A Panel Study of the Relationships Between Social Media Interactions and Adolescents’ Pro-Environmental Cognitions and Behaviors

Abstract

Despite the circulation of climate content on social media, little longitudinal research has explored their relations with pro-environmental attitudes and behaviors. Considering that individual behaviors, in conjunction with structural change, are critical to mitigate climate change, this two-wave panel study among 657 adolescents examined how social media interactions (i.e., exposure, liking, commenting, sharing and posting of climate messages) reciprocally related to adolescents’ pro-environmental cognitions (i.e., descriptive and injunctive norms, attitudes) and behavior. The study showed transactional relationships between self-posting and sharing of climate content over time. Pro-environmental behavior at Wave 1 (W1) positively related to all cognitive variables at Wave 2 (W2), yet no reciprocal relationship occurred as none of the cognitive variables (W1) predicted behavior (W2) over time. Moreover, with the exception of the positive link between “liking” (W1) and attitudes (W2), no (reciprocal) relationships between social media interactions and adolescents’ pro-environmental cognitions and behavior occurred over time.

Keywords

pro-environmental behaviors social media interactions adolescence longitudinal research

Previous studies have highlighted the role of pro-environmental behaviors (PEB) in managing the climate crisis (Stern, 2006). According to contemporary literature, holding strong pro-environmental cognitions (PEC; i.e., attitudes and norms) is an important predictor of PEB (Li et al., 2019). Interestingly, individuals who care about the climate crisis do not always engage in PEB nor hold PEC (Pezzullo & Cox, 2016). In particular, adolescents, despite being highly concerned about the personal impact of climate change, are more reluctant to engage in PEB (Corner et al., 2015; Grønhøj & Thøgersen, 2012). As such, research has questioned how contemporary adolescents, who differ from other generations (Andersen et al., 2021; Valkenburg & Peter, 2013), develop PEC and PEB. Literature has explained that adolescents are receptive to other people’s attitudes and behaviors regarding the environment (Casaló & Escario, 2016; Grønhøj & Thøgersen, 2009). Although research has examined the influence of peers and family as socialization agents, limited attention has been given to the role of social media, which is surprising considering its significance in recent protests.

Since 2019, social media have been labeled as a crucial tool for mobilizing youth for climate actions and in particular for the well-attended School Strikes for Climate (Boulianne et al., 2020). A Twitter analysis suggested that exposure to the school strike tweets of Greta Thunberg motivated users to share such messages (Boulianne et al., 2020). The echo chamber literature further suggests that youth who like and share pro-environmental messages are also the most exposed to such messages (Williams et al., 2015). However, little research has investigated adolescents’ social media interactions with pro-environmental messages, despite its importance in understanding how social media dynamics regarding pro-environmental topics evolve in youth and their potential links to their PEC and PEB.

Contemporary research among adults have yielded mixed findings regarding the associations between social media interactions, cognitions, and behaviors related to environmental actions (Ekström & Östman, 2015). This literature suggests that more consistent findings can be found when focusing on youth and distinguishing between different types of social media interactions. In addition, the scarcity of longitudinal research leaves many questions unanswered regarding the temporal dynamics of the connections between social media, cognitions, and behaviors (Ekström & Östman, 2015; Vissers et al., 2012).

To address the aforementioned gaps, the current two-wave panel study (N = 657) aims to test the potential reciprocal reinforcing relationships between different social media interactions (i.e., exposure, liking, commenting, sharing and posting of climate messages) and adolescents’ PEC and PEB. By investigating these relationships over time, this study provides valuable insights into the dynamics of social media interactions and their associations with cognitions and behaviors, particularly in the context of individual, low-threshold actions. Furthermore, by focusing on all-day behaviors, this study explores the possibility of “spill-over” effects, investigating whether engagement with general climate-related content on social media predicts a change in attitudes, norms, and tangible behaviors related to environmental conservation and sustainability. Such focus is different from prior research that typically examined engaging with climate-related content on social media in relation to attitudes toward the environmental crisis (see Casaló & Escario, 2018; Nelms et al., 2017).

Pro-Environmental Attitudes, Norms and Behaviors

Climate change poses a serious threat to the health and well-being of individuals worldwide (Fritze et al., 2008). To prevent and mitigate climate change, individuals have been engaging in a wide range of behaviors in the public and private spheres (Li et al., 2019). Among the individual efforts individuals can engage in are PEB, which can be defined as “actions contributing to environmental conservation, or human activity intended to protect natural resources, or at least reduce environmental deterioration” (Juárez-Nájera et al., 2010, p. 687). Many experts contend, however, that individual behaviors such as PEB do not have a significant effect in mitigating climate change in comparison to actions from corporations, governments, wealthy individuals, banks, and investors (IPCC, 2014; Nielsen et al., 2021). Indeed, studies suggest that the ecological footprints are asymmetrical and the responsibility to engage in PEB should depend on the ecological space occupied (Jagers et al., 2016; Nielsen et al., 2021). Other observations emphasize that while no solution is sufficient on its own, when combined with technological and structural change, behavior, lifestyle, and cultural changes can have a significant mitigation potential (IPCC, 2014; Newell et al., 2022; Song et al., 2019). Individual PEB, albeit limited, go hand-in-hand with collective action (Kenis & Mathijs, 2012) and are believed to minimize the negative impacts of humans on the environment (Newell et al., 2022; Stern, 2006).

One group of individuals that could be expected to engage in PEB is youth. Despite threatening populations globally, young people are in a unique position of vulnerability to the climate crisis as they face the legacy of decisions made by older generations of decision makers. The climate crisis is a defining event for today’s adolescents and young adults, and is believed to shape their values and behaviors (Benckendorff et al., 2012). In line with this, concern over climate change was found to be strongly marked by age, with adolescents being more likely to express concern over climate change (Hornsey et al., 2016; Tobler et al., 2012) and believe in its anthropogenic origins (Feldman, 2010; Hibberd & Nguyen, 2013) as compared to other age groups. Furthermore, as young people feel excluded from political participation (Gordon, 2009), they tend to resort to alternative means of engaging in politics, and, for instance, take action by making particular lifestyle choices (Micheletti & Stolle, 2010; Zamponi et al., 2022). At the same time, research also indicates that youth are more reluctant to engage in PEB than older people are (Corner et al., 2015; Grønhøj & Thøgersen, 2012). The potential explanations for this low engagement are manifold (see O'Brien et al., 2018) but one key reason might be that (young) people are quite skeptic regarding the effectiveness of individual PEB in mitigating climate change (Kenis & Mathijs, 2012). Consequently, as a variety of factors drive individuals’ engagement in PEB, climate concerns do not always translate into actual PEB (Pezzullo & Cox, 2016).

