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Abstract

Social media became the predominant medium for communicating, sharing updates, and monitoring other users. However, due to increasing use of social media, individuals might feel availability pressure to be online and production pressure to post content, which might result in negative consequences. The present study aims to disentangle the relationships between active social media use (private interactions), active public social media use (broadcasting), and passive social media use (monitoring) in relation to digital pressure and life satisfaction. The results of a two-wave panel survey with N = 415 late adolescents and young adults (M_age = 19.08, SD = 1.57, 58.31% female) reveal a longitudinal reciprocal relationship between active public use and production pressure. However, availability pressure positively predicts active social media use over time, suggesting increase in private interactions due to perceived peer’s expectations to be reachable over social media. In contrast, production pressure is related to lower active social media use, thus placing focus on active public broadcasting instead. Notwithstanding the reciprocal interplay between social media use and digital pressure, the present study does not demonstrate harmful consequences of digital pressure on life satisfaction.

Keywords

digital pressure late adolescents young adults social media use longitudinal study well-being

Social media rapidly became an essential part of adolescents’ communicative activities with peers. Adolescents report spending a considerable amount of time on various social media sites (Pew Research Center, 2022), which has led researchers to investigate potential benefits and risks for their well-being. Thus far, researchers have focused on understanding the differences in social media use in terms of quantity and intensity (Ellison et al., 2007), demonstrating non-linear correlational evidence showing that higher frequency of use, but also low frequency, might result in reduced well-being, while moderate social media use can have advantage to adolescents (the goldilocks hypothesis; Przybylski & Weinstein, 2017). However, the sheer amount of time spent on various social media sites does not reveal much about the actual use, that is, for what purpose and how social media is utilized to reach certain relational goals (Verduyn et al., 2022). Recently, based on a number of meta-reviews, researchers have suggested to conduct more longitudinal studies that differentiate between various activities on social media in connection to well-being outcomes (Meier & Reinecke, 2021; Valkenburg, Meier, & Beyens, 2022; Valkenburg, van Driel, & Beyens, 2022) Thus, the present study aims to account for different types of social media use over time by focusing on: (1) active social use in terms of (private) interpersonal interactions with friends, (2) active public use in terms of posting status updates and broadcasting but not interacting through private features, and (3) passive use in terms of monitoring other’s social media content without any social engagement (based on the PAUM model, Gerson et al., 2017).

As adolescents increasingly use social media in parallel with other activities, for example, during an interaction with friends or family, in the evenings before bedtime, during commuting, or at school, researchers have coined the phrase of being “permanently online and permanently connected” (Vorderer et al., 2016). When these activities overlap, they lead to the phenomenon of being available online at all times. In fact, recent data shows that approximately 46% of adolescents feel they are online “almost constantly” (Pew Research Center, 2022). This embeddedness in online activities and social media can not only distract youth’s daily lives but also have a significant impact on their cognitive and psychological states (Klimmt et al., 2017; Reinecke et al., 2018).

Peers start to play an important role during adolescence, and their influence should be acknowledged when it comes to significance of social media use (Knop et al., 2015). Not only can peers exert certain social pressure, but they could be the main reason for adolescents to turn to problematic social media use in order to keep on maintaining social relationships online (Throuvala et al., 2019). Therefore, in line with the differential susceptibility to media effects model (Valkenburg & Peter, 2013), social-context factors need to be investigated in order to understand the underlying mechanisms, predictors, and outcomes of social media use.

Because social media platforms are readily available anytime and anywhere through smartphones, the state of being permanently connected and available to other online contacts might become problematic (Freytag et al., 2021; Vorderer et al., 2016). As adolescents’ social relationships are largely maintained over social media, such circumstances could (a) instigate perceived peer pressure related to expectations to be available over social media use and (b) intensify social media use due to perceived digital pressure (e.g., through increased friendship maintenance, Hall & Baym, 2012, or feelings of being entrapped in mobile media use, Hall, 2017). The question about the influence of such digital pressure on youth’s life satisfaction becomes increasingly relevant. For example, recent research suggests that especially social pressure to be available might have detrimental impact on well-being (Halfmann & Rieger, 2019).

The present study aims to understand the reciprocal relationships between different types of social media use and adolescents’ perceived digital pressure to be available and post on social media that stems from their friends’ expectations. According to the differential susceptibility model (Valkenburg & Peter, 2013), a nuanced approach in investigating social susceptibility as a factor influencing (social) media use and its outcomes is indispensable. In order to take into consideration the significance of peer influence on social media we aim to investigate availability pressure, defined as perceived peer’s expectations to be on social media, and production pressure, defined as peer’s expectations to post and share content on social media.

Thus, the contributions of the present study are several. First, to extend the dichotomous differentiation of social media use, in this study we differentiate between active social media use and active public social media use that accounts for private interactions and public postings, respectively (Gerson et al., 2017). In addition, we include passive social media use as relevant measure that captures monitoring behavior in terms of observing other’s profiles. Second, by conducting a two-wave panel survey among late adolescents and young adults, we account for over time associations between overall social media use, perceived digital pressure from peers in terms of availability and production pressure, and ultimately youth’s life satisfaction.

