Social Isolation and Social Media Addiction: The Serial Mediation Roles of Social Anxiety and FoMO among Chinese University Students

Social Isolation and Social Media Addiction: The Serial Mediation Roles of Social Anxiety and FoMO among Chinese University Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Social Isolation and Social Media Addiction: The Serial Mediation Roles of Social Anxiety and FoMO among Chinese University Students Xian Zhao, Tongfeng Xu, Xiaosheng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7654856/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 18 You are reading this latest preprint version Abstract Purpose – This study investigates the direct and indirect relationships between social isolation and social media addiction among Chinese university students, with social anxiety and fear of missing out (FoMO) as serial mediators in the digital era. Design/methodology – Data were collected from 210 students across seven universities in different cities and academic majors using random sampling. Hypotheses were tested in SPSS and SmartPLS through four stages: (1) descriptive analysis, (2) confirmatory factor analysis, (3) structural model evaluation, and (4) mediation testing (direct, simple, and serial). Findings – Social isolation was positively associated with social media addiction, social anxiety, and FoMO. Social anxiety further increased FoMO and social media addiction, while FoMO also amplified social media addiction. Mediation analyses showed that social isolation indirectly predicted social media addiction via social anxiety (indirect effect = 0.121***), FoMO (indirect effect = 0.104***), and their sequential pathway (specific indirect effect = 0.161***). Descriptively, 60% of students (n = 126) reported above-average levels of social isolation, and 76.19% (n = 160) scored ≥ 19 on the Bergen Social Media Addiction Scale, indicating moderate to high addiction levels. Originality/value – By applying a tripartite theoretical framework, this study extends Sociometer Theory and identifies a novel intra-psychological mechanism linking social isolation to social media addiction. Importantly, recognizing these antecedent factors enables early detection and prevention, thereby reducing the prevalence and harms of social media addiction among university students. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Social media addiction social isolation social anxiety FoMO serial mediation university students Figures Figure 1 Figure 2 Figure 3 1. Introduction In today's digital age, Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties. Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles [ 1 ]. Among the 7.91 billion people in the world as of 2022, 4.62 billion active social media users, and the average time individuals spent using the internet was 6h 58min per day with an average use of social media platforms of 2h and 27min [ 2 ]. Social media has transformed how we live, work, and interact, offering unprecedented convenience and opportunities. Especially for modern university students, social media are not only used for social communication and entertainment, but also for sharing opinions, learning new things, building personal networks, and initiating collaboration [ 3 ]. A large-scale study of 737 university students examined prevalent social media behaviors, which include publishing life updates, expressing personal opinions, exchanging items, engaging in collaborative communication, and sharing academic resources [ 4 , 5 ]. Even though social media has some benefits, excessive use of it can lead to addiction and increase feelings of depression, and loneliness [ 6 ]. Addiction is a global healthcare burden, and behavioral addiction is an emerging activity that has evolved beyond substance abuse. Social media addiction, a type of behavioral addiction related to the compulsive use of social media and associated with adverse outcomes, has been discussed by scholars and practitioners alike [ 7 ]. This form of addiction involves persistent and uncontrollable use of social media platforms, often accompanied by withdrawal symptoms, mood regulation behaviors, and a strong compulsion to remain online—particularly among university students. The incidence rate of internet disorder (often encompassing social media addiction) among Chinese university students is as high as 11% [ 8 ]. For example, a 2024 study involving 4,101 Chinese university students found a negative correlation (r = -0.220, p < 0.001) between social media addiction and sleep quality, highlighting how addiction displaces time for rest and exacerbates mental health issues [ 9 ]. Additionally, manifestations of social media addiction—including compulsive engagement, adverse social consequences, and significant time displacement—are associated with declines in key academic performance indicators such as GPA, and course completion rates [ 10 ]. University students are in a psychologically and socially sensitive period characterized by identity formation, career planning, and value development. As noted in recent studies, this group tends to have high cognitive capacity but lower self-regulation, making them especially vulnerable to the addictive features of social media [ 11 , 12 ]. Given these risks, it is critical to examine the underlying causes of social media addiction in this population in order to develop effective interventions and mitigate its long-term consequences. Some studies have shown that social media addiction among university students is driven by factors such as the need for social validation[ 13 ], subjective psychological state triggered by campus isolation [ 14 ]. Extant empirical studies have demonstrated that social isolation is a significant antecedent to smartphone addiction. Social isolation can manifest as a subjective experience that may include feelings of not belonging to a group or the perceived disparity between actual and desired social interactions [ 15 ]. This phenomenon can be quantitatively assessed as an objective state, indicated by factors such as living in solitude, having limited social contact, and engaging in minimal social activities [ 16 ]. Recent studies show that social isolation is common among university students. One survey found that 64% of students self-isolated most or all of the time [ 17 ]. This issue is linked to factors such as lack of social support, language barriers, and caregiving responsibilities, which make some students more vulnerable [ 18 ]. In university, social isolation greatly limits the range of activities and socialization of college students, and smartphones have become their primary means of communication [ 19 ]. However, this increased reliance has also contributed to rising levels of social media addiction. Although extant studies have established social isolation as a key predictor of smartphone addiction, few studies have systematically examined the underlying mechanisms linking social media addiction to its effects on university campuses. Therefore, the complex interplay between social isolation and social media addiction among Chinese university students in today’s digital age remains insufficiently understood. According to Sociometer Theory (SMT), self-esteem functions as “an internal gauge of others’ evaluations of the individual” [ 20 ]. SMT provides a useful framework for explaining how social isolation may contribute to social media addiction. As Baumeister and Leary (1995) argued, the need to belong is a fundamental human motivation, and self-esteem operates as a sociometer that monitors one’s relational value and social acceptance [ 21 ]. When individuals involve in socially isolated, their sociometer signals low acceptance, which often evokes negative emotional states such as social anxiety [ 22 ]. Social anxiety is defined as “a state of anxiety resulting from the prospect or presence of interpersonal evaluation in real or imagined social settings” [ 23 ]. This heightened anxiety can, in turn, intensify fear of missing out (FoMO), defined as “a pervasive apprehension that others might be having rewarding experiences from which one is absent” and characterized by a strong desire to stay connected to others’ experiences [ 24 ]. Thus, social anxiety can be regarded as the immediate emotional response to perceived social exclusion, whereas FoMO reflects a subsequent cognitive–motivational process that further reinforces compensatory social media use. Together, these mechanisms suggest a serial mediation pathway in which social isolation leads to social anxiety, which then heightens FoMO, ultimately fostering social media addiction. However, few scholars have examined these four factors as part of a comprehensive, integrated framework. The present study attempts to fill these research gaps by constructing a serial mediation model grounded in Sociometer theory, incorporating social anxiety and FoMO as mediating variables to reveal the underlying mechanisms in the relationship between social isolation and social media addiction among Chinese university students. By clarifying these mechanisms, this study advances the understanding of psychological pathways that underlie social media addiction among Chinese university students—an increasingly urgent issue in the digital era that threatens both well-being and academic performance. Furthermore, it underscores the importance of cultivating supportive online and campus environments to reduce social isolation and to prevent or mitigate social media addiction. 2. Theoretical background and hypotheses 2.1 Social isolation and social media addiction Social isolation (SI) is an apparent (objective) or perceived (subjective) lack of contact between an individual and society [ 25 ]. It is defined by characteristics such as physical isolation, a reduced size or diversity of one's social network, and less frequent contact with family and friends [ 26 ]. Donovan and Blazer [ 26 ] proposed that the level of social isolation or integration is determined by four domains: marital status; the frequency of contact with others; participation in religious activities; and participation in other community groups. As a bio-psychosocial stressor, social isolation severely impacts long-term quality of life through its association with various health and mental conditions [ 27 ]. A synthesis of data across 16 independent longitudinal studies shows poor social relationships (social isolation) were associated with a 29% increase in the risk of heart disease and a 32% increase in the risk of stroke[ 28 ]. Furthermore, Shannon, Bush [ 28 ] also found that social isolation is consistently associated with higher levels of problematic social media use among adolescents and young adults (which is associated with mental health issues such as depression [r = 0.273, p < .001] and stress [r = 0.313, p < .001]), as lonely individuals tend to prefer online social interactions and are more inclined to seek social fulfillment online to compensate for social deficits in their offline lives. Social media addiction is defined as a form of addiction that harms the social, physical and psychological functionality of a person through excessive and increased frequency of social media use over time [ 29 ]. Excessive use and lack of control are main reasons for social media addiction or behavioral addiction disorder, which leads to social overload, envy and anxiety, similar to compulsive buying behaviors. Addicted young adults are more likely to experience changes in their own mood and personality patterns, as well as disruptions to their learning processes and academic lives [ 30 ]. According to Salari, Zarei [ 31 ], the pooled global prevalence of social network addiction among university students was 18.4%, with the highest prevalence (22.8%) found in Asian studies. Wang et al. (2024) found that social isolation in Chinese university students drives problematic social media use through escapism, creating a vicious cycle [ 32 ]. Santini, Thygesen [ 33 ] reported that social isolation in 2020 positively predicted social media addiction in 2021 among Danish students. From an psychological perspective, the Sociometer Theory proposes that individuals who belong and interact in social groups will have a greater chance at survival [ 34 ]. In order to maintain the relationship of interpersonal support, human beings require a mechanism to monitor the reaction of others, especially when someone does not provide support. Additionally, Armstrong, Phillips [ 35 ] suggested that internet addicts are using the Internet as an escape. Thus, we hypothesize that: H1. Social isolation is directly and positively related to university students’ social media addiction. 