Why social network site use fails to promote well-being? The roles of social overload and fear of missing out

Highlights

• There was no significant correlation between SNS use and subjective well-being.

• SNS use had a suppressing effect on subjective well-being via social overload.

• FoMO moderated the direct and indirect relations between SNS use and subjective well-being.

Abstract

Considering the popularity of social networking sites (SNSs) and the inconsistent results regarding the effect of SNS use on subjective well-being, this study intended to address the question “why SNS fails to predict subjective well-being” by investigating the suppressing role of social overload and moderating role of fear of missing out (FoMO). A sample of 1319 Chinese adolescents was recruited to complete measures on SNS use, social overload, FoMO and subjective well-being. Results showed that SNS use had a positively direct effect on subjective well-being, while the indirect effect via social overload in this association was significantly negative, suggesting that SNS use had a suppressing effect on well-being via social overload. FoMO moderated the suppressing effect of social overload; specifically, the indirect and direct effects were both more potent for adolescents with higher FoMO. Implications and limitations of this study are also discussed.

Introduction

Social networking sites (SNSs) today are one of the most popular online applications in modern information society. They have been widely used all over the world, which afford users the opportunity to establish, maintain and expand social networks (Su & Chan, 2017). As their numbers of users are considerably large and continuously growing, a lot of attention has been paid to exploring the association between SNS use and subjective well-being (i. e., people's assessment of their life; Valkenburg, Peter, & Schouten, 2006). However, the findings have been shown to be inconsistent. Some studies revealed the positive association between SNS use and well-being (e.g., Lönnqvist et al., 2016, Wheatley and Buglass, 2019), while others revealed the opposite (e.g., Reer et al., 2019, Sampasa-Kanyinga and Lewis, 2015), and two studies even found no significant correlation (Lee et al., 2011a, Utz and Breuer, 2017). To cope with this, some researchers dichotomize SNS activities into active and passive forms of usage (Frison and Eggermont, 2015, Orosz et al., 2016). Prior research has verified that active use of SNS is positively linked with well-being (Verduyn et al., 2015, Wang et al., 2014), while passive one demonstrates a negative link with it (Fardouly et al., 2015, Verduyn et al., 2015).

An alternative resolution may be to identify some mediators, suppressors (inconsistent mediators) or moderators that influence the association between SNS use and subjective well-being (Oh et al., 2014, Yoo and Jeong, 2017). According to some researchers, a mediator and suppressor are both used to account for the covariance between the independent variable (X) and the dependent one (Y). What makes them different is that the former provides an illustration how X influences Y, while the latter discloses the reason why X fails to predict Y (Ludlow and Klein, 2014, Wen and Ye, 2014). Apparently, the introduction of a suppressor, rather than a mediator, is more suitable for addressing the aforementioned problem. Besides, a moderator further helps identify the condition under which the suppressor exerts its best influence (Hayes, 2013). In other words, the suppression effect will vary with different levels of moderator. Thus, a combination of suppression and moderation models will contribute to a novel solution to the contradictory results on the relation between SNS and subjective well-being.

In addition, as adolescents have gradually migrated to SNSs and become a considerable group among SNS users (Ellison, Steinfield, & Lampe, 2011), research about adolescents’ use of SNSs calls for more attention. Based on these, the present study aimed to investigate the mechanisms underlying the association between SNS use and subjective well-being among adolescents by constructing a mixed model with a combination of suppressing and moderating effects. Identifying these mechanisms will contribute to a deeper understanding of the relationship between SNS use and well-being, as well as the prevention of possible risks of SNS use for adolescents.

Generally, SNSs are characterized by a personal profile consisting of static descriptive information and dynamic update of user content, and a publicly visible list of connections representing users' collection of online social relations (Verduyn, Ybarra, Résibois, Jonides, & Kross, 2017). Examples of popular SNSs include world-wide Facebook, LinkedIn and Instagram, as well as Qzone and Wechat Moments in China. With the development of information and communication technologies, these SNSs have enjoyed great popularity around the world in recent years. Take adolescent populations for example, a survey by China Internet Network Information Center (2017) reported that adolescents’ usage rate of Qzone and Wechat Moments were both over 65%. Facebook, one of the most popular SNSs in the world, has been adopted by 71% of adolescents aging from 13 to 17 years old (Lenhart, 2015).

