Psychosocial Factors Behind Fear of Missing Out (FOMO) Among Young Adults: The Effects of Phubbing, Social Comparison, Exhaustion, and Loneliness.

Psychosocial Factors Behind Fear of Missing Out (FOMO) Among Young Adults: The Effects of Phubbing, Social Comparison, Exhaustion, and Loneliness.

Ariff Md Ab Malik, Hanitahaiza Hairuddin*, Norhanis Ismail, Yasarah Syazana Shafi’I, Siti Aisyah Barkat Ali, Fatin Raudah Ramzah

Faculty of Business and Management, University Technology MARA, Puncak Alam Campus, 42300 Puncak Alam, Selangor, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.913COM0043

Received: 03 September 2025; Accepted: 08 September 2025; Published: 11 October 2025

ABSTRACT

In today’s hyperconnected society, Fear of Missing Out (FOMO) has emerged as a prominent psychological phenomenon particularly among young adults who are heavily engaged with social media. FOMO is often associated with negative psychological outcomes, including emotional well-being, online social anxiety, sleep disturbances and problematic digital behaviours. This study investigates the psychosocial factors contributing to FOMO among young adults by examining the roles of phubbing, social comparison, exhaustion, and loneliness.  Using cross-sectional research design, data were collected through an online survey from 134 young adult social media platform users. The data were analysed using SmartPLS to examine the relationships between the psychosocial factors and to identify the most significant predictors of FOMO. The findings show that social comparison is the most significant predictor to FOMO. This finding supporting the perspective of Social Comparison Theory that individuals evaluate themselves based on other people lives as presented in social media.  Phubbing and loneliness also demonstrated positive but modest association with FOMO. On the other hand, the findings show that exhaustion does not significantly influence FOMO. The study highlights that academic fatigue and burnout may not directly influence this phenomenon as strongly as interpersonal and social behaviour. The interventions that promote digital mindfulness, better social relationships, and effective academic mechanisms are needed in helping young adults to mitigate FOMO. The findings provide valuable insights for policymakers, educators, and mental health professionals that should play their roles as psychosocial drivers of FOMO. The practical direction for prevention and intervention strategies should be offered to protect the well-being of youth in digital environment.

Keywords: Fear of Missing Out (FOMO), Social Comparison, Phubbing, Loneliness, Social media Use

INTRODUCTION

The emergence of social media has reshaped the way of the young adults communicate, interact, and perceive their social environments. Undoubtedly, digital platforms such as Instagram, TikTok, and Facebook offer enormous opportunities for connectivity. Despite that, they also contribute to a psychological phenomenon that widely recognised as Fear of Missing Out (FOMO). FOMO is the widespread anxiety that others are having rewarding experiences without one’s participation, driving individuals to stay continually connected to social media platforms [1], [2], [3]. This fear is intensified by social media, where individuals are continuously exposed to ideal versions of other people lives [4]. For young adults, who are in a critical developmental phase, FOMO can result in emotional distress, compulsive phone usage, exhaustion, loneliness, and unhealthy social comparisons [5].

There are lack of studies about FOMO that focus of Malaysian young adults. However, cultural and social contexts can shape how FOMO plays out. FOMO might look different or have stronger effects in more collective societies like Malaysia, where the expectations of family and peer are strong. Due to that, it is important to study how FOMO affects Malaysian adult youths. Therefore, this study aims to explore the predictors of FOMO in social media use, focusing on the roles of phubbing, loneliness, social comparison, and exhaustion among Malaysian young adults.

LITERATURE REVIEW

A. Social Media and FOMO

Social media has become a significant part of our daily life. Apart from the benefits given by social media like connecting and sharing with other people, it has also resulted to the “Fear of Missing Out” (FOMO), particularly among the young people. Studies show that the more time people spend on social media, the more likely they are to feel FOMO. FOMO can negatively impact how people view themselves, affecting things like self-esteem and confidence [6]. Moreover, it often extends beyond the online world. [7] found that FOMO can lead to problematic social media use (PSMU) and may contribute to behaviours like excessive amount of alcohol consumption among college students. FOMO has been linked to PSMU and social media addiction [8], [2], [9], [10].