2.2 Serial Mediation Models Linking SI and SMA Social anxiety . Social anxiety is a condition characterized by a debilitating fear of, and avoidance of, various social situations [ 36 ]. Behavioral manifestations of social anxiety may include inadequate assertiveness, rigid body posture, poor eye contact, trembling, speaking in an excessively soft voice, mumbling, stuttering, nail-biting, heightened self-consciousness, and withdrawal from social groups or unfamiliar settings. Laldinpuii, Bhattacharjee [ 37 ] found that social anxiety can have harmful effects on various aspects of life, including interpersonal relationships, academic achievement, emotional well-being, and future career prospects. Researchers have postulated that individual differences in the development of social anxiety stem from a multidimensional relationship between genetic factors and environmental factors, such as parenting, peer relationships [ 38 ]. Social isolation refers to adverse peer relationships, which are rejected by peers[ 39 ]. In addition, studies by Ali, Ali [ 40 ] on problematic social media use have identified various predictors, including social anxiety. Zeng, Zhang [ 41 ] highlighted that academic anxiety mediates the relationship between social isolation and smartphone addiction, with the effect being moderated by non-communicative social media use. Based on the above, we hypothesize that: H2a. Social isolation may be positively related to university students’ social anxiety. H2b. Social anxiety may be negatively related to university students’ social media addiction. H2c. University students’ social anxiety may mediate the relationship between social isolation and social media addiction. Fear of missing out . Fear of Missing Out (FoMO) is defined as a pervasive apprehension that others might be having rewarding experiences from which one is absent. It is characterized by a compulsive desire to constantly stay connected with and updated about the activities of others [ 42 ]. Within the Chinese context, FoMO can be categorized into two dimensions: fear of missing novel information and fear of missing social opportunities [ 43 ]. Zhen, Liu [ 44 ] proposed that negative life events diminish the satisfaction of psychological needs and increase FoMO. Individuals use social media to satisfy various psychological needs, including entertainment, social interaction, information seeking and expressive information sharing. Specifically, students experiencing FoMO may feel a strong urge to stay updated on others' activities, prompting them to use social media for the latest news and information. Fioravanti, Casale [ 45 ] reported that social media are particularly appealing to individuals with high levels of FoMO. Additionally, a substantial body of studies indicated that FoMO acted as a mediator in explaining the link between specific negative life events and problematic social media use[ 46 , 47 ]. Hence, we hypothesize that: H3a. Social isolation may be negatively related to fear of missing out among university students. H3b. Fear of missing out may be negatively related to university students’ social media addiction. H3c. Fear of missing out may mediate the relationship between social isolation and social media addiction. Based on Sociometer Theory, the psychological mechanism that monitors acceptance and rejection from others in social relations and interactions is termed the “Sociometer”. Sociometer Theory provides a useful lens for understanding how individuals, such as Gen Z, react to feelings of social isolation and anxiety. Functioning as a psychological mechanism that monitors and responds to such threats to relational value [ 48 ], the sociometer motivates individuals to convey a desired self-image and achieve successful self-presentation. The level of self-esteem is able to accurately reflect one's sociometer condition [ 49 ]. Holas, Kowalczyk [ 50 ] indicated that self-esteem is a stronger predictor of social anxiety. Social anxiety can be primarily driven by perceived social isolation, which is defined as the perceived absence of satisfying social relationships accompanied by symptoms of psychological distress [ 51 ]. Tanrikulu and Mouratidis [ 52 ] suggested that adolescents with higher social anxiety may be more prone to FoMO, which, in turn, disrupts their school engagement. A recent longitudinal study further confirmed the mediating roles of FoMO in the influence on problematic social media use in a sample of Chinese college students [ 53 ]. Thus, students’ social anxiety and fear of missing out function sequentially as mediators that explain how social isolation ultimately increases social media addiction through a chain of psychological processes. Then we hypothesize that: H4a. University students’ social anxiety may be positively related to their fear of missing out. H4b. University students’ social anxiety and fear of missing out may sequentially mediate the relationship between their social isolation and social media addiction. The theoretical cross-level model is shown in Fig. 1 . 3. Methods 3.1 Measures A cross-sectional, descriptive survey was conducted to examine the relationships among social isolation, social anxiety, fear of missing out (FoMO), and university students’ social media addiction. To ensure the reliability and validity of the measurement instruments, all scales employed in this study were drawn from leading international journals and had previously been validated in the Chinese context. A double-blind translation procedure was implemented, involving multiple researchers and student reviewers. After two rounds of evaluation and discussion, the final versions of the scales were confirmed. Participants responded to all items using a 5-point Likert scale, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Social isolation. We adapted the 5-item Social Isolation Scale from Choi and Noh [ 54 ] for using in our study, including the example “I do not have anyone to socialize with.” Cronbach’s α for this scale was 0.855, indicating good reliability. Social anxiety. 5 items measuring social anxiety were adapted from Lyngdoh, El-Manstrly [ 51 ], including statements such as “I have difficulty talking with other people.” The Cronbach’s a for this scale was 0.869. Fear of missing out. We used 5 items to measure FoMO from Jabeen, Tandon [ 55 ], and some of the statements included, “I fear others have more rewarding experiences than me.” This scale demonstrated a Cronbach’s α of 0.885. Social media addiction. The 6-item Social Media Addiction Scale developed by Eichenberg, Schneider [ 56 ] was employed to assess university students’ addictive behaviors related to social media use. An example item is: “How often during the last six months have you spent a lot of time thinking about social media or planning the use of social media?” A higher BSMAS score reflects an elevated risk of social media addiction. Based on evidence from a large-scale Hungarian study involving 6,000 adolescents Bányai, Zsila [ 57 ], a cutoff score of 19 out of 30 has been proposed. The internal consistency of this scale was acceptable (Cronbach’s α = 0.875). 3.2 Samples and procedures The samples for this study were drawn from seven universities in China, encompassing a wide range of academic disciplines-including social sciences, engineering, medicine, business, and education—as well as diverse geographical regions such as Beijing, Guangdong, and Hubei, among others. This diversity captures a broad spectrum of student experiences and strengthens both the reliability and the generalizability of the findings across different institutional and cultural contexts. The sample size was estimated using a respondent-to-item ratio of 5:1 to 10:1 [ 58 ]. With 21 questionnaire items, this yielded a required range of 105–210 participants. To account for potential data loss and meet recommendations for parametric tests, we increased the sample size by approximately 15% [ 59 ]. Increasing the sample size is important because it avoids any expected loss of data such as withdrawals from the study or missing data. With the assistance of teachers at each participating university, the questionnaires were distributed to students through various channels, including email, WeChat, and QQ. A total of 300 students were invited, and 221 responses were received. After removing 11 invalid questionnaires, 210 students who completed all scale items were included in the final sample. Ethical considerations are integral to conducting ethical research, which involves doing better and avoiding harm [ 60 ]. These considerations also relate to the fairness of the research process. To uphold the ethics of this study, we obtained electronic informed consent from the participants, informing them that their participation was voluntary and that they could withdraw at any time if they felt uncomfortable answering the interview questions. We maintained the confidentiality of the participants’ identities and shared the study findings with them to ensure the fairness of the collected data, which they approved for publication. We expressed our gratitude to the participants for sharing their experiences. The data collection period lasted from July to August 2025. 3.3 Analytical strategy We used 2nd generation multivariate statistical techniques and assessed the hypothesized model using IBM SPSS Statistics 26.0 and SmartPLS 4 [ 61 ] in four stages: (1) descriptive analysis, (2) confirmatory factor analysis to evaluate the measurement model, (3) assessment of the structural model, and (4) examination of simple and serial mediation effects. 4. Data analysis 4.1 descriptive analysis A total of 76.19% of respondents (n = 160) scored at or above the established cut-off score of 19 on the Bergen Social Media Addiction Scale (BSMAS), indicating moderate to high levels of social media addiction [ 56 ]. Among these 160 participants, 98 were female and 62 were male. In terms of time since university enrollment, 53 had been enrolled for less than one year, 52 for two to three years, and 55 for more than three years, showing a relatively balanced distribution across academic stages. Regarding educational background, 66 participants held an associate degree, 64 held a bachelor’s degree, and 30 held a master’s degree or higher. The results of means, standard deviations, and correlation coefficients of all variables in this study are presented in Table 1 . As shown in the table, social isolation was significantly positively correlated with social anxiety (r = 0.578, p < 0.01), fear of missing out (r = 0.644, p < 0.01), and social media addiction (r = 0.547, p < 0.01). Social anxiety was also significantly positively correlated with fear of missing out (r = 0.599, p < 0.01) and social media addiction (r = 0.558, p < 0.01). In addition, fear of missing out was significantly positively correlated with social media addiction (r = 0.625, p < 0.01). These correlation results provide preliminary evidence for hypothesis testing. Overall, 60% of Chinese university students (n = 126) reported mean scores above the sample average of 3.56 on a 5-point scale, indicating higher levels of social isolation. Table 1 Descriptive Statistics for All Variables (N = 210) Variable Means SDs Social isolation Social anxiety Fear of missing out Social media addiction Social isolation 3.56 0.93 0.797 Social anxiety 3.35 0.92 0.578** 0.811 Fear of missing out 3.71 0.98 0.644** 0.599** 0.828 Social media addiction 4.43 1.07 0.547** 0.558** 0.625** 0.784 Notes: * p < 0.05, ** p < 0.01, *** p ≦ 0.001. The diagonal values (in bold) represent the square root of the Average Variance Extracted for each construct. Off-diagonal values represent the correlations between constructs. Source: The authors made it according to the questionnaire 4.2 Assessment of Measurement model To authenticate psychometric properties of the study’s constructs, a separate set of criteria were used for reflective and formative measures in Table 2 [ 62 ]. Both Cronbach’s alpha and composite reliability values are satisfactory, with Cronbach’s alpha ranging from 0.855 to 0.885 and composite reliability from 0.896 to 0.916. All outer loadings of the constructs exceed the recommended threshold of 0.70 (ranging from 0.741 to 0.861). Moreover, the average variance extracted (AVE) values for all constructs are above 0.50, confirming adequate convergent validity[ 63 , 64 ]. Table 2 Confirmatory Factor Analysis Construct Reflective measures Items Factor loading Composite reliability (rho_c) Cronbach's alpha AVE Social isolation SI1 0.783*** 0.896 0.855 0.634 SI2 0.761*** SI3 0.811*** SI4 0.762*** SI5 0.861*** Social anxiety SA1 0.827*** 0.905 0.869 0.657 SA2 0.796*** SA3 0.823*** SA4 0.808*** SA5 0.799*** Fear of missing out FoMO1 0.844*** 0.916 0.885 0.686 FoMO2 0.805*** FoMO3 0.801*** FoMO4 0.855*** FoMO5 0.834*** Social media addiction SMA1 0.741*** 0.906 0.875 0.615 SMA2 0.766*** SMA3 0.762*** SMA4 0.790*** SMA5 0.817*** SMA6 0.826*** Notes: * p < 0.05, ** p < 0.01, *** p ≦ 0.001. In order to test the significance of the inner model paths and outer loadings for all model tests, we performed the bootstrapping procedure with the option of no sign and 5,000 sub-samples [ 62 ]. Source: The authors made it according to the questionnaires 4.3 Assessment of