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Netiquette: ethic, education, and behavior on internet—a systematic literature review.

1. Introduction

2.1. search strategy, 2.2. inclosure criteria, 3.1. country, 3.4. methodological design, 3.5. main variables, 3.6. sample details, 3.7. measurement, 4. discussion, 5. conclusions, author contributions, acknowledgments, conflicts of interest.

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Soler-Costa, R.; Lafarga-Ostáriz, P.; Mauri-Medrano, M.; Moreno-Guerrero, A.-J. Netiquette: Ethic, Education, and Behavior on Internet—A Systematic Literature Review. Int. J. Environ. Res. Public Health 2021 , 18 , 1212. https://doi.org/10.3390/ijerph18031212

Soler-Costa R, Lafarga-Ostáriz P, Mauri-Medrano M, Moreno-Guerrero A-J. Netiquette: Ethic, Education, and Behavior on Internet—A Systematic Literature Review. International Journal of Environmental Research and Public Health . 2021; 18(3):1212. https://doi.org/10.3390/ijerph18031212

Soler-Costa, Rebeca, Pablo Lafarga-Ostáriz, Marta Mauri-Medrano, and Antonio-José Moreno-Guerrero. 2021. "Netiquette: Ethic, Education, and Behavior on Internet—A Systematic Literature Review" International Journal of Environmental Research and Public Health 18, no. 3: 1212. https://doi.org/10.3390/ijerph18031212

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Netiquette: Ethic, Education, and Behavior on Internet-A Systematic Literature Review

Affiliations.

• 1 Department of Education Sciences, University of Zaragoza, 50009 Zaragoza, Spain.
• 2 Department of Didactics and School Organization, University of Granada, 51001 Ceuta, Spain.
• PMID: 33572925
• PMCID: PMC7908275
• DOI: 10.3390/ijerph18031212

In this article, an analysis of the existing literature is carried out. It focused on the netiquette (country, date, objectives, methodological design, main variables, sample details, and measurement methods) included in the Web of Science and Scopus databases. This systematic review of the literature has been developed entirely according to the Preferred Reporting Items for Systematic Reviews (PRISMA). The initial search yielded 53 results, of which 18 exceeded the inclusion criteria and were analyzed in detail. These results show that this is a poorly defined line of research, both in theory and in practice. There is a need to update the theoretical framework and an analysis of the empirical proposals, whose samples are supported by students or similar. Knowing, understanding, and analyzing netiquette is a necessity in a society in which information and communication technologies (ICT) have changed the way of socializing and communicating. A new reality in which there is cyber-bullying, digital scams, fake news, and haters on social networks.

Keywords: ICT; digital competence; netiquette; social media; systematic review.

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Conflict of interest statement

The authors declare no conflict of interest.

Flow diagram of PRISMA Systematic…

Flow diagram of PRISMA Systematic Review about “netiquette.”

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• DOI: 10.1080/10447318.2023.2188534
• Corpus ID: 257644182

Netiquette as Digital Social Norms

• Maxi Heitmayer , Robin Schimmelpfennig
• Published in International journal of… 19 March 2023
• Computer Science, Sociology

9 Citations

Social normativity in the transition of new media: the case of facebook in vietnam, mastering digital ethic: uncovering the influence of self-control, peer attachment, and emotional intelligence on netiquette through adolescent social media exposure, the second wave of attention economics. attention as a universal symbolic currency on social media and beyond, x (twitter): a protective platform for personal revelations among indonesian lgbtq adolescents, exploring lecturers ethical dilemmas in digital communication: a case study of telegram usage in college education in russia, introducing smart machines technology and netiquette for highschool students, digital hygiene skills and cyberbullying reduction: a study among teenagers in kazakhstan, netiquette practices and perceptions in tesol-related online communities, online communities as a risk factor for gambling and gaming problems: a five-wave longitudinal study, 138 references, netiquette between students and their lecturers on facebook: injunctive and descriptive social norms.

• Highly Influential

Social Norms: A Review

Socially mediated publicness: an introduction, an approach to global netiquette research, rediscovering the netiquette: the role of propagated values and personal patterns in defining identity of the internet user., "to listen, share, and to be relevant" - learning netiquette by reflective practice, social media, work and nonwork interface: a qualitative inquiry, a focus theory of normative conduct: a theoretical refinement and reevaluation of the role of norms in human behavior, cultural evolution in the digital age, older adults’ perceived sense of social exclusion from the digital world, related papers.