One potential avenue for adolescents to engage in PEB is the development of PEC. Studies using behavioral theories, such as the theory of reasoned action (Fishbein & Ajzen, 1977), have found that attitudes (i.e., the way individuals feel toward a particular subject or behavior; Ajzen & Albarracín, 2007) and social norms may predict intentions to engage in PEB (de Leeuw et al., 2015; Kaiser & Gutscher, 2003). Social norms can be divided into injunctive norms, which refer to perceptions of what ought to be, and descriptive norms, which refer to perceptions of what people’s behavior actually is (Perkins & Berkowitz, 1986). The concept of reciprocal determinism, central to Social Cognitive Theory (SCT; Bandura, 1978, 2001), further warns about the bidirectional nature of the relationships between attitudes, norms, intentions and behavior. SCT has been widely employed in research exploring the relationship between attitudes and behaviors, proposing a model of triadic reciprocity wherein behavior, personal, and environmental factors are interdependent components that mutually influence one another. These factors should not be explored in isolation (Bandura, 1986). The theory proposes that both personal (in this study, attitudes) and environmental (in this study, norms) factors are affected by and in turn determine (intentions for) behaviors. Despite abundant research on the links between pro-environmental attitudes, norms and behaviors, longitudinal evidence is often lacking. Following the process of reciprocal determinism, we hypothesize that:

Hypothesis 1 (H1): Pro-environmental attitudes, norms and behaviors will be reciprocally related over time.

Social Media and Climate-Related Content: Understanding Youth Engagement

While older generations tend to rely on traditional media sources for (environmental) news consumption (Andersen et al., 2021), social media have become an increasingly important source of information on climate change among youth (e.g., Corner et al., 2015; Wu & Otsuka, 2021). Adolescents, in particular, use social media to access information on environmental issues and to encourage action through movements such as Youth for Climate or hashtags like #Savetheplanet (Biswas, 2021). With a staggering 63% of 13 to 18 years old using social media on a daily basis, spending an average of 70 min per day (Rideout & Robb, 2019), it is likely that youth are regularly exposed to climate-related messages on these platforms.

Aside from their prevalence, climate-related news on social media differs from traditional news in a number of ways. The sources of information on social media are diverse and include online newspapers, peers, activists (such as Greta Thunberg), celebrities, and even adolescents themselves. Additionally, political information on social media is more personally-relevant, directed toward the user (Andersen et al., 2021) and interwoven with entertainment content (Gonzalez et al., 2023). The latter aspect is particularly appealing to youth since they often perceive politics as restrictive and “boring” in terms of topics (Moeller et al., 2018). Finally, social media news items are interactive, thanks to features like sharing, liking, and commenting, allowing for self-expression and content creation (Belotti et al., 2022).

The unique nature of political information on social media leads adolescents to consume political (and in particular environmental) content in different ways than older generations. Social media, for instance, allow youth to post environmental content as an expression of their developing identities (Valkenburg & Peter, 2011). Moreover, social media provide ample opportunities to interact with peers on environmental content by sharing and discussing it on social media (Crone & Konijn, 2018). Such peer interactions are especially impactful in adolescence given the pivotal role peers play in this developmental phase (Nesi et al., 2018).

Furthermore, adolescents may be more susceptible to the influence of environmental content on social media than older users. During adolescence, individuals are still forming their political values, attitudes, and behaviors and are more open-minded and receptive to change (Andersen et al., 2021; Valkenburg & Peter, 2013). Research supporting this reasoning showed that exposure to political information in traditional news media had a stronger impact on political involvement among younger users compared to older users (Andersen et al., 2021).

Finally, as digital natives, adolescents are well-versed in the various ways they can engage with (climate) content on social media, such as passively consuming content through scrolling, broadcasting content through self-posting or sharing, and directly communicating with specific content and users through liking or commenting (Burke et al., 2011). However, a significant issue with much of the literature on the effects of social media interactions with climate content on political/learning outcomes is that it often aggregates social media measures (e.g., R. Han & Xu, 2020; W. Han et al., 2018). However, experts contend that this approach needs to be reframed because media use is far from unitary (Ryding & Kuss, 2020) and each type of use relies on distinct psychological mechanisms.

Mechanisms Underlying Different Social Media Interactions

Exposure

When users passively consume content, social reinforcement processes come into play. SCT (Bandura, 2001) suggests that users may learn from the content they are exposed to and may endorse the cognitions and behaviors that they observe as being modeled by attractive role models on social media (e.g., peers, influencers). There is a considerable amount of literature on the effects of exposure to various media content and environmental outcomes.

Using a cross-sectional design, R. Han and Xu (2020) found that social media exposure to environmental information was (indirectly) related to PEB. Similar relationships between social media exposure to climate content and PEB outcomes were found in other studies (e.g., Maran & Begotti, 2021). Although these exposure studies are insightful, they fail to consider that unlike traditional forms of media, social media use is also characterized by additional features that allow users to interact with the content in additional ways, such as liking, commenting, sharing existing content and self-posting (H. M. Kim, 2021).

Liking

In addition to exposure, users may like the relevant content. Likes usually indicate agreement, enjoyment, support or approval of a post (J. W. Kim, 2018; Park & Kaye, 2023). The “like” function is a popular feature of various social media platforms: Users are eight times more inclined to like a post compared with other potential reactive behaviors (sharing or commenting; Pelletier & Blakeney Horky, 2015). Liking content may mobilize similar psychological mechanisms of internalization as exposure. However, liking can also be seen as a more intense form of exposure (Hendrickse et al., 2017; Schreurs & Vandenbosch, 2022). Although liking requires relatively low effort and engagement with the observed content, it still necessitates more effort than mere exposure (D. H. Kim et al., 2021; Kümpel et al., 2015), making it possible that liking has a different impact on PEB and PEC than exposure.

However, evidence is mixed. A set of four experimental studies found that an intention to like persuasive content (e.g., social activist messages on the climate) was the strongest predictor of offline behavioral intentions, in line with the persuasive message (Alhabash et al., 2015). In contrast, a recent two-wave longitudinal study found no relationship between liking status updates or links about social issues and political learning (D. H. Kim et al., 2021). Therefore, research is still needed to further understand the potential associations between liking (environmental) content on social media and users’ attitudes and behaviors regarding the subjects covered in the content.

Commenting

In addition to liking, users may interact directly with existing content by commenting on it. Comments can be defined as “user-generated content published as a thread under items posted by users” (Kaur et al., 2019, p. 3). Compared with liking, commenting demonstrates a higher degree of engagement with the existing content. It also requires more cognitive effort because users should compose a message independently (D. H. Kim et al., 2021). The act of commenting places users as “active agents of information processing and externalizing” because they can reframe and re-evaluate the content they interact with (Choi, 2016; D. H. Kim et al., 2021, p. 4). In this realm, Pingree (2007) theorized how messages may affect their senders and said that expression (via comments) “can motivate exposure, attention and elaboration of media messages, and the act of message composition is often more effective at improving understanding than any act of reception should be” (p. 447). For these reasons, this social media behavior is likely to influence the user’s cognitions and/or behaviors. Yet, research that examines the relationship between commenting on climate content on social media and users’ PEC and PEB is lacking.

