hypothesis of social media in students

• Open access
• Published: 22 September 2023

How social media usage affects psychological and subjective well-being: testing a moderated mediation model

• Chang’an Zhang 1 ,
• Lingjie Tang 1 &
• Zhifang Liu 2  

BMC Psychology volume  11 , Article number:  286 ( 2023 ) Cite this article

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A growing body of literature demonstrates that social media usage has witnessed a rapid increase in higher education and is almost ubiquitous among young people. The underlying mechanisms as to how social media usage by university students affects their well-being are unclear. Moreover, current research has produced conflicting evidence concerning the potential effects of social media on individuals' overall well-being with some reporting negative outcomes while others revealing beneficial results.

To address the research gap, the present research made an attempt to investigate the crucial role of social media in affecting students’ psychological (PWB) and subjective well-being (SWB) by testing the mediating role of self-esteem and online social support and the moderation effect of cyberbullying. The data in the study were obtained from a sample of 1,004 college students (483 females and 521 males, M age  = 23.78, SD  = 4.06) enrolled at 135 Chinese universities. AMOS 26.0 and SPSS 26.0 as well as the Process macro were utilized for analyzing data and testing the moderated mediation model.

Findings revealed that social media usage by university students was positively associated with their PWB and SWB through self-esteem and online social support, and cyberbullying played a moderating role in the first phase of the mediation process such that the indirect associations were weak with cyberbullying reaching high levels.

These findings highlight the importance of discerning the mechanisms moderating the mediated paths linking social media usage by young adults to their PWB and SWB. The results also underline the importance of implementing measures and interventions to alleviate the detrimental impacts of cyberbullying on young adults’ PWB and SWB.

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Introduction

In this digital world, the utilization of social media has become a massive and meaningful part of our everyday life and has grown substantially in recent years [ 1 , 2 ]. People of all ages, adults and adolescents, utilize a diverse array of social media platforms to engage in meaningful connections, both in intimate settings with loved ones and in expansive networks encompassing friends, acquaintances, and professional peers [ 3 ]. It is worth emphasizing that the younger generation is dedicating an ever-growing portion of their time to engaging in online networking platforms, indulging in e-games, exchanging messages, and immersing themselves in various forms of social media [ 4 ]. As a result, there is growing attention among the scholars of social sciences paid to social media research. Despite a handful of studies that have been conducted to shed light on the reasons behind the excessive usage of social media, still literature exploring the potential consequences of utilizing social media is limited, particularly among college students in the context of China. Taking up this research gap, we intend to examine the effects of social media usage on students’ wellbeing, for example, PWB and SWB, which are two distinct but related dimensions of well-being.

Studies on well-being have been grounded on two different philosophical approaches: the hedonic perspective, which defines well-being as the pursuit of pleasure and avoidance of pain, and the eudaimonic perspective, which conceptualizes well-being as the extent to which an individual achieves their potential and experiences personal growth [ 5 ]. Most studies on the hedonic psychological perspective have focused on using SWB measures [ 6 ], whereas the eudaimonic approach, as proposed by Ryff [ 7 ], includes a multidimensional model of PWB consisting of six different aspects of positive functioning: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance [ 8 ]. Although researchers have different approaches, they generally agree that well-being should be understood as a complex concept that incorporates elements from both the hedonic and eudaimonic perspectives [ 5 , 9 ]. Moreover, many scholars recommended that both concepts of wellbeing be re-examined by conducting in-depth and larger research subjects involving diverse cultures and countries [ 10 ]. This is necessary and meaningful since existing studies are typically conducted with subjects in countries referred to as WEIRD (Western, Educated, Industrialized, Rich, Democratic). As such, in this study, we attempted to investigate the impact of social media usage on both PWB and SWB.

Existing literature has revealed that the use of social media is closely related to individuals’ well-being. Some studies found that social media usage can produce beneficial effects. For instance, social media can increase users’ sense of connectedness with others [ 4 ], thus reducing social isolation. Some other studies have demonstrated that engaging in social interactions through smartphones exquisitely enhances one's overall sense of well-being, as it remarkably diminishes feelings of loneliness and shyness [ 11 ] while providing a sense of intimacy [ 12 ], and mobile voice communication with loved ones is a powerful predictor of enhanced PWB [ 13 ]. Furthermore, numerous studies have revealed that the utilization of entertainment-motivated social media can help improve users’ self-disclosure [ 14 ], and facilitated social connections through social media platforms can decrease the sense of stigmatization [ 15 ] and enhance belongingness and social inclusion [ 16 ], contributing to increased SWB. However, some researchers have stressed that social media usage can occasionally divert users' attention from meaningful relationships and hinder social interactions [ 17 , 18 ] and a number of scholars have cautioned against the potential additive relationship with digital devices like smartphones if used excessively [ 12 , 19 ], possibly due to the fear of missing out [ 20 ]. The utilization of social media has unfortunately been linked to a range of distressing consequences including heightened feelings of anxiety [ 21 ], profound loneliness [ 22 ], and debilitating depression [ 23 ]. Additionally, it has been found to perpetuate a sense of social isolation, as well as engender a phenomenon known as "phubbing," whereby individuals become excessively engrossed in their smartphones, thereby compromising genuine interpersonal connections during in-person interactions [ 24 ].

The inconsistent research findings regarding the impact of social media on individuals’ well-being suggest that some factors may play a role in this mechanism. Actually, in addition to the direct association between social media usage and well-being, a number of studies have further identified mediators to investigate underlying mechanisms of this relationship. Previous studies have identified self-esteem and online social support as two promising mediators of the link between social media usage and PWB and SWB. And empirical studies have revealed that media attention and dependency were proven to improve individuals’ self-efficacy [ 25 ], thus increasing their self-esteem. Most importantly, people would rely more on social media, especially during the COVID-19 pandemic in China [ 26 ], to seek social support via the Internet as in-person social support was seriously reduced [ 27 ]. Moreover, social media usage like for informational uses was found to increase people’s self-esteem [ 28 ] and can provide an important avenue for obtaining online social support from friends, peers and important others [ 29 ], which, in turn, reinforce peoples’ PWB and SWB. Although previous studies on mediation effects of self-esteem and online social support have helped elucidate the complex relationship between social media and well-being, further exploration can be made. To test the concurrent mediating effects of self-esteem and online social support, which have been investigated separately in prior studies, would shed more light on the interplay between social media usage and well-being. Furthermore, researchers have acknowledged the importance of exploring the generalizability of their findings to different cultures, like Asian cultures, particularly Chinese culture where collectivism runs strong [ 30 ]. Because previous research indicated that individuals who recorded high collectivism were apt to experience higher levels of well-being, regardless of social media usage [ 15 ], suggesting that a hierarchical society with a strong collectivist culture can play an important role in the impact of people’s social media use on their well-being.

Another factor that intrigued us is cyberbullying. A review of literature on this topic concluded that cyberbully is prevalent on the Internet and some 11.2% to 56.9% of Chinese adolescents reported experiences of cyberbullying victimization, the second-highest median rate among nine nations surveyed in the study [ 31 ]. Similar to traditional bullying, cyberbullying as a victim via social media is founded to be closely related to a series of behavioral and psychological problems (e.g., depression, anxiety, post-traumatic stress disorder, and suicidal ideation) [ 32 , 33 ]. Cyberbullying victimization has also been found to reduce individuals’ self-esteem [ 34 ] and make them feel less inclined to engage with social media platforms and online communities [ 35 ], thus decreasing online social support from peers, friends, and family members. This analysis inspired us to examine whether cyberbullying acts as a moderator in the association between social media usage and well-being. Given the widespread occurrence and undesirable effects of cyberbullying, it is significant for scholars to explore its underlying mechanisms and underexamined consequences. Meanwhile, previous empirical investigations on cyberbullying have largely focused on children and teens [ 36 ]. There have been comparably fewer studies on the influence of cyberbullying on mental health among young adults, like college students, especially in China. In addition, cyberbullying may have a differential impact on adults vs.children. This is particularly true for cyberbullying on social media, as there are differences in the amount of time spent on social media and the specific platforms used by children and adults [ 37 ].

Against the above background and in line with previous studies [ 16 , 38 , 39 , 40 ] we formulated a moderated mediation model to test the roles of self-esteem and online social support as mediators and cyberbullying as a moderator in the relationship of social media and PWB and SWB. Figure  1 presents our moderated mediation model.

Proposed moderated mediation model

Literature review and hypotheses development

Students’ social media usage and well-being.

University students utilize the Internet for various reasons, including leisure activities like participating in online communities or playing games, educational tasks such as completing assignments or applying for scholarships, and practical activities such as researching companies for job interviews. Previous studies have unveiled the rising popularity of social media among students, while more recent investigations have underscored the profound impact that the usage of social media has on their PWB and SWB [ 41 , 42 ]. Research studies have observed a directly or indirectly positive relationship of social media usage with students’ PWB [ 43 , 44 ] and SWB [ 41 , 42 ]. Specifically, PWB serves as a crucial determinant of the overall quality of life, referring to individuals' emotional states and appraisals of their existence [ 45 ], and can include a multiple of dimensions such as autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance [ 8 ]. The utilization of social media by students offers them a broader platform to voice their opinions and emotions regarding their rights, fostering their self-assurance and confidence, and bolstering their knowledge and understanding [ 46 ]. During times of crisis like during the period of COVID-19, the utilization of social media platforms by students presents a valuable avenue for stress relief as they can openly express their thoughts and receive advice from others on how to navigate and overcome the challenging circumstances they find themselves in [ 47 ]. In addition, researchers have also revealed that students’ frequent social media usage to exchange thoughts and strengthen bonds with family and friends can have a positive impact on their PWB by reducing loneliness [ 11 ] and social isolation [ 48 ], and strengthening life satisfaction [ 49 ]. Based on these findings, we can make this hypothesis;

H1a: Social media usage among university students is positively related to their PWB

SWB refers to an individual's overall contentment and happiness, taking into account their personal perception of the significance they place on various aspects of their life. Put simply, SWB encompasses a comprehensive assessment of one's life, encompassing both cognitive evaluations of life satisfaction (cognition) and emotional assessments of feelings and moods (emotion) [ 50 ]. This concept is a growing area of concern in light of the increase in mental health issues in higher education [ 51 ]. A decline in SWB is frequently observed prior to the onset of more severe mental health problems and behavioral issues, including but not limited to depression, suicidal tendencies, and dropping out of college [ 52 , 53 ]. However, some studies have linked social media usage to better SWB. For instance, prior research has demonstrated that social media platforms like Facebook can contribute to users’ accrual of network social capital, thus bolstering SWB [ 54 ]. Also, positive feedback received from individuals with whom one interacts online can significantly enhance overall well-being and mental health. And more frequent quality-based online communication with relatives, friends, family members, and relevant others was also found to have positive impacts on SWB [ 55 ] through lowered depression over time [ 56 ] and enhanced life satisfaction [ 55 ].

Moreover, according to the flow theory, individuals can experience a state of flow when they direct their attention toward accomplishing a specific task or overcoming a challenge in order to attain certain objectives [ 57 ]. This state of flow is characterized by a sense of fulfillment, enhanced cognitive abilities, heightened motivation, and overall happiness [ 58 ]. That is to say, flow improves people’s SWB. To experience a flow state, three conditions need to be fulfilled: having a clear goal and a perceived challenge, maintaining a balance between the difficulty of the challenge and one's skill level, and receiving immediate feedback on progress. Social media, with its enjoyable and controllable nature, provides these conditions and allows users to have an immersive experience, making it a significant source of flow experiences and contributing to people's SWB. In light of this principle, as students increase their usage of social media, they allocate a greater portion of their focus and energy toward engaging with these platforms. In the process of pursuing their objectives, such as engaging in lively conversations with friends via popular messaging applications like WeChat and QQ, or exhibiting their picturesque travel snapshots on platforms like Weibo, they might unexpectedly receive affirming feedback and positive responses from their virtual connections. This immersive and seamless flow experience not only enables individuals to unwind and experience a heightened sense of contentment but also directly enhances their overall sense of SWB. Along this line, we can propose the following hypothesis;

H1b: Social media usage by university students is positively associated with their SWB.

Self-esteem and online social support as mediators

Self-esteem refers to an individual's enduring attitude, whether positive or negative, towards oneself that remains consistent regardless of various circumstances and the passage of time [ 59 , 60 ]. Self-esteem is crucial, especially for young individuals, as they are going through a period of forming their identity, and feedback about themselves can greatly impact their self-esteem [ 61 ]. Research has demonstrated that individuals who possess high self-esteem often experience lower levels of aggressive negative emotions and depression compared to those with low self-esteem [ 62 , 63 ]. Research also revealed that self-esteem functions as an important and positive predictor of PWB and SWB [ 64 ] and success later in life [ 65 ]. By contrast, people who have low self-esteem are likely to be socially anxious, shy, lonely, and introverted. Individuals who experience a decrease in their self-esteem frequently limit their interactions with others, which can impede the formation of close and supportive relationships that are crucial for their overall well-being [ 66 ]. Additionally, they tend to have less stable and satisfying relationships compared to those with high self-esteem [ 67 ]. Furthermore, individuals with low self-esteem tend to engage in self-victimization and shift blame onto others when faced with social failures, rather than acknowledging their own choices. These tendencies lead to avoidance of social interactions, unfamiliar situations, and a general disconnection from society, which in turn heighten the chances of developing social anxiety and depression [ 68 ].

However, interacting with others on social media can generate favorable impacts on one's self-esteem when individuals experience a feeling of belonging and receive encouragement and assistance from their online connections. In the study by Apaolaza et al. [ 69 ], people socializing on social media sites can experience a rise in self-esteem and improvement in their SWB. Moreover, receiving positive feedback on social media can also help boost self-esteem, as others' responses to an individual's posts are usually positive. Studies have shown that the number of likes on social networking sites like Facebook is linked to higher self-esteem [ 70 ]. In more recent research using objective data, it was revealed that Facebook 'likes' have a positive association with happiness, as they boost self-esteem [ 71 ]. Similarly, engaging in self-reflection on social media can have a positive effect on one's self-esteem. By allowing users to carefully select and present information about themselves, social media enables individuals to highlight their positive attributes and experiences, which can boost their self-esteem when they review their profile or past interactions with others [ 40 , 72 ]. As a result, we hypothesized that;

H2a: There exists a mediating role of self-esteem in the relationship between social media usage by university students and their PWB and SWB.

Social support, being one of the most prominent factors that provide protection, plays a crucial and indispensable role in the prevention of mental illnesses [ 73 , 74 ]. It serves as a vital element in safeguarding individuals from the onset and development of psychological disorders [ 75 ]. When individuals received increased levels of social support, they experienced a decrease in feelings of loneliness and an increase in overall happiness [ 76 ]. Online social support refers to the emotional, informational, and instrumental support received through the Internet, as well as the feeling of connection and acceptance from friends, family, and other individuals within one's social circles. Online social support represents the extension of social support that is traditionally available in the physical world to the virtual realm of cyberspace and can enhance the well-being and overall health of individuals, both physically and mentally. This support is facilitated by online platforms and serves as a source of comfort, guidance, and a sense of belonging in times of need. It encompasses various forms of assistance, ranging from empathetic conversations and advice to tangible resources and assistance [ 77 , 78 ]. Through online social support, individuals are able to seek solace, share their experiences, and build meaningful relationships with others, ultimately enhancing their overall well-being and social connectedness in the digital realm. Past research has indicated that the utilization of mobile social media platforms can effectively fortify individuals' connections with others, thus offering them online social support, which in turn aids in the improvement of their well-being [ 79 , 80 ]. A recent review by Gilmour et al. [ 81 ] discovered that using social networking sites like Facebook for seeking social support can enhance users’ overall well-being, as well as improve both physical and mental health. Additionally, it was found to decrease instances of mental illnesses such as depression, anxiety, and loneliness. Thus, online social support seems to have promising effects on young people’s well-being. Along this line, we made the following hypotheses;

H2b: There exists a mediating role of online social support in the relationship between social media usage by university students and their PWB and SWB.

