||
| Withdrawal | |||||||
| Male | 41.9% | 41% | .056a | 0.813 | 1.04 | 0.753 | 1.436 |
| Female | 58.1% | 59% | |||||
| Persistence | |||||||
| Male | 38.9% | 45% | 2.701a | 0.100 | 0.776 | 0.573 | 1.05 |
| Female | 61.1% | 55% | |||||
| Displacement | |||||||
| Male | 40.3% | 42% | .219a | 0.640 | 0.931 | 0.691 | 1.255 |
| Female | 59.7% | 58% | |||||
| Problems | |||||||
| Male | 46.7% | 39.9% | 2.271a | 0.132 | 1.32 | 0.919 | 1.896 |
| Female | 53.3% | 60.1% | |||||
| Deception | |||||||
| Male | 41.1% | 41.4% | .005a | 0.942 | 0.988 | 0.712 | 1.371 |
| Female | 58.9% | 58.6% | |||||
| Escape | |||||||
| Male | 35.9% | 52.6% | 18.206a | 0.000 | 0.505 | 0.368 | 0.692 |
| Female | 64.1% | 47.4% | |||||
| Conflict | |||||||
| Male | 51.6% | 39.2% | 6.507a | 0.011 | 1.658 | 1.122 | 2.452 |
| Female | 48.4% | 60.8% | |||||
Table 5.
Comparison between physical activity and SMD.
| Activity | Yes | No | chi-square | P-value | OR | 95% Confidence Interval | |
| Preoccupation | Lower | Upper | |||||
| No | 10.3% | 5.7% | 5.379a | 0.020 | 1.911 | 1.097 | 3.329 |
| Yes | 89.7% | 94.3% | |||||
| Tolerance | |||||||
| No | 12.1% | 5.8% | 8.361a | 0.004 | 2.238 | 1.281 | 3.908 |
| Yes | 87.9% | 94.2% | |||||
| Withdrawal | |||||||
| No | 10.1% | 6.5% | 2.801a | 0.094 | 1.614 | 0.917 | 2.838 |
| Yes | 89.9% | 93.5% | |||||
| Persistence | |||||||
| No | 9.1% | 5.3% | 3.474a | 0.062 | 1.78 | 0.964 | 3.287 |
| Yes | 90.9% | 94.7% | |||||
| Displacement | |||||||
| No | 12.2% | 4% | 17.054a | 0.000 | 3.348 | 1.834 | 6.112 |
| Yes | 87.8% | 96% | |||||
| Problems | |||||||
| No | 10% | 7% | 1.527a | 0.217 | 1.478 | 0.793 | 2.755 |
| Yes | 90% | 93% | |||||
| Deception | |||||||
| No | 7.2% | 7.8% | .057a | 0.812 | 0.928 | 0.501 | 1.719 |
| Yes | 92.8% | 92.2% | |||||
| Escape | |||||||
| No | 7.4% | 8.1% | .124a | 0.725 | 0.901 | 0.505 | 1.608 |
| Yes | 92.6% | 91.9% | |||||
| Conflict | |||||||
| No | 9% | 7.3% | .408a | 0.523 | 1.252 | 0.627 | 2.5 |
| Yes | 91% | 92.7% | |||||
Table 6.
Comparison between academic performance and SMD.
| Academic Performance | Yes | No | chi-square | P-value | OR | 95% Confidence Interval | |
| Preoccupation | Lower | Upper | |||||
| Good Performance | 85.7% | 82.5% | 1.324a | 0.250 | 1.271 | 0.844 | 1.913 |
| Needs Improvement | 14.3% | 17.5% | |||||
| Tolerance | |||||||
| Good Performance | 86.9% | 83.2% | 1.032a | 0.310 | 1.341 | 0.76 | 2.365 |
| Needs Improvement | 13.1% | 16.8% | |||||
| Withdrawal | |||||||
| Good Performance | 84.8% | 83.4% | .227a | 0.633 | 1.113 | 0.718 | 1.725 |
| Needs Improvement | 15.2% | 16.6% | |||||
| Persistence | |||||||
| Good Performance | 81.8% | 86.9% | 3.242a | 0.072 | 0.680 | 0.446 | 1.037 |
| Needs Improvement | 18.2% | 13.1% | |||||
| Displacement | |||||||
| Good Performance | 80.3% | 86.6% | 5.133a | 0.023 | 0.633 | 0.425 | 0.942 |
| Needs Improvement | 19.7% | 13.4% | |||||
| Problems | |||||||
| Good Performance | 90% | 82.2% | 5.368a | 0.021 | 1.953 | 1.099 | 3.470 |
| Needs Improvement | 10% | 17.8% | |||||
| Deception | |||||||
| Good Performance | 85% | 83.3% | .323a | 0.570 | 1.138 | 0.728 | 1.779 |
| Needs Improvement | 15% | 16.7% | |||||
| Escape | |||||||
| Good Performance | 84.4% | 82.5% | .442a | 0.506 | 1.152 | 0.759 | 1.747 |
| Needs Improvement | 15.6% | 17.5% | |||||
| Conflict | |||||||
| Good Performance | 86.9% | 83.2% | 1.032a | 0.310 | 1.341 | 0.76 | 2.365 |
| Needs Improvement | 13.1% | 16.8% | |||||
Table 7.