Differential Approaches to Social Media Use

The existing empirical research on social media use can be divided into two dominating areas (Weinstein, 2018). The first area reflects opportunities and benefits of social media use for individuals’ well-being, which is particularly relevant against the background of the rapid increase of various social media platforms (Orben, 2020). A variety of social media platforms are used on mobile devices, predominantly for instant messaging services that help individuals with relationship formation and relationship maintenance (e.g., Burke et al., 2011; Hall & Baym, 2012). This online connectivity adds another layer to the bridging and bonding social capital (Ellison et al., 2007; Phua et al., 2017) and exerts positive influence on well-being (Chan, 2015, 2018).

The second area reflects negative outcomes of social media use and deals with potential risks for ones’ well-being (e.g., Keles et al., 2020). For example, earlier research on adolescents’ and adults’ social media use established negative outcomes such as upward social comparison (Frison & Eggermont, 2016; Schmuck et al., 2019), loneliness, and depressive mood (Frison & Eggermont, 2015; Matthes et al., 2020; Wang et al., 2018). All of these outcomes are relevant indicators of individuals’ well-being, especially in the long term.

However, researchers point to the necessity of distinguishing between the outcomes of social media use that are contingent on the way social media are used. In line with this reasoning, in a recent meta-analysis, researchers demonstrated that different types of social media use, such as interactivity and self-presentation, are associated with higher well-being, while solely consuming social media content is associated with lower well-being (Liu et al., 2019). Furthermore, a meta-analytic evidence has shown a small negative association between social media use and mental health outcomes (Meier & Reinecke, 2021) arguing that not only different communication channels and types of interaction matter, but also different indicators in terms of well-being and mental health.

One of the primary goals of this study is to establish and further elucidate different activities related to social media use. Since the dawn of social networking sites, users have been exposed to an increasing number of different features and affordances provided by social media platforms such as Facebook (e.g., Burke & Kraut, 2016; Frison & Eggermont, 2015). Specifically, Facebook allows for different types of communication practices over the platform. Previous research has therefore distinguished between “(1) targeted communication to a specific person (one-to-one communication), (2) one-click communication consisting of low-effort feedback in a form of a ‘like’, and (3) broadcast communication (one-to-many) that refers to posting status updates on ones’ own wall in order to reach wider audience” (Burke & Kraut, 2016, p. 266). Similarly, past studies differentiated between active and passive use of Facebook (Frison & Eggermont, 2016) suggesting that non-interactive activities are also relevant part of Facebook use.

In recent meta-reviews, academics have raised concerns regarding the dichotomous approach and differential outcomes of active and passive social media use on well-being (Valkenburg, Meier, & Beyens, 2022; Valkenburg, van Driel, & Beyens, 2022) emphasizing the need to precisely define and account for different types of social media use over time. The majority of reviewed studies argue that active social media use results in positive outcomes, whereas passive social media use leads to negative outcomes. However, research has demonstrated that such differentiation does not always yield previously assumed positive versus negative outcomes (Beyens et al., 2020), especially when looking into a variety of active social media uses, such as private and public social interactions (Frison & Eggermont, 2020). Therefore, the goal of the present study is to extend the twofold difference in social media usage types and simultaneously investigate outcomes of active social media use (private interpersonal social interactions) in comparison to active public social media use (broadcasting to wider audiences) and passive social media use (monitoring other people’s social media profiles).

Social Media Use and Perceived Digital Pressure

As social media features expand, so do the activities that can be carried out through social media platforms. Researchers have so far mainly accounted for two main processes, namely, active use that refers to social interactions, and passive use that refers to observing other users’ profiles. However, a third factor representing active non-social use has emerged as a relevant feature of particularly Facebook use (Gerson et al., 2017). While active use refers to private online interactions, active non-social use refers to positing content publicly without interacting, thus comprising both active and passive characteristics. In that sense, active non-social use includes public posting on one’s own profiles that others can see. Taking all three activities together, the present study suggests that overall immersion in private and public social media use likely creates cognitive preoccupation with social interactions, content production, and peer monitoring that can result in negative consequences such as perceived pressure.

Perceived pressure refers to norms or “perceived societal expectation to function digitally” (Büchi et al., 2019, p. 4). As such, it is a contextual factor concerning social setting. We define digital pressure as peer’s expectations to be available through social media (availability stress, Steele et al., 2020) but also to produce and post own content. In this study, we therefore differentiate between availability pressure that is characterized by high responsiveness on social media, and production pressure that refers to peer expectations to create content and post on social media. Both types of digital pressure reflect overarching perceived social pressure to be reachable, responsive, and active in social media communication (Büchi et al., 2019). Such pressure has been referred to as entrapment (Hall, 2017; Hall & Baym, 2012), regarded as a dialectical tradeoff that is unique to mobile communication technologies (Hall & Baym, 2012). The internalization of social expectations manifests when individuals’ start to perceive strong social pressure to be accessible (Hall, 2017). Hall (2017) further explains that “expectations of closeness and availability are accompanied by a competing desire to be independent and unavailable” (p. 149), which creates a contradiction. A desire to be autonomous clashes with the need to fulfill the expectations of others that may bring relational satisfaction (Halfmann & Rieger, 2019). Therefore, it becomes evident that independent social media use largely relates to other users’ expectations, especially in regard to close social ties.