In addition, it has been revealed that self-esteem is a crucial individual factor affecting social support [ 82 ]. Researchers contend that people having greater self-esteem are more inclined to have positive self-evaluations [ 83 ], gain acceptance from others [ 84 ], and exhibit proactive and optimistic behaviors in online contexts [ 85 ]. As a result, they are more likely to receive social support and assistance from their online communities. In comparison, individuals with lower self-esteem typically have negative opinions about themselves, display more negative behavior online, and may not receive as much social support on the Internet [ 86 ]. Furthermore, empirical studies also found a positive relationship between the two variables [ 87 , 88 ]. Given the literature review, we proposed;

H2c: University students’ self-esteem is positively related to their online social support.

Cyberbullying as a moderator

Cyberbullying, according to Rafferty and Vander Ven [ 88 ], was depicted as ‘repeated unwanted, hurtful, harassing, and threatening interaction through electronic communication media’. In contrast to conventional websites, social media platforms provide users with the unique opportunity to selectively share information and content by adjusting their account settings. This remarkable feature has granted young individuals an unprecedented level of access to personal information, as well as a readily accessible platform to exploit this information to their advantage when interacting with others. Cyberbullying can manifest itself across various platforms such as text messages, electronic mail, online chat rooms, and social networking sites. It has emerged as a substantial public health worry due to its potential to induce mental and behavioral health complications, along with an elevated susceptibility to suicidal tendencies [ 89 ]. In fact, cyberbullying poses a detrimental impact on all groups of people who have access to technology, but its consequences are particularly severe for students due to their vulnerable age and susceptibility to online harassment [ 90 ].

According to existing literature, individuals who fall victim to cyberbullying commonly experience a range of psychological issues, including but not limited to stress, depression, feelings of isolation, loneliness, low self-esteem, low academic success, fear of attending school, heightened levels of social anxiety and suicidal ideations [ 91 ]. Furthermore, numerous research studies have consistently demonstrated that cyberbullying inflicts severe emotional and physiological harm upon vulnerable individuals who find themselves unable to defend against such attacks [ 92 ], decreasing their SWB [ 93 ] and causing psychological challenges, such as behavioral issues, alcohol consumption, smoking, and diminished dedication to their academic pursuits [ 94 ]. Due to the detrimental impact of cyberbullying on individuals' well-being, it hinders students' academic success as they struggle to overcome the emotional distress caused by this form of harassment. It was revealed that cyberbullying victimization is strongly associated with various psychological issues such as anxiety, depression, substance abuse, diminished self-esteem, interpersonal difficulties, strained familial relationships, and subpar academic performance among university students [ 95 ].

Research consistently reveals that individuals who are bullied typically have lower levels of self-esteem compared to those who are not victimized [ 34 , 96 ]. And empirical studies based on student samples also confirmed that experience of cyberbullying as a victim was found to be correlated with significantly lower levels of self-esteem [ 94 , 97 ]. In a more recent study based on Chinese university students, Ding et al. [ 98 ] also observed a negative association between cyberbullying and self-esteem. On the other hand, cyberbullying often comes in many forms, such as being ignored, disrespected, threatened, made fun of, and harassed, causing psychological and emotional distress for the victim. Such undesirable feelings and experiences may dampen their motivation and weaken their enthusiasm to engage with online communities [ 35 ], thus decreasing potential online social support they would receive from peers, friends, family members, educators, and romantic partners. Also, cyberbullying erodes the trust individuals have in their online connections so that they would become more cautious about sharing personal information or expressing their thoughts and feelings online [ 99 ], thus hindering the development of genuine connections and limiting the depth of online social support received. In addition, continuous exposure to cyberbullying can damage a person's self-esteem, self-confidence and self-worth, resulting in a wrong belief that they are undeserving of support or that others will not empathize with their experiences [ 95 , 100 ] which may lead to refraining from seeking or accepting online social support. And those suffering from cyberbullying may also choose not to seek online or offline social support due to fear or anxiety, which would in turn have an adverse impact on their well-being [ 101 ].

Based on these findings, it can be inferred that the occurrence of cyberbullying might impact the connection between students' engagement with social media platforms and the positive outcomes it typically fosters. Thus, we hypothesized that;

H3a: Cyberbullying moderates the relationship between social media usage by university students and their self-esteem, wherein the relationship is weaker when cyberbullying is high.

H3b: Cyberbullying moderates the relationship between social media usage by university students and their online social support, wherein the relationship is weaker when cyberbullying is high.

H3c: Cyberbullying moderates the relationship between social media usage by university students and their PWB, wherein the relationship is weaker when cyberbullying is high.

H3d: Cyberbullying moderates the relationship between social media usage by university students and their SWB, wherein the relationship is weaker when cyberbullying is high.

Methodology

Participants and procedure.

The data for the present study were collected via an online survey carried out from April 2023 to May 2023. The survey was based on Wenjuanxing ( www.wjx.cn ), a widely accepted and professional online survey platform for questionnaire design and data collection in China. Questionnaire links can be sent to participants through various social media platforms, such as WeChat, QQ, Weibo, and email. Once the survey is finished, the statistical charts can be downloaded to a Word document for SPSS analysis online, or the original data can be downloaded to Excel and imported into SPSS software for further analysis. It has advantages due to its high efficiency, high quality and low cost. In the present study, questionnaires were designed in Chinese using Wenjuanxing and were then distributed and collected via WeChat and QQ, two popular social platforms that many Chinese people use on a daily basis.

A total of 1,301 active responses were recorded in a response to 1,500 distributed questionnaires (86.73% response rate). Each individual who took part in the research willingly agreed to participate and were given the assurance that their answers would be kept confidential, anonymous, and solely used for the purpose of conducting the study. Since the current study aimed at investigating the influence of social media usage, those who had no access to electronic devices or reported having not used any social media platforms were excluded ( N  = 9). And following careful data cleansing, the final sample comprised 1,004 students, and their major characteristics are displayed in Table 1 . The research participants consisted of both undergraduate (825) and graduate students (179) enrolled in 135 universities and colleges throughout China. Of the total participants, 48.11% were female students and 68.92% were from single-child families. The age range of the sample ranged from 18 to 31 years ( M  = 23.78, SD  = 4.06).

Scale items used in the present study were drawn from the extant literature; thus, well established and validated scales widely applied in prior studies were employed to measure the various constructs in the model shown in Fig.  1 . Given that the respondents in the study are Chinese, the English-language scales used for measuring social media usage and cyberbullying were translated into Chinese. To guarantee that the language was consistent in its meaning, a technique known as back-translation designed by Brislin [ 102 ] was employed. Specifically, this process involved the translation of items from English to Chinese by a bilingual linguist and the back-translation by another bilingual scholar. The other scales we employed were Chinese versions with valid and reliable psychometric properties.

Social media usage scale

In order to assess individuals' engagement on online social platforms, the researchers chose the 9-item general social media usage subscale from the Media and Technology Usage and Attitude Scale (MTUAS) devised by Rosen et al. [ 103 ]. The original MTUAS scale was designed to assess technology and media usage as well as attitudes toward technology. It consists of 60 questions, each of which measures 1 of 11 usage subscales of the questionnaire, and the subscales can be applied collectively or separately. Participants were requested to provide information regarding how often they engage in various activities on social media platforms (e.g., “Read postings; Comment on postings, status updates, photos, etc.”). Each participant assessed the accuracy of the statements using a frequency scale that ranged from 1 ( never ) to 10 ( all the time ) with higher scores indicating more social media usage. According to Rosen et al. [ 103 ] and Barton et al. [ 104 ], the general social media usage scale demonstrated good reliability and validity with the alpha coefficient calculated at 0.97 and 0.90, respectively. In the current study, the measure showed good reliability (Cronbach’s α = 0. 906).

Cyberbullying scale

An instrument devised by Ybarra et al. [ 105 ] captures the prevalence of an individual experiencing aggressive behavior online across various digital media platforms and electronic devices. The four-item self-report scale assesses the frequency of being subjected to such behaviors within the preceding year on a 5-point Likert scale with response options ranging from 1 ( not sure ) to 5 ( often ). Sample statements include: (a) “Someone made a rude or mean comment to me online”, (b) “Someone sent a text message that said rude or mean things”. Higher scores represent greater levels of cyberbullying as a victim. In the present study, the reliability of the scale calculated based on the current sample was high (Cronbach’s α = 0.818).

Self-esteem scale

The Rosenberg Self-Esteem Scale (RSES; Rosenberg, [ 59 ]) was adopted to assess global self-esteem with 10 statements on a 4-point Likert scale. This measure has already been translated into Chinese, demonstrating reliable and adequate psychometric properties [ 85 , 106 ]. Participants’ response categories were set as 1( strongly disagree ) and 4 ( strongly agree ). Example questions include: (a) “I feel that I have a number of good qualities,” and (b) “I take a positive attitude toward myself.” The five negatively worded items on the scale were reverse scored and the height of the scores taken from the measure suggests that a respondent’s self-esteem is high. For the present study, the measure demonstrated good reliability (Cronbach’s α = 0.945).

Online social support scale

The measure of online social support an individual receives was adapted from the Chinese short version of the Online Social Support Scale (OSSS-CS) developed by Zhou and Cheng [ 107 ] as this 20-item instrument has been translated into Chinese and has been tested in Chinese populations demonstrating good internal consistency and high construct validity for its four subscales: esteem/emotional support (0.92), social companionship (0.80), informational support (0.98), and instrumental support (0.92). These four factors were also validated based on confirmatory factor analysis (CFA). Example items include: (a) “People encourage me when I am online”, (b) “People help me learn new things when I am online”, and (c) “When I am online, people help me with school or work”. Participants were asked to rate the frequency of social support in these dimensions they received from the online world and their responses were recorded on a 5-point Likert scale with anchors of 1 ( never ) and 5 ( a lot ). Higher scores indicate greater online social support. In the present study, the measure demonstrated good reliability (Cronbach’s α = 0.956).

The PWB of the participants was evaluated using a shorter Chinese version for Ryff and Keyes’ [ 8 ] PWB Scale [ 108 ]. The 18-item scale is broken down into six different facets: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. Each aspect was measured by three items and the response to the individual questions was reverse-coded and configured with a 7-point Likert scale, ranging from 1 ( strongly agree ) to 7 ( strongly disagree ). Example items are: (a) “I tend to be influenced by people with strong opinions,” (b) “I have not experienced many warm and trusting relationships with others," and (c) "In many ways I feel disappointed about my achievements in life." Higher scores mean greater PWB. The shortened version scale has been adopted in a series of previous studies on Chinese samples with good internal consistency [ 109 ]. For the current study, the scale was reliable (Cronbach’s α = 0. 959).

The revised version of the College Student SWB Questionnaire (CSSWQ) with 16 self-report items that comprise four subscales was adopted to assess participants’ SWB in terms of academic efficacy, college gratitude, school connectedness, and academic satisfaction [ 53 ]. The four dimensions were measured using four items, respectively, on a 7-point Likert scale with anchors of 1 ( strongly disagree ) and 7 ( strongly agree ). Sample statements are: (a) “I have had a great academic experience at this college,” (b) “I am a diligent student,” and (c) “I feel thankful for the opportunity to learn so many new things." The overall well-being score was calculated by computing the average of all the items on the scale with higher scores reflecting better SWB. This scale has been translated into Chinese and validated on Chinese samples [ 110 ], revealing reliable and valid psychometric properties. In the present study, the measure demonstrated good reliability (Cronbach’s α = 0.953).

Statistical analysis

Before further analyses, we carried out a confirmatory factor analysis (CFA) using AMOS 26.0 to ensure the validity and reliability of the study variables. The potential common method variance (CMV) was checked considering self-report questionnaire was the principal method for obtaining data. After that, data analysis in the study was carried out in three steps using SPSS 26.0. Firstly, descriptive statistics and Pearson’s correlations were summarized and calculated. Then, to test the proposed hypotheses in the study, we employed Haye’s PROCESS macro Model 6 (version 3.4.1 software) [ 111 ] to test the mediating role of self-esteem and online social support in the relationship between social media usage and PWB and SWB. Finally, Haye’s PROCESS macro Model 85 [ 111 ] was conducted to test whether the first stage of indirect relationships and the direct association between social media usage and PWB and SWB was moderated by cyberbullying. In the process, all variables were standardized and the interaction terms were computed from the standardized variables. The bias-corrected percentile bootstrap method and 95% confidence intervals (CI) were applied. If the effect does not include 0 in the 95% CI, it is considered to be statistically significant. Moreover, the simple slope analysis was employed to evaluate the moderating effects [ 112 ]. We plotted the relationship between the independent variable (social media usage) and the dependent variables (self-esteem and online social support) when the levels of the moderator variable (cyberbullying) were one standard deviation below and one standard deviation above mean value of the moderator variable. In addition, demographic variables (i.e., gender, age, family origin) were controlled during the analyses. A p -value of < 0.05 was considered to be statistically significant.

Validity, construct reliability, and common method variance

The content validity and reliability of the study variables analyzed through CFA are displayed in Table 2 . As shown in the table, the item loadings of all factors in the study exceed the threshold value of 0.60 as recommended by Hair et al. [ 113 ]. To ensure the convergent validity of our model, we conducted an analysis of the composite reliability (CR), average variance extracted (AVE), and Cronbach alpha (CA) of all the constructs. The findings from this analysis revealed that the CR and CA values for all the constructs exceeded the recommended threshold of 0.70, indicating a high level of internal consistency. Additionally, construct validity is also confirmed because the AVE values for all the constructs were also above the suggested threshold of 0.50, as advised by previous research studies [ 114 , 115 ]. To assess the discriminant validity of our study, we employed the methodology suggested by Fornell and Larcker [ 114 ]. Our approach involved examining the square root values of AVE for each construct and comparing them with their respective inter-correlations. Considering that the square root of AVE for each factor is greater than its correlations with other factors, it can be concluded that discriminant validity is also established (see Tables 2 and 3 for comparison).

In order to minimize the risk of CMV in our data, we implemented multiple strategies to ensure the accuracy and reliability of the self-reported answers provided by the participants. For instance, as a procedural measure, we took into consideration the suggestions put forward by Podsakoff et al. [ 116 ] to address any potential concerns regarding the anonymity and confidentiality of our participants. We took great care in ensuring our participants that their identities would be kept strictly confidential, and that any information they shared would be treated with the highest level of confidentiality. Additionally, we employed the Herman single-factor test, as recommended by Podsakoff et al. [ 116 ], to evaluate the potential threat of CMV in our study. The results of this test indicated that the first factor accounted for 33.97% of the variance, suggesting that there is no significant problem of CMV present in our study.

