2 Correlates of Collective Behavioral Engagement
Catherine Garton
Department of Psychology, Stanford University
2.1 Part I: Introduction to Collective Behavioral Engagement
2.1.1 Chapter Rationale: The Case for Collective Behaviors
As articulated in the previous chapter [1], there is now consensus that climate solutions will require revamping system-wide policies, practices, and infrastructure in order to change our global energy usage (IPCC Sixth Assessment Report, 2022; Rees & Bamberg, 2014). Individuals who do not have special access to high-level decision-making can only catalyze this change by engaging in collective initiatives — behaviors or policies designed to alter broad public processes outside of themselves.
Borrowing from Stern’s (2000) widely-used taxonomy of environmental behavior, private-sphere environmentalism focuses on personal, household, and consumer behaviors, such as recycling, carpooling, home weatherization, dietary changes, travel reduction, etc. The most effective private-sphere behaviors are addressed in Chapter X ({Ariella’s}). These stand in contrast to public-sphere behaviors, which seek to target collective decision-making, whether at the local, corporate, or governmental level. For any given individual, this could include engagement with policy (such as climate policy support, pro-climate voting, and contacting elected officials) and/or engagement in groups (such as participation in climate initiatives, group organizing, or collective protest).
While individual behavior and collective action are not mutually exclusive, there is reason to carefully choose which to prioritize. Research on “single-action bias” shows that people often take one action to alleviate feelings of concern, and they often neglect taking further steps that would be incrementally protective (Weber, 1997). It is thought that the first action is often enough to pacify one’s affective feelings of worry, irrespective of the behavior’s relative effectiveness. Thus, mis-prioritizing effort in the private-sphere domain could be considered a large opportunity cost, when the alternative might be channeling energy into collective initiatives that would reduce emissions at scale.
Private-sphere behaviors may have a different psychological profile than public-sphere behaviors. Behaviors in each sphere typically load onto different psychometric factors, and different psychological predictors are associated with each (Stern, 2000). For example, in their data, Hall and colleagues (2018) showed that private-sphere actions were more likely to be endorsed by climate skeptics, whereas policy changes were more likely to be endorsed by climate believers. They also showed that private-sphere behaviors were weakly associated with public-sphere behaviors and policy support, with pro-environmental behaviors only correlating with support for various climate policies between r = -.08 to .19 (Hall et al., 2018). It is also worth noting that private sphere behaviors also often depend more on the affordances of one’s environment (e.g., the availability of public transport).
Thus, this chapter seeks to identify the state of psychological science on predictors of engagement in collective climate action. Note that terms such as “collective engagement”, “collective action”, and “public-sphere behavior” will be used interchangeably throughout the chapter, and they refer to any variable measuring either policy support or actions designed to alter broad public processes beyond the individual.
2.1.2 Chapter Scope and Approach
This chapter reviews any scientific articles since 2015 which measured policy support or public-sphere behaviors that (a) related to climate change specifically, not environmentalism more generally, (b) were measured in terms of current willingness, prospective intentions, or past measured actions, and (c) were assessed in relation to other psychological2 constructs. Because there are relatively few papers that focus explicitly on public-sphere behaviors, we included samples drawn from countries besides the U.S.
Despite perhaps weaker or indirect significance, we chose to include papers that measured mere behavioral willingness or intention, because influential theories of behavior change (e.g., the theory of planned behavior; Azjen, 1991) suggest that the biggest and most proximal predictor of someone’s behavior is their intention to execute it. This is mirrored in some findings from the climate behavior literature as well (Bamberg et al., 2015). However, it is also true that empirical predictors of actual behavior are almost invariably weaker than predictors of behavioral intentions (Hornsey et al., 2016). Therefore, if both behavioral intentions and actual behaviors were measured, we only synthesized the findings for actual behaviors, unless the findings were contradictory in a useful or telling way.
