hypothesis about impact of social media

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Effects of Social Media Use on Psychological Well-Being: A Mediated Model

Dragana ostic, sikandar ali qalati, belem barbosa, syed mir muhammad shah, esthela galvan vela, ahmed muhammad herzallah.

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Edited by: Heyla A. Selim, King Saud University, Saudi Arabia

Reviewed by: Elizabeth A. Boyle, University of the West of Scotland, United Kingdom; Barbara Caci, University of Palermo, Italy

*Correspondence: Sikandar Ali Qalati [email protected] ; [email protected]

Esthela Galvan Vela [email protected]

This article was submitted to Human-Media Interaction, a section of the journal Frontiers in Psychology

†ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342

Sikandar Ali Qalati orcid.org/0000-0001-7235-6098

Belem Barbosa orcid.org/0000-0002-4057-360X

Esthela Galvan Vela orcid.org/0000-0002-8778-3989

Feng Liu orcid.org/0000-0001-9367-049X

Received 2021 Mar 10; Accepted 2021 May 25; Collection date 2021.

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.

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Introduction

The use of social media has grown substantially in recent years (Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” (Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media (Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction (Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being (Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction (Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction (Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out (Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression (Dhir et al., 2018 ; Reer et al., 2019 ), social isolation (Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others (Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others (Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers (Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities (Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel (Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas (Carlson et al., 2016 ), which consequently may be significantly correlated to social support (Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage (Karikari et al., 2017 ), particularly regarding its societal implications (Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts (Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam ( 1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen ( 2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam ( 1995 , 2000 ) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties (Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being (Bano et al., 2019 ). Indeed, Williams ( 2006 ) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital (Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen ( 2014 ) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions (Chen and Li, 2017 ). Abbas and Mesch ( 2018 ) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. ( 2017 ) also found positive effects of social media use on social capital. Similarly, Pang ( 2018 ) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. ( 2019 ) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim ( 2017 ) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being (Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities (Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” (Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity (Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities (Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression (Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation (Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation (Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation (Whaite et al., 2018 ).

Chappell and Badger ( 1989 ) stated that social isolation leads to decreased psychological well-being, while Choi and Noh ( 2019 ) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. ( 2012 ) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” (Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices (Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction (Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones (Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction (Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart (Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities (Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others (Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” (Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing (Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing (Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. ( 2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity (Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being (Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

Conceptual model.

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context (Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones (Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents (Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research (Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data (Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) (Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. ( 2017 ). Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan ( 2015 ). Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh ( 2019 ). Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban ( 2013 ). Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas ( 2018 ). Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. ( 2017 ). Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields (Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” (Sarstedt and Cheah, 2019 ). According to Ringle et al. ( 2015 ), this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah ( 2019 ) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. ( 2019 ) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data (Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 (Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske ( 1959 ), who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey (Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings (Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results (Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB (Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold (Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB (Hair et al., 2019 ). Hair et al. ( 2019 ) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. ( 1991 ) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 (Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability (Hair et al., 2012 ). Hair et al. ( 2017 ) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. ( 2019 ). According to Nunnally ( 1978 ), Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi ( 1988 ) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker ( 1981 ) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker ( 1981 ), the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Study measures, factor loading, and the constructs' reliability and convergent validity.

Discriminant validity and correlation.

Bold values are the square root of the AVE .

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. ( 2019 ) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power (Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen ( 1998 ) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's ( 1998 ) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Summary of path coefficients and hypothesis testing.

p-value < 0.05, t-value > 1.96 .

Structural model.

Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Goodness of fit → SRMR = 0.063; d_ULS = 1.589; d_G = 0.512; chi-square = 2,910.744 .

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results (Ringle et al., 2012 ). Hair et al. ( 2019 ) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively (Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. ( 2019 ) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit (Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's ( 2008 ) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes ( 2008 ) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. ( 2018 ), if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes ( 2008 ) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan ( 2015 ) and Ellison et al. ( 2007 ), who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. ( 2021 ), who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan ( 2015 ) and Karikari et al. ( 2017 ). Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. ( 2019 ), who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li ( 2017 ).

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation (Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar ( 2020 ). The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh ( 2019 ), social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. ( 2016 ), who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. ( 2016 ), Salehan and Negahban ( 2013 ), and Swar and Hameed ( 2017 ). The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. ( 2019 ), who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat ( 2019 ), who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee ( 2020 ), Chotpitayasunondh and Douglas ( 2016 ), Guazzini et al. ( 2019 ), and Tonacci et al. ( 2019 ), who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas ( 2018 ) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. ( 2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression (Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. ( 2018 ), who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research (Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim ( 2017 ), who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections (Putnam, 1995 , 2000 ) with heterogeneous weak ties (Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties (Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

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

Conflict of Interest

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

Funding. This study is supported by the National Statistics Research Project of China (2016LY96).

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• Published: 01 July 2020

The effect of social media on well-being differs from adolescent to adolescent

• Ine Beyens   ORCID: orcid.org/0000-0001-7023-867X 1 ,
• J. Loes Pouwels   ORCID: orcid.org/0000-0002-9586-392X 1 ,
• Irene I. van Driel   ORCID: orcid.org/0000-0002-7810-9677 1 ,
• Loes Keijsers   ORCID: orcid.org/0000-0001-8580-6000 2 &
• Patti M. Valkenburg   ORCID: orcid.org/0000-0003-0477-8429 1  

Scientific Reports volume  10 , Article number:  10763 ( 2020 ) Cite this article

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• Human behaviour

The question whether social media use benefits or undermines adolescents’ well-being is an important societal concern. Previous empirical studies have mostly established across-the-board effects among (sub)populations of adolescents. As a result, it is still an open question whether the effects are unique for each individual adolescent. We sampled adolescents’ experiences six times per day for one week to quantify differences in their susceptibility to the effects of social media on their momentary affective well-being. Rigorous analyses of 2,155 real-time assessments showed that the association between social media use and affective well-being differs strongly across adolescents: While 44% did not feel better or worse after passive social media use, 46% felt better, and 10% felt worse. Our results imply that person-specific effects can no longer be ignored in research, as well as in prevention and intervention programs.

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Introduction.

Ever since the introduction of social media, such as Facebook and Instagram, researchers have been studying whether the use of such media may affect adolescents’ well-being. These studies have typically reported mixed findings, yielding either small negative, small positive, or no effects of the time spent using social media on different indicators of well-being, such as life satisfaction and depressive symptoms (for recent reviews, see for example 1 , 2 , 3 , 4 , 5 ). Most of these studies have focused on between-person associations, examining whether adolescents who use social media more (or less) often than their peers experience lower (or higher) levels of well-being than these peers. While such between-person studies are valuable in their own right, several scholars 6 , 7 have recently called for studies that investigate within-person associations to understand whether an increase in an adolescent’s social media use is associated with an increase or decrease in that adolescent’s well-being. The current study aims to respond to this call by investigating associations between social media use and well-being within single adolescents across multiple points in time 8 , 9 , 10 .

Person-specific effects

To our knowledge, four recent studies have investigated within-person associations of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results 6 , 11 , 12 , 13 . Orben and colleagues 6 found a small negative reciprocal within-person association between the time spent using social media and life satisfaction. Likewise, Boers and colleagues 12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues 11 and Jensen and colleagues 13 did not find any evidence for within-person associations between social media use and depression.

Earlier studies that investigated within-person associations of social media use with indicators of well-being have all only reported average effect sizes. However, it is possible, or even plausible, that these average within-person effects may have been small and nonsignificant because they result from sizeable heterogeneity in adolescents’ susceptibility to the effects of social media use on well-being (see 14 , 15 ). After all, an average within-person effect size can be considered an aggregate of numerous individual within-person effect sizes that range from highly positive to highly negative.

Some within-person studies have sought to understand adolescents’ differential susceptibility to the effects of social media by investigating differences between subgroups. For instance, they have investigated the moderating role of sex to compare the effects of social media on boys versus girls 6 , 11 . However, such a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators (e.g., gender, age) does not provide insights into more fine-grained differences at the level of the single individual 16 . After all, while girls and boys each represent a homogenous group in terms of sex, they may each differ on a wide array of other factors.

As such, although worthwhile, the average within-person effects of social media on well-being obtained in previous studies may have been small or non-significant because they are diluted across a highly heterogeneous population (or sub-population) of adolescents 14 , 15 . In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media 17 , a viable explanation for the small and inconsistent findings in earlier studies may be that the effect of social media differs from adolescent to adolescent. The aim of the current study is to investigate this hypothesis and to obtain a better understanding of adolescents’ unique susceptibility to the effects of social media on their affective well-being.

Social media and affective well-being

Within-person studies have provided important insights into the associations of social media use with cognitive well-being (e.g., life satisfaction 6 ), which refers to adolescents’ cognitive judgment of how satisfied they are with their life 18 . However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions 18 ) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like conceptualizations of well-being 11 , 12 , 13 , that is, adolescents’ average well-being across specific time periods 18 , there is a lack of studies that focus on well-being as a momentary affective state. Therefore, we extend previous research by examining the association between adolescents’ social media use and their momentary affective well-being. Like earlier experience sampling (ESM) studies among adults 19 , 20 , we measured adolescents’ momentary affective well-being with a single item. Adolescents’ momentary affective well-being was defined as their current feelings of happiness, a commonly used question to measure well-being 21 , 22 , which has high convergent validity, as evidenced by the strong correlations with the presence of positive affect and absence of negative affect.

To assess adolescents’ momentary affective well-being (henceforth referred to as well-being), we conducted a week-long ESM study among 63 middle adolescents ages 14 and 15. Six times a day, adolescents were asked to complete a survey using their own mobile phone, covering 42 assessments per adolescent, assessing their affective well-being and social media use. In total, adolescents completed 2,155 assessments (83.2% average compliance).

We focused on middle adolescence, since this is the period in life characterized by most significant fluctuations in well-being 23 , 24 . Also, in comparison to early and late adolescents, middle adolescents are more sensitive to reactions from peers and have a strong tendency to compare themselves with others on social media and beyond. Because middle adolescents typically use different social media platforms, in a complementary way 25 , 26 , 27 , each adolescent reported on his/her use of the three social media platforms that s/he used most frequently out of the five most popular social media platforms among adolescents: WhatsApp, followed by Instagram, Snapchat, YouTube, and, finally, the chat function of games 28 . In addition to investigating the association between overall social media use and well-being (i.e., the summed use of adolescents’ three most frequently used platforms), we examined the unique associations of the two most popular platforms, WhatsApp and Instagram 28 .

Like previous studies on social media use and well-being, we distinguished between active social media use (i.e., “activities that facilitate direct exchanges with others” 29 ) and passive social media use (i.e., “consuming information without direct exchanges” 29 ). Within-person studies among young adults have shown that passive but not active social media use predicts decreases in well-being 29 . Therefore, we examined the unique associations of adolescents’ overall active and passive social media use with their well-being, as well as active and passive use of Instagram and WhatsApp, specifically. We investigated categorical associations, that is, whether adolescents would feel better or worse if they had actively or passively used social media. And we investigated dose–response associations to understand whether adolescents’ well-being would change as a function of the time they had spent actively or passively using social media.

The hypotheses and the design, sampling and analysis plan were preregistered prior to data collection and are available on the Open Science Framework, along with the code used in the analyses ( https://osf.io/nhks2 ). For details about the design of the study and analysis approach, see Methods.

In more than half of all assessments (68.17%), adolescents had used social media (i.e., one or more of their three favorite social media platforms), either in an active or passive way. Instagram (50.90%) and WhatsApp (53.52%) were used in half of all assessments. Passive use of social media (66.21% of all assessments) was more common than active use (50.86%), both on Instagram (48.48% vs. 20.79%) and WhatsApp (51.25% vs. 40.07%).

Strong positive between-person correlations were found between the duration of active and passive social media use (overall: r  = 0.69, p  < 0.001; Instagram: r  = 0.38, p  < 0.01; WhatsApp: r  = 0.85, p  < 0.001): Adolescents who had spent more time actively using social media than their peers, had also spent more time passively using social media than their peers. Likewise, strong positive within-person correlations were found between the duration of active and passive social media use (overall: r  = 0.63, p  < 0.001; Instagram: r  = 0.37, p  < 0.001; WhatsApp: r  = 0.57, p  < 0.001): The more time an adolescent had spent actively using social media at a certain moment, the more time s/he had also spent passively using social media at that moment.

Table 1 displays the average number of minutes that adolescents had spent using social media in the past hour at each assessment, and the zero-order between- and within-person correlations between the duration of social media use and well-being. At the between-person level, the duration of active and passive social media use was not associated with well-being: Adolescents who had spent more time actively or passively using social media than their peers did not report significantly higher or lower levels of well-being than their peers. At the within-person level, significant but weak positive correlations were found between the duration of active and passive overall social media use and well-being. This indicates that adolescents felt somewhat better at moments when they had spent more time actively or passively using social media (overall), compared to moments when they had spent less time actively or passively using social media. When looking at specific platforms, a positive correlation was only found for passive WhatsApp use, but not for active WhatsApp use, and not for active and passive Instagram use.

Average and person-specific effects

The within-person associations of social media use with well-being and differences in these associations were tested in a series of multilevel models. We ran separate models for overall social media use (i.e., active use and passive use of adolescents’ three favorite social media platforms, see Table 2 ), Instagram use (see Table 3 ), and WhatsApp use (see Table 4 ). In a first step we examined the average categorical associations for each of these three social media uses using fixed effects models (Models 1A, 3A, and 5A) to investigate whether, on average, adolescents would feel better or worse at moments when they had used social media compared to moments when they had not (i.e., categorical predictors: active use versus no active use, and passive use versus no passive use). In a second step, we examined heterogeneity in the within-person categorical associations by adding random slopes to the fixed effects models (Models 1B, 3B, and 5B). Next, we examined the average dose–response associations using fixed effects models (Models 2A, 4A, and 6A), to investigate whether, on average, adolescents would feel better or worse when they had spent more time using social media (i.e., continuous predictors: duration of active use and duration of passive use). Finally, we examined heterogeneity in the within-person dose–response associations by adding random slopes to the fixed effects models (Models 2B, 4B, and 6B).

Overall social media use.

The model with the categorical predictors (see Table 2 ; Model 1A) showed that, on average, there was no association between overall use and well-being: Adolescents’ well-being did not increase or decrease at moments when they had used social media, either in a passive or active way. However, evidence was found that the association of passive (but not active) social media use with well-being differed from adolescent to adolescent (Model 1B), with effect sizes ranging from − 0.24 to 0.68. For 44.26% of the adolescents the association was non-existent to small (− 0.10 <  r  < 0.10). However, for 45.90% of the adolescents there was a weak (0.10 <  r  < 0.20; 8.20%), moderate (0.20 <  r  < 0.30; 22.95%) or even strong positive ( r  ≥ 0.30; 14.75%) association between overall passive social media use and well-being, and for almost one in ten (9.84%) adolescents there was a weak (− 0.20 <  r  < − 0.10; 6.56%) or moderate negative (− 0.30 <  r  < − 0.20; 3.28%) association.