Preliminary analyses

Descriptive statistics and correlation matrix between the variables are reported in Table 3 . As expected, all proposed path variables were revealed to be intercorrelated significantly (see Table 3 ). Significant positive correlations were obtained between social media usage and PWB ( r  = 0.40, p  < 0.01) and SWB ( r  = 0.46, p  < 0.01), respectively with large effect sizes. Self-esteem and online social support were found to be positively associated with social media usage ( r  = 0.45, p  < 0.01; r  = 0.43, p  < 0.01), PWB ( r  = 0.54, p  < 0.01; r  = 0.55, p  < 0.01), and SWB ( r  = 0.50, p  < 0.01; r  = 0.53, p  < 0.01), respectively. In addition, cyberbullying was negatively related to self-esteem ( r  = -0.18, p  < 0.01), online social support ( r  = -0.20, p  < 0.01), PWB and SWB ( r  = -0.27, p  < 0.01; r  = -0.16, p  < 0.01), respectively whereas a positive association was observed between this variable and social media usage ( r  = 0.18, p  < 0.01). In general, no significant relationships were identified between the demographic variables and the other variables under investigation. We, therefore, included them as control variables in the follow-up analyses.

Testing for the mediating effect

To test the hypothesized relationship between social media usage and outcomes as well as the mediation of self-esteem and online social support, we utilized SPSS PROCESS macros [ 111 ]. The results presented in Table 4 revealed that social media usage was positively related to self-esteem ( B  = 0.20, t  = 15.75, p  < 0.001), online social support ( B  = 0.09, t  = 7.00, p  < 0.001), PWB ( B  = 0.11, t  = 4.78, p  < 0.001), and SWB ( B  = 0.19, t  = 8.36, p  < 0.001), confirming our hypotheses H1a and H1b. Moreover, the results further showed that self-esteem and online social support mediate the relationship between students’ usage of social media and their PWB and SWB. Specifically, social media usage was significantly and positively associated with PWB via self-esteem (indirect effect = 0.100, SE  = 0.01, 95% CI  = [0.075, 0.126]), via online social support (indirect effect = 0.046, SE  = 0.01, 95% CI  = [0.030, 0.063]), and via self-esteem and online social support (indirect effect = 0.058, SE  = 0.01, 95% CI  = [0.043, 0.074]). Similarly, the utilization of social media by students was also significantly and positively related to their SWB via self-esteem (indirect effect = 0.072, SE  = 0.02, 95% CI  = [0.049, 0.097]), online social support (indirect effect = 0.043, SE  = 0.01, 95% CI  = [0.027, 0.061]), and the two mediators (indirect effect = 0.054, SE  = 0.01, 95% CI  = [0.039, 0.070]). Thus, self-esteem and online social support acted as effective mediators in the association between social media usage and PWB and SWB, supporting H2a, H2b. Moreover, self-esteem had a significant and positive effect on online social support ( B  = 0.57, t  = 19.76, p  < 0.001), thus confirming H2c.

Testing for moderated mediation

In Hypothesis 3, cyberbullying was projected to moderate the first phase of the indirect associations as well as the direct relations between social media usage and PWB and SWB. To test these hypotheses, we performed a moderated mediation analysis by using Haye’s PROCESS macro [ 111 ] in SPSS and investigated Cyberbullying across the levels. Concerning the relationships among study variables, as shown in Table 5 , cyberbullying was negatively correlated with self-esteem ( B  = -0.24, t  = -10.24, p  < 0.001), online social support ( B  = -0.16, t  = -7.16, p  < 0.001), PWB ( B  = -0.30, t  = -7.67, p  < 0.001), and SWB ( B  = -0.19, t  = -4.67, p  < 0.001). The effect of social media usage on self-esteem ( B  = 0.22, t  = 17.69, p  < 0.001) and online social support ( B  = 0.12, t  = 9.12, p  < 0.001) was significant, and more importantly, this effect was moderated by cyberbullying ( B  = -0.11, t  = -7.30, p  < 0.001; B  = -0.10, t  = -6.66, p  < 0.001), respectively. Contrary to our H3c and H3d, the direct relationships between social media usage and PWB ( B  = 0.00, t  = 0.10, p  > 0.05) and SWB ( B  = 0.00, t  = 0.11, p  > 0.05) were not significantly moderated by cyberbullying. Furthermore, the bias-corrected percentile bootstrapping results revealed that the indirect effect of social media usage on PWB via self-esteem (Index of moderated mediation = -0.05, SE  = 0.01, 95% CI  = [-0.07, -0.03]) and online social support (Index = -0.04, SE  = 0.01, 95% CI  = [.-0.06, -0.03]) was moderated by cyberbullying. Likewise, the relationship between social media usage and SWB was indirect and moderated by cyberbullying via self-esteem (Index = -0.04, SE  = 0.01, 95% CI  = [-0.05, -0.02]) and online social support (Index = -0.04, SE  = 0.01, 95% CI  = [-0.06, -0.03]). In addition, results showed that the indirect effects of social media usage by students via self-esteem on their PWB (effect = 0.056, SE  = 0.01, 95% CI  = [0.036, 0.078]) and SWB (effect = 0.041, SE  = 0.01, 95% CI  = [0.024, 0.061]) were weaker at + 1SD than at -1SD (effect = 0.128, SE  = 0.02, 95% CI  = [0.093, 0.165]; effect = 0.094, SE  = 0.02, 95% CI  = [0.061, 0.130]), respectively. Also, a similar pattern was observed for the indirect effects of social media usage via online social support on PWB (effect = 0.019, SE  = 0.01, 95% CI  = [0.003, 0.036]) and SWB (effect = 0.019, SE  = 0.01, 95% CI  = [0.003, 0.037]) at higher level of cyberbullying than at lower level (effect = 0.082, SE  = 0.01, 95% CI  = [0.058, 0.107]; effect = 0.081, SE  = 0.01, 95% CI  = [0.055, 0.107]), respectively. These results have given support to our H3a and H3b.

For clarity, we also plotted graphical diagrams to better examine the role of cyberbullying as a moderator in the relations between social media usage and self-esteem (Fig.  2 ) and online social support (Fig.  3 ), separately for students experiencing low and high cyberbullying (at 1 SD below the mean and 1 SD above the mean, respectively). Simple slope tests suggested that the relationships between social media usage and self-esteem and online social support were statistically weaker respectively when at the higher level of cyberbullying.

Cyberbullying moderates the relationship between social media usage and self-esteem

Cyberbullying moderates the relationship between social media usage and online social support

In this study, a moderated mediation model was formulated to explore whether students’ utilization of social media would be indirectly associated with their PWB and SWB via self-esteem and online social support and whether the first phase of this indirect relationship and the direct correlation would be moderated by cyberbullying they have experienced. Although numerous studies have examined the impacts of social media usage among various groups of people, especially children, this study is one of the few that considers both PWB and SWB as outcome variables among Chinese university students, a sample that has been insufficiently examined. Moreover, this study provides a probable explanation as to why university students' frequent use of social media results in higher levels of PWB and SWB. Moreover, it is the first empirical study confirming the mediating roles of self-esteem and online social support underlying this linkage. The research findings further our understanding of how social media usage impacts users’ well-being and what role cyberbullying plays in the process.

Consistent with our expectations, social media usage by university students positively predicted their PWB and SWB; and self-esteem and online social support mediated the relationships, which extends previous theoretical and empirical studies. Specifically, it helps advance our understanding of the intricate relationship between social media usage and people’s well-being, especially PWB and SWB. Previous research on this association has generated varied results. Some studies have observed a negative relationship while others have acknowledged that a positive association exists as social media can facilitate online social connections [ 117 ] and reduce the levels of negative emotions and feelings, such as stress, loneliness, depression, and the sense of social isolation [ 48 ], thus beneficial to users’ PWB. The research findings suggest that incorporating social media into the daily lives of college students and actively engaging with shared content can have a profound impact on their self-esteem and access to diverse forms of online social support, which, in turn, has the potential to enhance their overall PWB and SWB. In previous empirical studies [ 118 , 119 ], self-esteem was mainly found to be positively correlated with several indicators of SWB including affect, meaning in life, and subjective vitality. The present study contributes to the existing body of research by specifically identifying the positive associations between self-esteem and both PWB and SWB in relation to the usage of social media platforms. In this competitive world, healthy self-esteem is required for university students to effectively deal with potential psychological distress that may arise in their academic and career pursuits. And in accordance with self-affirmation theory, greater self-esteem can work as a buffer against unpleasant and stressful experiences and failures [ 120 ]. Furthermore, Sociometer Theory [ 121 ] suggests that an individual's self-esteem is influenced by their sense of social acceptance and the importance placed on their relationships. This theory provides further insight into the strong correlation between self-esteem and PWB. In collectivistic cultures like China, where social bonds are highly valued, young adults place a great emphasis on their connections with others, particularly within their families and interpersonal relationships. As a result, individuals with higher levels of self-esteem are more likely to experience greater PWB, as their self-esteem serves as a potential indicator of their value within their social circles. In addition to self-esteem, our study also identified positive effects of online social support on students’ well-being consistent with prior research [ 122 ]. The reason behind this phenomenon can be attributed to the fact that students who have a vast network of connections on social media and dedicate a considerable amount of time to actively engaging in various interactions on these platforms are more likely to garner a substantial amount of support from their online acquaintances [ 123 ]. As the number of friends a user possesses increases, the probability of receiving positive and supportive comments on their status updates, appreciation for their uploaded photos, and congratulations for their personal accomplishments also increases. This correlation implies that a larger social circle enhances the likelihood of receiving encouragement and validation from friends. This particular positive experience, which is frequently absent in face-to-face interactions, can strengthen the feeling of being a part of a social network and instill a sense of being valued, respected, and esteemed among students. As a result, it can lead to the development of a positive psychological and emotional state, ultimately contributing to an elevated level of SWB [ 124 ].

Apart from the general mediation effect, it is important to highlight the significance of each individual stage within the mediation process. First, our research finding is in line with prior reports that social media usage increases users' self-esteem [ 69 , 70 ]. Previous research on self-esteem theories has identified a close relationship between the use of various social media sites such as Facebook, Twitter, and Instagram and users’ self-esteem [ 125 , 126 ], revealing that peer interaction and feedback on the self represents critical predictors of young adults’ self-esteem [ 127 ]. In addition to facilitating instant messaging and enabling activities like posting and commenting on photos, social media platforms offer a valuable channel for young people to receive feedback, interact with their peers, enhance their social skills, and gain insights by observing others [ 79 ]. College students in China use similar sites like WeChat and Weibo to portray a different version of themselves online by sharing their photos, videos, and other posts within their friend circles or beyond. The likes they receive on social media sites are regarded as verification for acceptance and approval within their groups of peers, which may, in turn, boost their self-esteem. Since the main objective of social media platforms is to encourage communication and connections between individuals, students who frequently use these sites will have a higher likelihood of actively engaging with their fellow peers and more opportunities to receive positive feedback on social network profiles compared to those who use social media less frequently, thus enhancing their self-esteem. And as predicted, students’ higher self-esteem predicted greater online social support, corresponding to research findings by Jin et al. [ 87 ] and Zheng et al. [ 82 ]. These findings align with the principles of Sociometer Theory [ 84 ], which suggests that there is a strong relationship between self-esteem and how individuals perceive acceptance from society and others. People with high self-esteem often feel valued, which in turn encourages them to engage in positive online communication, receive more affirmation and praise from others, and ultimately be accepted within online communities. On the contrary, individuals who possess low self-esteem often harbor a pessimistic outlook towards their own self-image, leading to more negative online interactions and making it harder for them to receive acceptance from online communities, thus hindering their ability to develop a robust online social support system [ 128 ].

Furthermore, in line with previous research [ 79 , 80 ], our findings indicate that there is a positive correlation between the amount of time students spend on social media and the level of online social support they receive or perceive online. Social support in an online setting has attracted the attention of scholars who have studied its prevalence within social networks. One example of this is when individuals show support for their peers by sharing or forwarding online news articles that would be beneficial to their friends in the digital realm. Moreover, public officials have also recognized the significance of social media in providing updates to citizens during critical events such as natural disasters, criminal incidents, or accidents. In such cases, these officials utilize their social media accounts to keep the public informed and engaged. Additionally, people are able to obtain interpersonal support by connecting and interacting with like-minded individuals on various social media platforms. This form of support, commonly referred to as peer support, serves as a valuable resource for college students seeking understanding, guidance, and empathy from others who share similar interests or experiences [ 129 ]. Moreover, a previous research study conducted on college students found that when seeking social support, students were more inclined to rely on social media platforms rather than seeking help from their parents or mental health professionals. Many of them believed that social media use provided them with positive experiences, offering a support network and helping them feel more connected with their friends. Additionally, the study indicated that students tended to gravitate towards communities composed of their peers who shared similar interests, such as fandom communities [ 130 ]. Building upon a series of similar findings, our study provides new empirical support for the positive effect of social media usage on online social support.

Meanwhile, we identified cyberbullying as a boundary condition variable in our research model. Specifically, the results indicated that the links between social media usage and their PWB and SWB via the two mediators: self-esteem and online social support were weaker for those students suffering greater levels of cyberbullying. In today's technologically advanced society, the issue of online bullying has become a prominent worry in numerous settings. The research we conducted has provided evidence that cyberbullying has the potential to diminish the positive effects that students typically derive from their use of social media. For individuals experiencing a low level of cyberbullying, self-esteem, and online social support can have significant beneficial effects on their PWB and SWB. Increased cyberbullying, however, leads to more psychological distress, reduced life satisfaction, increased depressive symptoms and anxiety [ 131 ], or even suicidal thoughts and attempts [ 132 ]. However, contrary to part of our hypotheses, cyberbullying did not moderate the direct relationship between social media usage and PWB and SWB. A probable explanation for this is that the relationship between social media usage, cyberbullying, and well-being is multifaceted and influenced by various factors. It is possible that other variables not considered in this study could be influencing these relationships. For instance, as evidenced by previous research [ 25 ], cultural and contextual factors like collectivism in Chinese culture can play an important role in the effects of media use on well-being. Meanwhile, as suggested by the Differential Susceptibility to Media Effects Model [ 133 ] and Cultivation Theory [ 134 ], sociocultural and psycho-demographic factors can also moderate social media effects by strengthening, diminishing, and/or moderating individuals’ cognitive, emotional, and behavioral responses to media. Another possible reason is that individuals affected by cyberbullying might have developed coping strategies or mechanisms (e.g., emotion-focused coping and avoidance-coping) to deal with cyberbullying to lessen its impact on their PWB and SWB [ 135 ]. These coping mechanisms might mitigate the expected moderating effect.

Limitations and future directions

The present investigation provides a more comprehensive insight into the intricate relationship between social media usage by Chinese university students and their PWB and SWB and how such relationship is mediated by self-esteem and online social support, and moderated by cyberbullying. However, several limitations should be taken into consideration when analyzing and interpreting the research findings.