FOMO occurs when people’s basic demands for connection, competence, or independence are not met. When individuals rely on social media to fulfil these needs, it can lead to problematic use, low mood, and even sleep issues.  As a mental and emotional state, FOMO often becomes stronger when people feel stressed or face emotional challenges. This situation will encourage them to check the social information more often [11]. FOMO has been shown to mediate the relationship between social media use and outcomes such as negative emotions, reduced self-esteem, and sleep disturbances [12].

FOMO can both a cause and a consequence of excessive use of social media. Individuals who already feel insecure or stressed are more prone to experience FOMO. As they succumb to these feelings, they become stuck in a cycle of compulsive checking, comparing, and worrying. Theories like social comparison, self-determination, and compensatory internet use all highlight different sides of the same problem of FOMO. While FOMO meets our natural social needs, it frequently makes us feel worse. [12], [13].

B. Psychosocial Factors of FOMO

Fear of Missing Out (FOMO) is formed by various psychosocial elements that influences people’s feelings, thoughts, and actions in digital environments. Phubbing, social comparison, exhaustion, and loneliness are the identified elements that lead to the beginning and development of FOMO.

Phubbing is the practice of concentrating on one’s smartphone while ignoring others during face-to-face interactions. This act is closely related with FOMO because the individuals who experience FOMO are more likely to engage in phubbing behaviour as they prefer to staying connected to online interactions [14], [15], [16]. Studies indicate that FOMO can predict phubbing behaviour both directly and indirectly through its relationship with PSMU [2], [17]. The mediating role of FOMO in the relationship between boredom and phubbing suggests that individuals may use their phones to alleviate the negative feelings associated with boredom [16]. Phubbing is positively correlated with FOMO, according to empirical research, indicating that excessive mobile phone use increases social anxiety [15], [17].

Social comparison, especially upward comparison on social media, is another critical factor influencing FOMO. Upward social comparison, where individuals compare themselves to those perceived as better off, is particularly harmful. It is linked to lower life satisfaction and greater psychological distress [18], [19]. The tendency to compare oneself to others on social media can lead to feelings of inadequacy and anxiety, driving FOMO [18], [19], [20]. This situation has not only dragged them to FOMO but also drives them into reckless behaviour such as unnecessary shopping and overspending [13]. [21] argued that these comparisons strongly drive FOMO, resulting in increased envy, anxiety, and lowered self-worth. [22] found that frequent exposure to idealized online images fosters feelings of inadequacy, leading young adults to involve in constant trend participation. Past research shows that individuals with a high tendency to compare themselves to others are more susceptible to FOMO and subsequent problematic social media use [13], [23].

FOMO is often caused by study or working exhaustion. Exhaustion is when someone feels extremely tired physically and/or mentally until they unable to do their normal activities. Earlier studies mainly linked exhaustion to job burnout, but recent studies show its importance in academic and digital environments. In occupational context, several studies indicate a positive correlation between working exhaustion or burnout and FOMO. A study on nurses found that higher levels of job burnout were associated with increased FOMO. The study reveals that the stress and detachment from social and personal activities due to demanding work schedules contribute to FOMO [24]. Similarly, another study on Chinese employees showed that FOMO was positively associated with job burnout [25]. In academic context, the pressure to succeed academically may drive students to seek distraction and validation through social media, leading to increased FOMO [26]. [27] found that students with high FOMO report higher stress levels, as they struggle to balance academic responsibilities with online engagement. [28] suggested that while academic burnout can encourage self-reflection, unmanaged FOMO worsens emotional fatigue and weakens resilience. The relationship between study exhaustion and FOMO suggests that academic stress can intensify the desire to stay connected and updated on social media, further reinforcing problematic usage patterns [26], [29]. Research also indicates that social media fatigue, which is closely linked to exhaustion, can predict FOMO and unhealthy engagement patterns online.

One of the main psychosocial aspects that associated with FOMO is loneliness because the individuals who feel lonely are more likely to turn to social media for emotional support, which can lead to development of FOMO [20], [30]. [31] showed that young adults who experience loneliness are more likely to rely on social media to meet social needs. However, these digital interactions often lack emotional depth, thereby intensifying feelings of isolation. [32] emphasized that individuals high in FOMO report higher levels of loneliness due to compulsive checking behaviours, which paradoxically increase social exclusion. Empirical studies consistently report that loneliness is positively correlated with FOMO and problematic social media use [1], [16].