The model with continuous predictors (Model 2A) showed that, on average, there was a significant dose–response association for active use. At moments when adolescents had used social media, the time they spent actively (but not passively) using social media was positively associated with well-being: Adolescents felt better at moments when they had spent more time sending messages, posting, or sharing something on social media. The associations of the time spent actively and passively using social media with well-being did not differ across adolescents (Model 2B).

Instagram use

As shown in Model 3A in Table 3 , on average, there was a significant categorical association between passive (but not active) Instagram use and well-being: Adolescents experienced an increase in well-being at moments when they had passively used Instagram (i.e., viewing posts/stories of others). Adolescents did not experience an increase or decrease in well-being when they had actively used Instagram. The associations of passive and active Instagram use with well-being did not differ across adolescents (Model 3B).

On average, no significant dose–response association was found for Instagram use (Model 4A): At moments when adolescents had used Instagram, the time adolescents spent using Instagram (either actively or passively) was not associated with their well-being. However, evidence was found that the association of the time spent passively using Instagram differed from adolescent to adolescent (Model 4B), with effect sizes ranging from − 0.48 to 0.27. For most adolescents (73.91%) the association was non-existent to small (− 0.10 <  r  < 0.10), but for almost one in five adolescents (17.39%) there was a weak (0.10 <  r  < 0.20; 10.87%) or moderate (0.20 <  r  < 0.30; 6.52%) positive association, and for almost one in ten adolescents (8.70%) there was a weak (− 0.20 <  r  < − 0.10; 2.17%), moderate (− 0.30 <  r  < − 0.20; 4.35%), or strong ( r  ≤ − 0.30; 2.17%) negative association. Figure  1 illustrates these differences in the dose–response associations.

The dose–response association between passive Instagram use (in minutes per hour) and affective well-being for each individual adolescent (n = 46). Red lines represent significant negative within-person associations, green lines represent significant positive within-person associations, and gray lines represent non-significant within-person associations. A graph was created for each participant who had completed at least 10 assessments. A total of 13 participants were excluded because they had completed less than 10 assessments of passive Instagram use. In addition, one participant was excluded because no graph could be computed, since this participant's passive Instagram use was constant across assessments.

WhatsApp use

As shown in Model 5A in Table 4 , just as for Instagram, we found that, on average, there was a significant categorical association between passive (but not active) WhatsApp use and well-being: Adolescents reported that they felt better at moments when they had passively used WhatsApp (i.e., read WhatsApp messages). For active WhatsApp use, no significant association was found. Also, in line with the results for Instagram use, no differences were found regarding the associations of active and passive WhatsApp use (Model 5B).

In addition, a significant dose–response association was found for passive (but not active) use (Model 6A). At moments when adolescents had used WhatsApp, we found that, on average, the time adolescents spent passively using WhatsApp was positively associated with well-being: Adolescents felt better at moments when they had spent more time reading WhatsApp messages. The time spent actively using WhatsApp was not associated with well-being. No differences were found in the dose–response associations of active and passive WhatsApp use (Model 6B).

This preregistered study investigated adolescents’ unique susceptibility to the effects of social media. We found that the associations of passive (but not active) social media use with well-being differed substantially from adolescent to adolescent, with effect sizes ranging from moderately negative (− 0.24) to strongly positive (0.68). While 44.26% of adolescents did not feel better or worse if they had passively used social media, 45.90% felt better, and a small group felt worse (9.84%). In addition, for Instagram the majority of adolescents (73.91%) did not feel better or worse when they had spent more time viewing post or stories of others, whereas some felt better (17.39%), and others (8.70%) felt worse.

These findings have important implications for social media effects research, and media effects research more generally. For decades, researchers have argued that people differ in their susceptibility to the effects of media 17 , leading to numerous investigations of such differential susceptibility. These investigations have typically focused on moderators, based on variables such as sex, age, or personality. Yet, over the years, studies have shown that such moderators appear to have little power to explain how individuals differ in their susceptibility to media effects, probably because a group-differential approach does not account for the possibility that media users may differ across a range of factors, that are not captured by only one (or a few) investigated moderator variables.

By providing insights into each individual’s unique susceptibility, the findings of this study provide an explanation as to why, up until now, most media effects research has only found small effects. We found that the majority of adolescents do not experience any short-term changes in well-being related to their social media use. And if they do experience any changes, these are more often positive than negative. Because only small subsets of adolescents experience small to moderate changes in well-being, the true effects of social media reported in previous studies have probably been diluted across heterogeneous samples of individuals that differ in their susceptibility to media effects (also see 30 ). Several scholars have noted that overall effect sizes may mask more subtle individual differences 14 , 15 , which may explain why previous studies have typically reported small or no effects of social media on well-being or indicators of well-being 6 , 11 , 12 , 13 . The current study seems to confirm this assumption, by showing that while the overall effect sizes are small at best, the person-specific effect sizes vary considerably, from tiny and small to moderate and strong.

As called upon by other scholars 5 , 31 , we disentangled the associations of active and passive use of social media. Research among young adults found that passive (but not active) social media use is associated with lower levels of affective well-being 29 . In line with these findings, the current study shows that active and passive use yielded different associations with adolescents’ affective well-being. Interestingly though, in contrast to previous findings among adults, our study showed that, on average, passive use of Instagram and WhatsApp seemed to enhance rather than decrease adolescents’ well-being. This discrepancy in findings may be attributed to the fact that different mechanisms might be involved. Verduyn and colleagues 29 found that passive use of Facebook undermines adults’ well-being by enhancing envy, which may also explain the decreases in well-being found in our study among a small group of adolescents. Yet, adolescents who felt better by passively using Instagram and WhatsApp, might have felt so because they experienced enjoyment. After all, adolescents often seek positive content on social media, such as humorous posts or memes 32 . Also, research has shown that adolescents mainly receive positive feedback on social media 33 . Hence, their passive Instagram and WhatsApp use may involve the reading of positive feedback, which may explain the increases in well-being.

Overall, the time spent passively using WhatsApp improved adolescents’ well-being. This did not differ from adolescent to adolescent. However, the associations of the time spent passively using Instagram with well-being did differ from adolescent to adolescent. This discrepancy suggests that not all social media uses yield person-specific effects on well-being. A possible explanation may be that adolescents’ responses to WhatsApp are more homogenous than those to Instagram. WhatsApp is a more private platform, which is mostly used for one-to-one communication with friends and acquaintances 26 . Instagram, in contrast, is a more public platform, which allows its users to follow a diverse set of people, ranging from best friends to singers, actors, and influencers 28 , and to engage in intimate communication as well as self-presentation and social comparison. Such diverse uses could lead to more varied, or even opposing responses, such as envy versus inspiration.

Limitations and directions for future research

The current study extends our understanding of differential susceptibility to media effects, by revealing that the effect of social media use on well-being differs from adolescent to adolescent. The findings confirm our assumption that among the great majority of adolescents, social media use is unrelated to well-being, but that among a small subset, social media use is either related to decreases or increases in well-being. It must be noted, however, that participants in this study felt relatively happy, overall. Studies with more vulnerable samples, consisting of clinical samples or youth with lower social-emotional well-being may elicit different patterns of effects 27 . Also, the current study focused on affective well-being, operationalized as happiness. It is plausible that social media use relates differently with other types of well-being, such as cognitive well-being. An important next step is to identify which adolescents are particularly susceptible to experience declines in well-being. It is conceivable, for instance, that the few adolescents who feel worse when they use social media are the ones who receive negative feedback on social media 33 .

In addition, future ESM studies into the effects of social media should attempt to include one or more follow-up measures to improve our knowledge of the longer-term influence of social media use on affective well-being. While a week-long ESM is very common and applied in most earlier ESM studies 34 , a week is only a snapshot of adolescent development. Research is needed that investigates whether the associations of social media use with adolescents’ momentary affective well-being may cumulate into long-lasting consequences. Such investigations could help clarify whether adolescents who feel bad in the short term would experience more negative consequences in the long term, and whether adolescents who feel better would be more resistant to developing long-term negative consequences. And while most adolescents do not seem to experience any short-term increases or decreases in well-being, more research is needed to investigate whether these adolescents may experience a longer-term impact of social media.

While the use of different platforms may be differently associated with well-being, different types of use may also yield different effects. Although the current study distinguished between active and passive use of social media, future research should further differentiate between different activities. For instance, because passive use entails many different activities, from reading private messages (e.g., WhatsApp messages, direct messages on Instagram) to browsing a public feed (e.g., scrolling through posts on Instagram), research is needed that explores the unique effects of passive public use and passive private use. Research that seeks to explore the nuances in adolescents’ susceptibility as well as the nuances in their social media use may truly improve our understanding of the effects of social media use.

Participants

Participants were recruited via a secondary school in the south of the Netherlands. Our preregistered sampling plan set a target sample size of 100 adolescents. We invited adolescents from six classrooms to participate in the study. The final sample consisted of 63 adolescents (i.e., 42% consent rate, which is comparable to other ESM studies among adolescents; see, for instance 35 , 36 ). Informed consent was obtained from all participants and their parents. On average, participants were 15 years old ( M  = 15.12 years, SD  = 0.51) and 54% were girls. All participants self-identified as Dutch, and 41.3% were enrolled in the prevocational secondary education track, 25.4% in the intermediate general secondary education track, and 33.3% in the academic preparatory education track.

The study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the University of Amsterdam and was performed in accordance with the guidelines formulated by the Ethics Review Board. The study consisted of two phases: A baseline survey and a personalized week-long experience sampling (ESM) study. In phase 1, researchers visited the school during school hours. Researchers informed the participants of the objective and procedure of the study and assured them that their responses would be treated confidentially. Participants were asked to sign the consent form. Next, participants completed a 15-min baseline survey. The baseline survey included questions about demographics and assessed which social media each adolescent used most frequently, allowing to personalize the social media questions presented during the ESM study in phase 2. After completing the baseline survey, participants were provided detailed instructions about phase 2.

In phase 2, which took place two and a half weeks after the baseline survey, a 7-day ESM study was conducted, following the guidelines for ESM studies provided by van Roekel and colleagues 34 . Aiming for at least 30 assessments per participant and based on an average compliance rate of 70 to 80% reported in earlier ESM studies among adolescents 34 , we asked each participant to complete a total of 42 ESM surveys (i.e., six 2-min surveys per day). Participants completed the surveys using their own mobile phone, on which the ESM software application Ethica Data was installed during the instruction session with the researchers (phase 1). Each 2-min survey consisted of 22 questions, which assessed adolescents’ well-being and social media use. Two open-ended questions were added to the final survey of the day, which asked about adolescents’ most pleasant and most unpleasant events of the day.

The ESM sampling scheme was semi-random, to allow for randomization and avoid structural patterns in well-being, while taking into account that adolescents were not allowed to use their phone during school time. The Ethica Data app was programmed to generate six beep notifications per day at random time points within a fixed time interval that was tailored to the school’s schedule: before school time (1 beep), during school breaks (2 beeps), and after school time (3 beeps). During the weekend, the beeps were generated during the morning (1 beep), afternoon (3 beeps), and evening (2 beeps). To maximize compliance, a 30-min time window was provided to complete each survey. This time window was extended to one hour for the first survey (morning) and two hours for the final survey (evening) to account for travel time to school and time spent on evening activities. The average compliance rate was 83.2%. A total of 2,155 ESM assessments were collected: Participants completed an average of 34.83 surveys ( SD  = 4.91) on a total of 42 surveys, which is high compared to previous ESM studies among adolescents 34 .

The questions of the ESM study were personalized based on the responses to the baseline survey. During the ESM study, each participant reported on his/her use of three different social media platforms: WhatsApp and either Instagram, Snapchat, YouTube, and/or the chat function of games (i.e., the most popular social media platforms among adolescents 28 ). Questions about Instagram and WhatsApp use were only included if the participant had indicated in the baseline survey that s/he used these platforms at least once a week. If a participant had indicated that s/he used Instagram or WhatsApp (or both) less than once a week, s/he was asked to report on the use of Snapchat, YouTube, or the chat function of games, depending on what platform s/he used at least once a week. In addition to Instagram and WhatsApp, questions were asked about a third platform, that was selected based on how frequently the participant used Snapchat, YouTube, or the chat function of games (i.e., at least once a week). This resulted in five different combinations of three platforms: Instagram, WhatsApp, and Snapchat (47 participants); Instagram, WhatsApp, and YouTube (11 participants); Instagram, WhatsApp, and chatting via games (2 participants); WhatsApp, Snapchat, and YouTube (1 participant); and WhatsApp, YouTube, and chatting via games (2 participants).

Frequency of social media use

In the baseline survey, participants were asked to indicate how often they used and checked Instagram, WhatsApp, Snapchat, YouTube, and the chat function of games, using response options ranging from 1 ( never ) to 7 ( more than 12 times per day ). These platforms are the five most popular platforms among Dutch 14- and 15-year-olds 28 . Participants’ responses were used to select the three social media platforms that were assessed in the personalized ESM study.

Duration of social media use

In the ESM study, duration of active and passive social media use was measured by asking participants how much time in the past hour they had spent actively and passively using each of the three platforms that were included in the personalized ESM surveys. Response options ranged from 0 to 60 min , with 5-min intervals. To measure active Instagram use, participants indicated how much time in the past hour they had spent (a) “posting on your feed or sharing something in your story on Instagram” and (b) “sending direct messages/chatting on Instagram.” These two items were summed to create the variable duration of active Instagram use. Sum scores exceeding 60 min (only 0.52% of all assessments) were recoded to 60 min. To measure duration of passive Instagram use, participants indicated how much time in the past hour they had spent “viewing posts/stories of others on Instagram.” To measure the use of WhatsApp, Snapchat, YouTube and game-based chatting, we asked participants how much time they had spent “sending WhatsApp messages” (active use) and “reading WhatsApp messages” (passive use); “sending snaps/messages or sharing something in your story on Snapchat” (active use) and “viewing snaps/stories/messages from others on Snapchat” (passive use); “posting YouTube clips” (active use) and “watching YouTube clips” (passive use); “sending messages via the chat function of a game/games” (active use) and “reading messages via the chat function of a game/games” (passive use). Duration of active and passive overall social media use were created by summing the responses across the three social media platforms for active and passive use, respectively. Sum scores exceeding 60 min (2.13% of all assessments for active overall use; 2.90% for passive overall use) were recoded to 60 min. The duration variables were used to investigate whether the time spent actively or passively using social media was associated with well-being (dose–response associations).