First, in our study, we employed a cross-sectional research design, which is not without its limitations, particularly the potential for common method variance (CMV). To address this concern, we implemented various measures, such as guaranteeing the confidentiality and anonymity of participants and conducting statistical analyses to confirm the absence of CMV. Nonetheless, we recognize that our model's credibility and validity could be further strengthened by employing a longitudinal research design or carrying out an experimental laboratory study. Second, it is important to approach the generalizability of the present findings with caution. It remains uncertain whether the findings in our study based on samples collected from Chinese universities can be applied to samples obtained in different contexts, populations (e.g., children, older adults), and countries. Therefore, more studies are warranted to examine these relationships in more diverse samples and contexts since it is noteworthy that social network sites may have different effects on individuals of different ages or nationalities. Third, given our failure to confirm hypotheses regarding cyberbullying moderating the impact of social media usage on PWB and SWB due to possible deficiencies in our research design, it is important to note that future studies should formulate a more comprehensive research design by taking into account a broader context and more factors (e.g., coping strategies, social contexts, cultural norms, and psycho-demographic factors) that may moderate social media impact on health outcomes. Meanwhile, given that some studies have found negative effects of excessive and problematic use of social media on users’ well-being, it is necessary for future studies to examine specific factors resulting in such detrimental outcomes, such as time spent on social media, active or passive social media use [ 136 ], and users’ motives [ 137 ]. Third, the current study found support for the important roles of self-esteem and online social support in explaining why social media usage can be beneficial to users’ PWB and SWB, yet some other factors may also take effect. A more extensive investigation is required in order to gain a comprehensive understanding of the specific circumstances under which predictor variables become significant and the ways in which they interact with online processes and individuals' overall well-being, such as positive and negative emotions while using various social networking sites, bridging and bonding social capital, social connectedness, social comparison, and interpersonal competence. In addition, more studies are needed to determine the circumstances in which social media usage can have positive effects, such as investigating whether social networking platforms that encourage more direct social interaction can improve well-being. Furthermore, future studies can also compare the different roles of direct contact and online contact via different social media platforms in affecting people’s overall well-being. Additionally, it could be further explored how previous experiences with specific social media platforms, potentially influenced by the age of the site and the user, impact the association between usage and PWB and SWB.

Theoretical and practical implications

Despite the limitations, this research has a series of important theoretical and practical implications. First, the current study is one of the few attempts to examine the impact of social media on well-being from both the hedonic and eudaimonic perspectives among university students in the context of China, contributing to the existing literature by empirically confirming the positive implications of social media usage on PWB and SWB. Second, this study extends the extant literature on social media by identifying a mediation pathway that includes self-esteem and online social support, underlying their positive effects. This finding helps shed light on how self-esteem within the theoretical context of Identity Theory and Sociometer Theory can be applied in the digital domain, opening up a new research trajectory to further exploring the effect of various dimensions of self-esteem on health outcomes within the framework of social media research. Also, the examination of online social support as a mediator aligns with communication and media theories that emphasize the importance of technology-mediated communication in shaping relationships and well-being. Moreover, it provides firm support for the Social Compensation hypothesis, which is concerned with how online interaction can generate a host of benefits for individuals struggling with face-to-face interaction due to lack of social skills or low well-being [ 133 ], especially during the pandemic. This can enrich our understanding of how these theories apply within a non-WEIRD cultural context, particularly considering the moderating role of cyberbullying. Lastly, another important contribution of our research is the investigation of the moderating role of cyberbullying, which was found to harm the positive utility of social media on students’ PWB and SWB via diminishing the beneficial effects of self-esteem and online social support. This serves as the core theoretical contribution of this study, adding to the previous body of literature on cyberbullying research, especially its moderating role.

In terms of practical contributions, our results highlight the importance and the beneficial outcomes of social media among college students on their overall well-being. This suggests that educational institutions, teachers, administrators, and parents should recognize the positive application of various social media platforms in academia and encourage rational social media use inside and outside schools. Then the positive effects of self-esteem and online social support indicate that students should communicate and interact more frequently with peers, friends, families and important others as a way to increase their self-esteem and seek more emotional and informational support as well as social companionship. However, the finding that cyberbullying victimization as a moderator can reduce the positive effects of social media usage on health outcomes through mediators of self-esteem and online social support indicates that it is important to empower students at-risk for cyberbullying victimization through prevention efforts. Self-esteem as a social construct is especially influenced by interactions with peers. Hence, it is crucial to offer opportunities for cyberbullying victims to connect with their peers, establish strong relationships, and develop meaningful friendships that contribute to their self-worth and foster a positive self-perception. In addition, as for those enduring cyberbullying-related psychological or behavioral problems (e.g., depression, anxiety, social isolation, and suicidal attempts), most Chinese university counselling centers could open online platforms for psychoeducation like training sessions and courses easily accessible through popular apps, such as WeChat and Tencent [ 138 ], and offer timely and target psychological interventions and counseling. Most importantly, given the prevalence of cyberbullying in China, it is imperative that universities initiate training programs and provide relevant curricula to empower students with basic skills and knowledge to recognize, prevent, and cope with cyberbullying. Bullying tracking software and similar practices can be utilized to prevent cyberbullying while using social media for academic purposes. The authorities may also implement more stringent laws and regulations against cyberbullying and online harassment to create a safe online environment.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Leong L-Y, Hew T-S, Ooi K-B, Lee V-H, Hew J-J. A hybrid SEM-neural network analysis of social media addiction. Expert Syst Appl. 2019;133:296–316. https://doi.org/10.1016/j.eswa.2019.05.024 .

Article   Google Scholar  

Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM, Liu F. Effects of social media use on psychological well-being: a mediated model. Front Psychol. 2021;12:678766. https://doi.org/10.3389/fpsyg.2021.678766 .

Article   PubMed   PubMed Central   Google Scholar  

Bayer JB, Triệu P, Ellison NB. Social media elements, ecologies, and effects. Annu Rev Psychol. 2020;71:471–97. https://doi.org/10.1146/annurev-psych-010419-050944 .

Article   PubMed   Google Scholar  

Twenge JM, Campbell WK. Media use is linked to lower psychological well-being: evidence from three datasets. Psychiatry Q. 2019;90(2):311–31. https://doi.org/10.1007/s11126-019-09630-7 .

Ryan RM, Deci EL. On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. Annu Rev Psychol. 2001;52:141–66. https://doi.org/10.1146/annurev.psych.52.1.141 .

Kahneman, D., Diener, E., & Schwarz, N. (Eds.). Well-being: The foundations of hedonic psychology. Russell Sage Foundation. 1999

Ryff CD. Beyond Ponce de Leon and life satisfaction: new directions in quest of successful ageing. Int J Behav Dev. 1989;12(1):35–55. https://doi.org/10.1177/016502548901200102 .

Ryff CD, Keyes CL. The structure of psychological well-being revisited. J Pers Soc Psychol. 1995;69(4):719–27. https://doi.org/10.1037//0022-3514.69.4.719 .

Diener, E. (Ed.). The science of well-being: The collected works of Ed Diener. Springer Science + Business Media.2009.  https://doi.org/10.1007/978-90-481-2350-6

Awad F, Mayasari R. Subjective well-being, psychological well-being, and islamic religiosity. Int J Sci Res (IJSR). 2015;4:1168–73.

Halston A, Iwamoto D, Junker M, Chun H. Social media and loneliness. Int J Psychol Stud. 2019;11:27. https://doi.org/10.5539/ijps.v11n3p27 .

Dalvi-Esfahani M, Niknafs A, Kuss DJ, Nilashi M, Afrough S. Social media addiction: applying the DEMA℡ approach. Telematics Informatics. 2019;43:101250. https://doi.org/10.1016/j.tele.2019.101250 .

Jiao Y, Jo M-S, Sarigöllü E. Social value and content value in social media: two paths to psychological well-being. J Organ Comput Electron Commer. 2017;27(1):3–24. https://doi.org/10.1080/10919392.2016.1264762 .

Kim JY, Chung N, Ahn KM. Why people use social networking services in Korea: the mediating role of self-disclosure on subjective well-being. Inf Dev. 2014;30(3):276–87. https://doi.org/10.1177/0266666913489894 .

Zsila Á, Reyes MES. Pros & cons: impacts of social media on mental health. BMC Psychology. 2023;11:201. https://doi.org/10.1186/s40359-023-01243-x .

Wei L, Gao F. Social media, social integration and subjective well-being among new urban migrants in China. Telematics Inform. 2017;34(3):786–96. https://doi.org/10.1016/j.tele.2016.05.017 .

Jimenez, Y., & Morreale, P. Social Media Use and Impact on Interpersonal Communication. In C. Stephanidis (Ed.), HCI International 2015—Posters’ Extended Abstracts, 2015; (pp. 91–96). Springer International Publishing.

Chotpitayasunondh V, Douglas KM. How, “phubbing” becomes the norm: the antecedents and consequences of snubbing via smartphone. Comput Hum Behav. 2016;63:9–18. https://doi.org/10.1016/j.chb.2016.05.018 .

Swar B, Hameed T. Fear of missing out, social media engagement smartphone addiction and distraction: moderating role of self-help mobile apps-based interventions in the youth. Int Conference Health Informatics. 2017. https://doi.org/10.5220/0006166501390146 .

Roberts JA, David ME. The social media party: Fear of missing out (FoMO), social media intensity, connection, and well-being. Int J Human-Computer Interaction. 2020;36(4):386–92. https://doi.org/10.1080/10447318.2019.1646517 .

Vannucci A, Flannery KM, Ohannessian CM. Social media use and anxiety in emerging adults. J Affect Disord. 2017;207:163–6. https://doi.org/10.1016/j.jad.2016.08.040 .

Kim Y, Lee M. Does social media use mitigate or exacerbate loneliness among korean older adults? focusing on the moderating role of media literacy. Soc Med + Soc. 2023;9(2):20563051231177960. https://doi.org/10.1177/20563051231177959 .

Dhir A, Yossatorn Y, Kaur P, Chen S. Online social media fatigue and psychological well-being—a study of compulsive use, fear of missing out, fatigue, anxiety and depression. Int J Inf Manage. 2018;40:141–52. https://doi.org/10.1016/j.ijinfomgt.2018.01.012 .

Chi LC, Tang TC, Tang E. The phubbing phenomenon: a cross-sectional study on the relationships among social media addiction, fear of missing out, personality traits, and phubbing behavior. Curr Psychol. 2022;41(2):1112–23. https://doi.org/10.1007/s12144-021-02468-y .

Gong J, Firdaus A, Said F, Ali I, Danaee M, Xu J. Pathways linking media use to wellbeing during the COVID-19 pandemic: a mediated moderation study. Soc Med + Soc. 2022;8:205630512210873. https://doi.org/10.1177/20563051221087390 .

Gong J, Zanuddin H, Hou W, Xu J. Media attention, dependency, self-efficacy, and prosocial behaviours during the outbreak of COVID-19: a constructive journalism perspective. Global Med China. 2022;7(1):81–98. https://doi.org/10.1177/20594364211021331 .

Cole DA, Nick EA, Zelkowitz RL, Roeder KM, Spinelli T. Online social support for young people: does it recapitulate in-person social support; can it help? Comput Hum Behav. 2017;68:456–64. https://doi.org/10.1016/j.chb.2016.11.058 .

Chen Y, Gao Q. Effects of social media self-efficacy on informational use, loneliness, and self-esteem of older adults. Int J Human-Computer Int. 2023;39(5):1121–33. https://doi.org/10.1080/10447318.2022.2062855 .

Haslam DM, Tee A, Baker S. The use of social media as a mechanism of social support in parents. J Child Fam Stud. 2017;26(7):2026–37. https://doi.org/10.1007/s10826-017-0716-6 .

Li LW, Liang J. Social exchanges and subjective well-being among older Chinese: does age make a difference? Psychol Aging. 2007;22(2):386–91. https://doi.org/10.1037/0882-7974.22.2.386 .

Brochado S, Soares S, Fraga S. A scoping review on studies of cyberbullying prevalence among adolescents. Trauma Violence Abuse. 2017;18(5):523–31. https://doi.org/10.1177/1524838016641668 .

van Geel M, Vedder P. Does cyberbullying predict internalizing problems and conduct problems when controlled for traditional bullying? Scand J Psychol. 2020;61(2):307–11. https://doi.org/10.1111/sjop.12601 .

Carvalho M, Branquinho C, Matos M. Cyberbullying and bullying: impact on psychological symptoms and well-being. Child Indicators Res. 2021;14:435–52. https://doi.org/10.1007/s12187-020-09756-2 .

Wachs S, Vazsonyi AT, Wright MF, KsinanJiskrova G. Cross-national associations among cyberbullying victimization, self-esteem, and internet addiction: direct and indirect effects of alexithymia. Front Psychol. 2020;11:1368. https://doi.org/10.3389/fpsyg.2020.01368 .

Hsieh Y-P. Parental psychological control and adolescent cyberbullying victimization and perpetration: The mediating roles of avoidance motivation and revenge motivation. Asia Pacific J Soc Work. 2020;30:212–26. https://doi.org/10.1080/02185385.2020.1776153 .

Kwan I, Dickson K, Richardson M, MacDowall W, Burchett H, Stansfield C, Brunton G, Sutcliffe K, Thomas J. Cyberbullying and children and young people’s mental health: a systematic map of systematic reviews. Cyberpsychol Behav Soc Netw. 2020;23(2):72–82. https://doi.org/10.1089/cyber.2019.0370 .

Auxier B, Anderson M. Social Media USE in 2021. 2021. Available online at:  https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/

Gulzar MA, Ahmad M, Hassan M, Rasheed MI. How social media use is related to student engagement and creativity: Investigating through the lens of intrinsic motivation. Behav Information Technol. 2022;41(11):2283–93. https://doi.org/10.1080/0144929X.2021.1917660 .

Wu H-Y, Chiou A-F. Social media usage, social support, intergenerational relationships, and depressive symptoms among older adults. Geriatr Nurs. 2020;41(5):615–21. https://doi.org/10.1016/j.gerinurse.2020.03.016 .

Cingel DP, Carter MC, Krause H-V. Social media and self-esteem. Curr Opinion Psychol. 2022;45:101304. https://doi.org/10.1016/j.copsyc.2022.101304 .

Wirtz D, Tucker A, Briggs C, Schoemann A. How and why social media affect subjective well-being: multi-site use and social comparison as predictors of change across time. J Happiness Stud. 2021;22:1673–91. https://doi.org/10.1007/s10902-020-00291-z .

Ye S, Ho KKW, Wakabayashi K, Kato Y. Relationship between university students’ emotional expression on tweets and subjective well-being: considering the effects of their self-presentation and online communication skills. BMC Public Health. 2023;23(1):594. https://doi.org/10.1186/s12889-023-15485-2 .

Chen YA, Fan T, Toma CL, Scherr S. International students’ psychosocial well-being and social media use at the onset of the COVID-19 pandemic: a latent profile analysis. Comput Human Behav. 2022;137:107409. https://doi.org/10.1016/j.chb.2022.107409 .

Yimer BL. Social media usage, psychosocial well-being and academic performance. Community Health Equity Res Policy. 2023;43(4):399–404. https://doi.org/10.1177/0272684X211033482 .

Diener E, Seligman MEP. Beyond money: toward an economy of well-being. Psycholog Sci Pub Interest. 2004;5(1):1–31. https://doi.org/10.1111/j.0963-7214.2004.00501001.x .

Shaheen MA. Use of social networks and information seeking behavior of students during political crises in Pakistan: a case study. The Intern Information Library Rev. 2008;40(3):142–7. https://doi.org/10.1016/j.iilr.2008.07.006 .

Shah S, Hussain K, Aftab A, Rizve R. Social media usage and students’ psychological well-being: an empirical analysis of District Mirpur, AJ&K, Pakistan. New Educ Rev. 2021;64:60–72. https://doi.org/10.15804/tner.2021.64.2.05 .

O’keeffe GS, Clarke-Pearson K, Council on Communications and Media. The impact of social media on children, adolescents, and families. Pediatrics. 2011;127(4):800–4. https://doi.org/10.1542/peds.2011-0054 .

Zhan L, Sun Y, Wang N, Zhang X. Understanding the influence of social media on people’s life satisfaction through two competing explanatory mechanisms. Aslib J Inf Manag. 2016;68(3):347–61. https://doi.org/10.1108/AJIM-12-2015-0195 .