Waeterloos et al. (2021) found that 17.5% of their adolescent sample had already commented on a climate-related social media post in a public manner, and 31.5% had done so in a private way (i.e., in a closed Facebook group). Positively commenting on a post can be seen as rewarding and approving the content commented upon. This reasoning is supported by D. H. Kim et al. (2021), who conducted a survey among American adults and found that commenting on and sharing existing political content related to users’ political knowledge (D. H. Kim et al., 2021). While causal effects need to be established in future experimental research, these findings do have implications for a possible relationship between positive comments on climate messages and the development and/or reinforcement of PEB and PEC.

Sharing

Finally, individuals can broadcast content by sharing existing messages or posting new content. Sharing is another central feature of social media, where users can repost existing content posted by other users, adding it to their profiles. When users share existing content, they become a secondary source of that information. This has several psychological benefits (Oeldorf-Hirsch & Sundar, 2015; Sundar & Nass, 2001), including feeling empowered and feeling a sense of influence (Stavrositu & Sundar, 2012). These experiences allow users to assert their identity and to act as a gatekeeper of information for their audience (Sundar, 2008). As a result, users gain a sense of responsibility over the shared issue, leading them to be more involved in that issue (Oeldorf-Hirsch & Sundar, 2015). This can also be true of self-posting behaviors, which place the user as a primary source of that information. Despite the low effort required to share an item, this behavior demonstrates a rather high level of engagement with the content (D. H. Kim et al., 2021) and requires greater commitment to the covered subject compared with other behaviors, such as likes (C. Kim & Yang, 2017).

As a public form of commitment, sharing likely influences users’ subsequent attitudes, norms and behaviors (see Valkenburg, 2017). Johnson and Van Der Heide (2015) supported this reasoning: In their findings, public content sharing had a strong positive effect on attitudes among users who often shared their tastes online. Sharing seemed to weaken attitudes for those who rarely did so. Similar relationships emerged in studies on political communication (de Zúñiga et al., 2014; Kalogeropoulos et al., 2017).

Self-posting

Content that users self-post or share is well considered because users attempt to satisfy status needs through this “public” act (H. Lee et al., 2013; Lu & Hsiao, 2007). In posting, users become vulnerable to public judgment in that the shared content will be (re)viewed by their audience (Rui & Stefanone, 2013). In self-posting, some kind of “verbal commitment” is involved, which research has found to be the strongest predictor of engaging in environmental behaviors (Arlt et al., 2011). Posting also requires more cognitive effort than, for instance, sharing or liking. Moreover, according to Valkenburg’s (2017) self-generated media effect perspective, posting behavior affects the poster’s feelings, norms, attitudes and behaviors so that they align with the content they posted.

There are few studies among adolescents on the self-posting of climate messages. In one of the few cross-sectional studies, Waeterloos et al. (2021) measured posting and sharing with the same item and found that 23.5% publicly posted or shared information concerning the climate issue on social media and that 30.5% did so privately in closed Facebook groups. Although only correlations were investigated, the teenagers engaging in sharing and posting behaviors belonged to “activist” categories, indicating that they were likely to engage in offline environmental actions such as protests and volunteering, to different extents.

In summary, the self-effect framework (Valkenburg, 2017) offers a comprehensive framework to understand the interweaving and reciprocity of self- and reception effects of social media interactions on PEC and PEB. Liking, commenting, sharing and self-posting can affect users’ cognitions and behaviors, either directly via an internalization process, such as self-perception, biased scanning, or public commitment, or indirectly via the moderating feedback of their audience (Valkenburg, 2017). The outcome of this process is an alignment between the content interacted with and the users’ norms, attitudes and behaviors. To fully understand the richness of adolescents’ social media experiences and the singular effects of these different types of social media interactions on PEC and PEB, the differentiation of interactions is essential.

Hypothesis 2 (H2): Exposure to, liking, commenting, sharing and self-posting climate content at Wave 1 (W1) will positively relate to PEC and PEB at Wave 2 (W2).

Research question 1 (RQ1): Do more effortful and engaged interactions (e.g., self-posting) have a stronger impact on PEC/PEB over time compared to less effortful and engaged interactions (e.g., liking)?

Reciprocal Pathways Between Social Media and Cognitions and Behavior

The aforementioned reasoning (see H1/H2 and RQ1) proposes unidirectional relationships between social media interactions with climate content and adolescents’ PEC and PEB. Yet, such relationships are likely reciprocal. As described above, SCT provides a framework to understand how psychological, external, and behavioral factors interact and influence each other, leading individuals to maintain, change, or reinforce their thoughts, feelings, and actions (Bandura, 2001). The reinforcing spirals theory (Slater, 2007) builds on SCT to further explain the dynamic interplay between behaviors and cognition in a media context, by suggesting a reciprocal relationship between media usages and users’ attitudes and behaviors. People who associate with a particular identity or lifestyle (e.g., eco-conscious) typically select media content that aligns with that identity or lifestyle (Slater, 2007). This media use, in turn, affects cognitive or behavioral outcomes resulting in mutual feedback loops and a potential alignment between the two. Thus, adolescents with an interest in climate issues may also be more likely to consume environmental content on social media, further reinforcing their interest in this topic.

Reinforcing spirals theory, developed for traditional media interactions, solely explains links between a user’s attitudes/behavior and media exposure behavior. However, we have reasons to believe that attitudes and behavior are also reciprocally related to different social media interactions (i.e., exposure, liking, commenting, sharing and posting of climate messages). Existing research has found that such social media interactions with political/environmental topics were influenced by pre-existing attitudes (Casaló & Escario, 2018), interests (Kalogeropoulos et al., 2017; Oeldorf-Hirsch & Sundar, 2015), and past behavior (Maki & Rothman, 2017). S. Lee and Xenos (2022) found further evidence for reciprocal pathways between political social media use (i.e., social media interactions with political content) and political participation. Therefore, it stands to reason that PEB/PEC positively predict adolescents’ social media engagement with environmental content.

Hypothesis 3 (H3): PEC and PEB at W1 will positively relate to exposure to, liking, commenting, sharing and self-posting climate content at W2.

Research also suggests that social media interactions reinforce each other over time. Drawing upon SCT and the reinforcing spirals, the self-effects framework (Valkenburg, 2017) posits that as social media users alternate between being senders and recipients of messages, their social media behaviors can encourage subsequent (social media) behaviors through internalization and feedback processes. For example, when a user posts a message, they may feel more connected to the topic and be more likely to share more messages about the same topic in the future.

Hypothesis 4 (H4): The various social media interactions with climate content will reinforce each other over time.

The Current Study

This study examines the bidirectional relationships between adolescents’ climate-related social media use, PEB and PEC using data from a two-wave panel study. Based on the reciprocal determinism argument of SCT (Bandura, 1978, 2001), it will examine the bidirectional relationships between PEC and PEB over time (H1). This study also takes an initial step in identifying the reciprocal relationships between social media interactions with climate content and PEB and PEC over time (H2-H3), and in exploring whether such associations differ based on the effort required by the interaction (RQ1). Finally, using the self-effects theory (Valkenburg, 2017), this study will analyze how different social media interactions might reinforce each other over time (H4).¹

Method

Sample

Data were collected from a two-wave panel study conducted in Flanders, Belgium. Secondary schools were randomly selected from a list of schools provided by the Flemish government and contacted by a team of researchers to invite them to participate in the research project. Ethical approval was received from the ethical committee of the host university. The two waves of data collection took place in October 2019 (W1) and February 2020 (W2).² Ten schools agreed to participate in the research project. All the initially participating schools participated again in the second wave. The educational staff received information about the research project. Active parental consent and student assent were required for all participants to fill out the questionnaire. The participants completed the surveys during the researchers’ visits to the schools, and confidentiality was guaranteed.