Use/no use of social media

Based on the duration variables, we created six dummy variables, one for active and one for passive overall social media use, one for active and one for passive Instagram use, and one for active and one for passive WhatsApp use (0 =  no active use and 1 =  active use , and 0 =  no passive use and 1 =  passive use , respectively). These dummy variables were used to investigate whether the use of social media, irrespective of the duration of use, was associated with well-being (categorical associations).

Consistent with previous ESM studies 19 , 20 , we measured affective well-being using one item, asking “How happy do you feel right now?” at each assessment. Adolescents indicated their response to the question using a 7-point scale ranging from 1 ( not at all ) to 7 ( completely ), with 4 ( a little ) as the midpoint. Convergent validity of this item was established in a separate pilot ESM study among 30 adolescents conducted by the research team of the fourth author: The affective well-being item was strongly correlated with the presence of positive affect and absence of negative affect (assessed by a 10-item positive and negative affect schedule for children; PANAS-C) at both the between-person (positive affect: r  = 0.88, p < 0.001; negative affect: r  = − 0.62, p < 0.001) and within-person level (positive affect: r  = 0.74, p < 0.001; negative affect: r  = − 0.58, p < 0.001).

Statistical analyses

Before conducting the analyses, several validation checks were performed (see 34 ). First, we aimed to only include participants in the analyses who had completed more than 33% of all ESM assessments (i.e., at least 14 assessments). Next, we screened participants’ responses to the open questions for unserious responses (e.g., gross comments, jokes). And finally, we inspected time series plots for patterns in answering tendencies. Since all participants completed more than 33% of all ESM assessments, and no inappropriate responses or low-quality data patterns were detected, all participants were included in the analyses.

Following our preregistered analysis plan, we tested the proposed associations in a series of multilevel models. Before doing so, we tested the homoscedasticity and linearity assumptions for multilevel analyses 37 . Inspection of standardized residual plots indicated that the data met these assumptions (plots are available on OSF at  https://osf.io/nhks2 ). We specified separate models for overall social media use, use of Instagram, and use of WhatsApp. To investigate to what extent adolescents’ well-being would vary depending on whether they had actively or passively used social media/Instagram/WhatsApp or not during the past hour (categorical associations), we tested models including the dummy variables as predictors (active use versus no active use, and passive use versus no passive use; models 1, 3, and 5). To investigate whether, at moments when adolescents had used social media/Instagram/WhatsApp during the past hour, their well-being would vary depending on the duration of social media/Instagram/WhatsApp use (dose–response associations), we tested models including the duration variables as predictors (duration of active use and duration of passive use; models 2, 4, and 6). In order to avoid negative skew in the duration variables, we only included assessments during which adolescents had used social media in the past hour (overall, Instagram, or WhatsApp, respectively), either actively or passively. All models included well-being as outcome variable. Since multilevel analyses allow to include all available data for each individual, no missing data were imputed and no data points were excluded.

We used a model building approach that involved three steps. In the first step, we estimated an intercept-only model to assess the relative amount of between- and within-person variance in affective well-being. We estimated a three-level model in which repeated momentary assessments (level 1) were nested within adolescents (level 2), who, in turn, were nested within classrooms (level 3). However, because the between-classroom variance in affective well-being was small (i.e., 0.4% of the variance was explained by differences between classes), we proceeded with estimating two-level (instead of three-level) models, with repeated momentary assessments (level 1) nested within adolescents (level 2).

In the second step, we assessed the within-person associations of well-being with (a) overall active and passive social media use (i.e., the total of the three platforms), (b) active and passive use of Instagram, and (c) active and passive use of WhatsApp, by adding fixed effects to the model (Models 1A-6A). To facilitate the interpretation of the associations and control for the effects of time, a covariate was added that controlled for the n th assessment of the study week (instead of the n th assessment of the day, as preregistered). This so-called detrending is helpful to interpret within-person associations as correlated fluctuations beyond other changes in social media use and well-being 38 . In order to obtain within-person estimates, we person-mean centered all predictors 38 . Significance of the fixed effects was determined using the Wald test.

In the third and final step, we assessed heterogeneity in the within-person associations by adding random slopes to the models (Models 1B-6B). Significance of the random slopes was determined by comparing the fit of the fixed effects model with the fit of the random effects model, by performing the Satorra-Bentler scaled chi-square test 39 and by comparing the Bayesian information criterion (BIC 40 ) and Akaike information criterion (AIC 41 ) of the models. When the random effects model had a significantly better fit than the fixed effects model (i.e., pointing at significant heterogeneity), variance components were inspected to investigate whether heterogeneity existed in the association of either active or passive use. Next, when evidence was found for significant heterogeneity, we computed person-specific effect sizes, based on the random effect models, to investigate what percentages of adolescents experienced better well-being, worse well-being, and no changes in well-being. In line with Keijsers and colleagues 42 we only included participants who had completed at least 10 assessments. In addition, for the dose–response associations, we constructed graphical representations of the person-specific slopes, based on the person-specific effect sizes, using the xyplot function from the lattice package in R 43 .

Three improvements were made to our original preregistered plan. First, rather than estimating the models with multilevel modelling in R 43 , we ran the preregistered models in Mplus 44 . Mplus provides standardized estimates for the fixed effects models, which offers insight into the effect sizes. This allowed us to compare the relative strength of the associations of passive versus active use with well-being. Second, instead of using the maximum likelihood estimator, we used the maximum likelihood estimator with robust standard errors (MLR), which are robust to non-normality. Sensitivity tests, uploaded on OSF ( https://osf.io/nhks2 ), indicated that the results were almost identical across the two software packages and estimation approaches. Third, to improve the interpretation of the results and make the scales of the duration measures of social media use and well-being more comparable, we transformed the social media duration scores (0 to 60 min) into scales running from 0 to 6, so that an increase of 1 unit reflects 10 min of social media use. The model estimates were unaffected by this transformation.

Reporting summary

Further information on the research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analysed during the current study is available in Figshare 45 . The preregistration of the design, sampling and analysis plan, and the analysis scripts used to analyse the data for this paper are available online on the Open Science Framework website ( https://osf.io/nhks2 ).

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Acknowledgements

This study was funded by the NWO Spinoza Prize and the Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to P.M.V. by the Dutch Research Council (NWO). Additional funding was received from the VIDI grant (NWO VIDI Grant 452.17.011) awarded to L.K. by the Dutch Research Council (NWO). The authors would like to thank Savannah Boele (Tilburg University) for providing her pilot ESM results.

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I.B., J.L.P., I.I.v.D., L.K., and P.M.V. designed the study; I.B., J.L.P., and I.I.v.D. collected the data; I.B., J.L.P., and L.K. analyzed the data; and I.B., J.L.P., I.I.v.D., L.K., and P.M.V. contributed to writing and reviewing the manuscript.

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Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10 , 10763 (2020). https://doi.org/10.1038/s41598-020-67727-7

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ORIGINAL RESEARCH article

Effects of social media use on psychological well-being: a mediated model.

• 1 School of Finance and Economics, Jiangsu University, Zhenjiang, China
• 2 Research Unit of Governance, Competitiveness, and Public Policies (GOVCOPP), Center for Economics and Finance (cef.up), School of Economics and Management, University of Porto, Porto, Portugal
• 3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan
• 4 CETYS Universidad, Tijuana, Mexico
• 5 Department of Business Administration, Al-Quds University, Jerusalem, Israel
• 6 Business School, Shandong University, Weihai, China

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.

Introduction

The use of social media has grown substantially in recent years ( Leong et al., 2019 ; Kemp, 2020 ). Social media refers to “the websites and online tools that facilitate interactions between users by providing them opportunities to share information, opinions, and interest” ( Swar and Hameed, 2017 , p. 141). Individuals use social media for many reasons, including entertainment, communication, and searching for information. Notably, adolescents and young adults are spending an increasing amount of time on online networking sites, e-games, texting, and other social media ( Twenge and Campbell, 2019 ). In fact, some authors (e.g., Dhir et al., 2018 ; Tateno et al., 2019 ) have suggested that social media has altered the forms of group interaction and its users' individual and collective behavior around the world.

Consequently, there are increased concerns regarding the possible negative impacts associated with social media usage addiction ( Swar and Hameed, 2017 ; Kircaburun et al., 2020 ), particularly on psychological well-being ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ). Smartphones sometimes distract their users from relationships and social interaction ( Chotpitayasunondh and Douglas, 2016 ; Li et al., 2020a ), and several authors have stressed that the excessive use of social media may lead to smartphone addiction ( Swar and Hameed, 2017 ; Leong et al., 2019 ), primarily because of the fear of missing out ( Reer et al., 2019 ; Roberts and David, 2020 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ), and “phubbing,” which refers to the extent to which an individual uses, or is distracted by, their smartphone during face-to-face communication with others ( Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ).

However, social media use also contributes to building a sense of connectedness with relevant others ( Twenge and Campbell, 2019 ), which may reduce social isolation. Indeed, social media provides several ways to interact both with close ties, such as family, friends, and relatives, and weak ties, including coworkers, acquaintances, and strangers ( Chen and Li, 2017 ), and plays a key role among people of all ages as they exploit their sense of belonging in different communities ( Roberts and David, 2020 ). Consequently, despite the fears regarding the possible negative impacts of social media usage on well-being, there is also an increasing number of studies highlighting social media as a new communication channel ( Twenge and Campbell, 2019 ; Barbosa et al., 2020 ), stressing that it can play a crucial role in developing one's presence, identity, and reputation, thus facilitating social interaction, forming and maintaining relationships, and sharing ideas ( Carlson et al., 2016 ), which consequently may be significantly correlated to social support ( Chen and Li, 2017 ; Holliman et al., 2021 ). Interestingly, recent studies (e.g., David et al., 2018 ; Bano et al., 2019 ; Barbosa et al., 2020 ) have suggested that the impact of smartphone usage on psychological well-being depends on the time spent on each type of application and the activities that users engage in.

Hence, the literature provides contradictory cues regarding the impacts of social media on users' well-being, highlighting both the possible negative impacts and the social enhancement it can potentially provide. In line with views on the need to further investigate social media usage ( Karikari et al., 2017 ), particularly regarding its societal implications ( Jiao et al., 2017 ), this paper argues that there is an urgent need to further understand the impact of the time spent on social media on users' psychological well-being, namely by considering other variables that mediate and further explain this effect.

One of the relevant perspectives worth considering is that provided by social capital theory, which is adopted in this paper. Social capital theory has previously been used to study how social media usage affects psychological well-being (e.g., Bano et al., 2019 ). However, extant literature has so far presented only partial models of associations that, although statistically acceptable and contributing to the understanding of the scope of social networks, do not provide as comprehensive a vision of the phenomenon as that proposed within this paper. Furthermore, the contradictory views, suggesting both negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Van Den Eijnden et al., 2016 ; Jiao et al., 2017 ; Whaite et al., 2018 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) and positive impacts ( Carlson et al., 2016 ; Chen and Li, 2017 ; Twenge and Campbell, 2019 ) of social media on psychological well-being, have not been adequately explored.

Given this research gap, this paper's main objective is to shed light on the effect of social media use on psychological well-being. As explained in detail in the next section, this paper explores the mediating effect of bonding and bridging social capital. To provide a broad view of the phenomenon, it also considers several variables highlighted in the literature as affecting the relationship between social media usage and psychological well-being, namely smartphone addiction, social isolation, and phubbing. The paper utilizes a quantitative study conducted in Mexico, comprising 940 social media users, and uses structural equation modeling (SEM) to test a set of research hypotheses.

This article provides several contributions. First, it adds to existing literature regarding the effect of social media use on psychological well-being and explores the contradictory indications provided by different approaches. Second, it proposes a conceptual model that integrates complementary perspectives on the direct and indirect effects of social media use. Third, it offers empirical evidence and robust statistical analysis that demonstrates that both positive and negative effects coexist, helping resolve the inconsistencies found so far in the literature. Finally, this paper provides insights on how to help reduce the potential negative effects of social media use, as it demonstrates that, through bridging and bonding social capital, social media usage positively impacts psychological well-being. Overall, the article offers valuable insights for academics, practitioners, and society in general.

The remainder of this paper is organized as follows. Section Literature Review presents a literature review focusing on the factors that explain the impact of social media usage on psychological well-being. Based on the literature review, a set of hypotheses are defined, resulting in the proposed conceptual model, which includes both the direct and indirect effects of social media usage on psychological well-being. Section Research Methodology explains the methodological procedures of the research, followed by the presentation and discussion of the study's results in section Results. Section Discussion is dedicated to the conclusions and includes implications, limitations, and suggestions for future research.

Literature Review

Putnam (1995 , p. 664–665) defined social capital as “features of social life – networks, norms, and trust – that enable participants to act together more effectively to pursue shared objectives.” Li and Chen (2014 , p. 117) further explained that social capital encompasses “resources embedded in one's social network, which can be assessed and used for instrumental or expressive returns such as mutual support, reciprocity, and cooperation.”

Putnam (1995 , 2000) conceptualized social capital as comprising two dimensions, bridging and bonding, considering the different norms and networks in which they occur. Bridging social capital refers to the inclusive nature of social interaction and occurs when individuals from different origins establish connections through social networks. Hence, bridging social capital is typically provided by heterogeneous weak ties ( Li and Chen, 2014 ). This dimension widens individual social horizons and perspectives and provides extended access to resources and information. Bonding social capital refers to the social and emotional support each individual receives from his or her social networks, particularly from close ties (e.g., family and friends).

Overall, social capital is expected to be positively associated with psychological well-being ( Bano et al., 2019 ). Indeed, Williams (2006) stressed that interaction generates affective connections, resulting in positive impacts, such as emotional support. The following sub-sections use the lens of social capital theory to explore further the relationship between the use of social media and psychological well-being.

Social Media Use, Social Capital, and Psychological Well-Being

The effects of social media usage on social capital have gained increasing scholarly attention, and recent studies have highlighted a positive relationship between social media use and social capital ( Brown and Michinov, 2019 ; Tefertiller et al., 2020 ). Li and Chen (2014) hypothesized that the intensity of Facebook use by Chinese international students in the United States was positively related to social capital forms. A longitudinal survey based on the quota sampling approach illustrated the positive effects of social media use on the two social capital dimensions ( Chen and Li, 2017 ). Abbas and Mesch (2018) argued that, as Facebook usage increases, it will also increase users' social capital. Karikari et al. (2017) also found positive effects of social media use on social capital. Similarly, Pang (2018) studied Chinese students residing in Germany and found positive effects of social networking sites' use on social capital, which, in turn, was positively associated with psychological well-being. Bano et al. (2019) analyzed the 266 students' data and found positive effects of WhatsApp use on social capital forms and the positive effect of social capital on psychological well-being, emphasizing the role of social integration in mediating this positive effect.