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• Published: 06 October 2020

Authentic self-expression on social media is associated with greater subjective well-being

• Erica R. Bailey   ORCID: orcid.org/0000-0002-2924-2500 1   na1 ,
• Sandra C. Matz   ORCID: orcid.org/0000-0002-0969-4403 1   na1 ,
• Wu Youyou 2 &
• Sheena S. Iyengar 1  

Nature Communications volume  11 , Article number:  4889 ( 2020 ) Cite this article

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Social media users face a tension between presenting themselves in an idealized or authentic way. Here, we explore how prioritizing one over the other impacts users’ well-being. We estimate the degree of self-idealized vs. authentic self-expression as the proximity between a user’s self-reported personality and the automated personality judgements made on the basis Facebook Likes and status updates. Analyzing data of 10,560 Facebook users, we find that individuals who are more authentic in their self-expression also report greater Life Satisfaction. This effect appears consistent across different personality profiles, countering the proposition that individuals with socially desirable personalities benefit from authentic self-expression more than others. We extend this finding in a pre-registered, longitudinal experiment, demonstrating the causal relationship between authentic posting and positive affect and mood on a within-person level. Our findings suggest that the extent to which social media use is related to well-being depends on how individuals use it.

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Introduction.

Social media can seem like an artificial world in which people’s lives consist entirely of exotic vacations, thriving friendships, and photogenic, healthy meals. In fact, there is an entire industry built around people’s desire to present idealistic self-representations on social media. Popular applications like FaceTune, for example, allow users to modify everything about themselves, from skin tone to the size of their physical features. In line with this “self-idealization perspective”, research has shown that self-expressions on social media platforms are often idealized, exaggerated, and unrealistic 1 . That is, social media users often act as virtual curators of their online selves 2 by staging or editing content they present to others 3 .

A contrasting body of research suggests that social media platforms constitute extensions of offline identities, with users presenting relatively authentic versions of themselves 4 . While users might engage in some degree of self-idealization, the social nature of the platforms is thought to provide a degree of accountability that prevents individuals from starkly misrepresenting their identities 5 . This is particularly true for platforms such as Facebook, where the majority of friends in a user’s network also have an offline connection 6 . In fact, modern social media sites like Facebook and Instagram are far more realistic than early social media websites such as Second Life, where users presented themselves as avatars that were often fully divorced from reality 7 . In line with this authentic self-expression perspective, research has shown that individuals on Facebook are more likely to express their actual rather than their idealized personalities 8 , 9 .

The desire to present the self in a way that is ideal and authentic is not mutually exclusive; on the contrary, an individual is likely to desire both simultaneously 10 . This occurs in part because self-idealization and authentic self-expression fulfill different psychological needs and are associated with different psychological costs. On the one hand, self-idealization has been called a “fundamental part of human nature” 11 because it allows individuals to cultivate a positive self-view and to create positive impressions of themselves in others 12 . In addition, authentic self-expression allows individuals to verify and affirm their sense of self 13 , 14 which can increase self-esteem 15 , and a sense of belonging 16 . On the other hand, self-idealizing behavior can be psychologically costly, as acting out of character is associated with feelings of internal conflict, psychological discomfort, and strong emotional reactions 17 , 18 ; individuals may also possess characteristics that are more or less socially desirable, bringing their desire to present themselves in an authentic way into conflict with their desire to present the best version of themselves.

Here, we explore the tension between self-idealization and authentic self-expression on social media, and test how prioritizing one over the other impacts users’ well-being. We focus our analysis on a core component of the self: personality 19 .  Personality captures fundamental differences in the way that people think, feel and behave, reflecting the psychological characteristics that make individuals uniquely themselves 20 , 21 . Building on the Five Factor Model of personality 22 , we test the extent to which authentic self-expression of personality characteristics are related to Life Satisfaction, hypothesizing that greater authentic self-expression will be positively correlated with Life Satisfaction. In exploratory analyses, we also consider whether this relationship is moderated by the personality characteristics of the individual. That is, not all individuals might benefit from authentic self-expression equally. Given that some personality traits are more socially desirable than others 23 , individuals who possess more desirable personality traits are likely to experience a reduced tension between self-idealization and authentic self-expression. Consequently, individuals with more socially desirable profiles might disproportionality benefit from authentic self-expression because the motivational pulls of self-idealization and authentic self-expression point in the same—rather than the opposite—direction.