In total,1,248 adolescents completed the survey at baseline (W1). There were 214 participants excluded because they failed the attention task, indicating that they did not fill in the survey honestly, 20 because of missing values in more than 30% of the items, and another 33 because of lacking an account on at least one of the following social media: Snapchat, Facebook, and Instagram. Of the remaining 948 participants who accurately completed the survey in W1, 657 also completed it in W2 (response rate = 69%). A multivariate analysis of covariance (MANCOVA; controlling for age, gender, socioeconomic status [SES] and social media use) using Pillai’s Trace, V = 0.01, F(7, 869) = 1.75, p = .095, ηp² = 0.01, showed no significant differences between those who participated only in the first wave and those who participated in both waves.

The final analytical sample consisted of 657 participants (68.2% girls) aged 13 to 19 years (M = 15.59, SD = 1.27). Most were born in Belgium (91.9%), and about 4 out of 10 participants had a mother (28.7%) or a father (28.8%) with a university degree.

Measures

For this study, the researchers created new measures. For each new scale, an in-depth review of the literature on social media, adolescence and PEB oriented the creation of the first set of items. Next, these items were reviewed and adapted by two researchers who are highly experienced in (social) media research among adolescents. Ultimately, a pilot study was conducted among 220 mid- to late-adolescents to check whether the wording of the items was age appropriate and the item interpretation was as intended. More information about this study is available upon emailing the first author. The full description of the items is available on OSF.³

Demographics

Gender (1 = boy, 2 = girl), age (continuous variable) and parents’ educational level (average of father’s and mother’s highest completed level of education serving as a proxy for participants’ SES) were considered.

Social Media Use

Participants indicated on which social media platforms they were active. Then, they were asked to evaluate the amount of daily time spent on (1) Facebook and (2) Instagram using a 5-point Likert scale ranging from 1 = less than 10 min a day to 5 = more than 2 hr a day. A social media use score was computed by averaging both items.

Exposure to Climate Content on Social Media

Participants indicated the extent to which they had encountered messages about the environment (e.g., actions of climate activists; the establishment of organizations such as Youth for Climate; or the importance of recycling for climate change) on Facebook, Instagram and Snapchat. Answers were scored on a 5-point Likert scale ranging from 1 = (almost) never to 5 = (almost) every day (M_W₁ = 2.73, SD_W₁ = 1.09, Range = 1–5; M_W₂ = 2.63, SD_W₂ = 1.08).

Self-Posting of Climate Content on Social Media

Participants indicated how often they engaged in posting a positive message about the climate on social media on a 6-point Likert scale ranging from 1 = (almost) never to 6 = (almost) every day (M_W₁ = 1.43, SD_W₁ = 0.82, Range = 1–6; M_W₂ = 1.40, SD_W₂ = 0.84).

Sharing of Climate Content on Social Media

Participants indicated on a 6-point Likert scale (1 = (almost) never; 6 = (almost) every day) how often they shared a positive message from someone else about the environment (M_W₁ = 1.86, SD_W₁ = 1.10, Range = 1–6; M_W₂ = 1.80, SD_W₂ = 1.04).

Liking Climate Content on Social Media

Participants indicated how often they liked a positive message from someone else about the climate on social media on a 6-point Likert scale ranging from 1 = (almost) never to 6 = (almost) every day (M_W₁ = 3.27, SD_W₁ = 1.67, Range = 1–6; M_W₂ = 2.88, SD_W₂ = 1.59).

Commenting on Climate Content on Social Media

Participants indicated how often they commented positively on a message from someone else about the climate on social media on a 6-point Likert scale ranging from 1 = (almost) never to 6 = (almost) every day (M_W₁ = 2.06, SD_W₁ = 1.47, Range = 1–6; M_W₂ = 1.86, SD_W₂ = 1.28).

Descriptive Norms

Based on Ajzen (2006), participants indicated how many of their peers are taking action for the climate (i.e., “My friends take action for the climate by engaging in pro-environmental behavior”). Answers were scored on a 7-point Likert scale ranging from 1 = completely disagree to 7 = completely agree (M_W₁ = 4.06, SD_W₁ = 1.49, Range = 1–7; M_W₂ = 3.91, SD_W₂ = 1.41).

Injunctive Norms

Following Ajzen (2006), participants estimated how many of their peers approved of taking action for the climate. Using a 7-point Likert scale from 1 = completely disagree to 7 = completely agree participants were asked to rate how much they agreed with the following statements: “My friends expect me to take action for the climate by engaging in pro-environmental behavior” and “My friends believe it is important that I take action for the climate on a regular basis.” Both item scores were averaged to create an estimate of the participants’ subjective norms regarding climate action (M_W₁ = 3.49, SD_W₁ = 1.50, Range = 1–7; M_W₂ = 3.46, SD_W₂ = 1.39).

Attitudes

Following Ajzen (2006), participants indicated their approval of (1) calling attention to the climate on social media, (2) setting up actions for climate change, (3) supporting actions for climate change, (4) recycling for the climate, and (5) investing in the climate personally, for example, by saving energy. Participants answered the five items on a 7-point Likert scale ranging from 1 = completely disagree to 7 = completely agree (M_W₁ = 5.64, SD_W₁ = 1.06, Range = 1–7; M_W₂ = 5.41, SD_W₂ = 1.24). The scale showed good internal consistency (α_W1 = 0.88, α_W2 = 0.91). Item scores were averaged to yield a measure of pro-environmental attitudes.

Behavior

We used the pro-environmental behavior scale (de Leeuw et al., 2015) to assess the extent to which participants performed eco-friendly behaviors. Participants indicated how often they engaged in 13 different eco-friendly behaviors (e.g., “I leave the water running while I brush my teeth” and “At home, I put my trash in the proper recycling bin”) on a 5-point Likert scale ranging from 1 = never to 5 = always. In both waves, we found that the item “When I am outside, I avoid littering” weakened internal reliability (α_W1 = 0.69, α_W2 = 0.70), possibly because it contains two negative words. Therefore, this item was removed, and the reliability was re-calculated using the remaining 12 items. The 12-item scale showed acceptable moderate internal consistency (α_W1 = 0.71, α_W2 = 0.72). Item scores were averaged to create an estimate of participants’ engagement in eco-friendly behavior (M_W₁ = 3.38, SD_W₁ = 0.56, Range = 1–5; M_W₂ = 3.28, SD_W₂ = 0.58).