Kim and Kim (2017) stressed the importance of having a heterogeneous network of contacts, which ultimately enhances the potential social capital. Overall, the manifest and social relations between people from close social circles (bonding social capital) and from distant social circles (bridging social capital) are strengthened when they promote communication, social support, and the sharing of interests, knowledge, and skills, which are shared with other members. This is linked to positive effects on interactions, such as acceptance, trust, and reciprocity, which are related to the individuals' health and psychological well-being ( Bekalu et al., 2019 ), including when social media helps to maintain social capital between social circles that exist outside of virtual communities ( Ellison et al., 2007 ).

Grounded on the above literature, this study proposes the following hypotheses:

H1a: Social media use is positively associated with bonding social capital.

H1b: Bonding social capital is positively associated with psychological well-being.

H2a: Social media use is positively associated with bridging social capital.

H2b: Bridging social capital is positively associated with psychological well-being.

Social Media Use, Social Isolation, and Psychological Well-Being

Social isolation is defined as “a deficit of personal relationships or being excluded from social networks” ( Choi and Noh, 2019 , p. 4). The state that occurs when an individual lacks true engagement with others, a sense of social belonging, and a satisfying relationship is related to increased mortality and morbidity ( Primack et al., 2017 ). Those who experience social isolation are deprived of social relationships and lack contact with others or involvement in social activities ( Schinka et al., 2012 ). Social media usage has been associated with anxiety, loneliness, and depression ( Dhir et al., 2018 ; Reer et al., 2019 ), and social isolation ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ). However, some recent studies have argued that social media use decreases social isolation ( Primack et al., 2017 ; Meshi et al., 2020 ). Indeed, the increased use of social media platforms such as Facebook, WhatsApp, Instagram, and Twitter, among others, may provide opportunities for decreasing social isolation. For instance, the improved interpersonal connectivity achieved via videos and images on social media helps users evidence intimacy, attenuating social isolation ( Whaite et al., 2018 ).

Chappell and Badger (1989) stated that social isolation leads to decreased psychological well-being, while Choi and Noh (2019) concluded that greater social isolation is linked to increased suicide risk. Schinka et al. (2012) further argued that, when individuals experience social isolation from siblings, friends, family, or society, their psychological well-being tends to decrease. Thus, based on the literature cited above, this study proposes the following hypotheses:

H3a: Social media use is significantly associated with social isolation.

H3b: Social isolation is negatively associated with psychological well-being.

Social Media Use, Smartphone Addiction, Phubbing, and Psychological Well-Being

Smartphone addiction refers to “an individuals' excessive use of a smartphone and its negative effects on his/her life as a result of his/her inability to control his behavior” ( Gökçearslan et al., 2018 , p. 48). Regardless of its form, smartphone addiction results in social, medical, and psychological harm to people by limiting their ability to make their own choices ( Chotpitayasunondh and Douglas, 2016 ). The rapid advancement of information and communication technologies has led to the concept of social media, e-games, and also to smartphone addiction ( Chatterjee, 2020 ). The excessive use of smartphones for social media use, entertainment (watching videos, listening to music), and playing e-games is more common amongst people addicted to smartphones ( Jeong et al., 2016 ). In fact, previous studies have evidenced the relationship between social use and smartphone addiction ( Salehan and Negahban, 2013 ; Jeong et al., 2016 ; Swar and Hameed, 2017 ). In line with this, the following hypotheses are proposed:

H4a: Social media use is positively associated with smartphone addiction.

H4b: Smartphone addiction is negatively associated with psychological well-being.

While smartphones are bringing individuals closer, they are also, to some extent, pulling people apart ( Tonacci et al., 2019 ). For instance, they can lead to individuals ignoring others with whom they have close ties or physical interactions; this situation normally occurs due to extreme smartphone use (i.e., at the dinner table, in meetings, at get-togethers and parties, and in other daily activities). This act of ignoring others is called phubbing and is considered a common phenomenon in communication activities ( Guazzini et al., 2019 ; Chatterjee, 2020 ). Phubbing is also referred to as an act of snubbing others ( Chatterjee, 2020 ). This term was initially used in May 2012 by an Australian advertising agency to describe the “growing phenomenon of individuals ignoring their families and friends who were called phubbee (a person who is a recipients of phubbing behavior) victim of phubber (a person who start phubbing her or his companion)” ( Chotpitayasunondh and Douglas, 2018 ). Smartphone addiction has been found to be a determinant of phubbing ( Kim et al., 2018 ). Other recent studies have also evidenced the association between smartphones and phubbing ( Chotpitayasunondh and Douglas, 2016 ; Guazzini et al., 2019 ; Tonacci et al., 2019 ; Chatterjee, 2020 ). Vallespín et al. (2017 ) argued that phubbing behavior has a negative influence on psychological well-being and satisfaction. Furthermore, smartphone addiction is considered responsible for the development of new technologies. It may also negatively influence individual's psychological proximity ( Chatterjee, 2020 ). Therefore, based on the above discussion and calls for the association between phubbing and psychological well-being to be further explored, this study proposes the following hypotheses:

H5: Smartphone addiction is positively associated with phubbing.

H6: Phubbing is negatively associated with psychological well-being.

Indirect Relationship Between Social Media Use and Psychological Well-Being

Beyond the direct hypotheses proposed above, this study investigates the indirect effects of social media use on psychological well-being mediated by social capital forms, social isolation, and phubbing. As described above, most prior studies have focused on the direct influence of social media use on social capital forms, social isolation, smartphone addiction, and phubbing, as well as the direct impact of social capital forms, social isolation, smartphone addiction, and phubbing on psychological well-being. Very few studies, however, have focused on and evidenced the mediating role of social capital forms, social isolation, smartphone addiction, and phubbing derived from social media use in improving psychological well-being ( Chen and Li, 2017 ; Pang, 2018 ; Bano et al., 2019 ; Choi and Noh, 2019 ). Moreover, little is known about smartphone addiction's mediating role between social media use and psychological well-being. Therefore, this study aims to fill this gap in the existing literature by investigating the mediation of social capital forms, social isolation, and smartphone addiction. Further, examining the mediating influence will contribute to a more comprehensive understanding of social media use on psychological well-being via the mediating associations of smartphone addiction and psychological factors. Therefore, based on the above, we propose the following hypotheses (the conceptual model is presented in Figure 1 ):

H7: (a) Bonding social capital; (b) bridging social capital; (c) social isolation; and (d) smartphone addiction mediate the relationship between social media use and psychological well-being.

Figure 1 . Conceptual model.

Research Methodology

Sample procedure and online survey.

This study randomly selected students from universities in Mexico. We chose University students for the following reasons. First, students are considered the most appropriate sample for e-commerce studies, particularly in the social media context ( Oghazi et al., 2018 ; Shi et al., 2018 ). Second, University students are considered to be frequent users and addicted to smartphones ( Mou et al., 2017 ; Stouthuysen et al., 2018 ). Third, this study ensured that respondents were experienced, well-educated, and possessed sufficient knowledge of the drawbacks of social media and the extreme use of smartphones. A total sample size of 940 University students was ultimately achieved from the 1,500 students contacted, using a convenience random sampling approach, due both to the COVID-19 pandemic and budget and time constraints. Additionally, in order to test the model, a quantitative empirical study was conducted, using an online survey method to collect data. This study used a web-based survey distributed via social media platforms for two reasons: the COVID-19 pandemic; and to reach a large number of respondents ( Qalati et al., 2021 ). Furthermore, online surveys are considered a powerful and authenticated tool for new research ( Fan et al., 2021 ), while also representing a fast, simple, and less costly approach to collecting data ( Dutot and Bergeron, 2016 ).

Data Collection Procedures and Respondent's Information

Data were collected by disseminating a link to the survey by e-mail and social network sites. Before presenting the closed-ended questionnaire, respondents were assured that their participation would remain voluntary, confidential, and anonymous. Data collection occurred from July 2020 to December 2020 (during the pandemic). It should be noted that, because data were collected during the pandemic, this may have had an influence on the results of the study. The reason for choosing a six-month lag time was to mitigate common method bias (CMB) ( Li et al., 2020b ). In the present study, 1,500 students were contacted via University e-mail and social applications (Facebook, WhatsApp, and Instagram). We sent a reminder every month for 6 months (a total of six reminders), resulting in 940 valid responses. Thus, 940 (62.6% response rate) responses were used for hypotheses testing.

Table 1 reveals that, of the 940 participants, three-quarters were female (76.4%, n = 719) and nearly one-quarter (23.6%, n = 221) were male. Nearly half of the participants (48.8%, n = 459) were aged between 26 and 35 years, followed by 36 to 35 years (21.9%, n = 206), <26 (20.3%, n = 191), and over 45 (8.9%, n = 84). Approximately two-thirds (65%, n = 611) had a bachelor's degree or above, while one-third had up to 12 years of education. Regarding the daily frequency of using the Internet, nearly half (48.6%, n = 457) of the respondents reported between 5 and 8 h a day, and over one-quarter (27.2%) 9–12 h a day. Regarding the social media platforms used, over 38.5 and 39.6% reported Facebook and WhatsApp, respectively. Of the 940 respondents, only 22.1% reported Instagram (12.8%) and Twitter (9.2%). It should be noted, however, that the sample is predominantly female and well-educated.

Table 1 . Respondents' characteristics.

Measurement Items

The study used five-point Likert scales (1 = “strongly disagree;” 5 = “strongly agree”) to record responses.

Social Media Use

Social media use was assessed using four items adapted from Karikari et al. (2017) . Sample items include “Social media is part of my everyday activity,” “Social media has become part of my daily life,” “I would be sorry if social media shut down,” and “I feel out of touch, when I have not logged onto social media for a while.” The adapted items had robust reliability and validity (CA = 783, CR = 0.857, AVE = 0.600).

Social Capital

Social capital was measured using a total of eight items, representing bonding social capital (four items) and bridging social capital (four items) adapted from Chan (2015) . Sample construct items include: bonging social capital (“I am willing to spend time to support general community activities,” “I interact with people who are quite different from me”) and bridging social capital (“My social media community is a good place to be,” “Interacting with people on social media makes me want to try new things”). The adapted items had robust reliability and validity [bonding social capital (CA = 0.785, CR = 0.861, AVE = 0.608) and bridging social capital (CA = 0.834, CR = 0.883, AVE = 0.601)].

Social Isolation

Social isolation was assessed using three items from Choi and Noh (2019) . Sample items include “I do not have anyone to play with,” “I feel alone from people,” and “I have no one I can trust.” This adapted scale had substantial reliability and validity (CA = 0.890, CR = 0.928, AVE = 0.811).

Smartphone Addiction

Smartphone addiction was assessed using five items taken from Salehan and Negahban (2013) . Sample items include “I am always preoccupied with my mobile,” “Using my mobile phone keeps me relaxed,” and “I am not able to control myself from frequent use of mobile phones.” Again, these adapted items showed substantial reliability and validity (CA = 903, CR = 0.928, AVE = 0.809).

Phubbing was assessed using four items from Chotpitayasunondh and Douglas (2018) . Sample items include: “I have conflicts with others because I am using my phone” and “I would rather pay attention to my phone than talk to others.” This construct also demonstrated significant reliability and validity (CA = 770, CR = 0.894, AVE = 0.809).

Psychological Well-Being

Psychological well-being was assessed using five items from Jiao et al. (2017) . Sample items include “I lead a purposeful and meaningful life with the help of others,” “My social relationships are supportive and rewarding in social media,” and “I am engaged and interested in my daily on social media.” This study evidenced that this adapted scale had substantial reliability and validity (CA = 0.886, CR = 0.917, AVE = 0.688).

Data Analysis

Based on the complexity of the association between the proposed construct and the widespread use and acceptance of SmartPLS 3.0 in several fields ( Hair et al., 2019 ), we utilized SEM, using SmartPLS 3.0, to examine the relationships between constructs. Structural equation modeling is a multivariate statistical analysis technique that is used to investigate relationships. Further, it is a combination of factor and multivariate regression analysis, and is employed to explore the relationship between observed and latent constructs.

SmartPLS 3.0 “is a more comprehensive software program with an intuitive graphical user interface to run partial least square SEM analysis, certainly has had a massive impact” ( Sarstedt and Cheah, 2019 ). According to Ringle et al. (2015) , this commercial software offers a wide range of algorithmic and modeling options, improved usability, and user-friendly and professional support. Furthermore, Sarstedt and Cheah (2019) suggested that structural equation models enable the specification of complex interrelationships between observed and latent constructs. Hair et al. (2019) argued that, in recent years, the number of articles published using partial least squares SEM has increased significantly in contrast to covariance-based SEM. In addition, partial least squares SEM using SmartPLS is more appealing for several scholars as it enables them to predict more complex models with several variables, indicator constructs, and structural paths, instead of imposing distributional assumptions on the data ( Hair et al., 2019 ). Therefore, this study utilized the partial least squares SEM approach using SmartPLS 3.0.

Common Method Bias (CMB) Test

This study used the Kaiser–Meyer–Olkin (KMO) test to measure the sampling adequacy and ensure data suitability. The KMO test result was 0.874, which is greater than an acceptable threshold of 0.50 ( Ali Qalati et al., 2021 ; Shrestha, 2021 ), and hence considered suitable for explanatory factor analysis. Moreover, Bartlett's test results demonstrated a significance level of 0.001, which is considered good as it is below the accepted threshold of 0.05.

The term CMB is associated with Campbell and Fiske (1959) , who highlighted the importance of CMB and identified that a portion of variance in the research may be due to the methods employed. It occurs when all scales of the study are measured at the same time using a single questionnaire survey ( Podsakoff and Organ, 1986 ); subsequently, estimates of the relationship among the variables might be distorted by the impacts of CMB. It is considered a serious issue that has a potential to “jeopardize” the validity of the study findings ( Tehseen et al., 2017 ). There are several reasons for CMB: (1) it mainly occurs due to response “tendencies that raters can apply uniformity across the measures;” and (2) it also occurs due to similarities in the wording and structure of the survey items that produce similar results ( Jordan and Troth, 2019 ). Harman's single factor test and a full collinearity approach were employed to ensure that the data was free from CMB ( Tehseen et al., 2017 ; Jordan and Troth, 2019 ; Ali Qalati et al., 2021 ). Harman's single factor test showed a single factor explained only 22.8% of the total variance, which is far below the 50.0% acceptable threshold ( Podsakoff et al., 2003 ).

Additionally, the variance inflation factor (VIF) was used, which is a measure of the amount of multicollinearity in a set of multiple regression constructs and also considered a way of detecting CMB ( Hair et al., 2019 ). Hair et al. (2019) suggested that the acceptable threshold for the VIF is 3.0; as the computed VIFs for the present study ranged from 1.189 to 1.626, CMB is not a key concern (see Table 2 ). Bagozzi et al. (1991) suggested a correlation-matrix procedure to detect CMB. Common method bias is evident if correlation among the principle constructs is >0.9 ( Tehseen et al., 2020 ); however, no values >0.9 were found in this study (see section Assessment of Measurement Model). This study used a two-step approach to evaluate the measurement model and the structural model.