Based on the theoretical framework of self-perception and attitude-behavior consistency, past behaviors can shape perceived norms and expectations (Zanna et al., 1981). The framework suggests that individuals cannot easily assess their attitudes and feelings unless they engage in a behavior that reflects the attitude as reliable information from which it is possible to infer about it.

Applied to the context of social media use, one’s own behavior leads to self-reflection and consequently to consonant norms and attitudes. For example, posting a lot of content leads to a belief that it is normative to engage in high frequency of posting on social media. Furthermore, in line with the norm of reciprocity in interpersonal relationships, active targeted interactions with specific social media ties foster a stronger sense of obligation to be readily available and engage in interactions (Burke & Kraut, 2016; Verduyn et al., 2022; Wenninger et al., 2019).

In the context of cohabiting romantic partners, Taylor and Bazarova (2021) have investigated the concept of connected availability, defined as the perception that a partner is continuously within reach through various communication channels. Findings of the longitudinal dyadic analysis suggested that always being within reach enhances feelings of security without necessarily causing added stress in romantic partnerships. However, less is known about the dual nature of always-on digital connectivity, its potential to enhance feelings of security, or the stress associated with constant availability in friendships.

As social demands of mobile availability (Thomée et al., 2011) continuously grow, it is to be expected that higher involvement in active social media use brings forward both availability and production pressure. To further explain the specificities of cognitive engagement with social media, researchers have developed a concept called “online vigilance” (Klimmt et al., 2017; Reinecke et al., 2018), suggesting three central factors: salience, reactibility, and monitoring.

Salience indicates “constant cognitive engagement with the online environment” (Reinecke et al., 2018, p. 4) that became part of individuals’ offline environment, for example, when users frequently think about their social media activities and people while being engaged in other situations in real life.

Reactibility is defined as a “continuous inclination to respond and to prioritize events and cues from the online sphere over the demands of the current offline environment” (Reinecke et al., 2018, p. 5).

Monitoring refers to checking, observing, maintaining an overview, and staying updated on current happenings in one’s social media repertoire of messages, threads, posts on timeline, news feeds, and other social media users. For the purpose of the present study, reactibility dimension is understood as active social and active public social media use in terms of private and public communication with other social media users, whereas monitoring reflects passive use. While online vigilance, including reactibility and monitoring, may exert positive effects on relatedness need satisfaction, it has also been shown that cognitive salience may result in negative, stress-related experiences, due to the constant focus and preoccupation with happenings in the online sphere (Freytag et al., 2021; Reinecke et al., 2018). Although passively consuming content and browsing through social media feeds is more prevalent on social media than active use, its stagnant, non-interactive role suggests a weaker influence on digital pressure to be available and produce content (as shown for different well-being outcomes (Burke & Kraut, 2016; Karsay et al., 2023; Verduyn et al., 2022; Wenninger et al., 2019)). Nevertheless, we account for the engagement in passive social media use as one of the main activities on social media. Thus, based on the theoretical framework and previous findings, we proposed the following set of hypotheses:

H1: Social media use, in terms of (a) active social and (b) active public social media use, will positively predict availability pressure over time, while (c) passive social media use will not.

H2: Social media use, in terms of (a) active social and (b) active public social media use, will positively predict production pressure over time, while (c) passive social media use will not.

Reciprocal Relationships Between Perceived Digital Pressure and Social Media Use

In the context of social media connections, motivations to engage in a certain type of social media use should be accounted for on the basis of peer influence. According to the social influence theory, Kelman (1958) argued that social influence, in the form of communicated information as external input, might impact individuals’ attitudes. His research focused on whether such attitude changes were temporary or lasting with the potential of becoming integrated into an individual’s value system. Kelman (1958) proposed that variations in attitudes and actions influenced by social factors might occur at different levels, reflecting three processes that may shape individual’s behavior in terms of compliance, identification, and internalization. For instance, the internalization mechanism reflects the acceptance of peer influence due to intrinsic fulfillment that brings rewarding experience because of alignment with others’ opinion. Thus, it becomes increasingly relevant to understand how individuals navigate perceived expectations and potential pressure to be available on social media. Based on the social influence theory, perceived digital pressure to be available and produce content on social media could lead to internalization of behavior, thus increasing engagement in social media.

Prior studies have suggested a concept of being entrapped in mobile media use (Hall, 2017). Such entrapment might bring about feelings of stress, anxiety, or guilt due to frequent responding to connection cues. In other words, feeling entrapped might drive social media use, but it also “reflects the socially situated patterns of media use that brought about the feeling of entrapment to begin with” (Hall, 2017, p. 150). This understanding of bidirectional relationship between feelings of entrapment and social media use suggests a self-reinforcing circle. Ultimately, as the goal is to advance social connections from the perspective of feeling entrapped, a possible bidirectional relationship between adherence to connection cues and social media use is to be expected.