Analytical Strategy

Descriptive statistics and zero-order correlations were analyzed and are displayed in Table 1. Measurement invariance over time and by gender was addressed in a structural equation model for all latent constructs (i.e., attitudes and behavior). In this model, the values of the construct at W1 were regressed on the values of this variable at W2 (e.g., self-posting of climate content on social media at W1 predicted this self-posting variable at W2). The model allowed covariances between the error terms of the same manifest items of latent constructs over time. Measurement equivalence was considered proven when the comparative fit index (CFI) values between the unconstrained model (i.e., factor loadings vary freely according to gender/wave) and the constrained model (i.e., factor loadings are constrained to be equal across gender/waves) were lower than 0.01 (Cheung & Rensvold, 2002).

Table 1.

Descriptive Statistics and Pearson Zero-Order Correlations.

				1.		2.		3.		4.		5.		6.		7.		8.		9.
		M (SD)	Range	W1	W2	W1	W2	W1	W2	W1	W2	W1	W2	W1	W2	W1	W2	W1	W2	W1	W2
1. Exposure	W1	2.73 (1.09)	1–5	1
	W2	2.63 (1.08)	1–5	.34^***	1
2. Attitudes	W1	5.64 (1.06)	1–7	.14^***	.02	1
	W2	5.41 (1.24)	1–7	.05	.03	.56^***	1
3. Liking	W1	3.27 (1.67)	1–6	.24^***	.10^*	.44^***	.38^***	1
	W2	2.88 (1.59)	1–6	.14^***	.18^***	.31^***	.38^***	.58^***	1
4. Posting	W1	1.43 (0.82)	1–6	.20^***	.13^***	.22^***	.16^***	.41^***	.35^***	1
	W2	1.40 (.84)	1–6	.11^**	.17^***	.12^**	.08^*	.30^***	.40^***	.48^***	1
5. Sharing	W1	1.86 (1.10)	1–6	.15^***	.09^*	.28^***	.22^***	.60^***	.47^***	.61^***	.42^***	1
	W2	1.80 (1.04)		.10^**	.14^***	.18^***	.17^***	.42^***	.60^***	.45^***	.67^***	.61^***	1
6. Commenting	W1	2.06 (1.47)	1–6	.21^***	.08^*	.30^***	.25^***	.57^***	.37^***	.48^***	.32^***	.48^***	.36^***	1
	W2	1.86 (1.28)	1–6	.11^**	.16^***	.21^***	.20^***	.43^***	.54^***	.36^***	.50^***	.39^***	.56^***	.55^***	1
7. Injunctive	W1	3.49 (1.50)	1–7	.10^*	−.02	.43^***	.35^***	.33^***	.25^***	.22^***	.13^***	.23^***	.15^***	.25^***	.21^***	1
	W2	3.46 (1.39)	1–7	.03	.04	.30^***	.34^***	.27^***	.28^***	.18^***	.15^***	.20^***	.19^***	.24^***	.25^***	.56^***	1
8. Descriptive	W1	4.06 (1.49)	1–7	.06	−.01	.44^***	.37^***	.29^***	.24^***	.12^***	.05	.16^***	.10^***	.26^***	.18^***	.71^***	.49^***	1
	W2	3.91 (1.41)	1–7	.01	.03	.36^***	.41^***	.26^***	.30^***	.11^**	.13^***	.16^***	.18^***	.19^***	.22^***	.47^***	.69^***	.51^***	1
9. Behavior (PEB)	W1	3.38 (0.56)	1–5	.04	−.01	.38^***	.37^***	.24^***	.20^***	.15^***	.06	.13^**	.07	.23^***	.17^***	.38^***	.31^***	.35^***	.34^***	1
	W2	3.28 (0.58)	1–5	−.01	−.04	.36^***	.41^***	.21^***	.22^***	.12^**	.03	.11^**	.10^*	.17^***	.16^***	.35^***	.37^***	.34^***	.44^***	.78^***	1

Note. The dichotomous variable gender is coded as follows: boy = 1, girl = 2. W1 = Wave 1, W2 = Wave 2.

***

p < .001. ^**p < .01. ^*p < .05.

To test our hypothesized relationships, longitudinal cross-lagged relationships between all main variables were examined using structural equation modeling (AMOS). The baseline values of age, parents’ educational level and general social media use were included as control variables. Covariances with the variables at W1 and predictive paths with the variables at W2 were included. The model included predictive paths from a particular construct at W1 to the same construct at W2 (e.g., posting behavior at W1 predicted posting behavior at W2). The fit was evaluated using a CFI (≥0.90), root mean square error of approximation (RMSEA; ≤0.08), and standardized root mean squared residual (SRMR; ≤0.08).

Variables measured through scales (e.g., PEB) were modeled as latent variables, with the items measuring the latent constructs as manifest variables. Single-item variables (e.g., descriptive norm) were entered as manifest variables. Covariances between error terms of the same manifest items of latent constructs between waves were also modeled as well as covariances between exogenous constructs of the same wave and error terms of endogenous constructs of the same wave.

Descriptive statistics further indicated that for all main variables, the skewness level was under 3.0 and the kurtosis level did not exceed 10.0. According to Kline (2010), the normality assumption was accepted. The data is available on https://osf.io/9mtq5/.

Results

The Measurement Model

Pro-environmental attitudes and PEB were specified as latent variables. All the indicator variables loaded significantly onto the respective latent factor (p < .001), with most factor loadings ranging from 0.35 to 0.90. Correlations among the main variables (i.e., exposure, liking, commenting, posting, sharing, injunctive norms and descriptive norms, attitudes and behavior) were added to the measurement model. These correlations were weak to moderate, but were all significant except for correlations between social media exposure and all other main variables and self-posting and PEB. The confirmatory factor analysis resulted in a low to moderate model fit (χ² = 2422.67, df = 924, χ²/df = 2.62, p < .001, CFI = 0.89, TLI = 0.87, RMSEA = 0.05) (Hu & Bentler, 1999). Inspection of the modification indices suggested that model fit would improve by allowing correlated errors: First, between two items of the PEB scale that refer to sorting trash (i.e., “At home, I put my trash in the proper recycling bin” and “At school, I put my trash in the proper recycling bin”) and second, between items of the attitudes scale that refer to conducting eco-friendly behavior (i.e., “I think it’s a good idea to recycle for the climate” and “I think it’s a good idea to invest in the climate myself by, e.g., saving energy”). These changes resulted in an improved model fit (χ² = 2135.43, df = 920, χ²/df = 2.32, p < .001, CFI = 0.91, TLI = 0.90, RMSEA = 0.05). All indicator variables continued to load significantly onto the attitudes or PEB latent factors (p < .001), and factor loadings were very similar to those in the original measurement model (range: 0.35−0.90), suggesting that the indicator variables adequately measured the latent constructs. All key variables exhibited invariance over time and by gender (ΔCFI = 0.001).

The Hypothesized Model

The structural model showed an acceptable fit (Hu & Bentler, 1999) with a chi-square value of 2,377.43 with 1,042 degrees of freedom, χ²/df = 2.82, p < .001, CFI = 0.91, RMSEA = 0.04, SRMR = 0.06. Significant and non-significant results are reported in Table 2 and represented in Figure 1.

Table 2.

Parameter Estimates.