Table 2 . Common method bias (full collinearity VIF).

Assessment of Measurement Model

Before conducting the SEM analysis, the measurement model was assessed to examine individual item reliability, internal consistency, and convergent and discriminant validity. Table 3 exhibits the values of outer loading used to measure an individual item's reliability ( Hair et al., 2012 ). Hair et al. (2017) proposed that the value for each outer loading should be ≥0.7; following this principle, two items of phubbing (PHUB3—I get irritated if others ask me to get off my phone and talk to them; PHUB4—I use my phone even though I know it irritated others) were removed from the analysis Hair et al. (2019) . According to Nunnally (1978) , Cronbach's alpha values should exceed 0.7. The threshold values of constructs in this study ranged from 0.77 to 0.903. Regarding internal consistency, Bagozzi and Yi (1988) suggested that composite reliability (CR) should be ≥0.7. The coefficient value for CR in this study was between 0.857 and 0.928. Regarding convergent validity, Fornell and Larcker (1981) suggested that the average variance extracted (AVE) should be ≥0.5. Average variance extracted values in this study were between 0.60 and 0.811. Finally, regarding discriminant validity, according to Fornell and Larcker (1981) , the square root of the AVE for each construct should exceed the inter-correlations of the construct with other model constructs. That was the case in this study, as shown in Table 4 .

Table 3 . Study measures, factor loading, and the constructs' reliability and convergent validity.

Table 4 . Discriminant validity and correlation.

Hence, by analyzing the results of the measurement model, it can be concluded that the data are adequate for structural equation estimation.

Assessment of the Structural Model

This study used the PLS algorithm and a bootstrapping technique with 5,000 bootstraps as proposed by Hair et al. (2019) to generate the path coefficient values and their level of significance. The coefficient of determination ( R 2 ) is an important measure to assess the structural model and its explanatory power ( Henseler et al., 2009 ; Hair et al., 2019 ). Table 5 and Figure 2 reveal that the R 2 value in the present study was 0.451 for psychological well-being, which means that 45.1% of changes in psychological well-being occurred due to social media use, social capital forms (i.e., bonding and bridging), social isolation, smartphone addiction, and phubbing. Cohen (1998) proposed that R 2 values of 0.60, 0.33, and 0.19 are considered substantial, moderate, and weak. Following Cohen's (1998) threshold values, this research demonstrates a moderate predicting power for psychological well-being among Mexican respondents ( Table 6 ).

Table 5 . Summary of path coefficients and hypothesis testing.

Figure 2 . Structural model.

Table 6 . Strength of the model (Predictive relevance, coefficient of determination, and model fit indices).

Apart from the R 2 measure, the present study also used cross-validated redundancy measures, or effect sizes ( q 2 ), to assess the proposed model and validate the results ( Ringle et al., 2012 ). Hair et al. (2019) suggested that a model exhibiting an effect size q 2 > 0 has predictive relevance ( Table 6 ). This study's results evidenced that it has a 0.15 <0.29 <0.35 (medium) predictive relevance, as 0.02, 0.15, and 0.35 are considered small, medium, and large, respectively ( Cohen, 1998 ). Regarding the goodness-of-fit indices, Hair et al. (2019) suggested the standardized root mean square residual (SRMR) to evaluate the goodness of fit. Standardized root mean square is an absolute measure of fit: a value of zero indicates perfect fit and a value <0.08 is considered good fit ( Hair et al., 2019 ). This study exhibits an adequate model fitness level with an SRMR value of 0.063 ( Table 6 ).

Table 5 reveals that all hypotheses of the study were accepted base on the criterion ( p -value < 0.05). H1a (β = 0.332, t = 10.283, p = 0.001) was confirmed, with the second most robust positive and significant relationship (between social media use and bonding social capital). In addition, this study evidenced a positive and significant relationship between bonding social capital and psychological well-being (β = 0.127, t = 4.077, p = 0.001); therefore, H1b was accepted. Regarding social media use and bridging social capital, the present study found the most robust positive and significant impact (β = 0.439, t = 15.543, p = 0.001); therefore, H2a was accepted. The study also evidenced a positive and significant association between bridging social capital and psychological well-being (β = 0.561, t = 20.953, p = 0.001); thus, H2b was accepted. The present study evidenced a significant effect of social media use on social isolation (β = 0.145, t = 4.985, p = 0.001); thus, H3a was accepted. In addition, this study accepted H3b (β = −0.051, t = 2.01, p = 0.044). Furthermore, this study evidenced a positive and significant effect of social media use on smartphone addiction (β = 0.223, t = 6.241, p = 0.001); therefore, H4a was accepted. Furthermore, the present study found that smartphone addiction has a negative significant influence on psychological well-being (β = −0.068, t = 2.387, p = 0.017); therefore, H4b was accepted. Regarding the relationship between smartphone addiction and phubbing, this study found a positive and significant effect of smartphone addiction on phubbing (β = 0.244, t = 7.555, p = 0.001); therefore, H5 was accepted. Furthermore, the present research evidenced a positive and significant influence of phubbing on psychological well-being (β = 0.137, t = 4.938, p = 0.001); therefore, H6 was accepted. Finally, the study provides interesting findings on the indirect effect of social media use on psychological well-being ( t -value > 1.96 and p -value < 0.05); therefore, H7a–d were accepted.

Furthermore, to test the mediating analysis, Preacher and Hayes's (2008) approach was used. The key characteristic of an indirect relationship is that it involves a third construct, which plays a mediating role in the relationship between the independent and dependent constructs. Logically, the effect of A (independent construct) on C (the dependent construct) is mediated by B (a third variable). Preacher and Hayes (2008) suggested the following: B is a construct acting as a mediator if A significantly influences B, A significantly accounts for variability in C, B significantly influences C when controlling for A, and the influence of A on C decreases significantly when B is added simultaneously with A as a predictor of C. According to Matthews et al. (2018) , if the indirect effect is significant while the direct insignificant, full mediation has occurred, while if both direct and indirect effects are substantial, partial mediation has occurred. This study evidenced that there is partial mediation in the proposed construct ( Table 5 ). Following Preacher and Hayes (2008) this study evidenced that there is partial mediation in the proposed construct, because the relationship between independent variable (social media use) and dependent variable (psychological well-being) is significant ( p -value < 0.05) and indirect effect among them after introducing mediator (bonding social capital, bridging social capital, social isolation, and smartphone addiction) is also significant ( p -value < 0.05), therefore it is evidenced that when there is a significant effect both direct and indirect it's called partial mediation.

The present study reveals that the social and psychological impacts of social media use among University students is becoming more complex as there is continuing advancement in technology, offering a range of affordable interaction opportunities. Based on the 940 valid responses collected, all the hypotheses were accepted ( p < 0.05).

H1a finding suggests that social media use is a significant influencing factor of bonding social capital. This implies that, during a pandemic, social media use enables students to continue their close relationships with family members, friends, and those with whom they have close ties. This finding is in line with prior work of Chan (2015) and Ellison et al. (2007) , who evidenced that social bonding capital is predicted by Facebook use and having a mobile phone. H1b findings suggest that, when individuals believe that social communication can help overcome obstacles to interaction and encourage more virtual self-disclosure, social media use can improve trust and promote the establishment of social associations, thereby enhancing well-being. These findings are in line with those of Gong et al. (2021) , who also witnessed the significant effect of bonding social capital on immigrants' psychological well-being, subsequently calling for the further evidence to confirm the proposed relationship.

The findings of the present study related to H2a suggest that students are more likely to use social media platforms to receive more emotional support, increase their ability to mobilize others, and to build social networks, which leads to social belongingness. Furthermore, the findings suggest that social media platforms enable students to accumulate and maintain bridging social capital; further, online classes can benefit students who feel shy when participating in offline classes. This study supports the previous findings of Chan (2015) and Karikari et al. (2017) . Notably, the present study is not limited to a single social networking platform, taking instead a holistic view of social media. The H2b findings are consistent with those of Bano et al. (2019) , who also confirmed the link between bonding social capital and psychological well-being among University students using WhatsApp as social media platform, as well as those of Chen and Li (2017) .

The H3a findings suggest that, during the COVID-19 pandemic when most people around the world have had limited offline or face-to-face interaction and have used social media to connect with families, friends, and social communities, they have often been unable to connect with them. This is due to many individuals avoiding using social media because of fake news, financial constraints, and a lack of trust in social media; thus, the lack both of offline and online interaction, coupled with negative experiences on social media use, enhances the level of social isolation ( Hajek and König, 2021 ). These findings are consistent with those of Adnan and Anwar (2020) . The H3b suggests that higher levels of social isolation have a negative impact on psychological well-being. These result indicating that, consistent with Choi and Noh (2019) , social isolation is negatively and significantly related to psychological well-being.

The H4a results suggests that substantial use of social media use leads to an increase in smartphone addiction. These findings are in line with those of Jeong et al. (2016) , who stated that the excessive use of smartphones for social media, entertainment (watching videos, listening to music), and playing e-games was more likely to lead to smartphone addiction. These findings also confirm the previous work of Jeong et al. (2016) , Salehan and Negahban (2013) , and Swar and Hameed (2017) . The H4b results revealed that a single unit increase in smartphone addiction results in a 6.8% decrease in psychological well-being. These findings are in line with those of Tangmunkongvorakul et al. (2019) , who showed that students with higher levels of smartphone addiction had lower psychological well-being scores. These findings also support those of Shoukat (2019) , who showed that smartphone addiction inversely influences individuals' mental health.

This suggests that the greater the smartphone addiction, the greater the phubbing. The H5 findings are in line with those of Chatterjee (2020) , Chotpitayasunondh and Douglas (2016) , Guazzini et al. (2019) , and Tonacci et al. (2019) , who also evidenced a significant impact of smartphone addiction and phubbing. Similarly, Chotpitayasunondh and Douglas (2018) corroborated that smartphone addiction is the main predictor of phubbing behavior. However, these findings are inconsistent with those of Vallespín et al. (2017 ), who found a negative influence of phubbing.

The H6 results suggests that phubbing is one of the significant predictors of psychological well-being. Furthermore, these findings suggest that, when phubbers use a cellphone during interaction with someone, especially during the current pandemic, and they are connected with many family members, friends, and relatives; therefore, this kind of action gives them more satisfaction, which simultaneously results in increased relaxation and decreased depression ( Chotpitayasunondh and Douglas, 2018 ). These findings support those of Davey et al. (2018) , who evidenced that phubbing has a significant influence on adolescents and social health students in India.

The findings showed a significant and positive effect of social media use on psychological well-being both through bridging and bonding social capital. However, a significant and negative effect of social media use on psychological well-being through smartphone addiction and through social isolation was also found. Hence, this study provides evidence that could shed light on the contradictory contributions in the literature suggesting both positive (e.g., Chen and Li, 2017 ; Twenge and Campbell, 2019 ; Roberts and David, 2020 ) and negative (e.g., Chotpitayasunondh and Douglas, 2016 ; Jiao et al., 2017 ; Choi and Noh, 2019 ; Chatterjee, 2020 ) effects of social media use on psychological well-being. This study concludes that the overall impact is positive, despite some degree of negative indirect impact.

Theoretical Contributions

This study's findings contribute to the current literature, both by providing empirical evidence for the relationships suggested by extant literature and by demonstrating the relevance of adopting a more complex approach that considers, in particular, the indirect effect of social media on psychological well-being. As such, this study constitutes a basis for future research ( Van Den Eijnden et al., 2016 ; Whaite et al., 2018 ) aiming to understand the impacts of social media use and to find ways to reduce its possible negative impacts.

In line with Kim and Kim (2017) , who stressed the importance of heterogeneous social networks in improving social capital, this paper suggests that, to positively impact psychological well-being, social media usage should be associated both with strong and weak ties, as both are important in building social capital, and hence associated with its bonding and bridging facets. Interestingly, though, bridging capital was shown as having the greatest impact on psychological well-being. Thus, the importance of wider social horizons, the inclusion in different groups, and establishing new connections ( Putnam, 1995 , 2000 ) with heterogeneous weak ties ( Li and Chen, 2014 ) are highlighted in this paper.

Practical Contributions

These findings are significant for practitioners, particularly those interested in dealing with the possible negative impacts of social media use on psychological well-being. Although social media use is associated with factors that negatively impact psychological well-being, particularly smartphone addiction and social isolation, these negative impacts can be lessened if the connections with both strong and weak ties are facilitated and featured by social media. Indeed, social media platforms offer several features, from facilitating communication with family, friends, and acquaintances, to identifying and offering access to other people with shared interests. However, it is important to access heterogeneous weak ties ( Li and Chen, 2014 ) so that social media offers access to wider sources of information and new resources, hence enhancing bridging social capital.

Limitations and Directions for Future Studies

This study is not without limitations. For example, this study used a convenience sampling approach to reach to a large number of respondents. Further, this study was conducted in Mexico only, limiting the generalizability of the results; future research should therefore use a cross-cultural approach to investigate the impacts of social media use on psychological well-being and the mediating role of proposed constructs (e.g., bonding and bridging social capital, social isolation, and smartphone addiction). The sample distribution may also be regarded as a limitation of the study because respondents were mainly well-educated and female. Moreover, although Internet channels represent a particularly suitable way to approach social media users, the fact that this study adopted an online survey does not guarantee a representative sample of the population. Hence, extrapolating the results requires caution, and study replication is recommended, particularly with social media users from other countries and cultures. The present study was conducted in the context of mainly University students, primarily well-educated females, via an online survey on in Mexico; therefore, the findings represent a snapshot at a particular time. Notably, however, the effect of social media use is increasing due to COVID-19 around the globe and is volatile over time.

Two of the proposed hypotheses of this study, namely the expected negative impacts of social media use on social isolation and of phubbing on psychological well-being, should be further explored. One possible approach is to consider the type of connections (i.e., weak and strong ties) to explain further the impact of social media usage on social isolation. Apparently, the prevalence of weak ties, although facilitating bridging social capital, may have an adverse impact in terms of social isolation. Regarding phubbing, the fact that the findings point to a possible positive impact on psychological well-being should be carefully addressed, specifically by psychology theorists and scholars, in order to identify factors that may help further understand this phenomenon. Other suggestions for future research include using mixed-method approaches, as qualitative studies could help further validate the results and provide complementary perspectives on the relationships between the considered variables.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Jiangsu University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

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

This study is supported by the National Statistics Research Project of China (2016LY96).