Previous literature on authentic self-expression has predominantly relied on self-reported perceptions of authenticity as (i) a state of feeling authentic 24 , or (ii) a judgement about the honesty or consistency of one’s self 25 . However, such self-reported measures have been shown to be biased by valence states, and social desirability 26 , 27 . To overcome these limitations, in Study 1 we introduce a measure of Quantified Authenticity. If authenticity is most simply defined as the unobstructed expression of one’s self 28 , then authenticity can be estimated as the proximity of an individual’s self-view and their observable self-expression. We calculate Quantified Authenticity by comparing self-reported personality to personality judgements made by computers on the basis of observable behaviors on Facebook (i.e., Likes and status updates).

By observing self-presentation on social media and comparing it to the individual’s self-view, we are able to quantify the extent to which an individual deviates from their authentic self. That is, we locate each individual on a continuum that ranges from low authenticity (i.e., large discrepancy between the self-view and observable self-expression) to high authenticity (i.e., perfect alignment between the self-view and observable self-expression). Importantly, our approach rests on the assumption that any deviation from the self-view on social media constitutes an attempt to present oneself in a more positive light, and therefore a form of self-idealization. While a deviation could theoretically indicate both self-idealization and self-deprecation, it is unlikely that users will deviate from their true selves in a way that makes them look worse in the eyes of others. A strength of our measures is that we do not postulate that self-idealization takes a particular form of deviation from the self or is associated with striving for a particular profile. Although research suggests that there are certain personality traits that are more desirable on average 29 , 30 , the extent to which a person sees scoring high or low on a given trait is likely somewhat idiosyncratic and depends—at least in part—on other people in their social network. For example, behaving in a more extraverted way might be self-enhancing for most people; however, there might be individuals for whom behaving in a more introverted way might be more desirable (e.g. because the norm of their social network is more introverted). Hence, our conceptualization of Quantified Authenticity allows for deviations in different directions (see Supplementary Information for more detail).

Quantified Authenticity and subjective well-being

In Study 1, we analyzed the data of 10,560 Facebook users who had completed a personality assessment and reported on their Life Satisfaction through the myPersonality application 31 , 32 . To estimate the extent to which their Facebook profiles represent authentic expressions of their personality, we compared their self-ratings to two observational sources: predictions of personality from Facebook Likes ( N  = 9237) 33 and predictions of personality from Facebook status updates ( N  = 3215) 34 . These are based on recent advances in the automatic assessment of psychological traits from the digital traces they leave on Facebook 35 . For each of the observable sources, we calculated Quantified Authenticity as the inverse Euclidean distance between all five self-rated and observable personality traits. Our measure of Quantified Authenticity exhibits a desirable level of variance, ranging all the way from highly authentic self-expression to considerable levels of self-idealization (see ridgeline plot of Quantified Authenticity calculated for self-language and Self-Likes in Supplementary Fig.  3 , see Supplementary Tables  1 and 2 for zero-order correlations among variables).

To test the extent to which authentic self-expression is related to Life Satisfaction, we ran linear regression analyses predicting Life Satisfaction from the two measures of Quantified Authenticity (Likes, status updates). The results support the hypothesis that higher levels of authenticity (i.e. lower distance scores) are positively correlated with Life Satisfaction (Table  1 , Model 1 without controls). These effects remained statistically significant when controlling for self-reported personality traits. Additionally, we included a control variable for the overall extremeness of an individual’s personality profile (deviation from the population mean across all five traits), as people with more extreme personality profiles might find it more difficult to blend into society and therefore experience lower levels of well-being 36 (see Table  1 , Model 2 with controls; the results are largely robust when controlling for gender and age, see Supplementary Table  3 ; see Supplementary Figs.  1 and 2 for interactions between individual self-reported and predicted personality traits).

To further explore the mechanisms of Quantified Authenticity, we conducted analyses that distinguished between normative self-enhancement (i.e., rating oneself as more Extraverted, Agreeable, Conscientiousness, Emotionally Stable, and Open-minded than is indicated by one’s Facebook behavior) from self-deprecation (i.e., rating oneself lower on all of these traits). While normative self-enhancement has a negative effect on well-being, normative self-deprecation has no effect. These findings suggest that self-enhancement specifically, rather than overall self-discrepancy/lack of authenticity, is detrimental to subjective well-being (see Supplementary Fig.  4 ).