McGillivray, M. Human Well-being: Issues, Concepts and Measures. In: McGillivray, M. (eds) Human Well-Being. Studies in Development Economics and Policy. Palgrave Macmillan, London. 2007. https://doi.org/10.1057/9780230625600_1

Twenge JM, Joiner TE, Rogers ML, Martin GN. Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. Adolescents after 2010 and links to increased new media screen time. Clin Psycholog Sci. 2018;6(1):3–17. https://doi.org/10.1177/2167702617723376 .

Keyes CL, Dhingra SS, Simoes EJ. Change in level of positive mental health as a predictor of future risk of mental illness. Am J Public Health. 2010;100(12):2366–71. https://doi.org/10.2105/AJPH.2010.192245 .

Renshaw TL. Psychometrics of the revised college student subjective well-being questionnaire. Can J Sch Psychol. 2018;33(2):136–49. https://doi.org/10.1177/0829573516678704 .

Wu M-S. The effects of facebook use on network social capital and subjective well-being: a generational cohort analysis from the Taiwan social change survey. Heliyon. 2023;9(4):e14969. https://doi.org/10.1016/j.heliyon.2023.e14969 .

Dienlin T, Masur PK, Trepte S. Reinforcement or displacement? The reciprocity of FtF, IM, and SNS communication and their effects on loneliness and life satisfaction. J Comput-Mediat Commun. 2017;22(2):71–87. https://doi.org/10.1111/jcc4.12183 .

Moukalled SH, Bickham DS, Rich M. Examining the associations between online interactions and momentary affect in depressed adolescents. Front Human Dynamics. 2021;3:624727. https://doi.org/10.3389/fhumd.2021.624727 .

Csikszentmihalyi M. Flow: The psychology of optimal experience. New York: Harper Perennial Modern Classics; 2008.

Google Scholar  

Moneta GB, Csikszentmihalyi M. The effect of perceived challenges and skills on the quality of subjective experience. J Pers. 1996;64(2):275–310. https://doi.org/10.1111/j.1467-6494.1996.tb00512.x .

Rosenberg M. Society and the adolescent self-image. Princeton, NJ: Princeton University Press; 1965.

Book   Google Scholar  

Brown, J. D., & Marshall, M. A. The Three Faces of Self-Esteem. In M. H. Kernis (Ed.), Self-esteem issues and answers: A sourcebook of current perspectives, 2006; (pp. 4–9). Psychology Press.

Valkenburg PM, Koutamanis M, Vossen HGM. The concurrent and longitudinal relationships between adolescents’ use of social network sites and their social self-esteem. Comput Hum Behav. 2017;76:35–41. https://doi.org/10.1016/j.chb.2017.07.008 .

Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215. https://doi.org/10.1037/0033-295X.84.2.191 .

Sowislo JF, Orth U. Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychol Bull. 2013;139(1):213–40. https://doi.org/10.1037/a0028931 .

ciçek I. Mediating role of self-esteem in the association between loneliness and psychological and subjective well-being in University students. Intern J Contemporary Educ Res. 2021;8:83–97. https://doi.org/10.33200/ijcer.817660 .

Orth U, Robins RW. The development of self-esteem. Curr Dir Psychol Sci. 2014;23(5):381–7. https://doi.org/10.1177/0963721414547414 .

Fatima M, Niazi S, Ghayas S. Relationship between self-esteem and social anxiety: role of social connectedness as a mediator. Pakistan J Soc Clin Psychol. 2017;15:12–7.

Pineiro, Carly Renee, "Social media use and self-esteem in undergraduate students". Theses and Dissertations. 2016;1484. https://rdw.rowan.edu/etd/1484

Tracy JL, Robins RW. “Death of a (Narcissistic) salesman:” an integrative model of fragile self-esteem: comment. Psychol Inq. 2003;14(1):57–62.

Apaolaza V, Hartmann P, Medina E, Barrutia JM, Echebarria C. The relationship between socializing on the Spanish online networking site Tuenti and teenagers’ subjective wellbeings: the roles of self-esteem and loneliness. Comput Hum Behav. 2013;29(4):1282–9. https://doi.org/10.1016/j.chb.2013.01.002 .

Burrow AL, Rainone N. How many likes did I get?: Purpose moderates links between positive social media feedback and self-esteem. J Exp Soc Psychol. 2017;69:232–6. https://doi.org/10.1016/j.jesp.2016.09.005 .

Marengo D, Montag C, Sindermann C, Elhai JD, Settanni M. Examining the links between active facebook use, received likes, self-esteem and happiness: a study using objective social media data. Telematics Informatics. 2021;58:101523. https://doi.org/10.1016/j.tele.2020.101523 .

Toma CL, Hancock JT. Self-affirmation underlies facebook use. Pers Soc Psychol Bull. 2013;39(3):321–31. https://doi.org/10.1177/0146167212474694 .

Lakey B, Orehek E. Relational regulation theory: a new approach to explain the link between perceived social support and mental health. Psychol Rev. 2011;118(3):482–95. https://doi.org/10.1037/a0023477 .

Brailovskaia J, Teismann T, Margraf J. Cyberbullying, positive mental health and suicide ideation/behavior. Psychiatry Res. 2018;267:240–2. https://doi.org/10.1016/j.psychres.2018.05.074 .

Calhoun CD, Stone KJ, Cobb AR, Patterson MW, Danielson CK, Bendezú JJ. The role of social support in coping with psychological trauma: an integrated biopsychosocial model for posttraumatic stress recovery. Psychiatry Q. 2022;93(4):949–70. https://doi.org/10.1007/s11126-022-10003-w .

Tian Q. Intergeneration social support affects the subjective well-being of the elderly: mediator roles of self-esteem and loneliness. J Health Psychol. 2016;21(6):1137–44. https://doi.org/10.1177/1359105314547245 .

Nick EA, Cole DA, Cho SJ, Smith DK, Carter TG, Zelkowitz RL. The online social support scale: measure development and validation. Psychol Assess. 2018;30(9):1127–43. https://doi.org/10.1037/pas0000558 .

Zhao C, Ding N, Yang X, Xu H, Lai X, Tu X, Lv Y, Xu D, Zhang G. Longitudinal effects of stressful life events on problematic smartphone use and the mediating roles of mental health problems in chinese undergraduate students. Front Pub Health. 2021;9:752210. https://doi.org/10.3389/fpubh.2021.752210 .

Boyd dm, Ellison NB. Social network sites: definition, history, and scholarship. J Computer-Mediated Commun. 2007;13(1):210–30. https://doi.org/10.1111/j.1083-6101.2007.00393.x .

Wenninger H, Krasnova H, Buxmann P. Understanding the role of social networking sites in the subjective well-being of users: a diary study. Eur J Inf Syst. 2019;28(2):126–48. https://doi.org/10.1080/0960085X.2018.1496883 .

Gilmour J, Machin T, Brownlow C, Jeffries C. Facebook-based social support and health: a systematic review. Psychology of Popular Media. 2020;9(3):328–46. https://doi.org/10.1037/ppm0000246 .

Zheng X, Wang Z, Chen H, Xie F. The relationship between self-esteem and internet altruistic behavior: the mediating effect of online social support and its gender differences. Person Individual Diff. 2021;172:110588. https://doi.org/10.1016/j.paid.2020.110588 .

Porter AC, Zelkowitz RL, Gist DC, Cole DA. Self-Evaluation and depressive symptoms: a latent variable analysis of self-esteem, shame-proneness, and self-criticism. J Psychopathol Behav Assess. 2019;41(2):257–70. https://doi.org/10.1007/s10862-019-09734-1 .

Leary MR, Tambor ES, Terdal SK, Downs DL. Self-esteem as an interpersonal monitor: the sociometer hypothesis. J Pers Soc Psychol. 1995;68(3):518–30. https://doi.org/10.1037/0022-3514.68.3.518 .

Wang Y, Nie R, Li Z, Zhou N. WeChat Moments use and self-esteem among Chinese adults: the mediating roles of personal power and social acceptance and the moderating roles of gender and age. Personality Individ Differ. 2018;131:31–7. https://doi.org/10.1016/j.paid.2018.04.012 .

Karaca A, Yildirim N, Cangur S, Acikgoz F, Akkus D. Relationship between mental health of nursing students and coping, self-esteem and social support. Nurse Educ Today. 2019;76:44–50. https://doi.org/10.1016/j.nedt.2019.01.029 .

Jin, G. Lu, L. Zhang, X. Li. The mediating role of college students’ online social support in the relationship between self-esteem and online deviant behavior. Psychological Techniques and Application, 2017;5 (6), 327–333

Rafferty R, Vander Ven T. “I hate everything about you”: a qualitative examination of cyberbullying and on-line aggression in a college sample. Deviant Behav. 2014;35(5):364–77. https://doi.org/10.1080/01639625.2013.849171 .

Zhu C, Huang S, Evans R, Zhang W. Cyberbullying among adolescents and children: a comprehensive review of the global situation, risk factors, and preventive measures. Front Pub Health. 2021;9:634909. https://doi.org/10.3389/fpubh.2021.634909 .

Hinduja S, Patchin JW. Cultivating youth resilience to prevent bullying and cyberbullying victimization. Child Abuse Negl. 2017;73:51–62. https://doi.org/10.1016/j.chiabu.2017.09.010 .

Ladd GW, Ettekal I, Kochenderfer-Ladd B. Peer victimization trajectories from kindergarten through high school: differential pathways for children’s school engagement and achievement? J Educ Psychol. 2017;109(6):826–41. https://doi.org/10.1037/edu0000177 .

Akbulut Y, Erişti B. Cyberbullying and victimisation among Turkish university students. Australas J Educ Technol. 2011;27:1155–70.

Hellfeldt K, López-Romero L, Andershed H. Cyberbullying and psychological well-being in young adolescence: the potential protective mediation effects of social support from family, friends, and teachers. Int J Environ Res Public Health. 2019;17(1):45. https://doi.org/10.3390/ijerph17010045 .

Cénat JM, Blais M, Hébert M, Lavoie F, Guerrier M. Correlates of bullying in Quebec high school students: the vulnerability of sexual-minority youth. J Affect Disord. 2015;183:315–21. https://doi.org/10.1016/j.jad.2015.05.011 .

Peled Y. Cyberbullying and its influence on academic, social, and emotional development of undergraduate students. Heliyon. 2019;5(3):e01393. https://doi.org/10.1016/j.heliyon.2019.e01393 .

Maurya C, Muhammad T, Dhillon P, Maurya P. The effects of cyberbullying victimization on depression and suicidal ideation among adolescents and young adults: a three year cohort study from India. BMC Psychiatry. 2022;22(1):599. https://doi.org/10.1186/s12888-022-04238-x .

Burns, M. L. Cyberbullying: reciprocal links with social anxiety, self-esteem and resilience in U.K. school children (Master's thesis, University of Chester, Chester, United Kingdom). 2017. Retrieved from  https://chesterrep.openrepository.com/handle/10034/620963 . Accessed 22 Aug 2023.

Ding Z, Wang X, Liu Q. The relationship between college students’ self-esteem and cyber aggressive behavior: the role of social anxiety and dual self-consciousness. Psychol Dev Educ. 2018;34(2):171–80.

Pieschl S, Porsch T. The complex relationship between cyberbullying and trust. Int J Dev Sustain. 2017;11:1–9. https://doi.org/10.3233/DEV-160208 .

Denche-Zamorano Á, Barrios-Fernandez S, Galán-Arroyo C, Sánchez-González S, Montalva-Valenzuela F, Castillo-Paredes A, Rojo-Ramos J, Olivares PR. Science mapping: a bibliometric analysis on cyberbullying and the psychological dimensions of the self. Int J Environ Res Public Health. 2022;20(1):209. https://doi.org/10.3390/ijerph20010209 .

Völlink T, Bolman CAW, Dehue F, Jacobs NCL. Coping with cyberbullying: differences between victims, bully-victims and children not involved in bullying. J Commun App Soc Psychol. 2013;23(1):7–24. https://doi.org/10.1002/casp.2142 .

Brislin RW. Comparative research methodology: cross-cultural studies. Int J Psychol. 1976;11:215–29. https://doi.org/10.1080/00207597608247359 .

Rosen LD, Whaling K, Carrier LM, Cheever NA, Rokkum J. The media and technology usage and attitudes scale: an empirical investigation. Comput Hum Behav. 2013;29(6):2501–11. https://doi.org/10.1016/j.chb.2013.06.006 .

Barton BA, Adams KS, Browne BL, Arrastia-Chisholm MC. The effects of social media usage on attention, motivation, and academic performance. Act Learn High Educ. 2021;22(1):11–22. https://doi.org/10.1177/1469787418782817 .

Ybarra ML, Espelage DL, Mitchell KJ. The co-occurrence of Internet harassment and unwanted sexual solicitation victimization and perpetration: associations with psychosocial indicators. The J Adolescent Health. 2007;41(6):S31-41.

Jiang H, Chen G, Wang T. Relationship between belief in a just world and Internet altruistic behavior in a sample of Chinese undergraduates: Multiple mediating roles of gratitude and self-esteem. Personality Individ Differ. 2017;104:493–8. https://doi.org/10.1016/j.paid.2016.09.005 .

Zhou Z, Cheng Q. Measuring online social support: development and validation of a short form for Chinese adolescents. Int J Environ Res Public Health. 2022;19(21):14058. https://doi.org/10.3390/ijerph192114058 .

Li R-H. Reliability and validity of a shorter Chinese version for Ryff’s psychological well-being scale. Health Educ J. 2014;73(4):446–52. https://doi.org/10.1177/0017896913485743 .

Tan Y, Huang C, Geng Y, Cheung SP, Zhang S. Psychological well-being in Chinese college students during the COVID-19 pandemic: roles of resilience and environmental stress. Front Psychol. 2021;12:671553. https://doi.org/10.3389/fpsyg.2021.671553 .

Zhang Y, Carciofo R. Assessing the wellbeing of Chinese university students: validation of a Chinese version of the college student subjective wellbeing questionnaire. BMC psychology. 2021;9(1):69. https://doi.org/10.1186/s40359-021-00569-8 .

Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach (2nd ed.). New York: The Guilford Press; 2018.

Aiken, L. S., & West, S. G. Multiple regression: Testing and interpreting interactions. Sage Publications, Inc. 1991

Hair J, Hollingsworth CL, Randolph AB, Chong AYL. An updated and expanded assessment of PLS-SEM in information systems research. Ind Manag Data Syst. 2017;117(3):442–58. https://doi.org/10.1108/IMDS-04-2016-0130 .

Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50. https://doi.org/10.2307/3151312 .

Hair, J. F, Hult, G. Tomas M, Ringle, C. M, & Sarstedt, M. A primer on partial least squares structural equation modeling (PLS-SEM). 2016;2nd ed. Los Angeles: SAGE.

Podsakoff PM, MacKenzie SB, Podsakoff NP. Sources of method bias in social science research and recommendations on how to control it. Annu Rev Psychol. 2012;63:539–69. https://doi.org/10.1146/annurev-psych-120710-100452 .

Wellman B. Computer networks as social networks. Science. 2001;293:2031–4. https://doi.org/10.1126/science.1065547 .

Diener E, Diener M. Cross-cultural correlates of life satisfaction and self-esteem. J Pers Soc Psychol. 1995;68(4):653–63. https://doi.org/10.1037//0022-3514.68.4.653 .

Steger MF, Frazier P, Oishi S, Kaler M. The meaning in life questionnaire: assessing the presence of and search for meaning in life. J Couns Psychol. 2006;53(1):80–93. https://doi.org/10.1037/0022-0167.53.1.80 .