Structural Model The structural model results, presented in Table 3 as well as in Fig. 2 , demonstrate the predictive validity and significance of the majority of the hypothesized paths. Table 3 Results of Model Path Testing Hypothesis Model Path Path coefficient / Specific indirect effect f² VIF HTMT ratio Test results H1 SI → SMA 0.248*** 0.063 1.708 0.629 Supported H2a SI → SA 0.578*** 0.501 1 0.670 Supported H2b SA → SMA 0.190*** 0.041 1.941 0.635 Supported H3a SI → FoMO 0.205*** 0.037 1.687 0.736 Supported H3b FoMO → SMA 0.465*** 0.221 1.708 0.707 Supported H4a SA → FoMO 0.599*** 0.558 1 0.679 Supported H2c SI → SA → SMA 0.121*** NA NA NA Supported H3c SI → FoMO → SMA 0.104*** NA NA NA Supported H4b SI → SA → FoMO → SMA 0.161*** NA NA NA Supported Notes: * p < 0.05, ** p < 0.01, *** p ≦ 0.001. SI is social isolation; SA is social anxiety; FoMO is fear of missing out; SMA is social media addiction. In order to test the significance of the inner model paths and outer loadings for all model tests, we performed the bootstrapping procedure with the option of no sign and 5,000 subsamples[ 62 ]. Source: The authors made it according to the questionnaires Bootstrapping with 5,000 subsamples confirmed that all direct paths were statistically significant (p ≦ 0.001). SI had a significant positive effect on SMA (β = 0.248, p = 0.001; f² = 0.063). SI also strongly predicted SA (β = 0.578, p < 0.001; f² = 0.501), while SA in turn positively influenced SMA (β = 0.190, p < 0.01; f² = 0.041). Furthermore, SI exerted a moderate effect on FoMO (β = 0.205, p < 0.001; f² = 0.037), and FoMO strongly predicted SMA (β = 0.465, p < 0.001; f² = 0.221). SA was also found to have a substantial positive effect on FoMO (β = 0.599, p < 0.001; f² = 0.558). The mediation analysis further showed that all indirect paths were significant. SA mediated the link between SI and SMA (β = 0.121, p < 0.001), while FoMO also mediated the SI–SMA relationship (β = 0.104, p < 0.001). Moreover, the sequential chain SI → SA → FoMO → SMA exerted a significant indirect effect (β = 0.161, p < 0.001). The values of effect sizes (f²) shown in Table 3 are all positive and greater than 0.02, indicating that each predictor contributes meaningfully to explaining variance in the dependent variables. Common method bias (CMB) is usually viewed as a threat to the analysis results when the survey method was self-reported [ 65 ]. According to Kock [ 66 ], if VIFs in the inner model resulting from a full collinearity test are equal to or lower than 3.3, the model can be considered free of CMB. Our results showed that all VIFs ranged from 1.000 to 1.941, thus, CMB is not an issue in this study. Moreover, an advanced procedure in the form of the heterotrait–monotrait (HTMT) ratio of correlations certifies discriminant validity with values smaller than a conservative cutoff criterion of 0.9 [ 67 ]. Additionally, the model demonstrated substantial explanatory power for all endogenous constructs. Specifically, it accounted for 33.4% of the variance in SA (R² = 0.334; adjusted R² = 0.331), 35.8% in FoMO (R² = 0.358; adjusted R² = 0.355), and explained 42.6% of the variance in the key outcome variable, SMA (R² = 0.426; adjusted R² = 0.421). According to Cohen’s (1988) guidelines, these R² values indicate that the model exhibits moderate to substantial explanatory power for the endogenous variables [ 68 ]. Predictive relevance was assessed using PLSpredict procedures. All Q² values exceeded zero for the endogenous constructs—SA (Q² = 0.323), FoMO (Q² = 0.322), and SMA (Q² = 0.271)—suggesting that the model demonstrates meaningful predictive power beyond a naïve benchmark [ 69 ]. 4.4 Analysis of Simple and Serial Mediation Models To compare and analyze the direct, simple, and serial mediation models, we applied a four-step procedure [ 70 ], as illustrated in Fig. 3 . First, Model 1, which included only the direct relationship between SI and SMA, showed a significant and positive effect (β = 0.547, p < 0.001), confirming H1. In steps 2 and 3, we added SA and FoMO as separate mediators to estimate simple mediation, resulting in Model 2 (SI–SA–SMA) and Model 3 (SI–FoMO–SMA), respectively. As shown in Fig. 3 , both models demonstrated significant direct and indirect effects. Specifically, in Model 2, the direct effect was β = 0.336 (p < 0.001) and the indirect effect was 0.212 (p < 0.001). In Model 3, the direct effect was β = 0.246 (p = 0.001) and the indirect effect was 0.301 (p < 0.001). The variance accounted for (VAF) was 38.76% for Model 2 and 54.93% for Model 3, indicating partial mediation since both values fall within the 20%–80% range [ 61 ]. These findings confirm hypotheses H2c and H3c. In the fourth step, both mediators were included simultaneously to form Model 4, which tested the serial mediation pathway. The sequential indirect effect of SA and FoMO was significant (β = 0.161, p < 0.001). The VAF for this model was 39.36%, confirming partial serial mediation. The direct path also remained significant (β = 0.248, p < 0.001), indicating that the mediation was partial and complementary in nature. The results provide empirical support for H4b. 5. Discussion This study examines the relationship between social isolation and social media addiction among Chinese university students, with a particular focus on the serial mediating roles of social anxiety and fear of missing out within the context of digital era. The findings show that 76.19% of respondents (n = 160) scored at moderate to high levels of social media addiction. This indicates that problematic social media use is prevalent among university students [ 71 ]. Notably, a higher proportion of female participants reported elevated addiction levels, which may be linked to differences in usage patterns, such as more frequent checking or greater social engagement[ 72 ]. Addiction levels were consistent across enrollment durations, suggesting that social media addiction can emerge at any stage of university life rather than being confined to the early or later years[ 73 ]. In contrast, students with higher education levels—particularly those pursuing a master’s degree or above—were less likely to exhibit high addiction scores, possibly reflecting stronger self-regulation or more effective time management skills [ 74 ]. Consistent with the proposed theoretical model, social isolation (SI) had a significant positive effect on social media addiction (SMA) (β = 0.248, p < 0.001), confirming H1. On the contrary,Vosoughi Motlagh, Kamjou [ 75 ] found that the use of social media can predict social isolation both directly and indirectly through the mediation of body image concern. Costin, Roman [ 76 ] demonstrated that long-term social isolation during the COVID-19 pandemic increased burnout, which was linked to decreased technology use, as individuals reported less engagement with digital platforms due to mental fatigue. The differing results of the two studies show that the link between social media use and social isolation is complex, shaped by factors like mental health, purpose of use, context (e.g. the pandemic), usage patterns, and study conditions- all of which can lead to opposite outcomes. It was also observed that SI significantly predicted SA (β = 0.578, p < 0.001, H2a), and FoMO (β = 0.205, p < 0.001, H3a). SA further influenced both SMA (β = 0.190, p < 0.001, H2b) and FoMO (β = 0.599, p < 0.001, H4a). FoMO also significantly predicted SMA (β = 0.465, p < 0.001, H3b). These are consistent with previous studies. Al Hussaini, Kausar [ 77 ] pointed out that the COVID-19 pandemic has markedly impacted mental health worldwide, with social isolation being a significant factor contributing to increased anxiety. Holte, Fisher [ 78 ] suggested that FoMO is less about missing out on specific experiences and more about being socially excluded. Świątek, Szcześniak [ 79 ] found that respondents with higher levels of anxiety reported more intense cognitive, behavioral, emotional, and overall online fatigue. Rifkin, Chan [ 80 ] claimed that FOMO is intensified when individuals feel concerned about their future sense of belonging to a social group, whether due to situational triggers (e.g., social media photos) or a chronic anxious attachment to that group. Kareem and Al-Munif [ 81 ] reported that there is a statistically significant positive correlation between Fear of Missing Out (FoMO) and excessive use of social media, with a correlation coefficient of 0.730. Regarding the simple mediation hypotheses, the indirect effect of SI on SMA through social anxiety (SA) was significant (β = 0.121, p < 0.001), supporting H2c. Similarly, a significant indirect pathway was identified through FoMO (β = 0.104, p < 0.001), confirming H3c. These results collectively indicate that both SA and FoMO function as important mediating variables that transmit the effect of social isolation to social media addiction. These findings align with earlier research. Zeng, Zhang [ 41 ] found that anxiety mediated the positive relationship between social isolation and smartphone addiction. Alabri [ 82 ] indicated that FoMO might enhance individuals’ need to stay connected and communicate with other people, in order to face the fear of being invisible in the world of social media in circumstances of physical isolation. Moreover, this finding reveals a serial compound mediation mechanism: social isolation contributes to heightened social anxiety, which subsequently intensifies fear of missing out, ultimately leading to increased levels of social media addiction (β = 0.161, p < 0.001). The findings echo those of earlier investigations. Li, Pan [ 83 ] found that older adults who were socially isolated were more likely to have emotional problems. They had a 1.77 times higher risk of depression and a 1.66 times higher risk of anxiety compared to those who weren’t isolated. He, Tan [ 84 ] reported that anxiety itself may lead to a more serious FoMO. Elhai, Yang [ 85 ] noted that multiple studies have identified Fear of Missing Out as a mediator in the relationship between psychopathological symptoms, such as anxiety, and Problematic Internet Use (PIU). Therefore, this finding provides a more nuanced understanding of the psychological dynamics underlying the pathway from social isolation to increased social media addiction. 5.1 Theoretical implications This study uses Sociometer Theory to explain how social isolation leads to social media addiction through social anxiety and Fear of Missing Out. The findings show that social isolation causes social anxiety, which then increases FoMO, ultimately driving social media addiction. Specifically, social anxiety is an emotional response to exclusion, while FoMO is a motivational process that encourages more social media use. This study extends Sociometer Theory by showing how these emotional and cognitive factors mediate the relationship between social isolation and social media addiction[ 86 ]. More broadly, this research contributes to Sociometer theory by emphasizing the crucial roles of social anxiety and FoMO in the pathway from social isolation to addiction. While previous studies have explored these factors individually, this study offers a comprehensive framework, specifically among Chinese university students[ 87 ]. It provides new insights into the intricate psychological mechanisms behind the growing prevalence of social media addiction in the digital age, enriching our understanding of how social and emotional dynamics influence digital behavior. 5.2 Practical implications This study offers important practical implications for educational policymakers, university students' families and teachers, and social media platform developers in the context of China’s digital era. First, for educational policymakers, it is essential to recognize social isolation and emotional distress as early indicators of social media addiction. Policies should support on-campus programs that promote real-life social interaction, such as peer mentorship, student clubs, and structured group activities. Mental health education should be integrated into the curriculum to help students understand and manage anxiety, while professional counseling centers must be made more accessible. Campus-wide early warning systems can help identify at-risk students and provide timely psychological support. Reducing digital dependence starts with creating a socially connected and emotionally safe school environment. Second, for university students' families and teachers, early detection and emotional support are crucial. Parents should watch for signs of social isolation and monitor their children’s social media use. When students lack connection at school, families can help fill the emotional gap[ 88 ], offering support and a sense of belonging to prevent social anxiety and FoMO. Close communication between home and school also helps guide students toward healthier social and digital habits. What’s more, university teachers ought to remain observant of students’ social participation both online and offline. Educators can facilitate inclusive classroom atmospheres and organize group activities that promote organic social bonding, thereby reducing over-reliance on virtual interaction. Finally, social media platforms should also take proactive steps to safeguard students’ mental health. Algorithms must be designed to reduce content that triggers FoMO, and instead promote psychologically positive interactions. Tools like focus modes, screen time limits, and scheduled downtime can further support balanced and intentional use. For example, integrating mental health resources from social media platforms, such as access to support communities or in-app emotional check-ins, can help students become more aware of their digital habits and emotional states [ 89 ]. By adopting a multi-stakeholder approach, these practical measures can collectively contribute to mitigating social media addiction among Chinese university students. 