To test the robustness of our effects, we regressed Life Satisfaction on three additional measures of Quantified Authenticity (i.e., calculated using Manhattan Distance, Cosine Similarity, and Correlational Similarity; see SI for details on these measures). In both comparison sets (likes and status updates), we found significant and positive correlations between the various ways of estimating Quantified Authenticity (see Supplementary Tables  1 and 2 ). The standardized beta-coefficients across all four metrics of Quantified Authenticity and observable sources are displayed in Fig.  1 . Despite variance in effect sizes across measures and model specifications, the majority of estimates are statistically significant and positive (11 out of 16). Importantly, no coefficients were observed in the opposite direction. These results suggest that those who are more authentic in their self-expression on Facebook (i.e., those who present themselves in a way that is closer to their self-view) also report higher levels of Life Satisfaction.

Figure 1 presents standardized beta coefficients for Quantified Authenticity using ordinary least squares regressions in 16 individual regressions predicting Life Satisfaction. Quantified Authenticity is significantly associated with Life Satisfaction in 11 out of the 16 models. Quantified Authenticity is measured as the consistency between self-reported personality and two other sources of personality data: language and Likes, respectively, (indicated in red and blue color). Quantified Authenticity is defined using four distance metrics, respectively: Manhattan, Euclidean, correlation, and cosine similarity (indicated with a letter in the dots). Models with and without control variables are indicated with dashed and solid line, respectively.

In exploratory analyses, we considered whether authenticity might benefit individuals of different personalities differentially. In order to examine this, we regressed Life Satisfaction on the interactions between Quantified Authenticity and each of the five personality traits (e.g., Quantified Authenticity × Extraversion). The results of these interaction analyses did not provide reliable evidence for the proposition that individuals with socially desirable profiles (i.e., high openness, conscientiousness, extraversion, agreeableness, and low neuroticism) benefit from authentic self-expression more than individuals with less socially desirable profiles (see Table  1 , Model 3). While the interactions of the five personality traits with Quantified Authenticity reached significance for some traits and measures, the results were not consistent across both observable sources of self-expression (Likes-based and Language-based). Consequently, we did not find reliable evidence that having a socially desirable personality profile boosts the effect of authenticity on well-being. Instead, individuals reported increased Life Satisfaction when they presented authentic self-expression, regardless of their personality profile.

The findings of Study 1 provide evidence for the link between authenticity on social media and well-being in a setting of high external validity. However, given the correlational nature of the study, we cannot make any claims about the causality of the effects. While we hypothesize that expressing oneself authentically on social media results in higher levels of well-being, it is also plausible that individuals who experience higher levels of well-being are more likely to express themselves authentically on social media. To provide evidence for the directionality of authenticity on well-being, we conducted a pre-registered, longitudinal experiment in Study 2 (see Fig.  2 for an illustration of the experimental design).

Figure 2 presents the longitudinal experimental study design for Study 2 with key timepoints, interventions, and surveys.

Experimental manipulation of authentic self-expression on well-being

We recruited 90 students and social media users at a Northeastern University to participate in a 2-week study ( M age  = 22.98, SD age  = 4.17, 72.22% female). The sample size deviates from our pre-registered sample size of 200. The reason for this is that the behavioral research lab of the university was shut down after the first wave of data collection due to the COVID-19 pandemic.

All participants completed two intervention stages during which they were asked to post on their social media profiles in a way that was: (1) authentic for 7 days and (2) self-idealized for 7 days. The order in which participants completed the two interventions was randomly assigned. This experimental set-up allowed us to study the effects of authentic versus idealized self-expression on social media in between-person (week 1) and within-person analyses (comparison between week 1 and week 2). All analyses were pre-registered prior to data collection 37 . Given the reduced sample size, the effects reported in this paper are all as expected in effect size, but only partially reached significance at the conventional alpha = 0.05 level. Consequently, we also consider effects that reach significance at alpha = 0.10 as marginally significant.

All participants completed a personality pre-screen (IPIP) 38 prior to beginning the study, and received personalized feedback report at the beginning of the treatment period (t0). Both the authentic and self-idealized interventions (see Methods for details) asked participants to reflect on that feedback report and identify specific ways in which they could alter their self-expression on social media to align their posts more closely with their actual personality profile (authentic intervention) or to align their posts more closely with how they wanted to be seen by others (see Supplementary Information for treatment text and examples of responses). The operationalization of the treatment follows our conceptualization of Quantified Authenticity in Study 1 in that it does not prescribe the direction of personality change (e.g. towards higher levels of extraversion). Instead, this design leaves it up to participants what posting in a more desirable way means in relation to their current profile.