Steele CM. The psychology of self-affirmation: Sustaining the integrity of the self. In L. Berkowitz (Ed.). Soc Psychol Stud Self. 1988;21:261–302 (Academic Press).

Leary, M. R., & Baumeister, R. F. The nature and function of self-esteem: Sociometer theory. In M. P. Zanna (Ed.), Advances in experimental social psychology, 2000; Vol. 32, pp. 1–62. Academic Press. https://doi.org/10.1016/S0065-2601(00)80003-9

Zheng Q, Yao T, Fan X. Improving customer well-being through two-way online social support. J Serv Theory Pract. 2016;26(2):179–202. https://doi.org/10.1108/JSTP-09-2014-0188 .

Nabi RL, Prestin A, So J. Facebook friends with (health) benefits? Exploring social network site use and perceptions of social support, stress, and well-being. Cyberpsychol Behav Soc Netw. 2013;16(10):721–7. https://doi.org/10.1089/cyber.2012.0521 .

Indian M, Grieve R. When facebook is easier than face-to-face: Social support derived from facebook in socially anxious individuals. Personality Individ Differ. 2014;59:102–6. https://doi.org/10.1016/j.paid.2013.11.016 .

Neira CJB, Barber BL. Social networking site use: Linked to adolescents’ social self-concept, self-esteem, and depressed mood. Aust J Psychol. 2014;66(1):56–64. https://doi.org/10.1111/ajpy.12034 .

Woods HC, Scott H. #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J Adolesc. 2016;51:41–9. https://doi.org/10.1016/j.adolescence.2016.05.008 .

Harter, S. The construction of the self: Developmental and sociocultural foundations (2nd ed.). The Guilford Press. 2012

Zhang H, Guan L, Qi M, Yang J. Self-esteem modulates the time course of self-positivity bias in explicit self-evaluation. PLoS ONE. 2013;8(12):Article e81169. https://doi.org/10.1371/journal.pone.0081169 .

Naslund JA, Aschbrenner KA, Marsch LA, Bartels SJ. The future of mental health care: peer-to-peer support and social media. Epidemiol Psychiatric Sci. 2016;25(2):113–22. https://doi.org/10.1017/S2045796015001067 .

Reining, Lauren; Drouin, Michelle; Toscos, Tammy; and Mirro, Michael J. "College students in distress: Can social media be a source of social support?". Presentations and Events. 2018;7. https://researchrepository.parkviewhealth.org/presentations/7

Cao X, Khan AN, Zaigham GHK, Khan NA. The stimulators of social media fatigue among students: role of moral disengagement. J Educ Computing Res. 2019;57(5):1083–107. https://doi.org/10.1177/0735633118781907 .

Sampasa-Kanyinga H, Hamilton HA. Social networking sites and mental health problems in adolescents: The mediating role of cyberbullying victimization. European Psychiatry. 2015;30(8):1021–7. https://doi.org/10.1016/j.eurpsy.2015.09.011 .

Valkenburg PM, Peter J. The differential susceptibility to media effects model. J Commun. 2013;63:221–43. https://doi.org/10.1111/jcom.12024 .

Gerbner G, Gross L. Living with television: the violence profile. J Commun. 1976;26(2):173–99. https://doi.org/10.1111/j.1460-2466.1976.tb01397.x .

Heiman T, Olenik-Shemesh D, Frank G. Patterns of coping with cyberbullying: emotional, behavioral, and strategic coping reactions among middle school students. Violence Vict. 2019;34(1):28–45. https://doi.org/10.1891/0886-6708.34.1.28 .

Valkenburg PM. Social media use and well-being: what we know and what we need to know. Curr Opinion Psychol. 2022;45:101294. https://doi.org/10.1016/j.copsyc.2021.12.006 .

Yang CC, Holden SM, Ariati J. Social media and psychological well-being among youth: the multidimensional model of social media use. Clin Child Fam Psychol Rev. 2021;24(3):631–50. https://doi.org/10.1007/s10567-021-00359- .

Zhou X, Snoswell CL, Harding LE, Bambling M, Edirippulige S, Bai X, Smith AC. The role of telehealth in reducing the mental health burden from COVID-19. Telemed E-Health. 2020;26(4):377–9. https://doi.org/10.1089/tmj.2020.0068 .

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Zhang, C., Tang, L. & Liu, Z. How social media usage affects psychological and subjective well-being: testing a moderated mediation model. BMC Psychol 11 , 286 (2023). https://doi.org/10.1186/s40359-023-01311-2

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CONCEPTUAL ANALYSIS article

The effect of social media on the development of students’ affective variables.

• 1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China
• 2 School of Marxism, Hohai University, Nanjing, Jiangsu, China
• 3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Aalbers, G., McNally, R. J., Heeren, A., de Wit, S., and Fried, E. I. (2018). Social media and depression symptoms: A network perspective. J. Exp. Psychol. Gen. 148, 1454–1462. doi: 10.1037/xge0000528

PubMed Abstract | CrossRef Full Text | Google Scholar

Abbott, J. (2017). Introduction: Assessing the social and political impact of the internet and new social media in Asia. J. Contemp. Asia 43, 579–590. doi: 10.1080/00472336.2013.785698

CrossRef Full Text | Google Scholar

Alahmar, A. T. (2016). The impact of social media on the academic performance of second year medical students at College of Medicine, University of Babylon, Iraq. J. Med. Allied Sci. 6, 77–83. doi: 10.5455/jmas.236927

Banjanin, N., Banjanin, N., Dimitrijevic, I., and Pantic, I. (2015). Relationship between internet use and depression: Focus on physiological mood oscillations, social networking and online addictive behavior. Comp. Hum. Behav. 43, 308–312. doi: 10.1016/j.chb.2014.11.013

Barry, C. T., Sidoti, C. L., Briggs, S. M., Reiter, S. R., and Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. J. Adolesc. 61, 1–11. doi: 10.1016/j.adolescence.2017.08.005

Chang, Y. (2012). The relationship between maladaptive perfectionism with burnout: Testing mediating effect of emotion-focused coping. Pers. Individ. Differ. 53, 635–639. doi: 10.1016/j.paid.2012.05.002

Charoensukmongkol, P. (2018). The impact of social media on social comparison and envy in teenagers: The moderating role of the parent comparing children and in-group competition among friends. J. Child Fam. Stud. 27, 69–79. doi: 10.1007/s10826-017-0872-8

Chukwuere, J. E., and Chukwuere, P. C. (2017). The impact of social media on social lifestyle: A case study of university female students. Gender Behav. 15, 9966–9981.

Google Scholar

Drouin, M., Reining, L., Flanagan, M., Carpenter, M., and Toscos, T. (2018). College students in distress: Can social media be a source of social support? Coll. Stud. J. 52, 494–504.

Dumitrache, S. D., Mitrofan, L., and Petrov, Z. (2012). Self-image and depressive tendencies among adolescent Facebook users. Rev. Psihol. 58, 285–295.

PubMed Abstract | Google Scholar

Fernyhough, C. (2008). Getting Vygotskian about theory of mind: Mediation, dialogue, and the development of social understanding. Dev. Rev. 28, 225–262. doi: 10.1016/j.dr.2007.03.001

Festinger, L. (1954). A Theory of social comparison processes. Hum. Relat. 7, 117–140. doi: 10.1177/001872675400700202

Fleck, J., and Johnson-Migalski, L. (2015). The impact of social media on personal and professional lives: An Adlerian perspective. J. Individ. Psychol. 71, 135–142. doi: 10.1353/jip.2015.0013

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am. Psychol. 56, 218–226. doi: 10.1037/0003-066X.56.3.218

Frison, E., and Eggermont, S. (2016). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Soc. Sci. Compu. Rev. 34, 153–171. doi: 10.1177/0894439314567449

Hanprathet, N., Manwong, M., Khumsri, J., Yingyeun, R., and Phanasathit, M. (2015). Facebook addiction and its relationship with mental health among Thai high school students. J. Med. Assoc. Thailand 98, S81–S90.

Hiver, P., and Al-Hoorie, A. H. (2019). Research Methods for Complexity Theory in Applied Linguistics. Bristol: Multilingual Matters. doi: 10.21832/HIVER5747

Iwamoto, D., and Chun, H. (2020). The emotional impact of social media in higher education. Int. J. High. Educ. 9, 239–247. doi: 10.5430/ijhe.v9n2p239

Keles, B., McCrae, N., and Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 25, 79–93. doi: 10.1080/02673843.2019.1590851

Ley, B., Ogonowski, C., Hess, J., Reichling, T., Wan, L., and Wulf, V. (2014). Impacts of new technologies on media usage and social behavior in domestic environments. Behav. Inform. Technol. 33, 815–828. doi: 10.1080/0144929X.2013.832383

Li, J.-B., Lau, J. T. F., Mo, P. K. H., Su, X.-F., Tang, J., Qin, Z.-G., et al. (2017). Insomnia partially mediated the association between problematic Internet use and depression among secondary school students in China. J. Behav. Addict. 6, 554–563. doi: 10.1556/2006.6.2017.085

Mathewson, M. (2020). The impact of social media usage on students’ mental health. J. Stud. Affairs 29, 146–160.

Neira, B. C. J., and Barber, B. L. (2014). Social networking site use: Linked to adolescents’ social self-concept, self-esteem, and depressed mood. Aus. J. Psychol. 66, 56–64. doi: 10.1111/ajpy.12034

O’Dea, B., and Campbell, A. (2011). Online social networking amongst teens: Friend or foe? Ann. Rev. CyberTher. Telemed. 9, 108–112.

Radovic, A., Gmelin, T., Stein, B. D., and Miller, E. (2017). Depressed adolescents positive and negative use of social media. J. Adolesc. 55, 5–15. doi: 10.1016/j.adolescence.2016.12.002

Sampasa-Kanyinga, H., and Lewis, R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychol. Behav. Soc. Network. 18, 380–385. doi: 10.1089/cyber.2015.0055

Sriwilai, K., and Charoensukmongkol, P. (2016). Face it, don’t Facebook it: Impacts of social media addiction on mindfulness, coping strategies and the consequence on emotional exhaustion. Stress Health 32, 427–434. doi: 10.1002/smi.2637

Stapel, D. A. (2007). “In the mind of the beholder: The interpretation comparison model of accessibility effects,” in Assimilation and Contrast in Social Psychology , eds D. A. Stapel and J. Suls (London: Psychology Press), 143–164.

Stapel, D. A., and Koomen, W. (2000). Distinctiveness of others, mutability of selves: Their impact on self-evaluations. J. Pers. Soc. Psychol. 79, 1068–1087. doi: 10.1037//0022-3514.79.6.1068

Tang, F., Wang, X., and Norman, C. S. (2013). An investigation of the impact of media capabilities and extraversion on social presence and user satisfaction. Behav. Inform. Technol. 32, 1060–1073. doi: 10.1080/0144929X.2013.830335

Tsitsika, A. K., Tzavela, E. C., Janikian, M., Ólafsson, K., Iordache, A., Schoenmakers, T. M., et al. (2014). Online social networking in adolescence: Patterns of use in six European countries and links with psychosocial functioning. J. Adolesc. Health 55, 141–147. doi: 10.1016/j.jadohealth.2013.11.010

Vernon, L., Modecki, K. L., and Barber, B. L. (2017). Tracking effects of problematic social networking on adolescent psychopathology: The mediating role of sleep disruptions. J. Clin. Child Adolesc. Psychol. 46, 269–283. doi: 10.1080/15374416.2016.1188702

Virden, A., Trujillo, A., and Predeger, E. (2014). Young adult females’ perceptions of high-risk social media behaviors: A focus-group approach. J. Commun. Health Nurs. 31, 133–144. doi: 10.1080/07370016.2014.926677

Wang, P., Wang, X., Wu, Y., Xie, X., Wang, X., Zhao, F., et al. (2018). Social networking sites addiction and adolescent depression: A moderated mediation model of rumination and self-esteem. Pers. Individ. Differ. 127, 162–167. doi: 10.1016/j.paid.2018.02.008

Weng, L., and Menczer, F. (2015). Topicality and impact in social media: Diverse messages, focused messengers. PLoS One 10:e0118410. doi: 10.1371/journal.pone.0118410

Yan, H., Zhang, R., Oniffrey, T. M., Chen, G., Wang, Y., Wu, Y., et al. (2017). Associations among screen time and unhealthy behaviors, academic performance, and well-being in Chinese adolescents. Int. J. Environ. Res. Public Health 14:596. doi: 10.3390/ijerph14060596

Zareen, N., Karim, N., and Khan, U. A. (2016). Psycho-emotional impact of social media emojis. ISRA Med. J. 8, 257–262.

Zhang, R. (2017). The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students. Comp. Hum. Behav. 75, 527–537. doi: 10.1016/j.chb.2017.05.043

Keywords : affective variables, education, emotions, social media, post-pandemic, emotional needs

Citation: Chen M and Xiao X (2022) The effect of social media on the development of students’ affective variables. Front. Psychol. 13:1010766. doi: 10.3389/fpsyg.2022.1010766

Received: 03 August 2022; Accepted: 25 August 2022; Published: 15 September 2022.

Reviewed by:

Copyright © 2022 Chen and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miao Chen, [email protected] ; Xin Xiao, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Association between social media use and students’ academic performance through family bonding and collective learning: The moderating role of mental well-being

• Published: 02 January 2024
• Volume 29 , pages 14059–14089, ( 2024 )

Cite this article

• Xueyuan Zhang   nAff1 ,
• Jaffar Abbas   ORCID: orcid.org/0000-0002-8830-1435 2 ,
• Muhammad Farrukh Shahzad 3 ,
• Achyut Shankar 4 , 5 , 6 ,
• Sezai Ercisli 7 &
• Dinesh Chandra Dobhal 8  

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The advent of the digital age represents a transformative era in which technology, primarily social media platforms, has become an integral part of the daily lives of individuals worldwide. Students are the most prolific users of social media, utilizing these platforms for a variety of purposes, including communication, information sharing, entertainment, and social networking. This study evaluated the connection between student social media use and academic performance through family bonding and collaborative learning. This research also explores how mental wellbeing moderates the link between students’ family bonding, collaborative learning, and academic performance. This research article analyzes a sample of 330 university students from the public and private sectors and tests the proposed hypothesized relationships. The study used the Partial Least Squares Structural Equation Modelling (PLS-SEM) methodological approach for evaluating proposed parameters. The findings indicated that social media use positively correlated with students’ academic performance. Second, family bonding and collaborative learning significantly moderated the association between students’ academic performance and social media use. Finally, mental well-being significantly moderated the connection between students’ collaborative learning, family bonding, and academic performance. This study’s findings contribute to the knowledge of global education with valuable insights into students’ psychological well-being and academic performance. In theory, the current research advances the scientific understanding of education by assessing social media usage’s effects on students’ academic performance and psychological well-being.

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Abaalzamat, K. H., Al-Sulaiti, K., Alzboun, N. M., & Khawaldah, H. A. (2021). The role of Katara Cultural Village in Enhancing and Marketing the image of Qatar: Evidence from TripAdvisor. SAGE Open, 11 (2), 21582440211022736. https://doi.org/10.1177/21582440211022737

Article   Google Scholar  

Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The impact of social media on learning behavior for sustainable education: Evidence of students from selected universities in Pakistan. Sustainability, 11 (6). https://doi.org/10.3390/su11061683

Abbas, J., Rehman, S., Aldereai, O., Al-Sulaiti, K. I., & Shah, S. A. R. (2023). Tourism management in financial crisis and industry 4.0 effects: Managers traits for technology adoption in reshaping, and reinventing human management systems. Human Systems Management, 42 (5), 1–18.