Based on identity processing theory (Berzonsky, 2011), people employ social-cognitive strategies to make decisions in three ways: informational, normative, and diffuse-avoidant. In this study, we focus on the normative style of identity processing that can be described as obedience to social expectations and external norms. Perceived social norms of social media users account for specific behaviors related to disclosure on social media (Masur et al., 2021). Perceived social expectations to behave in a certain way reflect the perceived pressure individuals feel to act under certain social influence that can become especially apparent on social media. Thus far, research did not sufficiently account for predictors of overall social media use in terms of perceived expectations and pressure to be available and post on social media. Thus, we refrained from suggesting hypotheses and based on the theoretical overview proposed research questions:

RQ1: How will availability pressure predict (a) active social media use, (b) active public social media use, and (c) passive social media use over time?

RQ2: How will production pressure predict (a) active social media use, (b) active public social media use, and (c) passive social media use over time?

Perceived Digital Pressure as a Predictor of Life Satisfaction

Subjective well-being is defined as individual’s evaluation of own life satisfaction (Diener et al., 1985). As part of hedonic well-being, Diener et al. (2018) define subjective well-being as a construct consisting of two components, affective well-being, in terms of positive and negative affect, and cognitive well-being, that is satisfaction with life. Given that life satisfaction represents more stable trait that situational state and subjective experience of how well one feels about their life, we focus on young adults’ subjective well-being measurement of life satisfaction based on previous empirical research in the area of social media use (e.g., Dienlin et al., 2017; Taylor & Bazarova, 2021).

Research indicates that higher satisfaction with life is strongly associated with better social relationships and health, longevity, work performance, and creativity (Diener et al., 2018). As healthy social relationships largely matter for life satisfaction, individuals thrive to maintain their relationships and fulfill their need to belong through social media (e.g., as established in computer-mediated communication, Tong & Walther, 2011). However, social media use on its own might not be a strong predictor of relatively stable life satisfaction (Dienlin & Johannes, 2020). Instead, individuals’ cognitive preoccupation and internalized expectations to be readily available and post updates on social media might be a more relevant predictor of life satisfaction because of the perceived pressure (Yang, 2022). In line with the “permanently online, permanently connected” mind-set, such cognitive preoccupation puts forward constant thinking and prioritizes fulfilling these social expectations (Klimmt et al., 2017), which can put additional pressure on individuals and thus affect their life satisfaction.

In fact, feelings of entrapment, along with expectations and perceived pressure to respond to one’s online contacts, were shown to be a positive predictor of friendship dissatisfaction (Hall & Baym, 2012). Furthermore, extant research has shown links between problematic social media use and lower well-being. Especially in the case of online vigilance or cognitive overload, studies have demonstrated that salience (i.e., thinking about social media connections) is negatively related to well-being (Johannes et al., 2021). Moreover, Hall (2017) found that feelings of entrapment, i.e., anxiety and stress related to responding and being consistently available through mobile phones, were significantly related to lower affective well-being. Therefore, we expected the same pattern to occur in the context of feeling pressured to be at one’s disposal and post content on social media that could diminish life satisfaction in the long term:

H3: Both (a) availability and (b) production pressure will negatively predict life satisfaction over time.

Method

We carried out a two-wave panel survey among late adolescents and young adults from Germany, recruited through a private survey company Dynata. The rationale for choosing the time interval between 4 to 5 months was based on previous long-term studies in a similar research area on social media use (Matthes et al., 2020; Van Zalk et al., 2011; Yao & Zhong, 2014) and theoretical suggestion emphasizing that “the optimal time lag” for research constructs regarding well-being is between 2 and 4 months (Dormann & Griffin, 2015, p. 497). The first survey wave was distributed in April 2021, while the second survey wave took place in August and September 2021. Prior to data collection, we obtained approval to conduct the study from the Institutional Review Board of the Department of Communication at the University of Vienna. It is relevant to note that this study is part of a larger project that deals with long-term relationships between adolescents’ social media use and well-being.

Participants

To be eligible to take part in the panel survey, participants had to provide their consent, be active on at least one social media platform, and be between 16 and 21 years old. We have excluded n = 120 unreliable respondents in the first wave to ensure sufficient data quality. The criteria for exclusion were not passing three attention checks, not being focused on answering the survey and responding to the survey below one-third of the sample’s duration median (> 30% faster based on minimum criteria outlined in Greszki et al., 2014).

A total of N = 978 participants (16 – 21 years, M_age = 19.08, SD_age = 1.57) completed the survey in the first wave. In the second wave, N = 415 (M_age = 18.91, SD_age = 1.55) took part. Attrition rate was 57.57%. The final sample of participants in both waves included 58.31% women, 41.69% men. Regarding education, 26.51% had low-level education (i.e., lower secondary or vocational school education), 42.41% had medium-level education (i.e., upper secondary education), and 31.08% had high-level education (i.e., completed upper secondary or university education).

We conducted chi-square and Welch’s t-tests to examine whether individuals who participated in both waves (n = 415) differed significantly from those who only participated in the first wave (n = 563). There were significant but very small differences for age, t(900.18) = −2.79, p = .005, Cohen’s d = −.18 and gender, χ²(2) = 8.04, p = .018, Cramér’s V = .09. Compared to the both-waves sample, the sample that consisted of participants who dropped out after the first wave had a lower ratio of women participants (52.2% vs. 58.3%) and was older (M = 19.20, SD = 1.58 vs. M = 18.91, SD = 1.55). No significant differences were found for education, χ²(2) = 1.53, p = .047, V = .04, active social media use, t(900.57) = −0.77, p = .440, d = −.02, active public social media use, t(895.43) = 0.67, p = .501, d = −.02, passive social media use, t(918.99) = −1.77, p = .077, d = .03, availability pressure, t(880.05) = 1.29, p = .196, d = −.03, production pressure, t(898.03) = 0.88, p = .381, d = −.02 and life satisfaction, t(894.88) = −1.77, p = .077, d = −.02. Thus, dropouts were random.