5.3 Limitations and future directions This study has several limitations that suggest valuable avenues for future research. First, the sample was limited to Chinese university students, whose social and psychological experiences are shaped by distinctive cultural and educational factors. Consequently, the findings may not be fully generalizable to other populations without further validation. Future studies should examine whether the observed relationships hold across different cultural and institutional settings. Second, the use of self-reported data may have introduced response distortion. To enhance validity, future studies should incorporate objective measures, such as behavioral records, third-party evaluations, or digital trace data [ 90 ]. Declarations Ethics approval and consent to participate Research involving human participants, materials, or data was performed in accordance with the Declaration of Helsinki. This research obtained research ethics document from the Research Ethics Committee of School of New Media, Beijing Institute of Graphic Communication(Certification Number: BIGC-2025010710), valid from July 11, 2025, to August 10, 2026. Anonymity was ensured, and informed consent was obtained online at the beginning of the survey. Consent to participate Declarations Written electronic informed consent was obtained from all participants (and/or their legal guardians, where applicable) between July and August 2025, prior to study participation. The consent process clearly explained the purpose of the research, emphasized voluntary participation, and assured anonymity. Consent for publication Not applicable. Availability of data and material The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research was funded by the university-level project “2025 Doctoral Research Start-up Fund” of Beijing Institute of Graphic Communication, Project No. Ed202504. Author Contributions XZ conceived the research, collected the data, performed the data analysis and interpretation, wrote, and revised the manuscript. TX guided the research ideas, helped revise the manuscript, and intensively edited the language of the manuscript. XL helped revise the manuscript and guided the journal selection. All authors contributed to the article and approved the submitted version. References Kaye, L. K. Exploring the socialness of social media. 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18:50:47","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178390,"visible":true,"origin":"","legend":"","description":"","filename":"ae8bbaebecfd460d9167352403bd9ffd1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7654856/v1/ddb9682e5df4e53d6b7a7a4f.xml"},{"id":95949107,"identity":"006aa3e2-c164-429d-ae19-4f9ef43b8bbe","added_by":"auto","created_at":"2025-11-14 18:50:47","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":193110,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7654856/v1/0d7e45832c040f2628459256.html"},{"id":96243649,"identity":"d4150500-b4f5-4758-abd9-a2be1e63046b","added_by":"auto","created_at":"2025-11-19 07:16:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53531,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework and Hypotheses\u003c/p\u003e\n\u003cp\u003eSource: The authors made it according to the questionnaires\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7654856/v1/564f57e0131afc503878d929.png"},{"id":96244207,"identity":"4fa7b38e-2ec5-4689-8991-713f37127dce","added_by":"auto","created_at":"2025-11-19 07:17:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58422,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Path modeling (path coefficients, loadings, and reliability measures)\u003c/p\u003e\n\u003cp\u003eNotes: SI is social isolation; SA is social anxiety; FoMO is fear of missing out; \u0026nbsp;SMA is social media addiction.\u003c/p\u003e\n\u003cp\u003eSource: The authors made it according to the questionnaires\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7654856/v1/fd46d00393367814118c2a0e.png"},{"id":95949101,"identity":"6643a005-67f3-416b-9d7e-41edbabf68da","added_by":"auto","created_at":"2025-11-14 18:50:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260442,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis and Comparison of Direct, Simple, and Serial Mediation Models (path coefficients, loadings, and reliability measures)\u003c/p\u003e\n\u003cp\u003eNotes: * p \u0026lt; 0.05,** p \u0026lt; 0.01, *** p ≦ 0.001. SI is social isolation; SA is social anxiety; FoMO is fear of missing out; \u0026nbsp;SMA is social media addiction.\u003c/p\u003e\n\u003cp\u003eSource: The authors made it according to the questionnaires\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7654856/v1/9efb64f191884c7a1fe06b50.png"},{"id":98813947,"identity":"a38ae757-c681-42f6-b7e6-29fbefc79b8a","added_by":"auto","created_at":"2025-12-22 16:08:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1440591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7654856/v1/aafb0b6b-fe16-4c7c-acae-531dffde005f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Isolation and Social Media Addiction: The Serial Mediation Roles of Social Anxiety and FoMO among Chinese University Students","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn today's digital age, Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties. Social networking sites such as Facebook, Instagram,\u003c/p\u003e\u003cp\u003eand TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the 7.91\u0026nbsp;billion people in the world as of 2022, 4.62\u0026nbsp;billion active social media users, and the average time individuals spent using the internet was 6h 58min per day with an average use of social media platforms of 2h and 27min [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Social media has transformed how we live, work, and interact, offering unprecedented convenience and opportunities. Especially for modern university students, social media are not only used for social communication and entertainment, but also for sharing opinions, learning new things, building personal networks, and initiating collaboration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A large-scale study of 737 university students examined prevalent social media behaviors, which include publishing life updates, expressing personal opinions, exchanging items, engaging in collaborative communication, and sharing academic resources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEven though social media has some benefits, excessive use of it can lead to addiction and increase feelings of depression, and loneliness [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Addiction is a global healthcare burden, and behavioral addiction is an emerging activity that has evolved beyond substance abuse. Social media addiction, a type of behavioral addiction related to the compulsive use of social media and associated with adverse outcomes, has been discussed by scholars and practitioners alike [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This form of addiction involves persistent and uncontrollable use of social media platforms, often accompanied by withdrawal symptoms, mood regulation behaviors, and a strong compulsion to remain online\u0026mdash;particularly among university students. The incidence rate of internet disorder (often encompassing social media addiction) among Chinese university students is as high as 11% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, a 2024 study involving 4,101 Chinese university students found a negative correlation (r = -0.220, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between social media addiction and sleep quality, highlighting how addiction displaces time for rest and exacerbates mental health issues [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, manifestations of social media addiction\u0026mdash;including compulsive engagement, adverse social consequences, and significant time displacement\u0026mdash;are associated with declines in key academic performance indicators such as GPA, and course completion rates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. University students are in a psychologically and socially sensitive period characterized by identity formation, career planning, and value development. As noted in recent studies, this group tends to have high cognitive capacity but lower self-regulation, making them especially vulnerable to the addictive features of social media [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given these risks, it is critical to examine the underlying causes of social media addiction in this population in order to develop effective interventions and mitigate its long-term consequences.\u003c/p\u003e\u003cp\u003eSome studies have shown that social media addiction among university students is driven by factors such as the need for social validation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], subjective psychological state triggered by campus isolation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Extant empirical studies have demonstrated that social isolation is a significant antecedent to smartphone addiction. Social isolation can manifest as a subjective experience that may include feelings of not belonging to a group or the perceived disparity between actual and desired social interactions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This phenomenon can be quantitatively assessed as an objective state, indicated by factors such as living in solitude, having limited social contact, and engaging in minimal social activities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent studies show that social isolation is common among university students. One survey found that 64% of students self-isolated most or all of the time [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This issue is linked to factors such as lack of social support, language barriers, and caregiving responsibilities, which make some students more vulnerable [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In university, social isolation greatly limits the range of activities and socialization of college students, and smartphones have become their primary means of communication [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, this increased reliance has also contributed to rising levels of social media addiction. Although extant studies have established social isolation as a key predictor of smartphone addiction, few studies have systematically examined the underlying mechanisms linking social media addiction to its effects on university campuses. Therefore, the complex interplay between social isolation and social media addiction among Chinese university students in today\u0026rsquo;s digital age remains insufficiently understood.\u003c/p\u003e\u003cp\u003eAccording to Sociometer Theory (SMT), self-esteem functions as \u0026ldquo;an internal gauge of others\u0026rsquo; evaluations of the individual\u0026rdquo; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. SMT provides a useful framework for explaining how social isolation may contribute to social media addiction. As Baumeister and Leary (1995) argued, the need to belong is a fundamental human motivation, and self-esteem operates as a sociometer that monitors one\u0026rsquo;s relational value and social acceptance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. When individuals involve in socially isolated, their sociometer signals low acceptance, which often evokes negative emotional states such as social anxiety [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Social anxiety is defined as \u0026ldquo;a state of anxiety resulting from the prospect or presence of interpersonal evaluation in real or imagined social settings\u0026rdquo; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This heightened anxiety can, in turn, intensify fear of missing out (FoMO), defined as \u0026ldquo;a pervasive apprehension that others might be having rewarding experiences from which one is absent\u0026rdquo; and characterized by a strong desire to stay connected to others\u0026rsquo; experiences [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, social anxiety can be regarded as the immediate emotional response to perceived social exclusion, whereas FoMO reflects a subsequent cognitive\u0026ndash;motivational process that further reinforces compensatory social media use. Together, these mechanisms suggest a serial mediation pathway in which social isolation leads to social anxiety, which then heightens FoMO, ultimately fostering social media addiction. However, few scholars have examined these four factors as part of a comprehensive, integrated framework.