If the method used by the original authors made it impossible to separate private and public sphere behaviors in their analysis, we did not synthesize these results3. Our goal was to be as empirically precise as possible, and we did not want to presume psychological equivalence of public- and private-sphere behaviors, in light of the differences described previously.
Where meta-analyses were available, we generally interpreted these results (while noting any concerns with methodological rigor and publication bias) and did not review its constituent studies. Studies conducted after the meta-analysis were still reviewed.
Finally, across all empirical papers, we opted to only interpret bivariate correlations, as opposed to multiple regression coefficients or mediation results. Our rationale was threefold. (1) Interpreting raw correlations facilitates consistent comparison across studies. Since some authors control for different covariates, interpreting beta coefficients is less standardized. (2) Many of the predictor variables measured in this literature are closely correlated, causing some to soak up more variance than others in an uninterpretable way. (3) If a relationship between two variables is only revealed through indirect mediation (with no direct correlation), this variable is unlikely to be the biggest lever for practitioners to pull in the real world. (4) It is impossible to determine if a mediation finding is causally structured in the implied way, without experimental control or longitudinal sampling.
2.2 Part II: What is Known
Before describing take-aways from the literature, we want to caution that skepticism should be applied to the synthesized findings that follow. While reassuring to find some consistent trends across papers, several limitations exist in the existing research. For example, there is a lot of heterogeneity in the way that the same constructs are measured across different papers. Such constructs are often measured with ad-hoc, unvalidated scales that make comparison difficult. Additionally, most work relies on cross-sectional data, which give us no way of understanding causal structures, and third-variable explanations are abundant. Finally, some papers use modest sample sizes (N <250), which are more likely to have noisy or spurious results, and we have not assessed potential publication bias across the literature. Few papers were pre-registered or provided access to replication data.
2.2.1 Theoretical work
Although the focus of this chapter is empirical work, it would not be complete without mention of the theories of collective engagement that have shaped construct formation and measurement. The purpose of this section is to provide context for why the same particular constructs appear repeatedly in the literature. Most of them arose from the earliest theorizing about behavior change, which has been built upon for decades. Although these constructs arguably pack a lot of explanatory power, they also potentially present the dilemma of looking for one’s keys under the streetlamp. In other words, just because these variables repeatedly appear in the literature does not necessarily mean that they are the most important or most relevant.
SIMCA, EMISCA, and SIMPEA are the models that have arguably inspired most of the recent validations and replications, both in the environmental domain and outside of it.
2.2.1.1 SIMCA, EMISCA, and SIMPEA
The Social Identity Model of Collective Action (SIMCA) developed out of a meta-analysis of 182 effects of three variables—perceived injustice, group efficacy, and group identity—which had formed the core pillars of prior research and theorizing on collective action, mostly outside of the domain of climate. Finding significant effects of all three variables, van Zomeren et al. (2008) proposed an overarching model combining them together and proposing that: collective action occurs when people identify with a group, which causes them to experience a shared sense of injustice, group-based affect, and collective efficacy.
The Encapsulation Model of the Social Identity of Collective Action (EMISCA), proposed by Thomas et al. (2012), is nearly identical to SIMCA, except that it reverses the causal order implied. In EMISCA, it is the perceptions of unfair treatment, combined with beliefs that collective participation will be effective, that cause someone to become socially identified with their group, thus spurring action on behalf of the group. In either case, the key variables contributed and highlighted by these models are collective efficacy, perceived injustice, affect, and social identification.
The social identity model of pro-environmental action (SIMPEA) was proposed by Fritsche et al. (2018) as an extension of previous models, adding that group-based environmental action is partly determined by perceptions of in-group pro-environmental norms and goals.
2.2.1.2 Foundational Frameworks
The models above were built upon prior theorizing from the five or so other foundational frameworks, described below.
Perhaps most influentially, social identity theory (Tajfel & Turner, 1979) identifies collective action as a behavior only undertaken by group members. In this framework, a salient group identity is the cornerstone of collective action, by which an individual is willing to take actions on behalf of their group. Based on 64 studies, Van Zomeren et al. (2008) reported in their meta-analysis a correlation of r =.38 between identification with the in-group and collective action.