Google Scholar  

Abbas, J., Wang, D., Su, Z., & Ziapour, A. (2021). The role of social media in the advent of COVID-19 pandemic: Crisis management, mental health challenges and implications. Risk Manag Healthc Policy, 14 , 1917–1932. https://doi.org/10.2147/RMHP.S284313

Abdullahi, Y. Y., Musa, M. M., Abubakar, I. B., & Yusif, N. D. (2019). The impact of social media on academic performance among undergraduate students of Bayero university, Kano. Asian Journal of Multidimensional Research (AJMR) , 8 (11), 54. https://www.iiardjournals.org/get/IJEE/VOL . 6 NO. 1 2020

Abi-Jaoude, E., Naylor, K. T., & Pignatiello, A. (2020). Smartphones, social media use and youth mental health. CMAJ, 192 (6), E136–E141. https://doi.org/10.1503/cmaj.190434

Ahmed, I., & Qazi, T. F. (2011). A look out for academic impacts of Social networking sites (SNSs): A student based perspective. Journal of Business, 5 (February 2004), 5022–5031. https://doi.org/10.5897/AJBM11.595

Akram, T., Lei, S., Haider, M. J., & Hussain, S. T. (2020). The impact of organizational justice on employee innovative work behavior: Mediating role of knowledge sharing. Journal of Innovation and Knowledge, 5 (2), 117–129. https://doi.org/10.1016/j.jik.2019.10.001

Al Halbusi, H., Al-Sulaiti, K., AlAbri, S., & Al-Sulaiti, I. (2023). Individual and psychological factors influencing hotel employee’s work engagement: The contingent role of self-efficacy. Cogent Business & Management, 10 (3), 2254914. https://doi.org/10.1080/23311975.2023.2254914

Al-Rahmi, W. M., Othman, M. S., & Musa, M. A. (2014). The improvement of students’ academic performance by using social media through collaborative learning in Malaysian higher education. Asian Social Science, 10 (8), 210–221. https://doi.org/10.5539/ass.v10n8p210

Al-Rahmi, W. M., & Zeki, A. M. (2017). A model of using social media for collaborative learning to enhance learners’ performance on learning. Journal of King Saud University - Computer and Information Sciences, 29 (4), 526–535. https://doi.org/10.1016/j.jksuci.2016.09.002

Al-Sulaiti, I., Al-Sulaiti, K., & Shah, S. A. R. (2023). Resetting the hospitality redux through country-of-origin effects: Role of tourism, culture, transportation and restaurants selection in arab countries. Country of Origin effects on Service evaluation (pp. 1–21). Qatar University Press.

Al-Sulaiti, K. I., & Baker, M. J. (1997). Qatari consumers perceptions and selections of domestic vs. Foreign airline services. In Department of Marketing, University of Strathclyde . https://doi.org/10.48730/rjp6-8433

Al-Sulaiti, K. (2002). Marketing educational services in Olatar: A multivariate analysis. Journal of International Marketing and Marketing Research, 27 (2), 87–98. https://cir.nii.ac.jp/crid/1130000793639643776 . Accessed 23 Feb 2023

Al-Sulaiti, K., & Al-Sulaiti, I. (2023). Tourists’ online information influences their dine-out behaviour: Country-of-origin effects as a moderator. Country of Origin effects on Service evaluation (pp. 1–20). Qatar University Press.

Alam, M. M. D., Alam, M. Z., Rahman, S. A., & Taghizadeh, S. K. (2021). Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach. Journal of Biomedical Informatics, 116 (February), 103722. https://doi.org/10.1016/j.jbi.2021.103722

Alnjadat, R., Hmaidi, M. M., Samha, T. E., Kilani, M. M., & Hasswan, A. M. (2019). Gender variations in social media usage and academic performance among the students of University of Sharjah. Journal of Taibah University Medical Sciences, 14 (4), 390–394. https://doi.org/10.1016/j.jtumed.2019.05.002

Alshuaibi, M. S. I., Alshuaibi, A. S. I., Shamsudin, F. M., & Arshad, D. A. (2018). Use of social media, student engagement, and academic performance of business students in Malaysia. International Journal of Educational Management, 32 (4), 625–640. https://doi.org/10.1108/IJEM-08-2016-0182

Alwagait, E., Shahzad, B., & Alim, S. (2015). Impact of social media usage on students academic performance in Saudi Arabia. Computers in Human Behavior, 51 , 1092–1097. https://doi.org/10.1016/j.chb.2014.09.028

Annamalai, N., Foroughi, B., Iranmanesh, M., & Buathong, S. (2020). Needs and Facebook addiction: How important are psychological well-being and performance-approach goals? Current Psychology, 39 (6), 1942–1953. https://doi.org/10.1007/s12144-019-00516-2

Ansari, J. A. N., & Khan, N. A. (2020). Exploring the role of social media in collaborative learning the new domain of learning. Smart Learning Environments, 7 (1), 1–16. https://doi.org/10.1186/s40561-020-00118-7

Aqeel, M., Rehna, T., & Shuja, K. H. (2022). Comparison of students’ Mental Wellbeing, anxiety, Depression, and Quality of Life during COVID-19’s full and partial (smart) lockdowns: A Follow-Up study at a 5-Month interval [Original Research]. Frontiers in Psychiatry, 13 , 835585. https://doi.org/10.3389/fpsyt.2022.835585

Asante, E., & Martey, E. M. (2015). Impact of social media usage on academic performance of tertiary institution students. Journal of Advance Research in Business Management and Accounting, 1 (1), 75–88. https://doi.org/10.53555/nnbma.v1i1.143 . ISSN: 2456–3544

Babin, B. J., Grif, M., & Jr, J. F. H. (2015). Heresies and sacred cows in scholarly marketing publications ☆ . 1–6. https://doi.org/10.1016/j.jbusres.2015.12.001

Bandura, A., & Walters, R. H. (1977). Social learning theory (Vol. 1). Prentice Hall: Englewood cliffs. Chicago.

Bano, S., Cisheng, W., Khan, A. N., & Khan, N. A. (2019). WhatsApp use and student’s psychological well-being: Role of social capital and social integration. Children and Youth Services Review, 103 (February), 200–208. https://doi.org/10.1016/j.childyouth.2019.06.002

Bekalu, M. A., McCloud, R. F., & Viswanath, K. (2019). Association of social media use with social well-being, positive mental health, and self-rated health: Disentangling routine use from emotional connection to use. Health Education and Behavior, 46 (2_suppl), 69–80. https://doi.org/10.1177/1090198119863768

Bhumika, T., Jyoti, Neha, G., & Santosh, K. (2022). Impact of social media on student life. I-Manager’s Journal on Information Technology , 11 (1), 35. https://doi.org/10.26634/jit.11.1.18565

Boahene, K. O., Fang, J., & Sampong, F. (2019). Social media usage and tertiary students’ academic performance: Examining the influences of academic self-efficacy and innovation characteristics. Sustainability (Switzerland), 11 (8), 2431. https://doi.org/10.3390/su11082431

Boer, M., van den Eijnden, R. J. J. M., Boniel-Nissim, M., Wong, S. L., Inchley, J. C., Badura, P., Craig, W. M., Gobina, I., Kleszczewska, D., Klanšček, H. J., & Stevens, G. W. J. M. (2020). Adolescents’ intense and problematic social media use and their well-being in 29 countries. Journal of Adolescent Health, 66 (6), S89–S99. https://doi.org/10.1016/j.jadohealth.2020.02.014

Boit, R. J., Eastern, A. C., Bayer, S., Birabwa, J. C., Mckoy, M., & Hestenes, L. L. (2023). Navigating educational success for their young children in the United States: Refugee families draw from their experiences. Navigating . https://doi.org/10.1007/s10643-023-01522-7

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. Internet and Higher Education, 27 , 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

Bufquin, D., Park, J. Y., Back, R. M., de Souza Meira, J. V., & Hight, S. K. (2021). Employee work status, mental health, substance use, and career turnover intentions: An examination of restaurant employees during COVID-19. International Journal of Hospitality Management, 93 (November 2020), 102764. https://doi.org/10.1016/j.ijhm.2020.102764

Chen, M. F., Wang, R. H., & Hung, S. L. (2015). Predicting health-promoting self-care behaviors in people with pre-diabetes by applying Bandura social learning theory. Applied Nursing Research, 28 (4), 299–304. https://doi.org/10.1016/j.apnr.2015.01.001

Chiumento, A., Rahman, A., Frith, L., Snider, L., & Tol, W. A. (2017). Ethical standards for mental health and psychosocial support research in emergencies: Review of literature and current debates. Globalization and Health, 13 (1), 1–19. https://doi.org/10.1186/s12992-017-0231-y

Coard, S. I., Kiang, L., Romero, M., Gonzalez, M. Y., L. M., & Stein, G. L. (2023). Talking through the tough: Identifying facilitating factors to preparation for bias and racial–ethnic discrimination conversations among families from minoritized ethnic–racial groups. Family Process , November 2022 , 1–16. https://doi.org/10.1111/famp.12878

Cornejo, R., Tentori, M., & Favela, J. (2013). Enriching in-person encounters through social media: A study on family connectedness for the elderly. International Journal of Human Computer Studies, 71 (9), 889–899. https://doi.org/10.1016/j.ijhcs.2013.04.001

Cui, L., & Li, Z. (2023). The influence of family function on online prosocial behaviors of high school students: A moderated chained mediation model. Frontiers in Psychology, 14 (March), 1–15. https://doi.org/10.3389/fpsyg.2023.1103897

Dabner, N. (2011). Design to support distance teacher education communities: A case study of a student-student e-mentoring initiative. SITE 2011–Society for Information Technology & Teacher Education International Conference , 2001 , 218–223.

Dale, C., & Pymm, J. M. (2009). Podagogy: The iPod as a learning technology. Active Learning in Higher Education, 10 (1), 84–96. https://doi.org/10.1177/1469787408100197

Davin, J. M. M. (2022). The mediating effect of cooperative learning application on the relationship between social media usage and language proficiency of students. EPRA International Journal of Research & Development (IJRD) , 7838 (February), 8–14. https://doi.org/10.36713/epra9479

de Leeuw, S., Haelermans, C., Jacobs, M., van der Velden, R., van Vugt, L., & van Wetten, S. (2023). The role of family composition in students’ learning growth during the COVID -19 pandemic. Journal of Marriage and Family , 807–828. https://doi.org/10.1111/jomf.12912

Delaney, C. L., & Byrd-Bredbenner, C. (2022). Family social support and weight-related behaviors of school-age children: An exploratory analysis. International Journal of Environmental Research and Public Health, 19 (14), 8501. https://doi.org/10.3390/ijerph19148501

Desi, W. (2023). Marital interaction and marital role on marital satisfaction of dual earner family. 2(2), 126–137. https://doi.org/10.29244/jcfcs.2.2.126-137

Dilci, T., & Eranıl, A. K. (2018). The impact of social media on children (pp. 1–10). https://doi.org/10.4018/978-1-5225-5733-3.ch001

Fan, X., Deng, N., Dong, X., Lin, Y., & Wang, J. (2019). Do others’ self-presentation on social media influence individual’s subjective well-being? A moderated mediation model. Telematics and Informatics, 41 (March), 86–102. https://doi.org/10.1016/j.tele.2019.04.001

Farrukh, M., Xu, S., Baheer, R., & Ahmad, W. (2023a). Unveiling the role of supply chain parameters approved by blockchain technology towards firm performance through trust: The moderating role of government support. Heliyon, 9 (11). https://doi.org/10.1016/j.heliyon.2023.e21831

Farrukh, M., Xu, S., Naveed, W., & Nusrat, S. (2023b). Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model. Heliyon, 9 (11). https://doi.org/10.1016/j.heliyon.2023.e21818

Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. International Journal of Phytoremediation, 21 (1), 7–23. https://doi.org/10.1080/08923640109527071

Gerson, J., Plagnol, A. C., & Corr, P. J. (2016). Subjective well-being and social media use: Do personality traits moderate the impact of social comparison on Facebook? Computers in Human Behavior, 63 , 813–822. https://doi.org/10.1016/j.chb.2016.06.023

Goodyear, V. A., Armour, K. M., & Wood, H. (2019). Young people and their engagement with health-related social media: New perspectives. Sport Education and Society, 24 (7), 673–688. https://doi.org/10.1080/13573322.2017.1423464

Gray, K., Chang, S., & Kennedy, G. (2010). Use of social web technologies by international and domestic undergraduate students: Implications for internationalising learning and teaching in Australian universities. Technology Pedagogy and Education, 19 (1), 31–46. https://doi.org/10.1080/14759390903579208

Guilmette, M., Mulvihill, K., Villemaire-Krajden, R., & Barker, E. T. (2019). Past and present participation in extracurricular activities is associated with adaptive self-regulation of goals, academic success, and emotional wellbeing among university students. Learning and Individual Differences, 73 (February), 8–15. https://doi.org/10.1016/j.lindif.2019.04.006

Gye-Soo, K. (2016). Partial least squares structural equation modeling(PLS-SEM): An application in customer satisfaction research. International Journal of U- and e- Service Science and Technology, 9 (4), 61–68. https://doi.org/10.14257/ijunesst.2016.9.4.07

Habes, M., Alghizzawi, M., Khalaf, R., Salloum, S. A., & Ghani, M. A. (2018). The relationship between social media and academic performance: Facebook perspective. International Journal of Information Technology and Language Studies (IJITLS), 2 (1), 12–18. http://journals.sfu.ca/ijitls

Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109 , 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19 (2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26 (2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling . SAGE Publications. https://books.google.com/books?id=5wmXDgAAQBAJ . Accessed 9 Mar 2023

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40 (3), 414–433. https://doi.org/10.1007/s11747-011-0261-6

Hamdollah, R., & Baghaei, P. (2016). Partial least squares structural equation modeling with R. Practical Assessment Research and Evaluation, 21 (1), 1–16. https://doi.org/10.7275/d2fa-qv48

Hardy, B. W., & Castonguay, J. (2018). The moderating role of age in the relationship between social media use and mental well-being: An analysis of the 2016 General Social Survey. Computers in Human Behavior, 85 , 282–290. https://doi.org/10.1016/j.chb.2018.04.005

Hightow-weidman, L. B., Muessig, K. E., Bauermeister, J., Zhang, C., Legrand, S., & Zhang, C. (2015). Youth, technology, and HIV: Recent advances and future directions . 500–515. https://doi.org/10.1007/s11904-015-0280-x

Huang, C. (2022). A meta-analysis of the problematic social media use and mental health. International Journal of Social Psychiatry, 68 (1), 12–33. https://doi.org/10.1177/0020764020978434

Ibrahim Khalil, A., Burayk Alharbi, N., Alhawasawi, Y., & Baker Albander, A. (2016). Prevalence of internet addiction among nursing students and the association with their academic performance and mental health. Athens Journal of Health, 3 (4), 291–306. https://doi.org/10.30958/ajh.3-4-2

Jiang, Y. (2021). Problematic social media usage and anxiety among university students during the COVID-19 pandemic: The mediating role of psychological capital and the moderating role of academic burnout. Frontiers in Psychology, 12 (February), 1–12. https://doi.org/10.3389/fpsyg.2021.612007

Kamal, S., & Arefin, M. S. (2016). Impact analysis of facebook in family bonding. Social Network Analysis and Mining, 6 (1), 1–14. https://doi.org/10.1007/s13278-015-0314-9

Kananifar, N., Muhamad, H., & Zarkesh, N. (2019). An evaluation of mental health based on the big five personality traits and machiavellianism in domestic and international students in Malaysia. Scholars Bulletin, 9771 (June), 192–201. https://doi.org/10.21276/sb.2019.5.5.4

Kane, G. C., & Fichman, R. G. (2009). The shoemaker’s children: Using wikis for information systems teaching, research, and publication. MIS Quarterly: Management Information Systems, 33 (1), 1–17. https://doi.org/10.2307/20650274

Khan, M. N., Ashraf, M. A., Seinen, D., Khan, K. U., & Laar, R. A. (2021). Social media for knowledge acquisition and dissemination: The impact of the COVID-19 pandemic on collaborative learning driven social media adoption. Frontiers in Psychology, 12 (May), 1–13. https://doi.org/10.3389/fpsyg.2021.648253

Kim, J., Suh, W., Kim, S., & Gopalan, H. (2012). Coping strategies to manage acculturative stress: Meaningful activity participation, social support, and positive emotion among Korean immigrant adolescents in the USA. International Journal of Qualitative Studies on Health and Well-Being, 1 , 1–11. https://doi.org/10.3402/qhw.v7i0.18870

Kline. (2005). Admixture analysis of a rural population of the state of Guerrero, Mexico. American Journal of Physical Anthropology, 128 (4), 861–869. https://doi.org/10.1002/ajpa.20227

Kobal, D., & Musek, J. (2001). Self-concept and academic achievement: Slovenia and France. Personality and Individual Differences, 30 (5), 887–899. https://doi.org/10.1016/S0191-8869(00)00081-7

Lau, W. W. F. (2017). Effects of social media usage and social media multitasking on the academic performance of university students. Computers in Human Behavior, 68 , 286–291. https://doi.org/10.1016/j.chb.2016.11.043

Legaree, B. A. (2015). Considering the changing face of social media in higher education. In FEMS Microbiology Letters (Vol. 362, Issue 16). https://doi.org/10.1093/femsle/fnv128

Legate, A. E., Hair, J. F., Chretien, J. L., & Risher, J. J. (2021). PLS-SEM: Prediction‐oriented solutions for HRD researchers. Human Resource Development Quarterly, 34 (1), 91–109.