Participants self-reported their subjective well-being as Life Satisfaction 39 , a single-item mood measure, and positive and negative affect 40 a week after the first intervention (t1), and a week after the second intervention (t2). This design allowed us to examine the causal nature of posting for a week in which participants posted authentically (“authentic, real, or true”), compared to a week in which they posted in a self-idealized way (“ideal, popular or pleasing to others”). Specifically, we hypothesized that individuals who post more authentically over the course of a week would self-report greater subjective well-being at the end of that week, both at the between and within-person level.

We examined the effect of authentic versus self-idealized expression at the between person level at t1 (see t1 in Fig.  3 ) using independent t -tests. Contrary to our expectations, we did not find any significant differences between the two conditions for any of the well-being indicators. This suggests that individuals in the authentic vs. self-idealized conditions did not differ from one another in their level of well-being after the first week of the study. However, when examining the effect within subjects using dependent t -tests we found that participants reported significantly higher levels of well-being after the week in which they posted authentically as compared to the week in which they posted in a self-idealized way. Specifically, the well-being scores in the authentic week were found to be significantly higher than in the self-idealized week for mood (mean difference = 0.19 [0.003, 0.374], t  = 2.02, d  = 0.43, p  = 0.046) and for positive affect (mean difference = 0.17 [0.012, 0.318], t  = 2.14, d  = 0.45, p  = 0.035), and marginally significant for negative affect (mean difference = −0.20 [−0.419, 0.016], t  = −1.84, d  = 0.39, p  = 0.069). There was no significant effect on Life Satisfaction (mean difference = 0.09 [−0.096, 0.274], t  = 0.96, d  = 0.20, p  = 0.342).

The bar chars illustrate the standardized mean of well-being indicators (mood, positive affect, negative affect, and Life Satisfaction) across two study time points by condition. The red bars indicate scores for the weeks in which participants were asked to post authentically, and the blue bars scores for the weeks in which they were asked to post in a self-idealized way. Error bars represent standard errors. The left-side panel presents Group A who received the authenticity treatment followed by the idealized treatment. The right-side panel presents Group B who received the idealized treatment followed by the authenticity treatment. This experiment was conducted once with independent samples in each group.

These findings are reflected in Fig.  3 which showcases the interactions between condition and time point. The graphs highlight that subjective well-being was higher in the weeks in which participants were asked to post authentically (red bars) compared to those in which they were asked to post in a self-idealized way (blue bars). While there was no difference in subjective well-being across conditions at t1, subjective well-being measures differed significantly between the authentic and self-idealized conditions at t2. We found no significant difference between conditions on Life Satisfaction (mean difference = 0.29 [−0.226, 0.798], t  = 1.11, d  = 0.23, p  = 0.270), however, we found a significant difference between conditions such that the group which received the authenticity treatment had greater positive affect (mean difference = 0.45 [0.083, 0.825], t  = 2.43, d  = 0.51 , p  = 0.017), lower negative affect (mean difference = −0.57 [−1.034, −0.113], t  = −2.47, d  = 0.52, p  = 0.015), and higher overall mood (mean difference = 0.40 [0.028, 0.775], t  = 2.14, d  = 0.45 , p  = 0.036).

The findings of the experiment provide support for the causal relationship between posting authentically, compared to posting in a self-idealized way, on the more immediate affective indicators of subjective-wellbeing, including mood and affect, but not on the more long-term, cognitive indicator of life satisfaction. This findings aligns with our pre-registration in that we had predicted mood and affect measures to be more sensitive to the treatment compared to Life Satisfaction, which is a broader global assessment one’s overall life 39 and less likely to change in the course of a week.

Additionally, the fact that we did not find significant effects in our between-subjects analysis in the first week of the study suggests that authentic self-expression might be difficult to manipulate in a one-off treatment as social media users are likely used to expressing themselves on social media both authentically and in a self-idealized way. Thus, when only one strategy is emphasized, participants might not shift their behavior. This is supported by the finding that participants did not differ significantly in their subjective experience of authenticity on social media at t1 (mean in authentic condition at t1 = 5.56, mean in self-idealized condition at t1 = 5.55, t  = 0.05, d  = 0.01, p  = 0.958; Participants responded to a single item, which read “This past week, I was authentic on social media” on a 7-point scale where 1 = strongly disagree and 7 = strongly agree), indicating that the between-subjects manipulation was unsuccessful in getting people to shift their behaviors more toward self-idealized or authentic self-expression compared to their baseline. However, the contrast of the two strategies highlighted in the within-subjects part of the study seems to have successfully shifted participants’ behavior. When compared within person, students did indeed report higher levels of experienced authenticity in their posting during the week in which they were instructed to post authentically (mean difference = 0.30 [0.044, 0.556], t  = 2.33, d  = 0.49, p  = 0.022).