Considering the large number of SNS users and the enormous amount of time in adopting these sites, an interest of research is to identify the effect of SNS use on people's subjective well-being. A plenty of recent studies have suggested that using SNS can positively affect well-being (e.g., Lönnqvist et al., 2016, Oh et al., 2014, Verduyn et al., 2017, Wang et al., 2014, Wheatley and Buglass, 2019). For example, the finding from a large-scale UK panel data demonstrated that greater use of SNS was associated with higher levels of life satisfaction (a key component of subjective well-being; Wheatley & Buglass, 2019). Pittman and Reich (2016) found a positive relationship between overall usage of Instagram and well-being. In contrast, there is also ample literature highlighting the dark side of SNS use in terms of decreased well-being. For instance, a longitudinal study by Kross et al. (2013) indicated that Facebook use predicted negative shifts on the two components (affect and life satisfaction) of subjective well-being over time. Some cross-sectional studies also indicate a negative relation between SNS and subjective well-being (Rae and Lonborg, 2015, Sagioglou and Greitemeyer, 2014). In addition, the studies by Lee et al. (2011a, 2011b) and Utz and Breuer (2017) even suggested a non-significant correlation between them.

To address the above inconsistencies, a typical resolution is to divide overall usage of SNS into active and passive one (Frison and Eggermont, 2015, Orosz et al., 2016). Active usage, including activities that facilitate direct exchanges with other users (e.g., direct communication, broadcasting; Burke, Kraut, & Marlow, 2011), has been reported to link positively with subjective well-being in both cross-sectional and longitudinal research (Verduyn et al., 2015, Wang et al., 2014, Wenninger et al., 2014). However, the opposite findings are consistently observed for passive usage (Fardouly et al., 2015, Shaw et al., 2015, Tandoc et al., 2015, Verduyn et al., 2015), which refers to activities that monitor other users' lives without direct exchanges (e.g., scanning other users’ profiles, pictures, and status updates). Further, there is great interest of research in examining the mediating mechanisms underlying the effect of SNS use on subjective well-being. For instance, perceived online social support, social capital and social connectedness (all of them are essentially perceived social support) are often mentioned as important mechanisms that account for the association between active use of SNS and subjective well-being (Frison and Eggermont, 2015, Kim and Lee, 2011). Social comparison and its induced envy are also frequently highlighted for their important role in mediating the association between passive use of SNS and subjective well-being (Krasnova et al., 2015, Tandoc et al., 2015).

Moreover, the introduction of suppressors may also lead to the settlement of the above problem. A suppressor refers to a third variable that “could increase the magnitude of the relationship” between the independent and dependent ones (MacKinnon, Krull, & Lockwood, 2000, p. 174). Usually, suppressors have been regarded as post hoc with little theoretical discussion in the introduction of literature. Some researchers, however, state that it is “reasonable, justifiable, and powerful” to work on an a priori suppressor variable design to address the question why the independent variables fails to predict the dependent one (Ludlow and Klein, 2014, MacKinnon et al., 2000, Wen and Ye, 2014). In other words, a suppression effect could be tested using a strategy of theory-based hypothesis testing instead of post-hoc determination. Therefore, it is reasonable to propose a theory-based suppressor to address the inconsistencies on the association between SNS use and subjective well-being.

Social overload refers to the negative perception on SNS usage when users receive too many social support requests and feel they are giving too much social support to other individuals embedded in their online social network (Maier, Laumer, Eckhardt, & Weitzel, 2012a). Namely, it can be seen as the burden of giving too much social support in SNS context (Maier, Laumer, Eckhardt, & Weitzel, 2014). According to Social Support Theory (SST), there are two different kinds of social support embedded in a social network, one is perceived from the perspective of receivers and the other is enacted from providers. Both of them are vital for network members for their relations with well-being (Cassel, 1976, Cobb, 1976). By definition, social overload can be considered as a kind of enacted social support.

Individual usage behavior has long been theorized to be a significant source of technology-related perceptions in general technology (Kim, 2009) and SNS research in particular (Maier, Laumer, Eckhardt, & Weitzel, 2012b). Social overload is a kind of SNS-related perception during adolescents' daily usage, and its relationship with SNS use can be explained by the essential features of the latter. Specifically, SNS is a useful online tool for establishing, maintaining and expanding social networks (Su & Chan, 2017), which facilitates not only the reception of perceived social support but also the request for actions by others giving social support (i.e. enacted social support). It is more likely for adolescents with frequent use of SNS to be exposed to more social support requests. More intensive SNS use, on the other hand, usually brings about larger and denser social network, which is closely related to more social requests (Baum and Koman, 1976, Evans and Lepore, 1993). Notably, social requests is a significantly precondition of adolescents' perception of social overload. Therefore, it is easy to make an inference that SNS use contributes to the development of social overload. Supporting this theoretical notion, some empirical studies have demonstrated the positive association between SNS use and social overload (Maier et al., 2012a, Manago et al., 2012). In addition, as a negative feeling in the context of SNS, social overload may further deteriorate individuals' psychosocial adaptations, such as greater SNS exhaustion (being tired of activities related to the usage of SNS, Ayyagari et al., 2011, Maier et al., 2012b), lower satisfaction of SNS use (Au et al., 2008, Maier et al., 2012b), as well as reduced self-esteem (Choi & Lim, 2016), all of which have negative effect on users’ well-being (Maier et al., 2014).