\u003c/p\u003e\u003cp\u003eThe present study attempts to fill these research gaps by constructing a serial mediation model grounded in Sociometer theory, incorporating social anxiety and FoMO as mediating variables to reveal the underlying mechanisms in the relationship between social isolation and social media addiction among Chinese university students. By clarifying these mechanisms, this study advances the understanding of psychological pathways that underlie social media addiction among Chinese university students\u0026mdash;an increasingly urgent issue in the digital era that threatens both well-being and academic performance. Furthermore, it underscores the importance of cultivating supportive online and campus environments to reduce social isolation and to prevent or mitigate social media addiction.\u003c/p\u003e"},{"header":"2. Theoretical background and hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Social isolation and social media addiction\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003cp\u003eSocial isolation (SI) is an apparent (objective) or perceived (subjective) lack of contact between an individual and society [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is defined by characteristics such as physical isolation, a reduced size or diversity of one's social network, and less frequent contact with family and friends [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Donovan and Blazer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] proposed that the level of social isolation or integration is determined by four domains: marital status; the frequency of contact with others; participation in religious activities; and participation in other community groups. As a bio-psychosocial stressor, social isolation severely impacts long-term quality of life through its association with various health and mental conditions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A synthesis of data across 16 independent longitudinal studies shows poor social relationships (social isolation) were associated with a 29% increase in the risk of heart disease and a 32% increase in the risk of stroke[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, Shannon, Bush [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] also found that social isolation is consistently associated with higher levels of problematic social media use among adolescents and young adults (which is associated with mental health issues such as depression [r\u0026thinsp;=\u0026thinsp;0.273, p\u0026thinsp;\u0026lt;\u0026thinsp;.001] and stress [r\u0026thinsp;=\u0026thinsp;0.313, p\u0026thinsp;\u0026lt;\u0026thinsp;.001]), as lonely individuals tend to prefer online social interactions and are more inclined to seek social fulfillment online to compensate for social deficits in their offline lives.\u003c/h2\u003e\u003cp\u003eSocial media addiction is defined as a form of addiction that harms the social, physical and psychological functionality of a person through excessive and increased frequency of social media use over time [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Excessive use and lack of control are main reasons for social media addiction or behavioral addiction disorder, which leads to social overload, envy and anxiety,\u003c/p\u003e\u003cp\u003esimilar to compulsive buying behaviors. Addicted young adults are more likely to experience changes in their own mood and personality patterns, as well as disruptions to their learning processes and academic lives [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. According to Salari, Zarei [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the pooled global prevalence of social network addiction among university students was 18.4%, with the highest prevalence (22.8%) found in Asian studies. Wang et al. (2024) found that social isolation in Chinese university students drives problematic social media use through escapism, creating a vicious cycle [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Santini, Thygesen [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] reported that social isolation in 2020 positively predicted social media addiction in 2021 among Danish students. From an psychological perspective, the Sociometer Theory proposes that individuals who belong and interact in social groups will have a greater chance at survival [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In order to maintain the relationship of interpersonal support, human beings require a mechanism to monitor the reaction of others, especially when someone does not provide support. Additionally, Armstrong, Phillips [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] suggested that internet addicts are using the Internet as an escape. Thus, we hypothesize that:\u003c/p\u003e\u003cp\u003eH1. Social isolation is directly and positively related to university students\u0026rsquo; social media addiction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Serial Mediation Models Linking SI and SMA\u003c/h2\u003e\u003cp\u003e\u003cem\u003eSocial anxiety\u003c/em\u003e. Social anxiety is a condition characterized by a debilitating fear of, and avoidance of, various social situations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Behavioral manifestations of social anxiety may include inadequate assertiveness, rigid body posture, poor eye contact, trembling, speaking in an excessively soft voice, mumbling, stuttering, nail-biting, heightened self-consciousness, and withdrawal from social groups or unfamiliar settings. Laldinpuii, Bhattacharjee [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] found that social anxiety can have harmful effects on various aspects of life, including interpersonal relationships, academic achievement, emotional well-being, and future career prospects. Researchers have postulated that individual differences in the development of social anxiety stem from a multidimensional relationship between genetic factors and environmental factors, such as parenting, peer relationships [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Social isolation refers to adverse peer relationships, which are rejected by peers[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In addition, studies by Ali, Ali [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] on problematic social media use have identified various predictors, including social anxiety. Zeng, Zhang [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] highlighted that academic anxiety mediates the relationship between social isolation and smartphone addiction, with the effect being moderated by non-communicative social media use. Based on the above, we hypothesize that:\u003c/p\u003e\u003cp\u003eH2a. Social isolation may be positively related to university students\u0026rsquo; social anxiety.\u003c/p\u003e\u003cp\u003eH2b. Social anxiety may be negatively related to university students\u0026rsquo; social media addiction.\u003c/p\u003e\u003cp\u003eH2c. University students\u0026rsquo; social anxiety may mediate the relationship between social isolation and social media addiction.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFear of missing out\u003c/em\u003e. Fear of Missing Out (FoMO) is defined as a pervasive apprehension that others might be having rewarding experiences from which one is absent. It is characterized by a compulsive desire to constantly stay connected with and updated about the activities of others [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Within the Chinese context, FoMO can be categorized into two dimensions: fear of missing novel information and fear of missing social opportunities [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Zhen, Liu [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] proposed that negative life events diminish the satisfaction of psychological needs and increase FoMO. Individuals use social media to satisfy various psychological needs, including entertainment, social interaction, information seeking and expressive information sharing. Specifically, students experiencing FoMO may feel a strong urge to stay updated on others' activities, prompting them to use social media for the latest news and information. Fioravanti, Casale [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] reported that social media are particularly appealing to individuals with high levels of FoMO. Additionally, a substantial body of studies indicated that FoMO acted as a mediator in explaining the link between specific negative life events and problematic social media use[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Hence, we hypothesize that:\u003c/p\u003e\u003cp\u003eH3a. Social isolation may be negatively related to fear of missing out among university students.\u003c/p\u003e\u003cp\u003eH3b. Fear of missing out may be negatively related to university students\u0026rsquo; social media addiction.\u003c/p\u003e\u003cp\u003eH3c. Fear of missing out may mediate the relationship between social isolation and social media addiction.\u003c/p\u003e\u003cp\u003eBased on Sociometer Theory, the psychological mechanism that monitors acceptance and rejection from others in social relations and interactions is termed the \u0026ldquo;Sociometer\u0026rdquo;. Sociometer Theory provides a useful lens for understanding how individuals, such as Gen Z, react to feelings of social isolation and anxiety. Functioning as a psychological mechanism that monitors and responds to such threats to relational value [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], the sociometer motivates individuals to convey a desired self-image and achieve successful self-presentation. The level of self-esteem is able to accurately reflect one's sociometer condition [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Holas, Kowalczyk [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] indicated that self-esteem is a stronger predictor of social anxiety. Social anxiety can be primarily driven by perceived social isolation, which is defined as the perceived absence of satisfying social relationships accompanied by symptoms of psychological distress [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Tanrikulu and Mouratidis [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] suggested that adolescents with higher social anxiety may be more prone to FoMO, which, in turn, disrupts their school engagement. A recent longitudinal study further confirmed the mediating roles of FoMO in the influence on problematic social media use in a sample of Chinese college students [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Thus, students\u0026rsquo; social anxiety and fear of missing out function sequentially as mediators that explain how social isolation ultimately increases social media addiction through a chain of psychological processes. Then we hypothesize that:\u003c/p\u003e\u003cp\u003eH4a. University students\u0026rsquo; social anxiety may be positively related to their fear of missing out.\u003c/p\u003e\u003cp\u003eH4b. University students\u0026rsquo; social anxiety and fear of missing out may sequentially mediate the relationship between their social isolation and social media addiction.\u003c/p\u003e\u003cp\u003eThe theoretical cross-level model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Measures\u003c/h2\u003e\u003cp\u003eA cross-sectional, descriptive survey was conducted to examine the relationships among social isolation, social anxiety, fear of missing out (FoMO), and university students\u0026rsquo; social media addiction. To ensure the reliability and validity of the measurement instruments, all scales employed in this study were drawn from leading international journals and had previously been validated in the Chinese context. A double-blind translation procedure was implemented, involving multiple researchers and student reviewers. After two rounds of evaluation and discussion, the final versions of the scales were confirmed. Participants responded to all items using a 5-point Likert scale, ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;).\u003c/p\u003e\u003cp\u003e\u003cem\u003eSocial isolation.\u003c/em\u003e We adapted the 5-item Social Isolation Scale from Choi and Noh [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] for using in our study, including the example \u0026ldquo;I do not have anyone to socialize with.