These psychosocial factors reveal the complex causes of FOMO by demonstrating the ways in which cognitive interests, working or academic stress, emotional health, and interpersonal behaviours interact to influence a person’s vulnerability to the disorder. The relationships in this study are also supported by the theoretical views. The Social Comparison Theory explains why people explains why people compare themselves to others in online environment, which reinforces FOMO when they see idealized depictions of other people’s lifestyles [4]. While, Compensatory Internet Use Theory by [33] suggests that individuals use social media as a coping mechanism for stressors such as exhaustion and loneliness, which can intensify FOMO. Self-Determination Theory highlights the importance of relatedness, explaining why unmet social needs contribute to heightened FOMO [34]. Guided by these insights and based on the discussion, the following hypotheses are offered:

H1: Phubbing, as a psychosocial factor, is positively associated with FOMO.

H2: Social comparison, as a psychosocial factor, is positively associated with FOMO.

H3: Exhaustion, as a psychosocial factor, is positively associated with FOMO.

H4: Loneliness, as a psychosocial factor, is positively associated with FOMO.

Figure 1 illustrates the relationship between the variables.

Figure 1: Conceptual Framework

METHODOLOGY

This research used a quantitative approach through a cross-sectional research design to examine psychosocial factors (phubbing, loneliness, social comparison and exhaustion) and FOMO. Therefore, this section describes the measures and item designs, the respondents and data collection procedures.

A. Measurement Instruments

The items of the survey instrument were measured on a five-point Likert scales and it consisted of five measures, which was adapted from past studies. For each of variables (phubbing, social comparison, exhaustion and FOMO), four (4) items were adopted and modified to social media context based on [11], [35], [36], [37]. While three (3) items of loneliness were adopted from [38]. Pre-test procedures were carried out before data was collected. For content validity, the instrument was evaluated by experts to make sure that the assessment is relevant and in line with the goals of the study. For the inconsistency scores, Cronbach’s alpha reliability tests were higher than 0.7 for all constructs which shows a good reliability of the item measurement.

B. Participants and Data Collection Procedures.

The population of this study was young adult social media users. For this study, the individuals age between 18-30 years can be categorised as young adults, aligning with the Federal Youth Policy in Malaysia. [39]. G*Power software was used to determine the sample size of this study.  Based on this tool, the minimum sample size required for this study was 95. Data were collected through Google Form. The participants were recruited through social media platform (WhatsApp, Instagram and Telegram) and a total of 134 responses were collected. The sample included 74 female respondents (55.2%) and 60 male respondents (44.8%). 73.9% of the respondents’ age were between 20 and 24 years old, 19.4% are between 25 to 30 years old and 6.7% between 18-20 years old.

FINDINGS

Data analysis was conducted by using SmartPLS version 4 to test the hypotheses. The analysis included measurement model evaluation and structural model evaluation.

A. Measurement Model

All items were tested for internal consistency reliability, convergent validity, and discriminant validity. The consistency validity and convergent validity were measured by using Cronbach’s Alpha (CA), Composite Reliability (CR), and Average Variance (AVE) as shown in Table 1. Based on the table, all CA values, ranged from 0.742 to 0.896 were exceeded the minimum value of threshold of 0.700. CR values ranged between 0.836 and 0.933, confirming the internal consistency validity is good across all constructs [40].  Convergent validity was used to examine the degree to which items of the same constructs are correlated. Table 1 shows that all the AVE’s value ranging from 0.571 to 0.822 were reached the threshold of 0.5 as recommended by [41]. The values indicate that each construct explains more than 50% of the variance of its indicators, showing adequate convergent validity for all constructs.  Therefore, both reliability and convergent validity were established for all constructs, and the measurement model is reliable and valid.

Table 1 : Reliability and Convergent Validity

Discriminant validity was examined to ensure that the constructs are distinct from each other. As shown in Table 2, all HTMT values are ranged between 0.304 and 0.666. Based on [40] and [42], HTMT values below 0.85 and 0.9 are acceptable and it indicates that the measurement items are measure different concepts. Hence, the measurement items are suitable for hypothesis testing within the structural model.