	Unstandardized Coeff. B	Standardized Coeff. β	SE		Unstandardized Coeff. B	Standardized Coeff. β	SE
Exp. (W1) → Exp. (W2)	.294^***	.296^***	.039	Attit. (W1) → Attit. (W2)	.511^***	.455^***	.052
Exp. (W1) → Liking (W2)	−.011	−.008	.049	Attit. (W1) → Exp. (W2)	−.046	−.051	.045
Exp. (W1) → Comm. (W2)	−.048	−.041	.040	Attit. (W1) → Liking (W2)	.022	.017	.056
Exp. (W1) → Sharing (W2)	−.014	−.015	.032	Attit. (W1) → Comm. (W2)	−.026	−.025	.047
Exp. (W1) → Posting (W2)	−.011	−.014	.028	Attit. (W1) → Sharing (W2)	−.005	−.005	.037
Exp. (W1) → Attit. (W2)	−.058	−.046	.043	Attit. (W1) → Posting (W2)	−.019	−.027	.032
Exp. (W1) → Injunct. (W2)	−.054	−.043	.043	Attit. (W1) → Injunct. (W2)	−.019	−.017	.050
Exp. (W1) → Descript. (W2)	−.068	−.053	.045	Attit. (W1) → Descript. (W2)	.096	.082	.052
Exp. (W1) → Behav. (W2)	−.010	−.056	.006	Attit. (W1) → Behav. (W2)	.008	.054	.006
Liking (W1) → Liking (W2)	.389^***	.410^***	.043	Injunct. (W1) → Injunct. (W2)	.332^***	.360^***	.045
Liking (W1) → Exp. (W2)	.021	.033	.035	Injunct. (W1) → Exp. (W2)	−.058	−.080	.040
Liking (W1) → Comm. (W2)	.092^*	.120^*	.036	Injunct. (W1) → Liking (W2)	−.024	−.023	.050
Liking (W1) → Sharing (W2)	.038	.062	.028	Injunct. (W1) → Comm. (W2)	.030	.035	.041
Liking (W1) → Posting (W2)	.015	.030	.025	Injunct. (W1) → Sharing (W2)	−.005	−.007	.032
Liking (W1) → Attit. (W2)	.090^*	.111^*	.038	Injunct. (W1) → Posting (W2)	.032	.058	.029
Liking (W1) → Injunct. (W2)	.027	.032	.039	Injunct. (W1) → Attit. (W2)	−.011	−.012	.044
Liking (W1) → Descript. (W2)	.050	.059	.040	Injunct. (W1)→Descript. (W2)	.121^**	.129^**	.047
Liking (W1) → Behav. (W2)	.003	.025	.005	Injunct. (W1) → Behav. (W2)	−.003	−.026	.006
Comm. (W1) → Comm. (W2)	.361^***	.415^***	.037	Descript.(W1)→Descript.(W2)	.285^***	.300^***	.046
Comm. (W1) → Exp. (W2)	−.007	−.010	.036	Descript. (W1) → Exp. (W2)	.033	.045	.039
Comm. (W1) → Liking (W2)	−.028	−.026	.045	Descript. (W1) → Liking (W2)	.070	.066	.049
Comm. (W1) → Sharing (W2)	.017	.024	.029	Descript. (W1) → Comm. (W2)	.010	.012	.041
Comm. (W1) → Posting (W2)	.045	.113	.026	Descript. (W1) → Sharing (W2)	−.004	−.006	.032
Comm. (W1) → Attit. (W2)	−.032	−.035	.040	Descript. (W1) → Posting (W2)	−.034	−.061	.028
Comm. (W1) → Injunct. (W2)	.038	.040	.040	Descript. (W1) → Attit. (W2)	.047	.051	.043
Comm. (W1) → Descript. (W2)	−.012	−.013	.042	Descript. (W1)→Injunct. (W2)	.151^***	.162^***	.044
Comm. (W1) → Behav. (W2)	−.005	−.042	.005	Descript. (W1) → Behav. (W2)	.006	.050	.006
Sharing (W1) → Sharing (W2)	.438^***	.463^***	.043	Behav. (W1) → Behav. (W2)	.450^***	.883^***	.078
Sharing (W1) → Exp. (W2)	−.013	−.013	.053	Behav. (W1) → Exp. (W2)	.101	.034	.182
Sharing (W1) → Liking (W2)	.234^***	.163^***	.066	Behav. (W1) → Liking (W2)	.392	.091	.230
Sharing (W1) → Comm. (W2)	.103^	.089^	.054	Behav. (W1) → Comm. (W2)	.118	.034	.189
Sharing (W1) → Posting (W2)	.124^**	.162^**	.038	Behav. (W1) → Sharing (W2)	−.017	−.006	.148
Sharing (W1) → Attit. (W2)	.012	.010	.058	Behav. (W1) → Posting (W2)	.000	.000	.131
Sharing (W1) → Injunct. (W2)	.026	.021	.059	Behav. (W1) → Attit. (W2)	.771^***	.209^***	.211
Sharing (W1) → Descript. (W2)	.034	.026	.061	Behav. (W1) → Injunct. (W2)	.468^*	.124^*	.206
Sharing (W1) → Behav. (W2)	.006	.033	.007	Behav. (W1) → Descript. (W2)	.602^**	.157^**	.216
Posting (W1) → Posting (W2)	.337^***	.330^***	.046
Posting (W1) → Exp. (W2)	.109	.082	.064
Posting (W1) → Liking (W2)	.126	.065	.080
Posting (W1) → Comm. (W2)	.103	.066	.066
Posting (W1) → Sharing (W2)	.170^**	.134^**	.052
Posting (W1) → Attit. (W2)	−.035	−.021	.071
Posting (W1) → Injunct. (W2)	.036	.021	.071
Posting (W1) → Descript.(W2)	−.040	−.023	.075
Posting (W1) → Behav. (W2)	−.010	−.043	.009

Note. For clarity, relations with control variables are not reported and significant relations are displayed in bold. W1 = Wave 1; W2 = Wave 2; Exp. = exposure; Comm. = commenting; Attit. = pro-environmental attitudes; descript. = descriptive norms; injunct. = injunctive norms; behav. = pro-environmental behavior.

***

p < .001. ^**p < .01. ^*p < .05. ^p < .06.

Figure 1.

The tested model.

PEB (W1) was positively related to all cognitive variables (W2); injunctive norms, β = .12, B = 0.47, SE = 0.21, p < .05, 95% CI [0.01, 0.19]; descriptive norms, β = .16, B = 0.60, SE = 0.22, p < .01, 95% CI [0.03, 0.24]; and attitudes, β = .21, B = 0.77, SE = 0.21, p < .001, 95% CI [0.10, 0.31]. Regarding the opposite relationships, none of the cognitive variables (W1) significantly predicted pro-environmental behavior (W2; H1). As expected, we found evidence for a bidirectional relation between injunctive and descriptive norms: Injunctive norms (W1) positively predicted descriptive norms (W2), β = .13, B = 0.12, SE = 0.05, p < .01, 95% CI [0.06, 0.20], and vice versa, descriptive norms (W1) positively predicted injunctive norms (W2), β = .16, B = 0.15 SE = 0.04, p < .001, 95% CI [0.09, 0.26].