Resource mobilization (McCarthy & Zald, 1997) has also garnered attention from collective action researchers. This is the idea that individuals join groups when they perceive the group has mobilized enough resources to accomplish its aims. This led to the introduction of variables like collective efficacy. Based on 53 studies, Van Zomeren et al. (2008) reported a meta-analytic effect size of r =.34 for the relationship between collective efficacy and collective action.
Intergroup emotions theory (Smith, 1993; Mackie et al., 2008) spotlights the key role of emotions in driving group-based behavior. According to this theory, group-based emotions are generated specifically as a result of one’s identification as a group member (e.g., pride, anger, guilt), and it propels behaviors on behalf of the group.Van Zomeren et al. (2008) report an average effect of group-based affect on collective behavior across 65 studies, r = .35.
The theory of planned behavior change, by Azjen (1991), posits that behaviors are directly preceded by behavioral intentions, and that behavioral intentions are influenced by three interdependent variables: attitudes toward the behavior, subjective norms about the behavior, and perceived behavioral control. Together, Azjen viewed that these could account for much of the variance in behavior.
Finally, the cost-benefit rational actor model, put forth by Olson (1965), suggests that free-riding is the most common outcome when there is a limited collective good. Thus, active participation in collective action is more likely if it is associated with benefits only obtainable through participation: such motives can include collecting the benefits of collective action goal, normative motives, and reward motives.
2.2.2 Empirical and Qualitative Work
The subsections below synthesize findings from psychological articles since 2015 that include measurement of collective climate engagement, either in the form of intention, behavior, or policy support. The section is organized around common themes—constructs that have been measured repeatedly across multiple papers. Note that all but two articles (one experimental and one qualitative) are cross-sectional.
2.2.2.1 Personal and collective efficacy beliefs
Six cross-sectional papers and one qualitative article addressed some form of efficacy in relation to collective action. Definitionally: personal efficacy refers to one’s belief that one is capable of having a positive impact in a group; collective efficacy refers to one’s belief that a particular group in question (or sometimes, groups in general) is capable of achieving its ends or making a difference.
Personal Efficacy. Personal efficacy was positively associated with collective behavior across five cross-sectional studies, ranging from r = .33-.62 (Bamberg et al., 2015; Furlong & Vignoles, 2021; Hornsey & Fielding, 2016; Gulliver et al., 2023; van Zomeren et al., 2019). Two of these papers measured self-reports of actual recent behavior among samples of individuals who were active in some type of environmental activism (Furlong & Vignoles, 2021; Gulliver et al., 2023), and both of these studies reported medium effect sizes of r = .33-.54. In open-ended responses, personal efficacy was frequently spontaneously mentioned as a reason why some people take on more activities (Gulliver et al., 2023). In a separate study of semi-structured interviews with four environmentally engaged individuals, one emergent theme was that these individuals felt simultaneously accomplished and indispensable within their initiatives, while soberly doubtful about the extent of their impact on climate change as a whole (Bührle & Kimmerle, 2021).
Collective Efficacy. Collective efficacy was positively associated with collective behavior in six studies, and not significant in one, with the significant effect sizes ranging from r = .19-.64 (Bamberg et al., 2015; Furlong & Vignoles, 2021; Gulliver et al., 2023; Rees & Bamberg, 2014; Sabherwal et al., 2021; van Zomeren et al., 2019). Of these studies, four measured hypothetical intentions as the outcome variable (Bamberg et al., 2015; Rees & Bamberg, 2014; Sabherwal et al., 2021; van Zomeren, 2019), and these generally had larger effect sizes (rs = .27-.64). The two studies that examined the relationship between collective efficacy and self-reported actual past behaviors ranged in effect size from not significant to r = .19-.24 (Furlong & Vignoles, 2021; Gulliver et al., 2023). In Bührle & Kimmerle’s (2021) analysis of semi-structured interviews, expectations of collective self-efficacy were spontaneously noted as important when deciding to pursue some form of engagement in the cause.