We often hear the advice to just be ourselves. Indeed, psychological theories have suggested that behaving in a way that is consistent with the self-view is beneficial for individual well-being 41 . However, prior investigations of authenticity and well-being have relied solely on self-reported measures which can be confounded by valence and social desirability biases. We estimated authenticity as the proximity between the self-view and self-expression on social media—which we termed Quantified Authenticity—and found that authentic self-expression on social media was correlated with greater Life Satisfaction, an important component of overall well-being. This effect was robust across two comparison points, computer modeled personality based on Facebook Likes and status updates. Our findings suggest that if users engage in self-expression on social media, there may be psychological benefits associated with being authentic. We replicate this finding in a longitudinal experiment with university students; being prompted to post in an authentic way was associated with more positive mood and affect, and less negative mood within participants. Contrary to our second hypothesis, we did not find consistent support for interactions between personality traits and authenticity, such that individuals with more socially desirable traits would benefit more from behaving authentically. Instead, our findings suggest that all individuals regardless of personality traits could benefit from being authentic on social media.

Our findings contribute to the existing literature by speaking directly to conflicting findings on the effects of social media use on well-being. Some studies find that social media use increases self-esteem and positive self-view 42 , while others find that social media use is linked to lower well-being 43 . Still, others find that the effect of social media on well-being is small 44 or non-existent 45 . In an attempt to reconcile these mixed findings, researchers have suggested that the extent to which social media platforms related to lower or higher levels of well-being might depend not on whether people use them but on how they use them. For example, research has shown that active versus passive Facebook use has divergent effects on well-being. While passively using Facebook to consume the content share by others was negatively related to well-being, actively using Facebook to share content and communicate was not 46 . We add to this growing body of research by suggesting that effects of social media use on well-being may also be explained by individual differences in self-expression on social media.

Our study has a number of limitations that should be addressed by future research. First, our analyses focused exclusively on the effects of authentic social media use on well-being, and cannot speak to the question of whether an authentic social media use is better or worse than not using social media at all. That is, even though using social media authentically is better than using it in a more self-idealizing way, the overall effect of social media use on well-being might still be a negative. Future research could address this question by directly comparing no social media use to authentic social media use in both correlational and experimental settings.

Second, our findings do not provide any insights into why individuals might behave more or less authentically. For example, a deviation from the self-view might be explained by a lack of self-awareness, or an intentional misrepresentation of the self. It is possible that depending on whether deviation is driven by intent or not, authenticity might be more or less strongly related to well-being. That is, the psychological costs of deviating from one’s self-view might be stronger when they are intentional such that the individual is fully aware of the fact that they are behaving in a self-idealizing way. Future research should explore this factor empirically.

Finally, the effects of authentic self-presentation on social media on well-being are robust but small (max(β) = 0.11) when compared to compared to other important predictors of well-being such as income, physical health, and marriage 47 , 48 , 49 . However, we argue that the effects described here are meaningful when trying to understand a complex and multifaceted construct such as Life Satisfaction. First, Study 1 captures authenticity using observations of actual behavior rather than self-reports. Given that such behavioral data captured in the wild do not suffer from the same response biases as self-reports which can inflate relationships between variables (e.g. common method bias 50 ), and are often noisier than self-reports, their effect sizes cannot be directly compared 51 . In fact, the effect sizes obtained in Study 2 which was conducted in a much more controlled, experimental setting shows that the effect of authenticity on subjective well-being is substantially larger when measured with more traditional methods (max(d) = 0.45). In addition, while other factors such as employment and health are stronger predictors of well-being, they can be outside of the immediate control of the individual. In contrast, posting on social media in a way that is more aligned with an individual’s personality is both up to the individual and relatively easy to change.