Regarding the links between social media interactions and PEB and PEC (H2), only liking (W1) positively predicted attitudes (W2), β = .11, B = 0.09, SE = 0.04, p < .05, 95% CI [0.04, 0.20]. For the reciprocal pathways, PEB and PEC (W1) did not relate to any of the social media interactions (W2; H3). For our social media variables (H4), we found a significant bidirectional relationship between the self-posting of climate content and the sharing of climate content. Self-posting (W1) positively predicted sharing (W2), β = .13, B = 0.17, SE = 0.05, p < .01, 95% CI [0.07, 0.20], and vice versa; sharing (W1) was positively related to self-posting (W2), β = .16, B = 0.12, SE = 0.04, p < .01, 95% CI [0.08, 0.24]. Furthermore, we observed unidirectional positive relationships between liking climate content (W1) and positively commenting on climate content (W2), β = .12, B = 0.09, SE = 0.04, p < .05, 95% CI [0.04, 0.19] and sharing climate content and liking climate content (W2), β = .16, B = 0.23, SE = 0.07, p < .001, 95% CI [0.09, 0.24].

Discussion

This longitudinal study aimed to broaden our understanding of how different types of social media interactions (e.g., exposure, liking, commenting, sharing and self-posting) with climate content reciprocally relate to PEB and PEC over time. Below, we discuss the most important findings and their theoretical implications.

Pro-Environmental Attitudes, Norms and Behaviors (H1)

We found a positive, unidirectional relationship between attitudes and descriptive norms, and positive bidirectional relationships between injunctive and descriptive norms. We did not find a positive relationship between pro-environmental attitudes (W1) and PEB (W2). This suggests an attitude–behavior gap, where being concerned about environmental issues does not necessarily translate into PEB (Pezzullo & Cox, 2016). Further data collection over a longer time-frame may be necessary to more strongly support this claim as attitudes stability is important to translate into behavior (Conner et al., 2022). The gap may also be partly attributed to the relative stability of PEB over time, as indicated by the stronger correlation between PEB at W1 and W2, and a relatively low variance (SD_W₁ = .56; SD_W₂ = .58). While adolescents may be reluctant to take action personally when the behaviors entail inconveniences or lifestyle changes (Hermans & Korhonen, 2017), our findings may also indicate that adolescents doubt PEB is useful in mitigating the negative impacts caused by humans on the environment. This is supported by previous qualitative research which found that people doubt the efficacy of individual behavior (Kenis & Mathijs, 2012), and the theory of planned behavior, which specifies that individuals’ perceived behavioral control over the outcome is an important factor to consider (Ajzen, 1991).

We did not find that attitudes and norms (W1) related to behavior (W2). The cross-lagged relationships were not significant when prior behavior was taken into account, despite the size of the correlation indices between PEC and PEB being moderate both synchronously and over time. We did find that behavior (W1) related to attitudes and norms (W2). The positive unidirectional relationship between behaviors and cognitions is in line with the SCT argument of reciprocal determinism (1978). This relationship provides new evidence for considering behavior not as an isolated outcome but rather a determinant of personal factors (i.e., attitudes) and environmental factors (i.e., social norms).

Social Media Interactions With Climate Content and PEC and PEB (H2 and RQ1)

Among all the examined social media interactions, only liking (W1) was related to pro-environmental attitudes (W2). However, there was no relation between positively commenting, sharing, or self-posting a message about the environment and pro-environmental attitudes, norms and behaviors. These findings are surprising considering these three forms of engagement require more cognitive effort than simply liking ( C. Kim & Yang, 2017; D. H. Kim et al., 2021). The reasons for these results are not yet clear and call for future research. One potential explanation could be that, when adolescents commented, shared, and/or posted environmental content, they received little support (e.g., low number of likes on their post/comment) or even negative feedback from their peers. The self-effects framework argues that the influence of one’s actions on one’s own cognitions and behaviors is moderated by the audience feedback (Valkenburg, 2017). Potentially, a positive relationship between social media interactions and PEB/PEC only exists when positive audience feedback occurs. Another explanation may be the low frequency of more effortful behaviors compared with liking, in our sample. Liking was more prevalent than the other examined behaviors, yet research claims that this particular behavior is often a habit (i.e., adolescents like almost everything their peers share; Yau & Reich, 2019). A high frequency might be necessary to evoke a cumulative effect and thus a change in adolescents’ PEC and PEB.

Moreover, Spence et al. (2010) found a trend among young people from Western countries toward psychological distancing in climate subjects. Youth acknowledged that climate change was happening now (YouGov, 2014), yet they also thought it primarily affected distant places (British Science Association, 2013; Perera & Hewege, 2013). Adolescents may like and comment on climate content, but they may not feel capable or responsible for addressing climate change in their immediate environment. In addition, narcissism may be a factor to consider in the future. Posting and sharing climate content may be part of users’ self-presentation strategies: Users may desire to be perceived as eco-conscious online, and not behave accordingly in the offline world. This reasoning corroborates with previous studies. While people with subclinical narcissistic tendencies were found to present themselves online as environmentally conscious to other people, their behaviors tended not to align with these self-presentations (Kesenheimer & Greitemeyer, 2021; Naderi, 2018). Finally, while our measures focused on exposure to and interaction with positive climate content, a social media experience is typically characterized by interactions with other types of content, including messages supporting a consumerist perspective (e.g., ads; Lapierre et al., 2017) and anti-environmental messages. It is likely that such interactions may compete with or neutralize the potential effects of positive climate content (Shehata et al., 2021). Future research should consider such counter-messages more explicitly.

In summary, this finding adds to the literature by suggesting that engagement with general climate-related content does not necessarily impact attitudes and behaviors related to specific, tangible environmental actions. This finding contradicts previous studies referring to these effects as “spill-over” effects (Evans et al., 2013; Kidwell et al., 2013). Schema theory (Bettman, 1979) helps explain the underlying cognitive mechanisms driving spill-over effects. When individuals engage with climate-related content, their existing schemas related to the environment and pro-environmental behaviors may be activated. This activation can lead to a broader impact on attitudes and behaviors beyond the specific content, as the activated schema influences an individual’s perception and interpretation of subsequent information or an individual’s tendency to engage in related behaviors. An example of a spill-over effect might be when exposure to a message about climate-related wildfires influences users to switch off lights at home. Departing from this literature, our result indicates that the effects of social media engagement on PEC and PEB may be more “siloed,” meaning that only engagement with content-specific posts may influence the specific attitude, norm or behavior portrayed in the post. For instance, a post about climate protests might not encourage users to recycle, but a specific post about recycling might. By highlighting the lack of direct influence from social media engagement on these low-threshold behaviors, our findings call for a more topic-focused approach in environmental communication research as the subject of the post may determine which particular environmental outcome is affected.⁴

Reciprocal Pathways (H3 and H4)