Conflict of Interest

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

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Keywords: smartphone addiction, social isolation, bonding social capital, bridging social capital, phubbing, social media use

Citation: Ostic D, Qalati SA, Barbosa B, Shah SMM, Galvan Vela E, Herzallah AM and Liu F (2021) Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Front. Psychol. 12:678766. doi: 10.3389/fpsyg.2021.678766

Received: 10 March 2021; Accepted: 25 May 2021; Published: 21 June 2021.

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Copyright © 2021 Ostic, Qalati, Barbosa, Shah, Galvan Vela, Herzallah and Liu. 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: Sikandar Ali Qalati, sidqalati@gmail.com ; 5103180243@stmail.ujs.edu.cn ; Esthela Galvan Vela, esthela.galvan@cetys.mx

† ORCID: Dragana Ostic orcid.org/0000-0002-0469-1342 Sikandar Ali Qalati orcid.org/0000-0001-7235-6098 Belem Barbosa orcid.org/0000-0002-4057-360X Esthela Galvan Vela orcid.org/0000-0002-8778-3989 Feng Liu orcid.org/0000-0001-9367-049X

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Social Media Use and Adolescents’ Self-Esteem: Heading for a Person-Specific Media Effects Paradigm

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Patti Valkenburg, Ine Beyens, J Loes Pouwels, Irene I van Driel, Loes Keijsers, Social Media Use and Adolescents’ Self-Esteem: Heading for a Person-Specific Media Effects Paradigm, Journal of Communication , Volume 71, Issue 1, February 2021, Pages 56–78, https://doi.org/10.1093/joc/jqaa039

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Eighteen earlier studies have investigated the associations between social media use (SMU) and adolescents’ self-esteem, finding weak effects and inconsistent results. A viable hypothesis for these mixed findings is that the effect of SMU differs from adolescent to adolescent. To test this hypothesis, we conducted a preregistered three-week experience sampling study among 387 adolescents (13–15 years, 54% girls). Each adolescent reported on his/her SMU and self-esteem six times per day (126 assessments per participant; 34,930 in total). Using a person-specific, N = 1 method of analysis (Dynamic Structural Equation Modeling), we found that the majority of adolescents (88%) experienced no or very small effects of SMU on self-esteem (−.10 < β < .10), whereas 4% experienced positive (.10 ≤ β ≤ .17) and 8% negative effects (−.21 ≤ β ≤ −.10). Our results suggest that person-specific effects can no longer be ignored in future media effects theories and research.

An important developmental task that adolescents need to accomplish is to acquire self-esteem, the positive and relative stable evaluation of the self. Adolescents’ self-esteem is an important predictor of a healthy peer attachment ( Gorrese & Ruggieri, 2013 ), psychological well-being ( Kernis, 2005 ), and success later in life ( Orth & Robins, 2014 ). In the past decade, a growing number of studies have investigated how adolescents’ social media use (SMU) may affect their self-esteem. Adolescents typically spend 2–3 hours per day on social media to interact with their peers and exchange feedback on their messages and postings ( Valkenburg & Piotrowski, 2017 ). Peer interaction and feedback on the self, both bedrock features of social media, are important predictors of adolescent self-esteem ( Harter, 2012 ). Therefore, understanding the effects of SMU on adolescents’ self-esteem is both important and opportune.

To our knowledge, 18 earlier studies have tried to assess the relationship between SMU and adolescents’ general self-esteem (e.g., Woods & Scott, 2016 ) or their domain-specific self-esteem (e.g., social self-concept; Blomfield Neira & Barber, 2014 ; Košir et al., 2016 ; Valkenburg et al., 2006 ). The ages of the adolescents included in these studies ranged from eight to 19 years. Fifteen of these studies are cross-sectional correlational (e.g., Cingel & Olsen, 2018 ; Meeus et al., 2019 ), two are longitudinal ( Boers et al., 2019 ; Valkenburg et al., 2017 ), and one is experimental ( Thomaes et al., 2010 ). Some of these studies have reported positive effects of SMU on self-esteem (e.g., Blomfield Neira & Barber, 2014 ), others have yielded negative effects (e.g., Woods & Scott, 2016 ), and yet others have found null effects (e.g., Košir et al., 2016 ). It is no wonder that the two meta-analyses on the relationship of SMU and self-esteem have identified their pooled relationships as “close to 0” ( Huang, 2017 , p. 351), “puzzling,” and “complicated” ( Liu & Baumeister, 2016 , p. 85).

While this earlier work has yielded important insights, it leaves two important gaps that may explain these weak effects and inconsistent results. A first gap involves the time frame in which SMU and self-esteem have been assessed in previous studies. Inherent to their design, the cross-correlational studies have measured SMU and self-esteem concurrently, at a single point in time. The two longitudinal studies have assessed both variables at three or four times, with one-year lags, with the aim to establish the potential longer-term effects of SMU on self-esteem ( Boers et al., 2019 ; Valkenburg et al., 2017 ). However, both developmental (e.g., Harter, 2012 ) and self-esteem theories (e.g., Rosenberg, 1986 ) argue that, in addition to such longer-term effects, adolescents’ self-esteem can fluctuate on a daily or even hourly basis as a result of their positive or negative experiences. These theories consider the momentary effects of SMU on self-esteem as the building blocks of its longer-term effects. Investigating such momentary effects of SMU on adolescents’ self-esteem is the first aim of this study.

A second gap in the literature that may explain the weak and inconsistent results in earlier work is that individual differences in susceptibility to the effects of SMU on self-esteem have hardly been taken into account. Studies that did investigate such differences have mostly focused on gender as a moderating variable, without finding any effect ( Kelly et al., 2018 ; Košir et al., 2016 ; Meeus et al., 2019 ; Rodgers et al., 2020 ). However, these null findings may be due to the high variance in susceptibility to the effects of SMU within both the boy and girl groups. After all, if differential susceptibility leads to positive effects among some girls and boys and to negative effects among others, the moderating effect of gender at the aggregate level would be close to zero. Therefore, the time is ripe to investigate differential susceptibility to the effects of SMU at the more fine-grained level of the individual rather than by including group-level moderators. Such an investigation would not only benefit media effects theories (e.g., Valkenburg & Peter, 2013 ), but also self-esteem theories that emphasize that the effects of environmental influences may differ from person to person (e.g., Harter & Whitesell, 2003 ). Investigating such person-specific susceptibility to the effects of SMU is, therefore, the second aim of this study.

To investigate the momentary effects of SMU on self-esteem (first aim), and to assess heterogeneity in these effects (second aim), we employed an experience sampling (ESM) study among 387 middle adolescents (13–15 years), whom we surveyed six times a day for three weeks (126 measurements per person). We measured SMU by asking adolescents on each measurement moment how much time in the past hour they had spent on the three most popular social media platforms among Dutch adolescents ( van Driel et al., 2019 ): Instagram, WhatsApp, and Snapchat. We focused on middle adolescence because this is the period of most significant fluctuations in self-esteem ( Harter, 2012 ). By employing a novel, person-specific method to analyze our intensive longitudinal data, we were able, for the first time, to assess the effects of SMU at the level of the individual adolescent, and to assess how these effects differ from adolescent to adolescent.

Social Media Use and Self-Esteem Level

Personality and social psychological research into the antecedents, consequences, and development of self-esteem has mostly focused on two aspects of self-esteem: self-esteem level and self-esteem instability. Most of this research has focused on self-esteem level, that is, whether it is high or low ( Crocker & Brummelman, 2018 ). This also holds for studies into the effects of SMU. For example, all of the 15 correlational studies have investigated whether adolescents who spend more time with social media report a lower (or higher) level of self-esteem compared to their peers who spend less time with social media (e.g., Apaolaza et al., 2013 , 12–17 years; Barthorpe et al., 2020 , 13–15 years; Bourke, 2013 , 12–16 years; Cingel & Olsen, 2018 , 12–18 years; Kelly et al., 2018 , 14 years; Morin-Major et al., 2016 , 12–17 years; O'Dea & Campbell, 2011 , M age 14; Rodgers et al., 2020 , M age 12.8; Thorisdottir et al., 2019 , 14–16 years; Valkenburg et al., 2006 , 10–19 years; van Eldik et al., 2019 , 9–13 years). In statistical terms, these studies have investigated the between -person relationship of SMU and self-esteem.

The majority of studies into the between-person relationship of SMU and self-esteem used Rosenberg’s (1965) self-esteem scale, which is the most commonly used survey measure to assess general, trait-like levels of self-esteem. These studies asked adolescents at one point in time to evaluate their selves in general or across a certain period in the past (e.g., in the past year). In the current study, we also investigated the between-person relationship between SMU and adolescents’ general levels of self-esteem. But unlike earlier studies, we assessed their levels of SMU and self-esteem by averaging the 126 momentary assessments of both variables across a three-week period. Such in situ assessments generally produce data with greater ecological validity because they are made in the natural flow of daily life, which reduces recall bias ( van Roekel et al., 2019 ). Given the inconsistent results in previous studies, the literature does not allow us to formulate a hypothesis on the between-person association between SMU and self-esteem level. Therefore, we investigated the following research question:

(RQ1) Do adolescents who spend more time with social media report a lower or higher level of self-esteem compared to adolescents who spend less time with social media?

Social Media Use and Self-Esteem Fluctuations

A second strand of personality and social psychological research has focused on the instability of self-esteem. Self-esteem instability refers to the extent to which self-esteem fluctuates within persons ( Kernis, 2005 ). Whereas research into the level of self-esteem has predominantly tried to establish differences in self-esteem between persons, work on self-esteem instability has focused on fluctuations in self-esteem within persons. Rosenberg (1986) distinguishes between two types of within-person self-esteem fluctuations: baseline and barometric instability. Baseline instability refers to potential within-person changes in levels of self-esteem that occur slowly and over an extended period of time. It has been shown, for example, that self-esteem decreases in early adolescence after which it may slowly and steadily increase again in later adolescence ( Harter & Whitesell, 2003 ). Barometric fluctuations, in contrast, reflect short-term within-person fluctuations in self-esteem as a result of one’s everyday positive and negative experiences. Rosenberg (1986) argued that such barometric fluctuations are particularly evident during adolescence, when adolescents typically experience enhanced uncertainty about their identity (i.e., how to define who they are and will become), intimacy (i.e., how to form and maintain meaningful relationships), and sexuality (e.g., how to cope with sexual desire and define their sexual orientation; Steinberg, 2011 ).

One of the aims of the current study is to investigate how SMU may induce within-person fluctuations in barometric self-esteem. Two earlier social media effects studies have focused on within-person effects, one longitudinal study ( Boers et al., 2019 , M age 17.7) and one experiment ( Thomaes et al., 2010 , 8–12 years). Using Rosenberg’s self-esteem scale, Boers et al. found negative within-person effects of SMU on baseline self-esteem. However, because the assessments of SMU and self-esteem were one year apart, and because short-term fluctuations can hardly be derived from designs with longer-term measurement intervals ( Keijsers & van Roekel, 2018 ), this study, although important, may not inform a hypothesis on the influences of SMU on barometric self-esteem.

A within-person experiment by Thomaes et al. (2010) does confirm self-esteem instability theories in the context of SMU. Thomaes et al. based their experiment on Leary and Baumeister’s (2000) Sociometer theory. Like Rosenberg’s theory of self-esteem, Sociometer theory proposes that self-esteem serves as a sociometer (cf. barometer) that gauges the degree of approval and disapproval from one’s social environment. An important proposition of Sociometer theory is that self-esteem changes are accompanied by changes in affect (mood and emotions). Self-esteem (and affect) goes up when people succeed or when others accept them, and it drops when people fail or when others reject them. The results of Thomaes et al. confirmed Sociometer theory: When preadolescents’ online social media profiles were approved by others, their self-esteem increased, and when their online profiles were disapproved, their self-esteem dropped.

In Thomaes et al.’s study, peer approval was experimentally manipulated so that one group of preadolescents (8-13 years) received positive feedback and an equally sized group received negative feedback on their online profiles. In reality, however, peer approval and disapproval in social media interactions are typically not as neatly balanced. In fact, studies have often reported a positivity bias in social media-based interactions (e.g., Reinecke & Trepte, 2014 ; Waterloo et al., 2017 ), meaning that social media users tend to share and receive more positive than negative information. This positivity bias also strongly holds for adolescent social media users. For example, among a national sample of adolescents, only 8% “sometimes” received negative feedback on their posts, whereas 91% “never” or “almost never” received such feedback ( Koutamanis et al., 2015 ). Therefore, on the basis of Sociometer theory, the positivity bias of social media interactions, and the findings of Thomaes et al., we expect an overall positive within-person effect of time spent with social media on adolescents’ self-esteem:

(H1) Overall, adolescents’ self-esteem will increase as a result of their time spent with social media in the past hour.

Heterogeneity in the Effects of Social Media Use on Self-esteem

Most media effects theories that have been developed during and after the 1970s agree that media effects are conditional, meaning that they do not equally hold for all media users (for a review see Valkenburg et al., 2016 ). These theories have sparked numerous media effects studies trying to uncover how certain dispositional, environmental, and contextual variables may enhance or reduce the cognitive, affective, and behavioral effects of media. In the past decade, this media effects research has resulted in an upsurge in meta-analyses of media effects, which not only helped integrating the findings in this vastly growing literature, but also pointed at the moderators that may explain differential susceptibility to media effects.

Despite their undeniable value, the effect sizes for both the main and moderating effects of media use that these meta-analyses have yielded typically range between r = .10 and r = .20 ( Valkenburg et al., 2016 ). Although small to medium effect sizes are common in many neighboring disciplines, some media scholars have argued that such small media effects defy common sense because everyday experience offers anecdotal evidence of strong media effects for some individuals ( Valkenburg et al., 2016 ). Moreover, qualitative studies have repeatedly confirmed that media users differ greatly in their responses to (social) media (e.g., Rideout & Fox, 2018 ). And studies on the emotional reactions to scary media content have reported extreme responses for particular individuals ( Cantor, 2009 ).

There is an apparent discrepancy between the magnitude of conditional media effects sizes reported in quantitative studies and meta-analyses on the one hand and the results of qualitative studies and anecdotal examples on the other. By focusing on group-level moderator effects, meta-analyses (and the studies on which they are based) invariably gloss over more subtle individual differences between people ( Pearce & Field, 2016 ). Diving deeper into these subtle individual differences, however, is only possible with research designs that are able to detect differences in person-specific effects. Such designs require a large number of assessments per person to derive conclusions about processes within single persons, as well as a sufficient number of participants for bottom-up generalization to sub-populations ( Voelkle et al., 2012 ).