Social media is a pervasive part of modern social life 52 . Nearly 80% of Americans use some form of social media, and three quarters of users check these accounts on a daily basis 53 . Many have speculated that the artificiality of these platforms and their trend towards self-idealization can be detrimental for individual well-being. Our results suggest that whether or not engaging with social media helps or hurts an individual’s well-being might be partly driven by how they use those platforms to express themselves. While it may be tempting to craft a self-enhanced Facebook presence, authentic self-expression on social media can be psychologically beneficial.

Study 1. Participants and procedure

Data were collected through the MyPersonality project, an application available on Facebook between 2007 and 2012 31 . Users of the app completed validated psychometric tests including a measure of the Big Five personality traits 22 , 54 , and received immediate feedback on their responses. A subsample of myPersonality users also agreed to donate their Facebook profile information—including their public profiles, their Facebook likes, their status updates, etc.—for research purposes. In addition, users could invite their Facebook friends to complete the personality questionnaire on their behalf, judging not their own personality but that of their friend.

To calculate authenticity, we developed a measure we refer to as Quantified Authenticity (QA). To compute this measure, we compared a person’s self-reported personality to two external criteria: (1) their personality as predicted from Facebook Likes, and (2) their personality as predicted from the language used in their status updates (see “Measures” section below for more information). The number of participants varied between the two samples based on exclusionary criteria. To be included in the Language-based model, individuals had to have posted at least 500 words of Facebook status updates ( N  = 3215). In the Likes-based model, only participants with 20 or more Likes were included ( N  = 9237).

Big Five personality

Participants’ personality was measured using the well-established Five Factor model of personality, also known as Big Five traits 54 , 55 . The Five Factor model posits five relatively stable, continuous personality traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The Big Five personality traits have been found to be stable across cultures, instruments, and observers 56 . Additionally, years of research have linked them to a broad variety of behaviors, preferences and other consequential outcomes, including well-being 57 and behavior on Facebook 58 .

Self-reported personality

Participants’ views of their own personalities are based on the well-established International Personality Item Pool or IPIP 38 . Participants included in the analyses responded to 20–100 questions using a 5 point Likert-scale where 1 =  strongly disagree to 5 = strongly agree.

Computer-based predictions of personality from likes and status updates

Recent methodological advances in machine learning have provided researchers with the ability to predict the personality of individuals from their social media profiles 33 , 34 , 35 . Here, we used personality prediction of personality from Facebook Likes and the language used in status updates. For Facebook Likes ( N  = 9327), we obtained the personality predictions made by Youyou and colleagues 33 , who used a 10-fold cross-validated LASSO regression to predict Big Five personality traits out of sample. On average, the predictions captured personality with an accuracy of r  = 0.56 (correlation between predicted and self-reported scores). For status updates ( N  = 3215), we obtained the predictions made by Park et al. 34 , who used cross-validated Ridge regression to infer personality from language features, such as individual words, combinations of words (n-grams), and topics. On average, the predictions captured personality with an accuracy of r  = 0.41 (correlation between predicted and self-reported scores).

Personality extremeness

We calculated extremeness of participants’ personality profiles as a control variable for our analyses by summing the absolute z -scores on all five traits. We include extremeness because extreme individual scores tend to produce larger absolute difference scores. Additionally, previous work has found that people with more extreme personality profiles might find it more difficult to blend into society and therefore experience lower levels of well-being 36 .

Self-ratings of well-being

Individuals reported their Life Satisfaction—a key component of subjective well-being—on a five-item scale 39 . The SWLS has been shown to be a meaningful psychological construct, correlated with a number of important life outcomes such as marital status and health 59 .

Quantified Authenticity

Quantified Authenticity was calculated in three steps. First, we z -standardized the personality scores on each of the three measures (self, Likes, language) to obtain a person’s relative standing on the five personality traits in comparison to the reference group. Second, we computed the distance between self-reported personality and each of the externally inferred personality profiles using Euclidean distance, a widely established distance measure, which has been used in previous psychological research 36 . To make our measure more intuitively interpretable, we finally subtracted the distance measure from zero to obtain a measure of Quantified Authenticity for which higher scores indicate higher levels of authenticity. See Eq. ( 1 ) below.

For individual i , x i is the Cartesian coordinate of the self-view in a 5 -dimensional personality space. For individual i, y i is the Cartesian coordinate of the language-, or likes-based personality. Our measure of Quantified Authenticity exhibited desirable level of variance, ranging all the way from highly authentic self-expression to considerable levels of self-idealization (see ridgeline plot of standardized Quantified Authenticity calculated based on Language and Likes in Supplementary Fig.  3 ). Additional information on the calculation of the three other metrics of Quantified Authenticity (i.e., Manhattan distance, correlational similarity, and cosine similarity) can be found in the SI.