Measures

The present study utilized short versions or adjusted high-loading items of established scales to avoid response fatigue while minimizing losses in measurement quality. Unless otherwise indicated, all items were measured with 5-point Likert-type scales ranging from 1 = “never” to 5 = “always” and were presented in a randomized order.

Social Media Use

Participants’ social media use was measured with three dimensions adapted from Gerson et al. (2017). To measure frequency of social media use participants were instructed to “Please indicate how often you typically engage in the following activities on your social media channels at this time.” For active social media use the items were: (1) “I comment on my friends’ posts”; (2) “I chat with my friends”; and (3) “I send private messages to my friends” (T1: ω = .76, M = 3.87, SD = 0.91; T2: ω = .77, M = 3.79, SD = 0.94). For active public social media use: (1) “I post or share publicly”; (2) “I post or share photos publicly”; and (3) “I post or share videos publicly” (T1: ω = .85, M = 2.52, SD = 1.01; T2: ω = .86, M = 2.51, SD = 1.05). For passive social media use: (1) “I look at my friends’ profiles”; (2) “I look at what others have posted or commented on”; and (3) “I look at photos of my friends” (T1: ω = .81, M = 3.72, SD = 0.91; T2: ω = .82, M = 3.74, SD = 0.91). When all three latent variables (dimensions) are entered, confirmatory factor analysis shows a good model fit: comparative fit index (CFI) = .96; Tucker-Lewis index (TLI) = .94, χ²/df = 2.44; root mean square error of approximation (RMSEA) = .06, 90% CIs [.05; .07].

Perceived Digital Pressure

We assessed participants’ digital pressure with two dimensions consisting of availability and production pressure with items adapted from Hall and Baym (2012). To gauge availability pressure, participants indicated how the following statements apply to them: (1) “My friends expect me to always be available on social media”; (2) “My friends expect me to be on social media every day”; and (3) “My friends expect to be able to communicate with me on social media all the time” (T1: ω = .87, M = 2.33, SD = 1.11; T2: ω = .83, M = 2.30, SD = 1.03). To gauge production pressure, participants indicated how the following statements apply to them: (1) “My friends expect me to constantly post something on my social media profiles”; (2) “My friends expect me to share something about my life on social media every day”; and (3) “My friends expect me to always post new content” (T1: ω = .91, M = 1.79, SD = 1.02; T2: ω = .90, M = 1.71, SD = 0.99). Confirmatory Factor Analysis showed a good model fit: CFI = .98; TLI = .96, χ²/df = 2.76; RMSEA = .07, 90% CIs [.05; .08].

Life Satisfaction

We assessed life satisfaction with four items from the satisfaction with life scale by Diener et al. (1985). On a 5-point Likert-type scale, ranging from 1 (“does not apply at all”) to 5 (“applies completely”), participants were asked to indicate their agreement with the following statements: “My life is going well”; “My life is just right the way it is”; “I have a good life”; and “I have what I want in life” (T1: ω = .84, M = 3.28, SD = 0.85; T2: ω = .85, M = 3.43, SD = 0.82). Confirmatory Factor Analysis showed a good model fit: CFI = .98; TLI = .96, χ²/df = 3.16; RMSEA = .08, 90% CIs [.05; .10].

Control Variables

We controlled for participants’ age, gender (coded as 1 = women vs. 0 = men and diverse), and education (coded as two dichotomous variables: [a] 1 = low-level vs. 0 = medium- and high-level, and [b] 1 = high-level vs. 0 = low- and medium-level) measured at Time 1.

Data Analysis

We used lavaan package (Rosseel, 2012) in R program to analyze the data with Structural Equation Modeling (SEM). To account for the missing values from participants that dropped out after the first wave, we employed the Full Information Maximum Likelihood (FIML) procedure, which accounts for values that were missing at random (Enders & Bandalos, 2001). We used the CFI, TLI, the chi-square to degrees of freedom ratio (χ²/df), and the RMSEA to establish the model fit indices (Byrne, 2001). In our SEM model, we controlled for all autoregressive paths (i.e., life satisfaction at Time 1 as a predictor of life satisfaction at Time 2) that allows us to explain changes in the dependent variables from T1 to T2 which are not explained by individuals’ baseline measures.¹ Data and analysis script are available at Open Science Framework: https://osf.io/ucf3x/.