Comparison between working memory and SMD.
| Working memory | |||||||
| Storage Domain | Yes | No | chi-square | P-value | OR | 95% Confidence Interval | |
| Preoccupation | Lower | Upper | |||||
| Good | 95.3% | 97.6% | 2.879a | 0.090 | 0.496 | 0.217 | 1.132 |
| Needs Improvement | 4.7% | 2.4% | |||||
| Tolerance | |||||||
| Good | 94.7% | 97.5% | 3.644a | 0.056 | 0.458 | 0.202 | 1.040 |
| Needs Improvement | 5.3% | 3.9% | |||||
| Withdrawal | |||||||
| Good | 94% | 97.8% | 6.865a | 0.009 | 0.349 | 0.154 | 0.793 |
| Needs Improvement | 6% | 2.2% | |||||
| Persistence | |||||||
| Good | 96.1% | 97.5% | 1.020a | 0.312 | 0.633 | 0.259 | 1.547 |
| Needs Improvement | 3.9% | 2.5% | |||||
| Displacement | |||||||
| Good | 95.3% | 97.8% | 3.324a | 0.068 | 0.466 | 0.201 | 1.078 |
| Needs Improvement | 4.7% | 2.2% | |||||
| Problems | |||||||
| Good | 94.7% | 97.2% | 2.378a | 0.123 | 0.511 | 0.214 | 1.217 |
| Needs Improvement | 5.3% | 2.8% | |||||
| Deception | |||||||
| Good | 95.7% | 97.1% | 0.946a | 0.331 | 0.660 | 0.284 | 1.533 |
| Needs Improvement | 4.3% | 2.9% | |||||
| Escape | |||||||
| Good | 96.3% | 97.4% | 0.622a | 0.430 | 0.687 | 0.269 | 1.754 |
| Needs Improvement | 3.7% | 2.6% | |||||
| Conflict | |||||||
| Good | 93.4% | 97.3% | 4.776a | 0.029 | 0.390 | 0.163 | 0.934 |
| Needs Improvement | 6.6% | 2.7% | |||||
Table 8.
Multiple regression analyses predicting social media disorder among the students.
| Variable | B | Sig. | aOR | 95% Confidence Interval for Exp(B) | ||
|---|---|---|---|---|---|---|
| Lower Bound | Upper Bound | |||||
| Needs Improvement | Constant | -1.283 | 0.037 | |||
| Gender | Male | 0.085 | 0.703 | 1.088 | 0.704 | 1.681 |
| Female | 0b | . | . | . | . | |
| Nationality | Georgian | 0.419 | 0.268 | 1.521 | 0.724 | 3.194 |
| International students | 0b | . | . | . | . | |
| Institute | UG | 0.196 | 0.583 | 1.216 | 0.605 | 2.446 |
| BSU | 0.537 | 0.124 | 1.71 | 0.863 | 3.388 | |
| EEU | 0b | . | . | . | . | |
| Physical activities | No | 0.781 | 0.023 | 2.183 | 1.114 | 4.281 |
| Yes | 0b | . | . | . | . | |
| Preoccupation | Yes | -0.368 | 0.130 | 0.692 | 0.43 | 1.114 |
| No | 0b | . | . | . | . | |
| Tolerance | Yes | 0.289 | 0.252 | 1.335 | 0.815 | 2.186 |
| No | 0b | . | . | . | . | |
| Withdrawal | Yes | -0.177 | 0.484 | 0.838 | 0.51 | 1.375 |
| No | 0b | . | . | . | . | |
| Persistence | Yes | 0.429 | 0.071 | 1.535 | 0.963 | 2.446 |
| No | 0b | . | . | . | . | |
| Displacement | Yes | 0.468 | 0.043 | 1.597 | 1.015 | 2.513 |
| No | 0b | . | . | . | . | |
| Problems | Yes | -0.735 | 0.025 | 0.479 | 0.252 | 0.913 |
| No | 0b | . | . | . | . | |
| Deception | Yes | 0.015 | 0.953 | 1.016 | 0.61 | 1.691 |
| No | 0b | . | . | . | . | |
| Escape | Yes | -0.286 | 0.222 | 0.751 | 0.475 | 1.189 |
| No | 0b | . | . | . | . | |
| Conflict | Yes | -0.244 | 0.473 | 0.784 | 0.402 | 1.526 |
| No | 0b | . | . | . | . | |
| Working Memory | Good | -0.907 | 0.067 | 0.404 | 0.153 | 1.065 |
| Needs Improvement | 0b | . | . | . | . | |
Reference category is good performance; aOR adjusted odd ratio.
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