2.2.2.2 Perceived behavioral control
Perceived behavioral control (PBC) refers to whether someone is capable of taking on activities based on their personal bandwidth (i.e., work schedule, childcare responsibilities, finances, opportunities for engagement in one’s environment, etc.). On surveys, it is often asked in terms of “how much control” someone has over whether they can participate.
This construct was addressed in two cross-sectional studies we found. PBC was positively associated with collective action, ranging from r = .19-.61. This relationship was weaker but still significant in the study that measured past actual behaviors (Gulliver et al., 2023) compared to the study that measured hypothetical intentions (Bamberg et al., 2015). In open-ended responses from participants, Gulliver and colleagues (2023) found that individuals most frequently reported PBC-related constraints as the main obstacle to taking on more involvement. These authors also collected data on the participants’ number of children, and having more children was associated with performing on fewer climate leadership actions (r = .15), which is consistent with a time-constraint account of PBC.
2.2.2.3 Identity 4
Identity can encompass many aspects of an individual’s self-concept, but in this literature, it is typically conceived of as either someone’s identification with a particular group or identification with nature. See Chapter X for a more comprehensive review of identity-related research.
Group-based Identity. Schulte et al. (2020) meta-analyzed 15 studies in which opinion-based social identity (e.g., identification with the environmental movement) was assessed as a predictor of participation in collective action, finding a significant correlation of r = 0.56. Two subsequent papers measuring identification with a particular group found it to be significantly positively related to collective engagement, as measured by actual behaviors, with correlations ranging from r = .23-.40 (Furlong & Vignoles, 2021; Gulliver et al., 20235). One caveat is that Gulliver and colleagues (2023) did not find this effect when looking at low-commitment participation, but only high-investment leadership activities. In open-ended responses, group commitment was frequently mentioned as a reason why volunteers choose to take on more activities (Gulliver et al., 2023). Additionally, Bührle and Kimmerle (2021) noted, based on their interviews of four engaged individuals, that their participants had “strongly internalized their group membership” and had developed a sense of project ownership and group solidarity.
Identification with Nature. With respect to identification with nature, there is a significant positive correlation with collective engagement, ranging from r =.10-.41. A meta-analysis of 75 correlational studies and 17 experimental studies summarized all empirical work prior to 2019 (Mackay & Schmitt, 2019) and found an effect size of r = .36 for the cross-sectional papers and r = .10 (d = .21) for the experimental papers, after accounting for publication bias. A recent cross-sectional study, published after this meta-analysis, found that environmental identity was associated with low-commitment participatory behavior (r =.25) and high-commitment leadership behavior (r =.41), measured in terms of past actual behavior (Gulliver et al., 2023).
Lastly, one article included a measure of global identification (i.e., identification with humanity) and found a non-significant correlation with past reported behaviors in their environmental group (Furlong & Vignoles, 2021).
2.2.2.4 Affect
In this literature, affect is typically measured as the extent to which the participant feels specific emotions when thinking about the climate crisis. The most commonly studied emotions are anger, worry, hope, and guilt. Generally, anger and worry have the most robust correlations with collective engagement, summarized below.
Anger. Across four cross-sectional papers reporting on anger, all found significant positive correlations between anger and collective intentions or behaviors, r = .15-.50 (Bamberg et al., 2015; Furlong & Vignoles, 2021; Rees & Bamberg, 2014; van Zomeren et al., 2019). Only Furlong & Vignoles (2021) examined real behaviors as an outcome, and they report a correlation coefficient of r =.23. From their qualitative interviews, Bührle & Kimmerle (2021) found that participants reported anger as being highly motivating initially, but fading over time in importance.