\u0026rdquo; Cronbach\u0026rsquo;s α for this scale was 0.855, indicating good reliability.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSocial anxiety.\u003c/em\u003e 5 items measuring social anxiety were adapted from Lyngdoh, El-Manstrly [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], including statements such as \u0026ldquo;I have difficulty talking with other people.\u0026rdquo; The Cronbach\u0026rsquo;s a for this scale was 0.869.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFear of missing out.\u003c/em\u003e We used 5 items to measure FoMO from Jabeen, Tandon [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and some of the statements included, \u0026ldquo;I fear others have more rewarding experiences than me.\u0026rdquo; This scale demonstrated a Cronbach\u0026rsquo;s α of 0.885.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSocial media addiction.\u003c/em\u003e The 6-item Social Media Addiction Scale developed by Eichenberg, Schneider [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] was employed to assess university students\u0026rsquo; addictive behaviors related to social media use. An example item is: \u0026ldquo;How often during the last six months have you spent a lot of time thinking about social media or planning the use of social media?\u0026rdquo; A higher BSMAS score reflects an elevated risk of social media addiction. Based on evidence from a large-scale Hungarian study involving 6,000 adolescents B\u0026aacute;nyai, Zsila [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], a cutoff score of 19 out of 30 has been proposed. The internal consistency of this scale was acceptable (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.875).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Samples and procedures\u003c/h2\u003e\u003cp\u003eThe samples for this study were drawn from seven universities in China, encompassing a wide range of academic disciplines-including social sciences, engineering, medicine, business, and education\u0026mdash;as well as diverse geographical regions such as Beijing, Guangdong, and Hubei, among others. This diversity captures a broad spectrum of student experiences and strengthens both the reliability and the generalizability of the findings across different institutional and cultural contexts.\u003c/p\u003e\u003cp\u003eThe sample size was estimated using a respondent-to-item ratio of 5:1 to 10:1 [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. With 21 questionnaire items, this yielded a required range of 105\u0026ndash;210 participants. To account for potential data loss and meet recommendations for parametric tests, we increased the sample size by approximately 15% [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Increasing the sample size is important because it avoids any expected loss of data such as withdrawals from the study or missing data. With the assistance of teachers at each participating university, the questionnaires were distributed to students through various channels, including email, WeChat, and QQ. A total of 300 students were invited, and 221 responses were received. After removing 11 invalid questionnaires, 210 students who completed all scale items were included in the final sample.\u003c/p\u003e\u003cp\u003eEthical considerations are integral to conducting ethical research, which involves doing better and avoiding harm [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These considerations also relate to the fairness of the research process. To uphold the ethics of this study, we obtained electronic informed consent from the participants, informing them that their participation was voluntary and that they could withdraw at any time if they felt uncomfortable answering the interview questions. We maintained the confidentiality of the participants\u0026rsquo; identities and shared the study findings with them to ensure the fairness of the collected data, which they approved for publication. We expressed our gratitude to the participants for sharing their experiences. The data collection period lasted from July to August 2025.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analytical strategy\u003c/h2\u003e\u003cp\u003eWe used 2nd generation multivariate statistical techniques and assessed the hypothesized model using IBM SPSS Statistics 26.0 and SmartPLS 4 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] in four stages: (1) descriptive analysis, (2) confirmatory factor analysis to evaluate the measurement model, (3) assessment of the structural model, and (4) examination of simple and serial mediation effects.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Data analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1 descriptive analysis\u003c/h2\u003e\u003cp\u003eA total of 76.19% of respondents (n\u0026thinsp;=\u0026thinsp;160) scored at or above the established cut-off score of 19 on the Bergen Social Media Addiction Scale (BSMAS), indicating moderate to high levels of social media addiction [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Among these 160 participants, 98 were female and 62 were male. In terms of time since university enrollment, 53 had been enrolled for less than one year, 52 for two to three years, and 55 for more than three years, showing a relatively balanced distribution across academic stages. Regarding educational background, 66 participants held an associate degree, 64 held a bachelor\u0026rsquo;s degree, and 30 held a master\u0026rsquo;s degree or higher.\u003c/p\u003e\u003cp\u003eThe results of means, standard deviations, and correlation coefficients of all variables in this study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As shown in the table, social isolation was significantly positively correlated with social anxiety (r\u0026thinsp;=\u0026thinsp;0.578, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), fear of missing out (r\u0026thinsp;=\u0026thinsp;0.644, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and social media addiction (r\u0026thinsp;=\u0026thinsp;0.547, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Social anxiety was also significantly positively correlated with fear of missing out (r\u0026thinsp;=\u0026thinsp;0.599, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and social media addiction (r\u0026thinsp;=\u0026thinsp;0.558, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, fear of missing out was significantly positively correlated with social media addiction (r\u0026thinsp;=\u0026thinsp;0.625, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These correlation results provide preliminary evidence for hypothesis testing. Overall, 60% of Chinese university students (n\u0026thinsp;=\u0026thinsp;126) reported mean scores above the sample average of 3.56 on a 5-point scale, indicating higher levels of social isolation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for All Variables (N\u0026thinsp;=\u0026thinsp;210)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeans\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSocial isolation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSocial anxiety\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFear of missing out\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSocial media addiction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial isolation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.797\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial anxiety\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.578**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.811\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFear of missing out\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.644**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.599**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.828\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial media addiction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.547**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.558**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.625**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.784\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;≦\u0026thinsp;0.001. The diagonal values (in bold) represent the square root of the Average Variance Extracted for each construct. Off-diagonal values represent the correlations between constructs.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: The authors made it according to the questionnaire\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Assessment of Measurement model\u003c/h2\u003e\u003cp\u003eTo authenticate psychometric properties of the study\u0026rsquo;s constructs, a separate set of criteria were used for reflective and formative measures in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Both Cronbach\u0026rsquo;s alpha and composite reliability values are satisfactory, with Cronbach\u0026rsquo;s alpha ranging from 0.855 to 0.885 and composite reliability from 0.896 to 0.916. All outer loadings of the constructs exceed the recommended threshold of 0.70 (ranging from 0.741 to 0.861). Moreover, the average variance extracted (AVE) values for all constructs are above 0.50, confirming adequate convergent validity[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct \u003c/p\u003e\u003cp\u003eReflective measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor loading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComposite reliability (rho_c)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCronbach's alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eSocial isolation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.783***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.761***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.811***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.762***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.861***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eSocial anxiety\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.827***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.796***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.823***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.808***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.799***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eFear of missing out\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.844***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.805***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.801***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.855***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.834***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eSocial media addiction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.741***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.766***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.762***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.790***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.817***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.826***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;≦\u0026thinsp;0.001. In order to test the significance of the inner model paths and outer loadings for all model tests, we performed the bootstrapping procedure with the option of no sign and 5,000 sub-samples [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: The authors made it according to the questionnaires\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Assessment of Structural Model\u003c/h2\u003e\u003cp\u003eThe structural model results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e as well as in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, demonstrate the predictive validity and significance of the majority of the hypothesized paths.