Our findings did not support the reinforcing spirals model (Slater, 2007) or previous research on the links between social media interactions and attitudes/behaviors (e.g., Casaló & Escario, 2018; Maki & Rothman, 2017). One exception was the positive relationship between “liking” and attitudes. Yet, there was no significant connection between all the other examined social media interactions and PEC and PEB (H2), and pre-existing PEC and PEB did not predict the social media interactions (H3). As previously explained, the foremost cause of this discrepancy may be the overall low prevalence of most social media interactions with climate messages. A low engagement may, in this view, signal low degrees of social media collective efficacy, meaning that some students believe social media are inappropriate as a political tool for the climate movement (Waeterloos et al., 2021). This is in line with Kenis and Mathijs (2012) who found that people often feel powerless to engage in the collective social actions they consider effective. However, their interview participants did engage in individual behavior change despite their understanding that such efforts are ineffective and insufficient in mitigating climate change. Furthermore, the lack of reciprocal relationships may be related to our research design. Our focus on relatively long-term effects may have failed to capture the immediate and short-term effects of PEB/PEC on engaging with environmental content (see Moeller et al., 2018). Indeed, a common challenge in investigating reinforcing relationships lies in the existence of differential time lags, as attitude to exposure lags may be more immediate than exposure to attitudes ones (Slater, 2007).

Even so, our study provides novel insights into the (reinforcing) relationships between different social media interactions. We found a significant bidirectional relationship between self-posting and the sharing of climate content. We also observed a unidirectional, positive relationship between liking (W1) and positively commenting on climate content, sharing and liking climate content (W2). This has implications, considering that liking (W1) was related to pro-environmental attitudes (W2). Some forms of less effortful involvement with the content, such as liking, seem to encourage other interactions that require more effort and engagement (e.g., commenting) later on. The (often) unidirectional relationships between individual behaviors are noteworthy because they seem to indirectly create a path toward attitudes. Indeed, self-posting was positively related to sharing. Then, sharing related to liking, which related positively to commenting and pro-environmental attitudes. Based on the self-effects theory (Valkenburg, 2017), if a user likes a message, it may enhance their sense of connection to a topic and internalization of the message. Consequently, this increased engagement could lead to more high-effort interactions with the topic in the future. Finally, the significant auto-regressive relationships on social media behavior showed that little variance was left to be explained (e.g., most of exposure to climate content at W2 is explained by exposure at W1). The existence of strong auto-regressive pathways in itself lends support for the fact that past behaviors tend to explain the performance of similar future behaviors.

In line with the Differential Susceptibility of Media Effects Model (DSMM; Valkenburg & Peter, 2013), future research should delve into various individual user characteristics and examine their role in potentially moderating the reciprocal relationships between social media interactions, PEB and PEC. For instance, investigating individuals’ past experiences with environmental crises (e.g., recent floods in Belgium or Germany), could provide valuable insights into the disconnection observed between online engagement and offline actions.

A relevant outcome variable to be considered in future research may be adolescents’ political developmental stage. By being active in climate conversations on social media, even slightly by liking posts, adolescents may enter the role of “standby citizens,” referring to citizens interested in politics, but currently inactive in political participation (Amnå & Ekman, 2014). Standby citizens stay informed about political issues and are willing to participate if needed, but they prioritize self-oriented values over more humanitarian and altruistic values. Social media interactions with (environmental) political topics may represent a more latent form of engagement by demonstrating interest, and this “pre-political” involvement could be channeled into future opportunities for participation (Ekman & Amnå, 2012; Waeterloos et al., 2021). Therefore, these adolescents may be open to being more involved in the future, or, should the opportunity to protest arise, may be ready to participate.

Limitations

Although our adolescent sample, the longitudinal design and distinguishing between differential social media interactions are key strengths of this research, several limitations should be noted. Firstly, small effect size estimates should be taken into account. Although we found similar effect sizes as previous research examining relationships between attitudes, norms and behavior (Collado et al., 2019; White et al., 2009), the effects remain small and should be considered accordingly. However, small effect sizes are common in media-effects research (Valkenburg & Peter, 2013), and given the relatively high frequency of perceived exposure to and low-effort interactions with climate content, such effects are likely to accumulate over time. Additionally, they do not necessarily reflect a small effect, but can also stem from methodological weaknesses such as media use measures that can be further improved (Valkenburg & Peter, 2013). This is especially relevant in this study as we relied on adolescents’ self-reports of their social media interactions with climate content. This is a typical approach in social media research, but it means that adolescents may have over- or underestimated the amount of exposure and interactions they had with the content of interest (Shi & Nagler, 2020; Verbeij et al., 2021). Future research should rely on experiments or data donations to better control and investigate the effects of interest. Secondly, the different social media interactions covered in this study are typical for Facebook and Instagram, yet do not all occur on other social media platforms, such as Snapchat or TikTok (e.g., liking is not possible on Snapchat). Future research should develop engagement scales adapted to (and thus more inclusive of) such “newer” social media platforms. A final limitation is the use of different measurement scales for assessing the frequency of exposure to and interaction with climate content on social media which were respectively measured with a 5-point and 6-point Likert scale. While the effects of this differentiation are believed to be minimal, this should be considered by future research to ensure consistency and comparability across these related constructs.

Despite its limitations, the present study advances our limited understanding of social media as potential socialization agents for adolescents’ PEB and PEC. More insight has been gained with regard to the accumulation of micro-dynamics of social media interactions into longer-term developmental processes of attitudes and behavior formation. As a whole, the evidence from this study intimates that the potential of social media effects on PEB should not be overstated, as only liking was found to be significantly related to pro-environmental attitudes. In addition, our findings regarding the singular relationships between the different social media interactions provide an encouragement to distinguish between various social media interactions rather than aggregating them into a unitary measure. Such findings also have value for other fields of research relying on social media interactions and which use aggregate measures (e.g., appearance or alcohol research). The insights provided by our study results add to a growing body of literature on PEB and may further inspire research questions on the role of social media in adolescents’ political and environmental development.

Footnotes

Author’ s note

Ann Rousseau is also affiliated to Amsterdam School of Communication Research, University of Amsterdam, Netherlands.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Research Foundation Flanders (FWO-Vlaanderen) under Grant 12U6419N.

ORCID iDs

Anaëlle Gonzalez

Ann Rousseau

Notes

Author Biographies

Anaëlle Gonzalez is a PhD researcher at the School for Mass Communication Research, KU Leuven, Belgium, supervised by L. Vandenbosch. Her research is situated at the intersection of communication science, psychology and political science, and focuses on investigating the role of social media and entertainment fiction in the development of the political self in adolescents.

Laura Vandenbosch is an associate Professor at the School for Mass Communication Research (BOF-ZAP research professorship grant), KU Leuven, Belgium. The relations between media and well-being are the core subject of her research, leading to international publications in several fields including developmental psychology, sexology, body image, social relationships and communication theory.

Ann Rousseau (PhD, KU Leuven) is a postdoctoral researcher at the Leuven School for Mass Communication Research, Faculty of Social Sciences, KU Leuven, Leuven, Belgium. She studies media use and media effects on body image, sexual development, and climate change engagement.

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