Results

Measurement Invariance

To establish metric and scalar longitudinal measurement invariance, we constrained all factor loadings of the same items as well as all latent means across two measurement points (Vandenberg & Lance, 2000). The constrained model revealed a good fit: CFI = .96; TLI = .96, χ²/df = 1.79; p < .001; RMSEA = .03, 90% CIs [.03; .03]. No significant differences between the latent means of active public social media use (p = .992), passive social media use (p = .958), availability pressure (p = .993), and production pressure (p = .052) between Time 1 and Time 2 were found, which confirms full metric and scalar invariance. Yet, the differences between active social media use and life satisfaction were significant over time. Therefore, we released the constraints on the intercepts of two items of active social use and three items of life satisfaction, which revealed no significant differences of active social media use between two time points (p = .390) and no significant differences of life satisfaction between two time points (p = .124). Therefore, for active social media use and life satisfaction full metric invariance and partial scalar invariance could be established. A nested model comparison of the constrained and the unconstrained measurement model showed no significant difference (Δχ² = 18.40, df = 21, p = .624).

Structural Equation Model

The hypothesized model revealed acceptable model fit: CFI = 0.95; TLI = 0.94, χ²/df = 2.11; p < .001; RMSEA = 0.04, SRMR = .07, 90% CIs [0.03; 0.04]. Table 1 shows an overview of the correlations between the main variables in the study. Table 2 presents an overview of the results.

Table 1.

Zero-Order Correlations.

Correlations	1	2	3	4	5	6	7	8
1. Active social media use (T1)	1
2. Active public social media use (T1)	.22***	1
3. Passive social media use (T1)	.35***	.25***	1
4. Availability pressure (T1)	.14***	.25***	.19***	1
5. Availability pressure (T2)	.14**	.23***	.09	.52***	1
6. Production pressure (T1)	.00	.30***	.03	.65***	.36***	1
7. Production pressure (T2)	.09*	.29***	.04	.43***	.73***	.57***	1
8. Life satisfaction (T1)	.03	−.02	.04	−.03	.01	−.01	.01	1
9. Life satisfaction (T2)	.03	.06	.04	.03	−.01	−.02	.03	.35***

Note. N_T1 = 978, N_T2 = 415; T1 = Time 1, T2 = Time 2.

p < .05, ** p < .01, *** p < .001.

Table 2.

Results of the Hypothesized Longitudinal Structural Equation Model.

Predictors (T1)	Availability pressure (T2)	Production pressure (T2)	Life satisfaction (T2)	Active SMU (T2)	Active public SMU (T2)	Passive SMU (T2)
Predictors (T1)	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)	b (SE)
Availability pressure	.43*** (.05)	−	.03 (.06)	.14** (.04)	−.07 (.06)	.02 (.06)
Production pressure	−	.54*** (.06)	−.02 (.07)	−.14** (.05)	.19** (.07)	−.02 (.07)
Life satisfaction	−	−	.52*** (.06)	.11* (.05)	.03 (.06)	.11 (.07)
Active SMU	.17 (.09)	.15 (.08)	−.05 (.09)	.53*** (.08)	−	−
Active public SMU	.09 (.06)	.11* (.05)	.08 (.05)	−	.52*** (.06)	−
Passive SMU	−.09 (.07)	−.07 (.07)	−.00 (.06)	−	−	.55*** (.06)
Age	.00 (.03)	.01 (.03)	−.01 (.03)	.05* (.02)	.01 (.03)	.04 (.03)
Gender^a	−.14 (.09)	−.15 (.10)	−.05 (.08)	.18* (.07)	.21* (.09)	.28** (.09)
Education (low)^b	.04 (.11)	−.06 (.12)	−.09 (.10)	.08 (.09)	−.10 (.12)	.05 (.11)
Education (high)^b	.10 (.12)	−.13 (.13)	.04 (.10)	−.13 (.09)	−.01 (.12)	−.09 (.12)
Adj. R²	.32	.34	.32	.42	.37	.36

Note. N_T1 = 978, N_T2 = 415; T1 = Time 1, T2 = Time 2, SMU = social media use.

Male is the reference category.

Medium-level education is the reference category.

p < .05, ** p < .01, *** p < .001.

In the first hypothesis, we assumed that active social media use and active public social media use would increase availability pressure over time while passive social media use would not have any influence. The results revealed no significant relationships between active social media use at Time 1 and availability pressure at Time 2, b = 0.17, SE = 0.09, β = 0.13, p = .068, CIs [−0.01; 0.27], active public social media use at Time 1 and availability pressure at Time 2, b = 0.09, SE = 0.06, β = 0.10, p = .121, CIs [−0.03; 0.23], and passive use at Time 1 and availability pressure at Time 2, b = −0.09, SE = 0.07, β = −0.09, p = .212, CIs [−0.22; 0.05]. Thus, we found no support for H1a, H1b, while H1c was supported.

Next, in the second hypothesis, we assumed that active social media use and active public social media use would positively predict higher production pressure after four months, whereas passive social media use would not have any influence. The results revealed that only active public social media use at Time 1 positively predicted production pressure at Time 2, b = 0.11, SE = 0.05, β = 0.11, p = .048, CIs [0.00; 0.22], while active social media use, b = 0.15, SE = 0.08, β = 0.11, p = .068, CIs [−0.01; 0.22], and passive use, b = −0.07, SE = 0.07, β = −0.06, p = .332, CIs [−0.19; 0.06], did not have significant relationships with production pressure over time. Therefore, H2b and H2c were supported, while H2a was not supported.