Table 2: HTMT Results

B. Structural Model

To test the hypothesises, a structural model was run to examine the relationship between the independent variables (phubbing, social comparison, exhaustion and loneliness) and dependent variable (FOMO). This model was evaluated by examining the value of path coefficients, t-value, p-value, effect sizes (f²) and R². A bootstrapping procedure with 5000 resamples and a one-tailed test with a significance level of 0.05 was used to test the significance of the hypothesis’s relationships. Table 3 shows the results of hypothesis testing.

Table 3 : Hypothesis Testing

R² (FOMO) = 0.422                Adjusted R² (FOMO) = 0.404

The result explained 42.2% of the variance in FOMO (R²=0.422, Adjusted R²=0.404), indicating moderate explanatory power [40]. The small difference between R² and Adjusted R² suggests that the model’s predictors make a significant contribution to explain FOMO. As shown in Table 4, all hypotheses are supported except H3. Among all the psychosocial factors, social comparison was the most significant factors (β = 0.410, p < 0.001, f² = 0.175), followed by loneliness (β = 0.212, p = 0.031, f² = 0.042) and phubbing (β = 0.137, p = 0.030, f² = 0.027). On the other hand, exhaustion did not significantly predict FOMO (β = 0.045, p = 0.318, f² = 0.002). The results indicate that FOMO is primarily driven by social and interpersonal factors rather than fatigue.

Table 4: Hypothesis Result

DISCUSSION

This study examines the psychosocial factors of FOMO among young adults. Phubbing, social comparison, exhaustion and loneliness are the focus of this study. The findings indicate that the model explained 42.2% of the variance in FOMO, reflecting a moderate level of explanatory power [40].  Social comparison has highly significant positive effect, strongly increases FOMO with largest effect size. Phubbing and loneliness have significant positive effect on FOMO. Phubbing slightly increases FOMO with small size effect while loneliness moderately increases FOMO with a small effect size.

A. Social Comparison as the Strongest Predictor

The results show that social comparison had a strong positive impact on FOMO (β = 0.410, p < 0.001, f² = 0.175). This finding supports social comparison theory [4], which suggests that people assess themselves by looking at others. In the context of social media, frequent exposure to idealized images of peers leads to upward comparisons. This can heighten feelings of exclusion and FOMO [2]. Previous studies consistently indicate that people who often engage in social comparison are more susceptible to FOMO. They are likely to see others enjoying rewarding experiences that they do not have [45], [46].  This study extends the previous findings by showing that social comparison is the most powerful factor compared to the other psychosocial factors. It shows that young adults can easily be influenced by the actions of their peers or people around them.

B. Loneliness and Phubbing as Significant Predictors

The study also found that loneliness positively predicted FOMO (β = 0.212, p = 0.031, f² = 0.042). It was supported by multiple studies that found a positive relationship between loneliness and FOMO [47]. A study of Vietnamese undergrad students revealed that loneliness is positively associated by both personal and societal aspects of FOMO [48]. Lonely people may turn to social media to fill unmet social needs. However, this can ironically increase their sense of being excluded from real-world interactions [21].  Cultural and environmental factors can also influence the relationship between loneliness and FOMO. [48] highlighted that uncertainty avoidance and independent thinking can affects the strength of the association between loneliness and FOMO. The studies in Asian countries like Vietnam and China indicates the positive relationship between loneliness and FOMO and this relationship is often mediated by additional factors such as social anxiety and smartphone addiction [48][49]. On the other hand, even though the studies from other regions also found the positive relationship between loneliness and FOMO but they are focusing more on the role of cultural variation in moderating the relationship [47].

Phubbing was also a significant, though weaker predictor of FOMO (β = 0.137, p = 0.030, f² = 0.027). This supports earlier studies that found positive relationship between phubbing and FOMO [50]. Heavy smartphone uses and ignoring face-to-face interactions can increase feelings of social disconnection and trigger FOMO [35]. Although the effect size is small, the finding aligns with the previous evidence that smartphone overuse and phubbing behaviours are both the causes and consequences of FOMO [16][50].