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Model Path Testing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel Path\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePath coefficient / Specific indirect effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ef\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHTMT ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTest results\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI \u0026rarr; SMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.248***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI \u0026rarr; SA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.578***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA \u0026rarr; SMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.190***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI \u0026rarr; FoMO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.205***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFoMO \u0026rarr; SMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.465***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA \u0026rarr; FoMO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.599***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI \u0026rarr; SA \u0026rarr; SMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.121***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI \u0026rarr; FoMO \u0026rarr; SMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.104***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSI \u0026rarr; SA \u0026rarr; FoMO \u0026rarr; SMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.161***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;≦\u0026thinsp;0.001. SI is social isolation; SA is social anxiety; FoMO is fear of missing out; SMA is social media addiction. In order to test the significance of the inner model paths and outer loadings for all model tests, we performed the bootstrapping procedure with the option of no sign and 5,000 subsamples[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: The authors made it according to the questionnaires\u003c/p\u003e\u003cp\u003eBootstrapping with 5,000 subsamples confirmed that all direct paths were statistically significant (p\u0026thinsp;≦\u0026thinsp;0.001). SI had a significant positive effect on SMA (β\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;=\u0026thinsp;0.001; f\u0026sup2; = 0.063). SI also strongly predicted SA (β\u0026thinsp;=\u0026thinsp;0.578, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; f\u0026sup2; = 0.501), while SA in turn positively influenced SMA (β\u0026thinsp;=\u0026thinsp;0.190, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; f\u0026sup2; = 0.041). Furthermore, SI exerted a moderate effect on FoMO (β\u0026thinsp;=\u0026thinsp;0.205, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; f\u0026sup2; = 0.037), and FoMO strongly predicted SMA (β\u0026thinsp;=\u0026thinsp;0.465, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; f\u0026sup2; = 0.221). SA was also found to have a substantial positive effect on FoMO (β\u0026thinsp;=\u0026thinsp;0.599, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; f\u0026sup2; = 0.558).\u003c/p\u003e\u003cp\u003eThe mediation analysis further showed that all indirect paths were significant. SA mediated the link between SI and SMA (β\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while FoMO also mediated the SI\u0026ndash;SMA relationship (β\u0026thinsp;=\u0026thinsp;0.104, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the sequential chain SI \u0026rarr; SA \u0026rarr; FoMO \u0026rarr; SMA exerted a significant indirect effect (β\u0026thinsp;=\u0026thinsp;0.161, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The values of effect sizes (f\u0026sup2;) shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e are all positive and greater than 0.02, indicating that each predictor contributes meaningfully to explaining variance in the dependent variables.\u003c/p\u003e\u003cp\u003eCommon method bias (CMB) is usually viewed as a threat to the analysis results when the survey method was self-reported [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. According to Kock [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], if VIFs in the inner model resulting from a full collinearity test are equal to or lower than 3.3, the model can be considered free of CMB. Our results showed that all VIFs ranged from 1.000 to 1.941, thus, CMB is not an issue in this study. Moreover, an advanced procedure in the form of the heterotrait\u0026ndash;monotrait (HTMT) ratio of correlations certifies discriminant validity with values smaller than a conservative cutoff criterion of 0.9 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, the model demonstrated substantial explanatory power for all endogenous constructs. Specifically, it accounted for 33.4% of the variance in SA (R\u0026sup2; = 0.334; adjusted R\u0026sup2; = 0.331), 35.8% in FoMO (R\u0026sup2; = 0.358; adjusted R\u0026sup2; = 0.355), and explained 42.6% of the variance in the key outcome variable, SMA (R\u0026sup2; = 0.426; adjusted R\u0026sup2; = 0.421). According to Cohen\u0026rsquo;s (1988) guidelines, these R\u0026sup2; values indicate that the model exhibits moderate to substantial explanatory power for the endogenous variables [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Predictive relevance was assessed using PLSpredict procedures. All Q\u0026sup2; values exceeded zero for the endogenous constructs\u0026mdash;SA (Q\u0026sup2; = 0.323), FoMO (Q\u0026sup2; = 0.322), and SMA (Q\u0026sup2; = 0.271)\u0026mdash;suggesting that the model demonstrates meaningful predictive power beyond a na\u0026iuml;ve benchmark [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Analysis of Simple and Serial Mediation Models\u003c/h2\u003e\u003cp\u003eTo compare and analyze the direct, simple, and serial mediation models, we applied a four-step procedure [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. First, Model 1, which included only the direct relationship between SI and SMA, showed a significant and positive effect (β\u0026thinsp;=\u0026thinsp;0.547, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming H1.\u003c/p\u003e\u003cp\u003eIn steps 2 and 3, we added SA and FoMO as separate mediators to estimate simple mediation, resulting in Model 2 (SI\u0026ndash;SA\u0026ndash;SMA) and Model 3 (SI\u0026ndash;FoMO\u0026ndash;SMA), respectively. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, both models demonstrated significant direct and indirect effects. Specifically, in Model 2, the direct effect was β\u0026thinsp;=\u0026thinsp;0.336 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the indirect effect was 0.212 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In Model 3, the direct effect was β\u0026thinsp;=\u0026thinsp;0.246 (p\u0026thinsp;=\u0026thinsp;0.001) and the indirect effect was 0.301 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The variance accounted for (VAF) was 38.76% for Model 2 and 54.93% for Model 3, indicating partial mediation since both values fall within the 20%\u0026ndash;80% range [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These findings confirm hypotheses H2c and H3c.\u003c/p\u003e\u003cp\u003eIn the fourth step, both mediators were included simultaneously to form Model 4, which tested the serial mediation pathway. The sequential indirect effect of SA and FoMO was significant (β\u0026thinsp;=\u0026thinsp;0.161, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The VAF for this model was 39.36%, confirming partial serial mediation. The direct path also remained significant (β\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the mediation was partial and complementary in nature. The results provide empirical support for H4b.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study examines the relationship between social isolation and social media addiction among Chinese university students, with a particular focus on the serial mediating roles of social anxiety and fear of missing out within the context of digital era. The findings show that 76.19% of respondents (n\u0026thinsp;=\u0026thinsp;160) scored at moderate to high levels of social media addiction. This indicates that problematic social media use is prevalent among university students [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Notably, a higher proportion of female participants reported elevated addiction levels, which may be linked to differences in usage patterns, such as more frequent checking or greater social engagement[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]. Addiction levels were consistent across enrollment durations, suggesting that social media addiction can emerge at any stage of university life rather than being confined to the early or later years[\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. In contrast, students with higher education levels\u0026mdash;particularly those pursuing a master\u0026rsquo;s degree or above\u0026mdash;were less likely to exhibit high addiction scores, possibly reflecting stronger self-regulation or more effective time management skills [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eConsistent with the proposed theoretical model, social isolation (SI) had a significant positive effect on social media addiction (SMA) (\u0026beta;\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming H1. On the contrary,Vosoughi Motlagh, Kamjou [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e] found that the use of social media can predict social isolation both directly and indirectly through the mediation of body image concern. Costin, Roman [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e] demonstrated that long-term social isolation during the COVID-19 pandemic increased burnout, which was linked to decreased technology use, as individuals reported less engagement with digital platforms due to mental fatigue. The differing results of the two studies show that the link between social media use and social isolation is complex, shaped by factors like mental health, purpose of use, context (e.g. the pandemic), usage patterns, and study conditions- all of which can lead to opposite outcomes. It was also observed that SI significantly predicted SA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.578, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, H2a), and FoMO (\u0026beta;\u0026thinsp;=\u0026thinsp;0.205, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, H3a). SA further influenced both SMA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.190, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, H2b) and FoMO (\u0026beta;\u0026thinsp;=\u0026thinsp;0.599, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, H4a). FoMO also significantly predicted SMA (\u0026beta;\u0026thinsp;=\u0026thinsp;0.465, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, H3b). These are consistent with previous studies. Al Hussaini, Kausar [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e] pointed out that the COVID-19 pandemic has markedly impacted mental health worldwide, with social isolation being a significant factor contributing to increased anxiety. Holte, Fisher [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e] suggested that FoMO is less about missing out on specific experiences and more about being socially excluded. Świątek, Szcześniak [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e] found that respondents with higher levels of anxiety reported more intense cognitive, behavioral, emotional, and overall online fatigue. Rifkin, Chan [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e] claimed that FOMO is intensified when individuals feel concerned about their future sense of belonging to a social group, whether due to situational triggers (e.g., social media photos) or a chronic anxious attachment to that group. Kareem and Al-Munif [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e] reported that there is a statistically significant positive correlation between Fear of Missing Out (FoMO) and excessive use of social media, with a correlation coefficient of 0.730.\u003c/p\u003e\n\u003cp\u003eRegarding the simple mediation hypotheses, the indirect effect of SI on SMA through social anxiety (SA) was significant (\u0026beta;\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H2c. Similarly, a significant indirect pathway was identified through FoMO (\u0026beta;\u0026thinsp;=\u0026thinsp;0.104, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming H3c. These results collectively indicate that both SA and FoMO function as important mediating variables that transmit the effect of social isolation to social media addiction. These findings align with earlier research. Zeng, Zhang [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] found that anxiety mediated the positive relationship between social isolation and smartphone addiction.\u003c/p\u003e\n\u003cp\u003eAlabri [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e] indicated that FoMO might enhance individuals\u0026rsquo; need to stay connected and communicate with other people, in order to face the fear of being invisible in the world of social media in circumstances of physical isolation.\u003c/p\u003e\n\u003cp\u003eMoreover, this finding reveals a serial compound mediation mechanism: social isolation contributes to heightened social anxiety, which subsequently intensifies fear of missing out, ultimately leading to increased levels of social media addiction (\u0026beta;\u0026thinsp;=\u0026thinsp;0.161, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The findings echo those of earlier investigations. Li, Pan [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e] found that older adults who were socially isolated were more likely to have emotional problems. They had a 1.77 times higher risk of depression and a 1.66 times higher risk of anxiety compared to those who weren\u0026rsquo;t isolated. He, Tan [\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e] reported that anxiety itself may lead to a more serious FoMO. Elhai, Yang [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e] noted that multiple studies have identified Fear of Missing Out as a mediator in the relationship between psychopathological symptoms, such as anxiety, and Problematic Internet Use (PIU). Therefore, this finding provides a more nuanced understanding of the psychological dynamics underlying the pathway from social isolation to increased social media addiction.