An important aim of this study is to capture such person-specific susceptibilities to the effects of SMU by employing a novel method of analysis: Dynamic Structural Equation Modeling (DSEM). DSEM is an advanced modeling technique that is suitable for analyzing intensive longitudinal data, that is, data with 20 to more than 100 repeated measurements that are typically closely spaced in time ( McNeish & Hamaker, 2020 ). DSEM combines the strengths of multilevel analysis and Structural Equation Modeling (SEM) with N  =   1 time-series analysis. N  =   1 time-series analysis enables researchers to establish the longitudinal (lagged) associations between SMU and self-esteem within single persons. The multilevel part of DSEM provides the opportunity to test whether the person-specific effect sizes of SMU on self-esteem differ between persons. Combining the power of a large number of assessments of single persons with a large sample, DSEM may help us answer the question: For how many adolescents does SMU support their self-esteem, for how many does it hinder their self-esteem, and for how many does it not affect their self-esteem?

Not only media effects theories, but also self-esteem theories give reason to assume person-specific effects of environmental influences on self-esteem. These theories agree that some individuals experience significant boosts (or drops) in self-esteem when they experience minor disapproval (or approval) from their peers, whereas the self-esteem of others may fluctuate only in case of serious self-relevant experiences ( Crocker & Brummelman, 2018 ). For example, a study by Harter and Whitesell (2003) showed that 59% of adolescents were prone to self-esteem fluctuations, whereas 41% were not or less prone to such fluctuations. Based on these insights of self-esteem theories, it is likely that the effects of SMU will also differ from adolescent to adolescent. Due to the positivity bias of social media interactions, we expect that most adolescents will experience increases in self-esteem as a result of their SMU in the past hour, whereas a smaller group will experience decreases in self-esteem, and for another smaller group of adolescents their SMU will be unrelated to their self-esteem. Therefore, we hypothesize:

(H2) The effect of time spent with social media on self-esteem will vary from adolescent to adolescent.

Participants

This preregistered study is part of a larger project on the psychosocial consequences of SMU. The present study uses data from the first three-week experience sampling method (ESM) wave of this project that took place in December 2019. The sample consisted of 387 early and middle adolescents (13- to 15-year-olds; 54% girls; M age = 14.11, SD = .69) from a large secondary school in the southern area of The Netherlands. Participants were enrolled in three different levels of education: 44% were in lower prevocational secondary education (VMBO), 31% in intermediate general secondary education (HAVO), and 26% in academic preparatory education (VWO). Of all participants, 96% was born in The Netherlands and self-identified as Dutch, 2% was born in another European country, and 2% in a country outside Europe. The sample was representative of this area in The Netherlands in terms of educational level and ethnic background ( Statistics Netherlands, 2020 ).

The study was approved by the Ethics Review Board of the University of Amsterdam. Before the start of the study parents gave written consent for their child’s participation in the study, after they had been extensively informed about the goals of the study. At the end of November 2019, participants took part in a baseline session during school hours. Researchers informed participants of the aims and procedure of the study and assured them that their responses would be treated confidentially. Participants were provided with detailed instructions about the ESM study that started in the week following upon the baseline survey. They were instructed on how to install the ESM software application (Ethica Data) on their phones, and how to answer the different types of ESM questions. At the end of the baseline session, participants completed an initial ESM survey on their use of different social media platforms, which we used to personalize subsequent ESM surveys. In case of questions or problems with the installment of the software, three researchers were present to help out.

ESM study . In the three-week ESM study, participants completed six 2-minute surveys per day in response to notifications from their mobile phones. The first and last ESM surveys contained 24 questions, whereas each of the other four ESM surveys consisted of 23 questions. Each ESM survey assessed, among other variables not reported in this study, participants’ self-esteem and their SMU. Participants received questions about their time spent with Instagram, WhatsApp, and Snapchat if they had indicated in the baseline session that they used these platforms more than once per week. In case participants did not use any of these platforms more than once a week, they were surveyed about other platforms that they did use (e.g., YouTube or gaming). If they did not use any other platforms either, they received other questions to ensure that each participant received the same number of questions. In total, 375 (97%) participants received questions about WhatsApp, 345 participants (89%) about Instagram, and 285 (73%) about Snapchat.

Sampling scheme . In total, participants received 126 ESM surveys (i.e., 21 days * 6 assessments a day) at random time points within fixed intervals. The sampling scheme was tailored to the school’s schedule and participants’ weekday and weekend routines to avoid that participants received notifications during class hours and while sleeping in on the weekends. Five to ten minutes after each ESM notification, participants received an automatic reminder. We have uploaded our entire notification scheme with the response windows on OSF .

Monitoring plan/incentives. We regularly messaged adolescents to check whether we could help with any technical issues and to motivate them to fill out as many ESM surveys as possible. Adolescents received a small gadget for participating in the baseline session, and a compensation of €0.30 for each completed ESM survey. In addition, each day we held a lottery, in which four participants who had completed all six ESM surveys the day before could win €25.

Compliance. We sent out 48,762 surveys (i.e., 387 × 126) to participants. Due to unforeseen technical problems with the Ethica software, 862 ESM surveys did not reach participants. As a result, 47,900 ESM surveys were received, and 34,930 surveys were completed. This led to a compliance rate of 73%, which is good in comparison with previous ESM studies among adolescents ( van Roekel et al., 2019 ). On average, participants completed 90.26 ESM surveys ( SD = 23.84).

A priori power-analyses. The number of assessments was determined based on the fact that a minimum of 50–100 assessments per participant is recommended to conduct N  =   1 time-series analyses ( Voelkle et al., 2012 ). In order to obtain at least 50 assessments per participant, we took a conservative approach and scheduled for a total of 126 assessments. A priori power analyses indicated that a number of 300 participants would suffice to reliably detect small effect sizes with a minimum power of .80 and significance levels of p = .05.

Time spent with social media . To obtain an ecologically valid ESM assessment of time spent with social media, we asked participants at each assessment how much time in the past hour they had spent with the three most popular platforms: WhatsApp, Instagram, and Snapchat. For each platform, we selected the most popular activities ( van Driel et al., 2019 ). For Instagram, we asked: How much time in the past hour have you spent… (1) sending direct messages on Instagram? (2) reading direct messages on Instagram? (3) viewing posts/stories of others on Instagram? For WhatsApp, we asked: How much time in the past hour have you spent… (4) sending messages on WhatsApp? (5) reading messages on WhatsApp? For Snapchat we asked: How much time in the past hour have you spent… (6) viewing snaps of others on Snapchat? (7) viewing stories of others on Snapchat? (8) sending snaps on Snapchat? Response options for each of these activities were measured with a Visual Analog Scale (VAS) that ranged from 0 to 60 minutes with one-minute intervals.

Participants’ scores on these activities were summed for each of the three platforms. For some assessments this summation led to time estimations exceeding 60 min. For WhatsApp this pertained to 0.85% of all 34,127 assessments, for Instagram to 2.40% of all 31,718 assessments, and for Snapchat to 3.87% of all 26,533 assessments. As indicated in our preregistration , these scores were recoded to 60 min. In a next step, the indicated times spent with WhatsApp, Instagram, and Snapchat were summed to create a variable “time spent with social media.” The summation of the three platforms again led to some estimations exceeding 60 min (i.e., 10.64% of all 34,686 estimations). In accordance with our preregistration, these scores were recoded to 60 min.

Self-esteem. Based on Rosenberg’s (1965) self-esteem scale, and studies establishing the validity of single-item measures of self-esteem (e.g., Robins et al., 2001 ), we presented participants with the question: “How satisfied do you feel about yourself right now?” We used a 7-point response scale ranging from 0 (not at all) to 6 (completely), with 3 (a little) as the midpoint.

Method of Analysis

As preregistered , we employed Dynamic Structural Equation Modeling (DSEM) for intensive longitudinal data in Mplus Version 8.4. Following the recommendations of McNeish and Hamaker (2020) , we estimated a two-level autoregressive lag-1 model (AR[1] model) with self-esteem as the outcome. At the within-person level (level 1), we specified SMU in the past hour as the time-varying covariate of self-esteem (to investigate H1), while controlling for the autoregressive effect of self-esteem (i.e., self-esteem predicted by lag-1 self-esteem). At the between-person level (level 2), we included the latent mean level of self-esteem and the latent mean of SMU in the past hour, and the correlation between these mean levels (to investigate RQ1). Finally, we included the between-person variances around the within-person effects of SMU on self-esteem (i.e., random effects to investigate H2).

Before estimating the model, we checked the required assumption of stationarity, that is, whether the mean of the outcome did not systematically change during the study ( McNeish & Hamaker, 2020 ). To do so we compared a two-level fixed effect model with day of study predicting self-esteem with an intercept-only model (i.e., a model without predictors). The assumption of stationarity was confirmed: Day of the study explained only 0.82% of the within-person variance in self-esteem.

Model specifications . By default, DSEM uses Bayesian Markov Chain Monte Carlo (MCMC) for model estimation. We followed our preregistered plan of analyses and ran the DSEM model with a minimum of 5,000 iterations. Before interpreting the estimates, we checked whether the model converged following the procedure of Hamaker et al. (2018) . Model convergence is considered successful when the Potential Scale Reduction (PSR) values are very close to 1 ( Gelman & Rubin, 1992 ), and the trace plots for each parameter look like fat caterpillars. We interpreted the parameters with the Bayesian credible intervals (CIs), as well as the Bayesian p- values. The hypotheses are confirmed if the 95% CIs for the effect of SMU on self-esteem (within-level; H1) and for the variance around this effect (between-level; H2) do not contain 0. Further details of the analytical strategy can be found in the preregistration of the study.

Correlations and Descriptives

Table 1 presents the means, standard deviations (SDs), ranges, and the within-person, between-person, and intra-class correlations (ICCs) of time spent with social media (SMU) and self-esteem. As the table shows, the average level of self-esteem was high ( M  =   4.09, SD = 1.12, range = 0–6). Participants spent on average almost 17 minutes (range 0–60 min.) with social media in the hour before each measurement occasion. The between-person association of the mean level of SMU with the mean level of self-esteem was significantly negative ( r = −.14, p = .005). The within-person correlation was close to zero ( r = −.01, p = .028), but significant (due to the high power of the study).

Descriptive Statistics and Within-Person, Between-Person, and Intra-Class Correlations of Time Spent with Social Media (SMU) and Self-Esteem

Mean scores reflect average number of minutes spent with social media in the past hour.

Within-person association ( p = .028) between SMU and self-esteem.

between-person association ( p = .005) between SMU and self-esteem.

The Intra-Class Correlations (ICCs) were .45 for self-esteem and .48 for SMU, which means that 45% of the variance in self-esteem and 48% of the variance in SMU was explained by differences between participants (i.e., between-person variance), whereas the larger part of these variances (55% and 52%) was explained by fluctuations within participants (i.e., within-person variance). These ICCs confirm that our sampling scheme of six assessments a day was appropriate for assessing within-person fluctuations in self-esteem and SMU and led to data with sufficient within-person variance for DSEM analyses.

DSEM Results

In all the steps of the analysis strategy, we followed our preregistered plan . We first ran a DSEM model with a minimum of 5,000 iterations (and a default maximum of 50,000 iterations) and one-hour time intervals (TINTERVAL = 1). This model did not converge: The Potential Scale Reduction (PSR) convergence criterion reached 1.354, which is not close enough to 1. As recommended by McNeish and Hamaker (2020) , in a next step, we improved the model setup by increasing the time interval from 1 to 2 hours (TINTERVAL = 2). This model converged well and before the 5,000 iterations. The PSR for this model was 1.006. Visual inspection of the trace plots confirmed that convergence was successful. Finally, we also ran a model with 10,000 iterations to exclude the possibility that the PSR value of 5,000 iterations was close to 1 by chance ( Schultzberg & Muthén, 2018 ). This model reached a PSR of 1.002, and its results did not deviate from the model with 5,000 iterations.

Investigating Research Question and Hypotheses

To answer our research question (RQ1), we investigated the between-person association between SMU and self-esteem. The DSEM analyses revealed a significantly negative association of −.147 between SMU and participants’ level of self-esteem, meaning that participants who spent more time with social media across the three weeks had a lower average level of self-esteem compared to participants who spent less time with social media across this period ( Table 2 ).

DSEM Results of the Between-Person Associations and Within-Person Effects of Time Spent with Social Media (SMU) and Self-Esteem (S-E)

The relationship between SMU and β rβ reflects the extent to which the within-person effect of momentary SMU on momentary S-E depends on the average level of adolescents’ SMU;

The relationship between S-E and β β reflects the extent to which the within-person effect of momentary SMU on momentary S-E depends on adolescents’ average level of S-E;

The 95% Credible Interval of the variance around the effect of SMU on S-E indicates that the within-person effect of SMU on S-E differed among participants. b ’s are unstandardized; β β’s are standardized using the STDYX Standardization in Mplus; p -values are one-tailed Bayesian p -values ( McNeish & Hamaker, 2020 ).

Our first hypothesis (H1) predicted an overall positive within-person effect of SMU on self-esteem. This within-person effect represents the average changes in self-esteem (i.e., self-esteem controlled for self-esteem at t −1) as a result of SMU in the previous hour. This hypothesis did not receive support. Despite the high power of the study, the within-person effect was nonsignificant (β = −.009), meaning that, on average, participants’ self-esteem did not increase nor decrease as a result of their SMU in the previous hour ( Table 2 ).

Our second hypothesis (H2), which predicted that the within-person effect of SMU on changes in self-esteem would differ from participant to participant, did receive support ( Table 2 : random effect = 0.006, p = .000). This random effect means that there was significant variance between participants in the extent to which their SMU in the previous hour predicted changes in their self-esteem.

Figure 1 shows the distribution of the person-specific standardized effect sizes for the effect of SMU on changes in self-esteem. These effect sizes ranged from β = −.21 to β = +.17 across participants. As the bar graph shows, the majority of participants (88%) experienced no or very small positive or negative effects of their SMU (i.e., −.10 < β < .10) on changes in self-esteem, whereas a small group of participants (4%) experienced positive (.10 ≤ β ≤ .17), and another small group (8%) experienced negative effects (−.21 ≤ β ≤ -.10) of SMU on changes in self-esteem. Figure 2 presents the N  =   1 time-series plots of three participants, one who experienced a positive, one who experienced a negative, and one who experienced a null-effect of SMU on self-esteem.

Range of the Standardized Person-Specific Effects of SMU on in Self-Esteem.

Note. The vertical black line represents the mean of the person-specific effects ( β = −.009).

Three N = 1 time-series plots picturing the effects of SMU on self-esteem (S-E).