Thus, both loneliness and phubbing contribute to FOMO, but their impact is smaller compared to social comparison. This suggests that social and behavioural factors play a secondary role.

C. Exhaustion as a Non-Significant Predictor

Interestingly, the findings of this study reveal that exhaustion did not significantly predict FOMO among young adults (β = 0.045, p = 0.318, f² = 0.002). One possible explanation is that FOMO is more closely tied to social and relational factors, such as social comparison, sense of belonginess and social disconnection [11]. In contrast, exhaustion often results in self-unwinding and disengagement from social and digital activities [51]. When people are exhausted, they may prioritize recovery such as sleeping, avoiding screens, reducing social commitments, rather than actively monitoring others’ activities online, which would otherwise trigger FOMO [52]. The non-significant result indicates that FOMO is less about energy levels and more about social-psychological factors such as loneliness, social comparison, and smartphone-related habits.

CONCLUSIONS

This study examined the psychological and behavioural factors that predict fear of missing out (FOMO) among young adults, focusing on loneliness, phubbing, social comparison, and exhaustion. Using SmartPLS, the findings showed that the model accounted for 42.2% of the variance in FOMO, which indicates moderate explanatory power. Among the predictors, social comparison was the strongest and most significant factor affecting FOMO. This highlights how upward comparisons on social media shape feelings of exclusion and missed experiences. Loneliness and phubbing also served as significant predictors, but their impacts were weaker. These results show that feelings of disconnection and behaviours related to smartphone use can worsen the experience of FOMO. On the other hand, exhaustion did not significantly affect FOMO. This suggests that FOMO mainly results from social and relational dynamics instead of academic or working pressure. The findings support the importance of social comparison theory [4] and self-determination theory [34] in understanding FOMO. Practically, the study points to the need for interventions that address excessive social comparison and digital dependency while encouraging meaningful offline connections to reduce loneliness. Overall, the study contributes to the growing understanding of FOMO by identifying its key predictors for young adults. It also provides valuable insights for educators, counsellors, and policymakers in crafting strategies to promote healthier digital engagement.

ACKNOWLEDGMENT

We would like to express our sincere gratitude to the Faculty of Business and Management Puncak Alam Campus, Selangor Branch, Malaysia for their continuous support and encouragement throughout the development of this paper.