Based on these, it is possible that SNS use increases social overload, which in turn contributes to negative consequences. In line with this theoretical notion, one study based on structural equation model revealed a negatively indirect effect of SNS use on SNS exhaustion and satisfaction via social overload (Maier et al., 2012b). However, the direct and overall effect of SNS usage was not examined in this study. It was possible that the total effect of SNS use on subjective well-being was suppressed as the direct effect and indirect effect via social overload were opposite in direction. In other words, SNS failed to predict subjective well-being due to its role in increasing social overload, as the latter was a risk factor for subjective well-being. Given the analysis above, it was hypothesized that:

Hypothesis 1

Social overload would suppress the association between SNS use and well-being.

Although SNS use may reduce well-being through the increased social overload, it is possible that not all users are equally influenced. As demonstrated by Valkenburg, Peter and Walther's (2015) summary regarding the trends and commonalities among prominent theories of media effects, this relation may differ for users with different levels of dispositional traits, such as FoMO.

Defined as a pervasive apprehension that others might be having rewarding experiences from which one is absent, FoMO is characterized by the desire to stay continually connected with what others are doing (Przybylski, Murayama, DeHaan, & Gladwell, 2013). Although the construct of FoMO is usually depicted and measured in an online context, neither its definition nor the items of measurement refer to online behavior (Alt, 2018, Przybylski et al., 2013). Therefore, FoMO could be considered as a dispositional trait characterized by the general fear of an individual of missing out on something (Wegmann, Oberst, Stodt, & Brand, 2017). Empirical research has found that FoMO is associated with higher levels of SNS-related stress (Beyens et al., 2016, Fox et al., 2015), deterioration of physical health and mental well-being (Buglass et al., 2017, Stead and Bibby, 2017), as well as poor sleep quality (Adams et al., 2016, Scott et al., 2016).

Apart from its directly negative effect on individuals, FoMO may also further moderate the influences of SNS use on individuals' adaptation through multiple mechanisms. First, with regarding to cognitive mechanisms, people with high FoMO might be expected to show more attentional bias towards threat (Bradley et al., 1999, Mogg and Bradley, 2002) because of a close relation between FoMO with trait anxiety (Przybylski et al., 2013). According to the feature of selected attentional bias in different levels of trait anxiety (Koster, Crombez, Verschuere, Van Damme, & Wiersema, 2006), people with high FoMO might be more susceptible to the threatening information on the SNS. Second, with regarding to motivational mechanisms, individuals high in FoMO are more likely to use maladaptive methods for fulfilling their psychological needs, such as problematic or addictive social media or smartphone use that may ultimately worsen their social or academic life (e.g., Elhai et al., 2016, Wolniewicz et al., 2017). Third, with regarding to the mechanisms of metacognition, people with high FoMO are thought to have more positive metacognitions towards SNS use, i.e., a specific form of expectancy of the positive role SNS usage play in controlling and regulating cognition and emotion (Casale, Rugai, & Fioravanti, 2018). Therefore, they are more likely to try various functions of SNS and subsequently be vulnerable to the threatening information in it. Fourth, according to its definition and features, individuals high in FoMO are more likely to stay continually connected with others’ ongoing activities (Przybylski et al., 2013). Consequently, it is more susceptible for them to engage in compulsive SNS use (Oberst et al., 2017, Wolniewicz et al., 2017), which would in turn contribute to poor perceptions or negative cognitive states (Brand et al., 2016, Lin et al., 2013), as well as lower well-being (Marino et al., 2018, Van der Aa et al., 2009). Due to the aforementioned analysis, it is reasonable to infer that:

Hypothesis 2

FoMO would moderate the relation between SNS use and social overload, with the relation being stronger for adolescents with higher levels with FoMO.

Hypothesis 3

FoMO would moderate the relation between SNS use and subjective well-being, with the relation being stronger for adolescents with lower levels of FoMO.

In conclusion, based on the perspective of effects of suppression and moderation, the aim of the present study was to investigate the mechanism underlying the association between SNS use and subjective well-being among adolescents by constructing a mixed model: (a) whether social overload suppressed the relation between SNS use and subjective well-being, (b) whether FoMO moderated the relation between SNS use and social overload, and (c) whether FoMO moderated the suppressing effect of social overload in the relationship between SNS use and subjective well-being. This study could not only shed more light on how and when SNS use fails ro predict well-being, but also provide some useful visions for practical intervention.