Note . The x -axes represent the measurement moments (range 1–126). The y -axes represent the co-fluctuations in SMU (blue lines, range 0–60 minutes/10) and S-E (yellow lines, range 0–6). The top plot belongs to a participant who experienced a positive effect of SMU on S-E ( β = .174). The SMU and S-E of this participant regularly co-fluctuated (e.g., around moment 40 and around moment 41). The middle plot is from a participant who experienced a negative effect ( β β = −.196): When the SMU of this participant increased, his/her S-E dropped (e.g., around moment 56), and vice versa (e.g., around moment 21). The bottom plot is from a participant who experienced no effects ( β = .013): At some moments, the S-E of this participant increased after his/her SMU increased (e.g., around moment 45), at othermoments her/his S-E dropped after his/her SMU went up (e.g., moment 72), resulting in a net effect close to zero.

Exploratory Analyses

In addition to our preregistered hypotheses, we ran four exploratory analyses. In a first step, we investigated potential platform differences. Because earlier studies into the relationship between SMU and self-esteem did not investigate differential effects of different platforms, we summed adolescents’ use of Instagram, Snapchat, and WhatsApp to create our SMU measure. To explore potential platforms differences, we reran our analyses separately for each of the three platforms. Our results did not show significant differences in the between-person relationships and within-person effects of the use of these platforms on self-esteem (see Supplement 1).

In a second step, we ran a multilevel model without controlling for self-esteem at the previous assessment. Given that DSEM models are rather stringent and that sizeable differences in effect sizes between lagged and non-lagged media effects have been reported ( Adachi & Willoughby, 2015 ), we wanted to get insight into these differences. All other model specifications of the multilevel model were identical to the initial DSEM model. The associations between SMU and self-esteem in the multilevel model ranged from β = −.34 to β = +.33. Consistent with the DSEM model, the average within-person association of SMU and self-esteem was close to zero (β = −.007, p = .162, CI = [−0.022, 0.007] compared to β = −.009 in the DSEM model).

In a third step, we explored whether the person-specific within-person effects of SMU on self-esteem (i.e., the βs) differed for adolescents with different mean levels of SMU or different mean levels of self-esteem. As Table 2 shows, the cross-level interaction of participants’ mean levels of SMU with the β’s was non-significant, indicating that adolescents with higher mean levels of SMU did not experience a more negative (or positive) within-person effect of SMU on their self-esteem than their peers with lower SMU. The cross-level interaction of self-esteem and the βs did reveal that the within-person effect of SMU on self-esteem depended on adolescents’ mean level of self-esteem: Adolescents with lower average levels of self-esteem had a more positive within-person effect of SMU on self-esteem than adolescents with higher average levels of self-esteem, and vice versa.

In a final step, we investigated a between-person hypothesis of one of the anonymous reviewers, who suggested to check whether adolescents with moderate SMU would experience higher trait levels of self-esteem than those with low and high SMU. We investigated this potential inverted U-shaped relationship between SMU and self-esteem by following the two-step hierarchical regression analysis used by Cingel and Olsen (2018) . At step 1 of this regression analysis, we found a negative linear relationship between SMU and self-esteem (β = − .145, p = .005; R 2 = .021, see also Table 1 ). At step 2, we found no significant curvilinear relationship between SMU and self-esteem, because the added squared SMU term did not result in a significant change in the explained variance (Δ R 2 = .001, Δ F (1, 380) = .516, p = .473).

Sensitivity Analysis

As preregistered , we conducted a validation check to examine whether participants’ answers were trustworthy according to the following criteria: (1) inconsistency of participants’ within-person response patterns, (2) outliers, (3) unserious responses (e.g., gross comments) to the open question in the ESM study. Based on these criteria, we considered the responses of eight participants as potentially untrustworthy, because they violated criterion 1 and 2 ( n  =   4) or criterion 1 and 3 ( n  =   4). As a sensitivity analysis, we reran the DSEM analysis without these eight participants. The results of both the between-person and within-person associations did not deviate from those of the full sample.

The two existing meta-analyses on the relationship of SMU and self-esteem assessed the effects of their included empirical studies as weak and their results as mixed ( Huang, 2017 ; Liu & Baumeister, 2016 ). The between-person associations reported in empirical studies on SMU and self-esteem ranged from +.22 ( Apaolaza et al., 2013 ) to − .28 ( Rodgers et al., 2020 ). In the current study, the between-person association between SMU and self-esteem fits within this range: We found a negative relationship of r = − .15 between SMU and self-esteem (RQ1), meaning that adolescents who spent more time on social media across a period of three weeks reported a lower level of self-esteem than adolescents who spent less time on social media. This negative relationship pertained to the summed usage of Instagram, Snapchat, and WhatsApp, but did not differ for the usage of each of the separate platforms.

In addition, although we hypothesized a positive overall within -person effect of SMU on self-esteem (H1), we found a null effect. However, this overall null effect must be interpreted in light of the supportive results for our second hypothesis (H2), which predicted that the effect of SMU on self-esteem would differ from adolescent to adolescent. We found that the majority of participants (88%) experienced no or very small positive or negative effects of SMU on changes in self-esteem ( − .10 < β < .10), whereas one small group (4%) experienced positive effects (.10 ≤ β ≤ .17), and another small group (8%) negative effects of SMU ( − .21 ≤ β ≤ − .10) on self-esteem.

The person-specific effect sizes reported in the current study pertain to SMU effects on changes in self-esteem (i.e., self-esteem controlled for previous levels of self-esteem). As Adachi and Willoughby (2015 , p. 117) argue, such effect sizes are often “dramatically” smaller than those for outcomes that are not controlled for their previous levels. Indeed, when we checked this assumption of Adachi & Willoughby, the associations between SMU and self-esteem not controlled for its previous levels resulted in a considerably wider range of effect sizes (β = − .34 to β = +.33) than those that did control for previous levels (β = − . 21 to β = +.17). To account for a potential undervaluation of effect sizes in autoregressive models, Adachi and Willoughby (2015 , p. 127) proposed “a more liberal cut-off for small effects in autoregressive models (e.g., small = .05).” In this study, we followed our preregistration and interpreted effect sizes ranging from − .10 < β < +.10 as non-existent to very small. However, if we would apply the guideline proposed by Adachi and Willoughby (2015) to our results, the distribution of effect sizes would lead to 21% negative susceptibles, 16% positive susceptibles, and 63% non-susceptibles.

Our results showed that the effects of SMU on self-esteem are unique for each individual adolescent, which may, in turn, explain why the two meta-analyses evaluated the effects of their included studies as weak and their results as inconsistent. First, our results suggest that these effects were weak because they were diluted across a heterogeneous sample of adolescents with different susceptibilities to the effects of SMU. This suggestion is supported by comparing our overall within-person effect (β = − .01, ns) with the full range of person-specific effects, which ranged from moderately negative to moderately positive. Second, the effects reported in earlier studies may have been inconsistent because these studies may, by chance, have slightly oversampled either “positive susceptibles” or “negative susceptibles.” After all, if a sample is somewhat biased towards positive susceptibles, the results would yield a moderately positive overall effect. Conversely, if a sample is somewhat biased towards negative susceptibles the results would report a moderately negative overall effect.

It may seem reassuring at first sight that the far majority of participants in our study did not experience sizeable negative effects of SMU on their self-esteem. However, as illustrated in the bottom N  =   1 time-series plot in Figure 2 , for some participants, their non-significant within-person effect may result from strong social media-induced ups and downs in self-esteem, which cancelled each other out across time, resulting in a net null effect. However, as the two upper time-series plots in Figure 2 show, not only the non-susceptibles, but also the positive and negative susceptibles sometimes experienced effects in the opposite direction: The positive susceptibles occasionally experienced negative effects, while the negative susceptibles occasionally experienced positive effects.

Although DSEM models enable researchers to demonstrate how within-person effects of SMU differ across persons, they do not (yet) allow us to statistically evaluate the presence of both positive and negative effects within one and the same person (Hamaker, 2020, personal communication). A possibility to analyze the combination of positive and negative effects within persons may soon be offered by even more advanced modeling strategies than DSEM, which are currently undergoing a rapid development. Among those promising developments are regime switching models ( Lu et al., 2019 ), which provide the opportunity to establish the co-occurrence of both positive and negative effects of SMU within single persons.

Explanatory Hypotheses and Avenues for Future Research

Although our study allowed us to reveal the prevalence of positive susceptibles, negative susceptibles, and non-susceptibles among participants, it did not investigate why and when some adolescents are more susceptible to SMU than others. Our exploratory results did show that adolescents with a lower mean level of self-esteem, experienced a more positive within-person effect of SMU on self-esteem than adolescents with a higher mean level of self-esteem. This latter result may point to a social compensation effect ( Kraut et al., 1998 ), indicating that adolescents who are low in self-esteem may successfully seek out social media to enhance their self-esteem. Our DSEM analysis did not reveal differences in the within-person effects of SMU on self-esteem among adolescents with high and low SMU, suggesting that the positive effects among some adolescents cannot be attributed to modest SMU, whereas the negative effects among other adolescents cannot be attributed to excessive SMU.

An important next step is to further explain why adolescents differ in their susceptibility to SMU. A first explanation may be that adolescents differ in the valence (the positivity or negativity) of their experiences while spending time on social media. It is, for example, possible that the positive susceptibles experience mainly positive content on social media, whereas the negative susceptibles experience mainly negative content. In this study, we focused on time as a predictor of momentary ups and downs in self-esteem. However, most self-esteem theories emphasize that it is the valence rather than the duration of social experiences that results in self-esteem fluctuations. It is assumed that self-esteem goes up when we succeed or when others accept us, and drops when we fail or when others reject us ( Leary & Baumeister, 2000 ). Future research should, therefore, extend our study by investigating to what extent the valence of experiences on social media accounts for differences in susceptibility to the effects of SMU above and beyond adolescents’ time spent on social media.

A second explanation as to why adolescents differ in their susceptibility to the effects of SMU may lie in person-specific susceptibilities to the positivity bias in SM. Our first hypothesis was based on the idea that the sharing of positively biased information would elicit reciprocal positive feedback from fellow users, which, in turn, would lead to overall improvements in self-esteem. However, our results suggest that, for some adolescents, this positivity bias may lead to decreases in self-esteem, for example, because of their tendency to compare themselves to other social media users who they perceive as more beautiful or successful. This tendency towards social comparison may lead to envy (e.g., Appel et al., 2016 ) and decreases in self-esteem ( Vogel et al., 2014 ).

Until now, studies investigating the positive feedback hypothesis have mostly focused on the positive effects of feedback on self-esteem (e.g., Valkenburg et al., 2017 ), whereas studies examining the social comparison hypothesis have mainly focused on the negative effects of social comparison on self-esteem (e.g., Vogel et al., 2014 ). However, both the positive feedback hypothesis and the social comparison hypothesis are more complex than they may seem at first sight. First, although most adolescents receive positive feedback while using social media, a minority frequently receives negative feedback ( Koutamanis et al., 2015 ), and may experience resulting decreases in self-esteem. Likewise, although social comparison may lead to envy, it may also lead to inspiration (e.g., Meier & Schäfer, 2018 ), and resulting increases in self-esteem. Future research should attempt to reconcile these explanatory hypotheses by investigating who is particularly susceptible to positive and/or negative feedback, and who is particularly susceptible to the positive (e.g., inspiration) and/or negative (e.g., envy) effects of social comparison on social media.

Another possible explanation for differences in person-specific effects of SMU on self-esteem may lie in differences in the specific contingencies on which adolescents’ self-esteem is based. Self-esteem contingency theory ( Crocker & Brummelman, 2018 ) recognizes that people differ in the areas of life that serve as the basis of their self-esteem ( Jordan & Zeigler-Hill, 2013 ). For example, for some adolescents their physical appearance may serve as the basis of their self-esteem, whereas others may base their self-esteem on peer approval. Different contexts may also activate different self-esteem contingencies ( Crocker & Brummelman, 2018 ). On the soccer field, athletic ability is valued, which may activate the athletic ability contingency in this context. On social media, physical appearance and peer approval may be relevant, so that these contingencies may particularly be triggered in the social media context. It is conceivable that adolescents who base their self-esteem on appearance or peer approval may be more susceptible to the effects of SMU than adolescents who base their self-esteem less on these contingencies, and this is, therefore, another important avenue for future research.

Stimulating Positive and Mitigating Negative Effects

Our results suggest that for the majority of adolescents the momentary effects of SMU are small or negligible. As discussed though, all adolescents—whether they are positive susceptibles, negative susceptibles, or non-susceptibles—may occasionally experience social media-induced drops in self-esteem. Social media have become a fixture in adolescents’ social life, and the use of these media may thus result in negative experiences among all adolescents. Therefore, not only the negative susceptibles, but all adolescents need their parents or educators to help them prevent, or cope with, these potentially negative experiences. Parents and educators can play a vital role in enhancing the positive effects of SMU and combatting the negative ones. Helping adolescents prevent or process negative feedback and explaining that the social media world may not be as beautiful as it often appears, are important ingredients of media-specific parenting as well as school-based media literacy programs.

Although this study was designed to contribute to (social) media effects theories and research, our analytical approach may also have social benefits. After all, N  =   1 time-series plots could not only be helpful for theory building, but also for person-specific advice to adolescents. These plots give a comprehensive snapshot of each adolescent’s experiences and responses across more or less prolonged time periods. Such information could greatly help tailoring prevention and intervention strategies to different adolescents. After all, only if we know which adolescents are more or less susceptible to the negative and positive effects of social media, are we able to adequately target prevention and intervention strategies at these adolescents.

Towards a Personalized Media Effects Paradigm

Insights into person-specific susceptibilities to certain environmental influences is burgeoning in several disciplines. For example, in medicine, personalized medicine is on the rise. In education, personalized learning is booming. And in developmental psychology, differential susceptibility theories are among the most prominent theories to explain heterogeneity in child development. Although N  =   1 or idiographic research is now progressively embraced in multiple disciplines, spurred by recent methodological developments, it has a long history behind it. In fact, in the first two decades of the 20th century, scholars such as Piaget, Pavlov, and Thorndike often conducted case-by-case research to develop and test their theories bottom up (i.e., from the individual to the population; Robinson, 2011 ). However, in the 1930s, idiographic research soon lost ground to nomothetic approaches, certainly after Francis Galton attached the term nomothetic to the aggregated group-based methodology that is still common in quantitative research ( Robinson, 2011 ). However, due to technological advancements, it has become feasible to collect masses of intensive longitudinal data from masses of individuals on the uses and effects of social media (e.g., through ESM, tracking). Moreover, rapid developments in data mining and statistical methods now also enable researchers to analyze highly complex N  =   1 data, and by doing so, to develop and investigate media effects and other communication theories bottom-up rather than top-down (i.e., from the population to the individual). We hope that this study may be a very first step to a personalized media effects paradigm.

Additional Supporting Information may be found in the online version of this article.

This study was funded by an NWO Spinoza Prize and a Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to Patti Valkenburg by the Dutch Research Council (NWO). Additional funding was received from a VIDI grant (NWO VIDI Grant 452.17.011) awarded to Loes Keijsers.

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