REFERENCES

1. Uram, P., & Skalski, S. (2022). Still logged in? The link between Facebook addiction, FOMO, self-esteem, life satisfaction and loneliness in social media users. Psychological Reports, 125(1), 218-231.
2. Franchina, V., Vanden Abeele, M., Van Rooij, A. J., Lo Coco, G., & De Marez, L. (2018). Fear of missing out as a predictor of problematic social media use and phubbing behavior among Flemish adolescents. International journal of environmental research and public health, 15(10), 2319.
3. Gao, X., Li, J., Bai, X., Zhou, Y., & Jiang, X. (2025). Understanding daily problematic social media use of young adults: the role of trait-and state-fear of missing out. Psychology & Health, 1-13.
4. Festinger, L. (1954). A theory of social comparison processes. Human relations, 7(2), 117-140.
5. Roberts, J. A., & David, M. E. (2020). The social media party: Fear of missing out (FOMO), social media intensity, connection, and well-being. International Journal of Human–Computer Interaction, 36(4), 386-392.
6. Seth, A., Singh, P., & Imran, M. (2024). The mediating role of FOMO on the relationship between social media engagement and core self-evaluation. In Health Psychology in Integrative Health Care (pp. 11-15). Routledge.
7. Gómez-Martínez, M., & Rosa-Alcázar, A. I. (2025). Fear of missing out, social media use and alcohol consumption in college students. A path analysis model. Anales de Psicología, 41(1).
8. Li, Y. Y., Koning, I. M., Finkenauer, C., Boer, M., & van den Eijnden, R. J. (2024). The bidirectional relationships between fear of missing out, problematic social media use and adolescents’ well-being: A random intercept cross-lagged panel model. Computers in Human Behavior, 154, 108160.
9. Reyes, M. E. S., Marasigan, J. P., Gonzales, H. J. Q., Hernandez, K. L. M., Medios, M. A. O., & Cayubit, R. F. O. (2018). Fear of missing out and its link with social media and problematic internet use among Filipinos. North American Journal of Psychology, 20(3).
10. Brailovskaia, J., & Margraf, J. (2024). From fear of missing out (FOMO) to addictive social media use: The role of social media flow and mindfulness. Computers in Human Behavior, 150, 107984.
11. Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Fear of missing out scale. Computers in human behavior.
12. Radiotin, A., & Roussos, P. (2024). Relationship between FOMO, problematic social media use, self-esteem, negative affectivity, and physical exercise: A structural equation model. Journal of Technology in Behavioral Science, 9(2), 313-324.
13. Kumar, S., & Kumar, S. (2024). Compulsive Use of Social Media and Unrestrained Buying Behavior: Delineating the Role of Social Comparison, Fear of Missing Out and Self Esteem. Journal of Internet Commerce, 23(4), 503–532. https://doi.org/10.1080/15332861.2024.2421731
14. Kaviya, G., & Likitha, S. (2025). The Influence of Personality Traits on Smartphone Addiction, Phubbing, and Mental Well-Being. In Smartphone Addiction, Phone Snubbing, and Effects on Interpersonal Relationships and Mental Health (pp. 237-264). IGI Global Scientific Publishing.
15. Fang, J., Wang, X., Wen, Z., & Zhou, J. (2020). Fear of missing out and problematic social media use as mediators between emotional support from social media and phubbing behavior. Addictive behaviors, 107, 106430.
16. Correa-Rojas, J., Grimaldo-Muchotrigo, M., & Espinoza, E. M. (2022). FOMO, Facebook Addiction, and Loneliness as Determinants of Phubbing in University Students from Lima.
17. Al-Saggaf, Y. (2021). Phubbing, fear of missing out and boredom. Journal of Technology in Behavioral Science, 6(2), 352-357.
18. Berkout, O. V., & Flynn, M. K. (2025). Social Media Utilization, Fear of Missing Out, Upward Social Comparison, and Self-Compassion as Predictors of Psychological Distress and Life Satisfaction Among Individuals with Mental Health Concerns. Journal of Technology in Behavioral Science, 10(1), 7-14.
19. Saritepeci, M., & Kurnaz, M. F. (2024). Antecedents and consequences of FOMO for neuroticism, openness and social influence: Investigating the moderating effect. Personality and Individual Differences, 225, 112657.
20. Servidio, R., Craig, F., Soraci, P., Boca, S., Pisanti, R., Demetrovics, Z., & Griffiths, M. D. (2025). Loneliness moderates the predictive effect of the trait-state FOMO pathway on problematic social media use. Addictive Behaviors Reports, 100612.
21. Dinh, T. C. T., & Lee, Y. (2022). “I want to be as trendy as influencers”–how “fear of missing out” leads to buying intention for products endorsed by social media influencers. Journal of Research in Interactive Marketing, 16(3), 346-364.
22. Rautela, S., & Sharma, S. (2022). Fear of missing out (FOMO) to the joy of missing out (JOMO): shifting dunes of problematic usage of the internet among social media users. Journal of Information, Communication and Ethics in Society, 20(4), 461-479.
23. Uram, P., & Skalski, S. (2022). Still logged in? The link between Facebook addiction, FOMO, self-esteem, life satisfaction and loneliness in social media users. Psychological Reports, 125(1), 218-231.