To account for possible reciprocal influences, we proposed two research questions asking whether availability pressure would increase active social, active public, and passive social media use after 4 months. As an answer to RQ1, the results revealed that availability pressure at Time 1 positively predicted active social use at Time 2, b = 0.14, SE = 0.04, β = 0.22, p = .001, CIs [0.09; 0.35]. In RQ2, we were interested if production pressure would predict active social, active public and passive social media use over time. The findings showed that production pressure at Time 1 positively predicted active public social media use at Time 2, b = 0.19, SE = 0.07, β = 0.19, p = .008, CIs [0.05; 0.33] and negatively predicted active social use at Time 2, b = −0.14, SE = 0.05, β = −0.19, p = .004, CIs [−0.33; −0.06].

In the third hypothesis, we expected both availability and production pressure to decrease life satisfaction over time. The findings showed no significant relationships between availability pressure at Time 1 and life satisfaction at Time 2, b = 0.03, SE = 0.06, β = 0.04, p = .587, CIs [−0.12; 0.21] as well as between production pressure at Time 1 and life satisfaction at Time 2, b = −0.02, SE = 0.07, β = −0.02, p = .823, CIs [−0.19; 0.15].

Regarding control variables, we found that gender positively predicted active social media use at Time 2, b = 0.20, SE = 0.07, β = 0.29, p = .007, CIs [0.04; 0.25], active public social media use, b = 0.21, SE = 0.09, β = 0.22, p = .021, CIs [0.02; 0.20], and passive use, b = 0.28, SE = 0.09, β = 0.31, p = .003, CIs [0.05; 0.25] meaning that women participants engaged in more overall social media use than men. Moreover, higher age related to higher active social media use over time, b = 0.05, SE = 0.02, β = 0.07, p = .039, CIs [0.01; 0.22]. Although not hypothesized, the results of the cross-lagged autoregressive model revealed that life satisfaction positively predicted active social media use over time, b = 0.11, SE = 0.05, β = 0.12, p = .040, CIs [0.01; 0.24].

Discussion

Previous studies have focused primarily on the differentiation between positive outcomes of active social media use in contrast to negative outcomes of passive social media use on well-being, while research on reciprocal relationships regarding private and public social media use in terms of active social, active public as well as passive use, perceived digital pressure, and life satisfaction is scant. The main goal of the current study was therefore to examine the processes by which three different types of social media use may be reciprocally related to perceived digital pressure and consequentially to life satisfaction.

The findings showed that only active public social media use positively predicted production pressure over time and this relationship was reciprocal. According to the self-perception and attitude-behavior consistency model, posting a lot might have formed an expectation and a norm to behave in that way that is reflected through others. Production pressure reinforced active public use which is in line with the social influence theory and the process of internalizing peer expectations to behave in a certain way (Kelman, 1958). We find that external input affects individuals’ subsequent behavior and might become integrated into an individual’s value system over time. Finding a longitudinal reciprocal relationship between active public social media use (public posting and sharing of content to wider audience) and production pressure, suggests a self-reinforcing spiral between perceived digital pressure to deliver and fulfill expectations of social network friends. In addition, according to the perceived social norms, social media users might refrain from disappointing their peers and succumb to their expectations to create content, post photographs, videos, and status updates. This way of self-exposure can further enhance relationships and serve as an additional online tool to keep the social network informed and updated about one’s life. This longitudinal finding is in line with the propositions of entrapment (Hall, 2017; Hall & Baym, 2012) demonstrated in previous studies in the context of texting and calling through mobile phones. Extending this, our study confirms a similar pattern for engaging in public social media use due to the perceived peer expectations to post and share content over social media platforms.

Contrary to expectations, the findings showed that production pressure negatively predicts active social use over time. Interestingly, this result suggests that higher pressure to produce and broadcast content on social media publicly could diminish or take precedence over (private) social interactions. Such distinction has been hinted at in recent reviews that emphasize the conceptual and empirical difference between public and private social media use (Valkenburg et al., 2022). Perhaps the expectation to post more on social media reduces the motivation or need to communicate interpersonally in private chats. In addition, a larger number of social media contacts can be involved in individuals’ public posts. Because public communication is one-to-many, it could leave the impression that users keep updating larger social network instead of only selected contacts in one-to-one communication in private chats (e.g., Burke & Kraut, 2016).

Furthermore, as expected, availability pressure positively predicted active social media use, suggesting that perceived peer expectations to be reachable and responsive over social media increase active social interaction over time. This finding is in line with previous studies suggesting that digital pressure might be confounded with the perceived social connectedness (Büchi et al., 2019). However, we did not find a reciprocal relationship. Thus, we can confirm a one-way temporal relationship, suggesting that availability pressure matters for active social engagement and possibly drives higher communicative use in the long run. In that context, again confirming social influence theory (Kelman, 1958) and internalization processes of social media behaviors through identity processing framework (Yang, 2022), leads us to believe that peer expectations and perceived availability pressure are the reason to actively engage in social interactions and information sharing (Knop et al., 2015; Yang, 2022).