\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Theoretical implications\u003c/h2\u003e\n \u003cp\u003eThis study uses Sociometer Theory to explain how social isolation leads to social media addiction through social anxiety and Fear of Missing Out. The findings show that social isolation causes social anxiety, which then increases FoMO, ultimately driving social media addiction. Specifically, social anxiety is an emotional response to exclusion, while FoMO is a motivational process that encourages more social media use. This study extends Sociometer Theory by showing how these emotional and cognitive factors mediate the relationship between social isolation and social media addiction[\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eMore broadly, this research contributes to Sociometer theory by emphasizing the crucial roles of social anxiety and FoMO in the pathway from social isolation to addiction. While previous studies have explored these factors individually, this study offers a comprehensive framework, specifically among Chinese university students[\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e]. It provides new insights into the intricate psychological mechanisms behind the growing prevalence of social media addiction in the digital age, enriching our understanding of how social and emotional dynamics influence digital behavior.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Practical implications\u003c/h2\u003e\n \u003cp\u003eThis study offers important practical implications for educational policymakers, university students\u0026apos; families and teachers, and social media platform developers in the context of China\u0026rsquo;s digital era.\u003c/p\u003e\n \u003cp\u003eFirst, for educational policymakers, it is essential to recognize social isolation and emotional distress as early indicators of social media addiction. Policies should support on-campus programs that promote real-life social interaction, such as peer mentorship, student clubs, and structured group activities. Mental health education should be integrated into the curriculum to help students understand and manage anxiety, while professional counseling centers must be made more accessible. Campus-wide early warning systems can help identify at-risk students and provide timely psychological support. Reducing digital dependence starts with creating a socially connected and emotionally safe school environment.\u003c/p\u003e\n \u003cp\u003eSecond, for university students\u0026apos; families and teachers, early detection and emotional support are crucial. Parents should watch for signs of social isolation and monitor their children\u0026rsquo;s social media use. When students lack connection at school, families can help fill the emotional gap[\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e], offering support and a sense of belonging to prevent social anxiety and FoMO. Close communication between home and school also helps guide students toward healthier social and digital habits. What\u0026rsquo;s more, university teachers ought to remain observant of students\u0026rsquo; social participation both online and offline. Educators can facilitate inclusive classroom atmospheres and organize group activities that promote organic social bonding, thereby reducing over-reliance on virtual interaction.\u003c/p\u003e\n \u003cp\u003eFinally, social media platforms should also take proactive steps to safeguard students\u0026rsquo; mental health. Algorithms must be designed to reduce content that triggers FoMO, and instead promote psychologically positive interactions. Tools like focus modes, screen time limits, and scheduled downtime can further support balanced and intentional use. For example, integrating mental health resources from social media platforms, such as access to support communities or in-app emotional check-ins, can help students become more aware of their digital habits and emotional states [\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eBy adopting a multi-stakeholder approach, these practical measures can collectively contribute to mitigating social media addiction among Chinese university students.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Limitations and future directions\u003c/h2\u003e\n \u003cp\u003eThis study has several limitations that suggest valuable avenues for future research. First, the sample was limited to Chinese university students, whose social and psychological experiences are shaped by distinctive cultural and educational factors. Consequently, the findings may not be fully generalizable to other populations without further validation. Future studies should examine whether the observed relationships hold across different cultural and institutional settings. Second, the use of self-reported data may have introduced response distortion. To enhance validity, future studies should incorporate objective measures, such as behavioral records, third-party evaluations, or digital trace data [\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch involving human participants, materials, or data was performed in accordance with the Declaration of Helsinki. This research obtained research ethics document from the Research Ethics Committee of School of New Media, Beijing Institute of Graphic Communication(Certification Number: BIGC-2025010710), valid from July 11, 2025, to August 10, 2026. Anonymity was ensured, and informed consent was obtained online at the beginning of the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten electronic informed consent was obtained from all participants (and/or their legal guardians, where applicable) between July and August 2025, prior to study participation. The consent process clearly explained the purpose of the research, emphasized voluntary participation, and assured anonymity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the university-level project \u0026ldquo;2025 Doctoral Research Start-up Fund\u0026rdquo; of Beijing Institute of Graphic Communication, Project No. Ed202504.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXZ conceived the research, collected the data, performed the data analysis and interpretation, wrote, and revised the manuscript. TX guided the research ideas, helped revise the manuscript, and intensively edited the language of the manuscript. XL helped revise the manuscript and guided the journal selection. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKaye, L. K. Exploring the socialness of social media. \u003cem\u003eComputers Hum. Behav. Rep.\u003c/em\u003e ;\u003cb\u003e3\u003c/b\u003e. (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePellegrino, A., Stasi, A. \u0026amp; Bhatiasevi, V. Research trends in social media addiction and problematic social media use: A bibliometric analysis. \u003cem\u003eFront. Psychiatry\u003c/em\u003e ;\u003cb\u003e13\u003c/b\u003e. (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlshamrani, S., Abusnaina, A., Abuhamad, M., Nyang, D. \u0026amp; Mohaisen, D. 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The impact of multimorbidity level and functional limitations on the accuracy of using self-reported survey data compared to administrative data to measure general practitioner and specialist visits in community-living adults. \u003cem\u003eBMC Health Serv. Res.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 1\u0026ndash;10 (2021).\u003c/span\u003e\u003c/li\u003e\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Social media addiction, social isolation, social anxiety, FoMO, serial mediation, university students","lastPublishedDoi":"10.21203/rs.3.rs-7654856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7654856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e \u0026ndash; This study investigates the direct and indirect relationships between social isolation and social media addiction among Chinese university students, with social anxiety and fear of missing out (FoMO) as serial mediators in the digital era.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign/methodology\u003c/b\u003e \u0026ndash; Data were collected from 210 students across seven universities in different cities and academic majors using random sampling. Hypotheses were tested in SPSS and SmartPLS through four stages: (1) descriptive analysis, (2) confirmatory factor analysis, (3) structural model evaluation, and (4) mediation testing (direct, simple, and serial).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFindings\u003c/b\u003e \u0026ndash; Social isolation was positively associated with social media addiction, social anxiety, and FoMO. Social anxiety further increased FoMO and social media addiction, while FoMO also amplified social media addiction. Mediation analyses showed that social isolation indirectly predicted social media addiction via social anxiety (indirect effect\u0026thinsp;=\u0026thinsp;0.121***), FoMO (indirect effect\u0026thinsp;=\u0026thinsp;0.104***), and their sequential pathway (specific indirect effect\u0026thinsp;=\u0026thinsp;0.161***). Descriptively, 60% of students (n\u0026thinsp;=\u0026thinsp;126) reported above-average levels of social isolation, and 76.19% (n\u0026thinsp;=\u0026thinsp;160) scored\u0026thinsp;\u0026ge;\u0026thinsp;19 on the Bergen Social Media Addiction Scale, indicating moderate to high addiction levels.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOriginality/value\u003c/b\u003e \u0026ndash; By applying a tripartite theoretical framework, this study extends Sociometer Theory and identifies a novel intra-psychological mechanism linking social isolation to social media addiction. Importantly, recognizing these antecedent factors enables early detection and prevention, thereby reducing the prevalence and harms of social media addiction among university students.\u003c/p\u003e","manuscriptTitle":"Social Isolation and Social Media Addiction: The Serial Mediation Roles of Social Anxiety and FoMO among Chinese University Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 18:50:42","doi":"10.21203/rs.3.rs-7654856/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T08:47:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T06:44:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T23:16:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173865103697741943491565291363782117149","date":"2025-11-21T00:12:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T17:35:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221784983449664516674998514904822994890","date":"2025-11-19T17:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216868708310271348536421026333952076050","date":"2025-11-18T15:14:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91591276264151863377059586310177807559","date":"2025-11-18T06:45:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T03:27:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40742679312106187514425957446380626175","date":"2025-11-18T01:47:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"796060796267122431088872999116697804","date":"2025-11-17T14:45:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307872920652099031885646847255074397678","date":"2025-11-17T14:14:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94291501345478562933411041151972390415","date":"2025-11-10T00:58:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238896276174476685009647633486531340069","date":"2025-11-05T10:28:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T10:04:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T07:01:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T11:14:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-19T05:40:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63a9920a-034d-4d30-87fa-8b577d6d70fc","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57718976,"name":"Health sciences/Health care"},{"id":57718977,"name":"Biological sciences/Psychology"},{"id":57718978,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-22T16:01:48+00:00","versionOfRecord":{"articleIdentity":"rs-7654856","link":"https://doi.org/10.1038/s41598-025-33176-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-21 15:56:57","publishedOnDateReadable":"December 21st, 2025"},"versionCreatedAt":"2025-11-14 18:50:42","video":"","vorDoi":"10.1038/s41598-025-33176-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33176-3","workflowStages":[]},"version":"v1","identity":"rs-7654856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7654856","identity":"rs-7654856","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}