Li, X., Dongling, W., Baig, N. U. A., & Zhang, R. (2022). From cultural tourism to social entrepreneurship: Role of social value creation for environmental sustainability [Original Research]. Frontiers in Psychology, 13 , 925768. https://doi.org/10.3389/fpsyg.2022.925768

Lopez-Fernandez, O., Freixa-Blanxart, M., & Honrubia-Serrano, M. L. (2013). The problematic internet entertainment use scale for adolescents: Prevalence of problem internet use in Spanish high school students. Cyberpsychology Behavior and Social Networking, 16 (2), 108–118. https://doi.org/10.1089/cyber.2012.0250

Luqman, A., Talwar, S., Masood, A., & Dhir, A. (2021). Does enterprise social media use promote employee creativity and well-being? Journal of Business Research, 131 (November 2020), 40–54. https://doi.org/10.1016/j.jbusres.2021.03.051

Maqableh, M., Rajab, L., Quteshat, W., Masa’deh, R. M. T., Khatib, T., & Karajeh, H. (2015). The impact of social media networks websites usage on students’ academic performance. Communications and Network, 07 (04), 159–171. https://doi.org/10.4236/cn.2015.74015

Maqsood, A., Rehman, G., & Mubeen, R. (2021). The paradigm shift for educational system continuance in the advent of COVID-19 pandemic: Mental health challenges and reflections. Current Research in Behavioral Sciences, 2 , 100011. https://doi.org/10.1016/j.crbeha.2020.100011

Martins, J. M., Muhammad, F. S., & Shuo, X. (2023a). Examining the factors influencing entrepreneurial intention to initiate new ventures: Focusing on knowledge of entrepreneurial skills, ability to take risk and entrepreneurial innovativeness in open innovation business model. Research Square , 1125–1146. https://doi.org/10.21203/rs.3.rs-2664778/v1

Martins, J. M., Shahzad, M. F., & Javed, I. (2023b). Assessing the Impact of Workplace Harassment on Turnover Intention: Evidence from the Banking Industry. 7(5), 1699–1722. https://doi.org/10.28991/ESJ-2023-07-05-016

Martins, J. M., Shahzad, M. F., & Xu, S. (2023c). Factors influencing entrepreneurial intention to initiate new ventures: Evidence from university students. Journal of Innovation and Entrepreneurship . https://doi.org/10.1186/s13731-023-00333-9

McMillan, S. J., & Hwang, J. S. (2002). Measures of perceived interactivity: An exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. Journal of Advertising, 31 (3), 29–42. https://doi.org/10.1080/00913367.2002.10673674

Meng, Q., Yan, Z., Shankar, A., & Subramanian, M. (2023). Human–computer interaction and digital literacy promote educational learning in pre-school children: Mediating role of psychological resilience for kids’ mental well-being and school readiness. International Journal of Human–Computer Interaction, 39 (20), 1–15.

Mohr, S., Preisig, M., Fenton, B. T., & Ferrero, F. (1999). Validation of the French version of the parental bonding instrument in adults. Personality and Individual Differences, 26 (6), 1065–1074. https://doi.org/10.1016/S0191-8869(98)00210-4

Muro, A., Soler, J., Cebolla, À., & Cladellas, R. (2018). A positive psychological intervention for failing students: Does it improve academic achievement and motivation? A pilot study. Learning and Motivation, 63 (June), 126–132. https://doi.org/10.1016/j.lmot.2018.04.002

Nurudeen, M., Abdul-Samad, S., Owusu-Oware, E., Koi-Akrofi, G. Y., & Tanye, H. A. (2023). Measuring the effect of social media on student academic performance using a social media influence factor model. Education and Information Technologies, 28 (1), 1165–1188. https://doi.org/10.1007/s10639-022-11196-0

Pasek, J., More, E., & Hargittai, E. (2009). Facebook and academic performance: Reconciling a media sensation with data. First Monday , 14 (5). https://doi.org/10.5210/fm.v14i5.2498

Pellerone, M., Martinez Torvisco, J., Razza, S. G., Lo Piccolo, A., Guarnera, M., Rosa, L., & Commodari, E. (2023). Relational competence, school adjustment and emotional skills: A cross-sectional study in a group of junior and high school students of the sicilian hinterland. International Journal of Environmental Research and Public Health, 20 (3), 1–20. https://doi.org/10.3390/ijerph20032182

Ponnusamy, S., Iranmanesh, M., Foroughi, B., & Hyun, S. S. (2020). Drivers and outcomes of Instagram addiction: Psychological well-being as moderator. Computers in Human Behavior, 107 (January), 106294. https://doi.org/10.1016/j.chb.2020.106294

Popescu, E. (2014). Providing collaborative learning support with social media in an integrated environment. World Wide Web, 17 (2), 199–212. https://doi.org/10.1007/s11280-012-0172-6

Qureshi, M. A., Khaskheli, A., Qureshi, J. A., Raza, S. A., & Yousufi, S. Q. (2021). Factors affecting students’ learning performance through collaborative learning and engagement. Interactive Learning Environments, 0 (0), 1–21. https://doi.org/10.1080/10494820.2021.1884886

Rahman, T., Rahman, M. M., Sabur, M. A., & Huda, M. N. (2022). Identified factors of the effects of excessive usages of smartphone on health of the university students: A study on Islamic university, Kushtia, Bangladesh. Archives of Business Research, 10 (9), 102–111. https://doi.org/10.14738/abr.109.12994

Rasmussen, E. E., Punyanunt-Carter, N., LaFreniere, J. R., Norman, M. S., & Kimball, T. G. (2020). The serially mediated relationship between emerging adults’ social media use and mental well-being. Computers in Human Behavior, 102 (May 2019), 206–213. https://doi.org/10.1016/j.chb.2019.08.019

Raza, S. A., Qazi, W., & Umer, B. (2019). Examining the impact of case-based learning on student engagement, learning motivation and learning performance among university students. Journal of Applied Research in Higher Education, 12 (3), 517–533. https://doi.org/10.1108/jarhe-05-2019-0105

Richards, D., Caldwell, P. H. Y., & Go, H. (2015). Impact of social media on the health of children and young people. In Journal of Paediatrics and Child Health, 51 (12), 1152–1157. https://doi.org/10.1111/jpc.13023 . John Wiley and Sons, Ltd.

Roscoe, A. M., Lang, D., & Sheth, J. N. (1975). Follow-Up methods, questionnaire length, and Market differences in mail surveys. Journal of Marketing, 39 (2), 20. https://doi.org/10.2307/1250111

Rouis, S., Limayem, M., & Salehi-Sangari, E. (2011). Impact of Facebook usage on students’ academic achievement: Role of self-regulation and trust. Electronic Journal of Research in Educational Psychology, 9 (3), 961–994. https://doi.org/10.25115/ejrep.v9i25.1465

Santoveña-Casal, S. (2019). The impact of social media participation on academic performance in undergraduate and postgraduate students. International Review of Research in Open and Distance Learning, 20 (1), 126–143. https://doi.org/10.19173/irrodl.v20i1.3751

Sarstedt, M., Hair, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2021). How to Specify, Estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27 (3), 197–211.

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. Handbook of Market Research (pp. 587–632). Springer. https://doi.org/10.1007/978-3-319-57413-4_15

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 1–40). Springer International Publishing.

Shahzad, F., Shahzad, M. F., Dilanchiev, A., & Irfan, M. (2022). Modeling the influence of paternalistic leadership and personality characteristics on alienation and organizational culture in the aviation industry of Pakistan: The mediating role of cohesiveness. Sustainability (Switzerland) , 14 (22).  https://doi.org/10.3390/su142215473

Shahzad, M. F., Khan, K. I., Saleem, S., & Rashid, T. (2021). What factors affect the entrepreneurial intention to start-ups? The role of entrepreneurial skills, propensity to take risks, and innovativeness in open business models. https://doi.org/10.3390/joitmc7030173

Shaw, J. M., Mitchell, C. A., Welch, A. J., & Williamson, M. J. (2015). Social media used as a health intervention in adolescent health: A systematic review of the literature. Digtal Health, 1 , 205520761558839. https://doi.org/10.1177/2055207615588395

Shuja, K. H. (2022). Criminal recidivism in Pakistan: A grounded theory of social & environmental causes and psychological consequences. Nature-Nurture Journal of Psychology, 2 (2), 41–53.

Sinha, C. (2023). Knowledge about family and school contribution in academic achievement: The context of schooling and social representations in India. Journal of Education, 203 (1), 154–172. https://doi.org/10.1177/00220574211025977

Smith, B. G., & Gallicano, T. D. (2015). Terms of engagement: Analyzing public engagement with organizations through social media. Computers in Human Behavior, 53 , 82–90. https://doi.org/10.1016/j.chb.2015.05.060

Sun, Y. (2023). The role of family on internet addiction: A model analysis of co-parenting effect the role of family on internet addiction. Cogent Social Sciences, 9 (1), 0–18. https://doi.org/10.1080/23311886.2022.2163530

Tajvidi, R., & Karami, A. (2021). The effect of social media on firm performance. Computers in Human Behavior, 115 , 105174. https://doi.org/10.1016/j.chb.2017.09.026

Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48 (1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005

Article   MathSciNet   Google Scholar  

Tess, P. A. (2013). The role of social media in higher education classes (real and virtual)-A literature review. Computers in Human Behavior, 29 (5), A60–A68. https://doi.org/10.1016/j.chb.2012.12.032

Twenge, J. M., & Martin, G. N. (2020). Gender differences in associations between digital media use and psychological well-being: Evidence from three large datasets. Journal of Adolescence, 79 (November 2018), 91–102. https://doi.org/10.1016/j.adolescence.2019.12.018

Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44 (1), 119–134. https://doi.org/10.1007/s11747-015-0455-4

Wang, Q., Chen, W., & Liang, Y. (2011). The effects of social media on college students. RSCH5500-Research & Analysis , 13 . https://doi.org/10.1111/j.1548-1379.2010.01107.x

Whelan, E., Golden, W., & Tarafdar, M. (2022). How technostress and self-control of social networking sites affect academic achievement and wellbeing. Internet Research, 32 (7), 280–306. https://doi.org/10.1108/INTR-06-2021-0394

Whelan, E., Islam, A. K. M. N., & Brooks, S. (2020). Applying the SOBC paradigm to explain how social media overload affects academic performance. Computers and Education, 143 (November 2018), 103692. https://doi.org/10.1016/j.compedu.2019.103692

Widmer, E. D. (2006). Who are my family members? Bridging and binding social capital in family configurations. Journal of Social and Personal Relationships, 23 (6), 979–998. https://doi.org/10.1177/0265407506070482

Williams, A. L., & Merten, M. J. (2011). iFamily: Internet and social media technology in the family context . 40 (2), 150–170. https://doi.org/10.1111/j.1552-3934.2011.02101.x

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of Power, Bias, and Solution Propriety. Educational and Psychological Measurement, 76 (6), 913–934.

Xu, S., Wang, Z., & David, P. (2022). Social media multitasking (SMM) and well-being: Existing evidence and future directions. Current Opinion in Psychology, 47 , 101345. https://doi.org/10.1016/j.copsyc.2022.101345

Yıldırım, M., Özaslan, A., & Arslan, G. (2022). Perceived risk and parental coronavirus anxiety in healthcare workers: A moderated mediation role of coronavirus fear and mental well-being. Psychology Health and Medicine, 27 (5), 1095–1106. https://doi.org/10.1080/13548506.2021.1871771

Yosep, I., Hikmat, R., & Mardhiyah, A. (2023). Nursing intervention for preventing cyberbullying and reducing its negative impact on students: A scoping review. Journal of Multidisciplinary Healthcare, 16 , 261–273. https://doi.org/10.2147/JMDH.S400779

Yu, S., Draghici, A., Negulescu, O. H., & Ain, N. U. (2022). Social media application as a new paradigm for business communication: The role of COVID-19 knowledge, Social Distancing, and preventive attitudes [Original Research]. Frontiers in Psychology, 13 , 903082. https://doi.org/10.3389/fpsyg.2022.903082

Zang, J., Kim, Y., & Dong, J. (2022). New evidence on technological acceptance model in preschool education: Linking project-based learning (PBL), mental health, and semi-immersive virtual reality with learning performance. Frontiers in Public Health, 10 , 964320. https://doi.org/10.3389/fpubh.2022.964320

Zhao, L. (2023). Social media addiction and its impact on college students’ academic performance: The mediating role of stress. Asia-Pacific Education Researcher, 32 (1), 81–90. https://doi.org/10.1007/s40299-021-00635-0

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Xueyuan Zhang

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Jaffar Abbas

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Muhammad Farrukh Shahzad

Department of Cyber Systems Engineering, WMG, University of Warwick, Coventry, CV74AL, UK

Achyut Shankar

Centre of Research Impact and Outreach, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

School of Computer Science Engineering, Lovely Professional University, Phagwara, 144411, Punjab, India

Department of Horticulture, Faculty of Agriculture, Atatürk Üniversitesi, Erzurum, 25240, Turkey

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Zhang, X., Abbas, J., Shahzad, M.F. et al. Association between social media use and students’ academic performance through family bonding and collective learning: The moderating role of mental well-being. Educ Inf Technol 29 , 14059–14089 (2024). https://doi.org/10.1007/s10639-023-12407-y

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