Study 2. Participants and procedure

All study procedures were approved by the Columbia University Human Research Protection Office and informed consent was received from all study participants. Prior to completing the study, participants completed a pre-screening survey. This included a number of questions related to their social media activity and the BFI-2S as a measure of their Big Five personality traits 60 . Participants who qualified for the study were randomly assigned to one of two groups depicted as “Group A” and “Group B” in Fig.  3 ). Both groups received both interventions (authentic and self-idealized), however they received the treatments in a different order.

The study took place over the course of 2 weeks. On the first day of the study, participants received an email, which included the results of their personality test taken in the pre-screen. They then self-reported their baseline subjective well-being (t0). At the end of the survey, half of the students were asked to use the personality feedback to list three ways in which they could express themselves more authentically over the next week on social media. The second group was asked to list with three ways to express themselves in a more self-idealized way.

At the end of the first week, participants received an email with the second survey link. They completed the same subjective well-being measures (t1; Day 0–7), and were shown their personality feedback again as a reminder. The students who were previously assigned to the authentic condition were now asked to list three ways to express themselves in a more self-idealized way (based on their personality profile), and vice versa (reversing the intervention assignments). At the end of the second week, participants received an email with the final survey link. They completed the same subjective well-being measures (t2; Day 7–14).

Subjective well-being

Individuals reported their Life Satisfaction on the same five-item scale as Study 1 39 . In addition, participants responded to positive and negative affect 40 and a single-item general mood measure.

Preregistration note

We had pre-registered the use of the Positive and Negative Affect Scale 61 . However, due to an oversight of the research team, we accidentally collected data using the Brief Mood Inventory Scale 40 . In the SI, we replicate the results using a subset of items, which overlap between the BMIS and the PANAS-X. Given that the two scales are highly correlated, share the same format, and even share some of the same descriptors, we do not expect that the results would have been different when using the PANAS scale.

Reporting summary

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

Data availability

Data for Study 1 are available upon request to the authors. Data for Study 2 relevant to the analyses described are available on our OSF page ( https://osf.io/fxav6/ ). Source data are provided with this paper.

Code availability

Code to reproduce the analyses for Study 1 and Study 2 described herein is available on OSF ( https://osf.io/fxav6/ ).

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Acknowledgements

We thank Blaine Horton, Jon Jachimowicz, Maya Rossignac-Milon, and Kostadin Kushlev for critical feedback which substantially improved this paper.

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Digital Etiquette in University Students’ Communicative Practice

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Complying with the requirements of digital etiquette is becoming an obligatory norm for building effective communication in the Internet space: in messengers, social networks, blogs, and electronic letters. The youth as a part of the society actively using the digital forms of communication demands special attention and special efforts for forming communicative competences in students, including in the sphere of introducing the digital etiquette norms into the practice of everyday communications. The research object is the speech etiquette of students used in the digital environment. The study is aimed at revealing the features of using the digital etiquette in the students’ communicative practice. The research involved analysis, synthesis, summarization of theoretical knowledge on the topic, and polling of 102 university students. The authors regretfully mark that the students’ general level of culture is not sufficiently high, and they often violate the boundaries when communicating both with each other and professors. The respondents assert that during communication in social networks they are most of all annoyed by late messages and violation of their personal boundaries (35.7% each). 86.7% of the respondents realize the need to use respectful forms of address, 34.7% pointed out the importance of observing the working time, and 23.5% is convinced that one may write personal messages, especially to a professor, only on very important and pressing matters. To solve the problems in the sphere of digital etiquette, it is necessary to introduce the digital etiquette as a discipline to be studied at university.

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

Dragana ostic.

1 School of Finance and Economics, Jiangsu University, Zhenjiang, China

Sikandar Ali Qalati

Belem barbosa.

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

Syed Mir Muhammad Shah

3 Department of Business Administration, Sukkur Institute of Business Administration (IBA) University, Sukkur, Pakistan

Esthela Galvan Vela

4 CETYS Universidad, Tijuana, Mexico

Ahmed Muhammad Herzallah

5 Department of Business Administration, Al-Quds University, Jerusalem, Israel

6 Business School, Shandong University, Weihai, China

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

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 ):

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.

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

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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