24. Qutishat, M., & Al Sabei, S. (2024). Relationship between burnout and fear of missing out among nurses in Oman: Implication for nursing practice. Journal of Education and Health Promotion, 13(1), 495.
25. Shi, G., Yan, W., Pang, H., Liu, Z., & Zheng, X. (2024). Workplace fear of missing out and job burnout: The chain mediating effect of psychological detachment and work–family conflict. Social Behavior and Personality: an international journal, 52(5), 13008E-13016E.
26. Al-Busaidi, A. S., Dauletova, V., & Al-Wahaibi, I. (2023). The role of excessive social media content generation, attention seeking, and individual differences on the fear of missing out: a multiple mediation model. Behaviour & Information Technology, 42(9), 1389-1409.
27. Lu, K., Zhu, J., Pang, F. et al. (2024). Understanding college students’ test anxiety in asynchronous online courses: the mediating role of emotional engagement. Int J Educ Technol High Educ 21, 50. https://doi.org/10.1186/s41239-024-00482-1.
28. Thanzami, V. (2022). Risk Factors for Smartphone Overuse Among University Students in Malaysia. In: Sia, S.K., Crane, L.S., Jain, A.K., Bano, S. (eds) Understanding Psychology in the Context of Relationship, Community, Workplace and Culture. Springer, Singapore. https://doi.org/10.1007/978-981-19-2693-8_10.
29. Al-Jallad, M. S., & Radwan, A. F. (2021). Exploring social media fatigue among youth in the United Arab Emirates. Journal of Print and Media Technology Research, 10(3), 163-178.
30. Doktorová, D., EL MGHARI, S., & Tokovská, M. (2025). Social Media Engagement And Identity: The Loneliness And FOMO Experience Of Young People. Communication Today, 16(1).
31. Bettmann, J. E., Anstadt, G., Casselman, B., & Ganesh, K. (2021). Young adult depression and anxiety linked to social media use: Assessment and treatment. Clinical Social Work Journal, 49(3), 368-379.
32. Koban, K., Stevic, A., & Matthes, J. (2023). A tale of two concepts: differential temporal predictions of habitual and compulsive social media use concerning connection overload and sleep quality. Journal of Computer-Mediated Communication, 28(2), zmac040.
33. Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in human behavior, 31, 351-354.
34. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67.
35. Chotpitayasunondh, V., & Douglas, K. M. (2018). The effects of “phubbing” on social interaction. Journal of applied social psychology, 48(6), 304-316.
36. Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in social comparison: development of a scale of social comparison orientation. Journal of personality and social psychology, 76(1), 129.
37. Maslach, C., Jackson, S. E., & Leiter, M. P. (1997). Maslach burnout inventory. Scarecrow Education.
38. Hughes, M. E., Waite, L. J., Hawkley, L. C., & Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on aging, 26(6), 655-672.
39. Sinar Daily. (2023, November 23). New youth age limit comes into force Jan 1, 2026. Sinar Daily.
40. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature.
41. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
42. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135.
43. Buglass, S. L., Binder, J. F., Betts, L. R., & Underwood, J. D. (2017). Motivators of online vulnerability: The impact of social network site use and FOMO. Computers in Human Behavior, 66, 248-255.
44. Elhai, J. D., Gallinari, E. F., Rozgonjuk, D., & Yang, H. (2020). Depression, anxiety and fear of missing out as correlates of social, non-social and problematic smartphone use. Addictive behaviors, 105, 106335.
45. Stead, H., & Bibby, P. A. (2017). Personality, fear of missing out and problematic internet use and their relationship to subjective well-being. Computers in Human Behavior, 76, 534-540.
46. Salmela‐Aro, K., & Upadyaya, K. (2014). School burnout and engagement in the context of demands–resources model. British journal of educational psychology, 84(1), 137-151.
47. Wang, H., & Xia, X. (2025). The relationship between perceived social (dis) connectedness and fear of missing out (FoMO): a meta-analysis. Current Psychology, 1-17.
48. Van Huynh, C., Phan, H. T., Hua, T. D., Tran-Thi, T. H., & Tran-Chi, V. L. (2022). Relationship between the fear of missing out and loneliness among university students in COVID-19 pandemic. Pegem Journal of Education and Instruction, 12(3), 44-48.
49. Li, T., Chen, B., Li, Q., Wu, X., Li, Y., & Zhen, R. (2023). Association between bullying victimization and post-traumatic stress disorders among Chinese adolescents: a multiple mediation model. BMC psychiatry, 23(1), 758.
50. Ansari, S., Azeem, A., Khan, I., & Iqbal, N. (2024). Association of phubbing behavior and fear of missing out: A systematic review and meta-analysis. Cyberpsychology, Behavior, and Social Networking, 27(7), 467-481.
51. Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: taking stock and looking forward. Journal of occupational health psychology, 22(3), 273.
52. Alonzo, R., Hussain, J., Stranges, S., & Anderson, K. K. (2021). Interplay between social media use, sleep quality, and mental health in youth: A systematic review. Sleep medicine reviews, 56, 101414.