In line with previous research (Verduyn et al., 2022; Wenninger et al., 2019), there was no significant reciprocal relationships between passive social media use and perceived digital pressure, suggesting that monitoring other users and information on social media does not create self-reinforcing internalizing social media behavior and potentially does not count toward cognitive preoccupation with social media. One reason for this null finding could lie in the typical feature of passive use that does not include any targeted social engagement making it difficult to sufficiently fulfill social needs and enhance peer expectations to be on social media. In other words, spending time on social media and browsing through its content without interactions does not create pressure and vice versa, peer pressure does not enhance passive type of social media use.

Finally, the results revealed that both dimensions of digital pressure were not related to life satisfaction (while controlling for different types of social media use), suggesting that perceived pressure to be available and produce social media content does not affect youth’s life satisfaction over time. This finding lends support to studies pointing to the minimal or even lack of impact social media has on youth’s well-being (Orben, 2020; Schemer et al., 2021). In fact, finding no relationship between perceived digital pressure that reflects cognitive preoccupation with online environment in the form of peer expectations, and life satisfaction that reflects offline environment, is a valuable contribution to previous research suggesting that social media have detrimental outcomes on well-being. Although digital pressure could be regarded as a harmful consequence, it seems to not be as strong to influence subjective well-being that consists of many other arguably more relevant factors, such as health, satisfactory social relationships, safety, income, fulfillment, etc. (Diener et al., 2018). This null finding could suggest that youth places more focus on other dimensions of their lives. In line with the longitudinal study by Taylor and Bazarova (2021) that was focused on romantic partnerships, our results did not show negative outcomes of enhanced connected availability in the context of friendships over time. It is good news that digital pressure does not interfere with youth’s life satisfaction although it reflects peers expectations that could be relevant for fulfilling social relationships.

More longitudinal research is necessary, however, to explore these differences in more detail. Nevertheless, the current study offers an important first step in exploring the reciprocal links between the three types of social media use and perceived digital pressure. Given that digital stress can mediate the relationship between social media use and psychosocial functioning (Steele et al., 2020), perceived digital pressure to be responsive and post on social media may mediate the association between social media use and life satisfaction, and such as relationship should be confirmed or refuted in a three-wave panel design.

Limitations and Future Research

Even though the present study offers valuable insight into the reciprocal associations between three types of social media use and digital pressure, it is still subject to some limitations that represent possible future research endeavors. First, data collection was based only on a two-wave panel survey with a small sample size, thereby not providing possibility for causal inferences. Instead, we could test possible temporal associations that may reflect the potential ongoing interplay between overall social media use and digital pressure. Future longitudinal research employing a minimum of three or more survey waves with larger samples as well as experimental studies are needed to conduct within-person analyses and to elucidate cause and effect relationships.

Second, we did not include separate measures of different social media platforms; instead, we collapsed three different types of social media use under one general scale reflecting all social media. In this sense, it is also possible that some types of social media use differ regarding the platform; for example, social media that are designed and used only for private communication do not allow for active public use as much as social media platforms that unify private and public communication. Thus, further differences in relation to perceived digital pressure stemming from different social media platforms may be detected in future research. Finally, we relied on self-report measures of one demographic in a specific country of interest, thus limiting the generalizability of our results. As social media use is prevalent across generations, cultures, and continents, further studies investigating perceived digital pressure in relation to social media use in a variety of demographics and contextual settings are indispensable.

Conclusion

Focusing on youths’ well-being is one of the biggest societal concerns regarding their future in increasingly digitized societies. Given that social media repertoire perpetually grows, it became crucial to investigate perceived expectations related to social media use. The present study sheds light on the complex dynamics between different types of social media use and perceived availability and production pressure. The findings show that availability pressure drives active social interactions, while production pressure is reciprocally related to active public social media use, suggesting that posting publicly creates a reinforcing circle related to experiencing stress. Interestingly, both forms of digital pressure stemming from peer expectations show different influences on active social and active public social media use but do not relate to life satisfaction. Thus, it becomes clearer that social media engagement might not impact subjective well-being in the long run. These findings emphasize the need for further research on the internalizing processes influenced by perceived peers’ expectations in digital environments, which can contribute to a more comprehensive understanding of the effects of social media on youths’ well-being.

Footnotes

Data Availability Statement

The research data used in this study is freely available in a public data repository of the Open Science Framework: .

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Austrian Science Fund (FWF) as part of the project “Social Media Use and Adolescents’ Well-Being” (P 33413-G).

ORCID iD

Anja Stevic

Notes

Author Biography

Anja Stevic (PhD, University of Vienna) is a Postdoctoral Researcher in the Department of Communication at the University of Vienna. Her research interests include social media and smartphone use, mobile communication, and online dating in relation to well-being.

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Under Pressure? Longitudinal Relationships between Different Types of Social Media Use,Digital Pressure,and Life Satisfaction

Abstract

Keywords

Differential Approaches to Social Media Use

Social Media Use and Perceived Digital Pressure

Reciprocal Relationships Between Perceived Digital Pressure and Social Media Use

Perceived Digital Pressure as a Predictor of Life Satisfaction

Method

Participants

Measures

Social Media Use

Perceived Digital Pressure

Life Satisfaction

Control Variables

Data Analysis

Results

Measurement Invariance

Structural Equation Model

Discussion

Limitations and Future Research

Conclusion

Footnotes

Data Availability Statement

Declaration of Conflicting Interests

Funding

ORCID iD

Notes

Author Biography

References