Worry. Three cross-sectional papers examined worry or anxiety, with mixed results. Furlong & Vignoles (2021) found no relationship between fear and self-reported behaviors among Extinction Rebellion members. On the other hand, Goldberg et al. (2021) found a significant positive association between fear and climate policy support (no r value available), and Hornsey & Fielding (2016) found a strong correlation between anxiety and self-reported public-sphere action likelihood, r = .56. These results may be compatible, given that they measure different outcome variables. Policy support and behavioral intentions require less active engagement than actual behaviors typically performed by Extinction Rebellion members, so it may be that those who have severe climate anxiety are reluctant to engage more directly. Among the four climate activists who provided semi-structured interviews, their fear reportedly increased over the time (Bührle & Kimmerle (2021).
Risk Perceptions. Following evidence that risk perceptions are driven by affect processes as much as (or perhaps more than) analytic processes, paired with the close conceptual proximity to worry or fear, I include papers on risk perception here (Weber, 2006). Four papers found positive correlations, r = .63-.66. Two papers found a positive relationship between the perception of future harm and climate policy support, with r =.63 (no r value available for one article) (Goldberg et al., 2021; Xie et al., 2019). Two papers also found a positive relationship between concern about climate risks and behavioral willingness, with r = .66 (again, no r value available for one paper) (Hall et al., 2018; Latkin et al., 2021).
Hope. Hope was measured by three studies, with mixed results. In two studies, it was not correlated with public-sphere behaviors (Furlong & Vignoles, 2021; Hornsey & Fielding, 2016). With respect to behavioral intentions, two studies found significant associations of r = .26-.27 (Furlong & Vignoles, 2021; van Zomeren et al., 2019), while the third found null results (Hornsey & Fielding, 2016). It is worth considering whether hope is already embedded within the construct of collective efficacy, or whether it merits separate evaluation.
Guilt. Guilt was measured by three cross-sectional studies, which together suggest a positive correlation with intentions, but not actual behavior. Rees & Bamberg (2014) and Hornsey and Fielding (2016) both found significant positive correlations between guilt/shame and collective behavioral intentions (r = .26 and .48, respectively). Furlong & Vignoles (2021) also found a positive correlation when measuring behavioral intentions. However, when the same participants reported their actual past actions, there was no correlation with their feelings of guilt (Furlong & Vignoles, 2021). Guilt was not mentioned by the participants in the interviews performed by Bührle & Kimmerle (2021).
Sadness. Sadness was measured by one cross-sectional study, which found a significant positive correlation with public-sphere action likelihood, r = .59 (Hornsey & Fielding, 2016).
2.2.2.6 Climate beliefs
Belief in climate change is typically measured in a few different ways, which we have organized in the following way: Climate change is real refers to belief in global warming, irrespective of cause; Climate change is human-caused refers specifically to belief that global warming is anthropogenic; Climate impacts are beliefs that demonstrate understanding of the negative impacts of climate change; and Climate change response is the belief that one knows how to effectively respond to climate change (measured by one study).
Meta-Analytic Findings. A meta-analysis by Hornsey and colleagues (2016) analyzed 25 polls & 171 academic studies across 56 nations. They found a significant, positive average effect of climate beliefs (broadly construed) on public-sphere behavioral intentions, r = .32, and public-sphere behavioral actions, r = .19. They also measured policy support, and despite a significant positive relationship, they found that the link between climate change beliefs and policy support got smaller the more specific and concrete the measure of policy support, and the more the policy implied personal cost on behalf of the respondent. All of the relationships found were stronger in American samples.
Climate Change is Real. Two subsequent cross-sectional studies measured belief in climate change, finding significant relationships around r = .55 (one r not available, but reported a significant beta coefficient) (Hall et al., 2018; Goldberg et al., 2021).
Climate Change is Human-Caused. Three subsequent cross-sectional studies specifically measured belief in anthropogenic climate change, again finding significant positive correlations, ranging from r = .15-.66 (Hall et al., 2018; Goldberg et al., 2021; Xie et al., 2019). The wide range may be due to country differences, as Hall et al. (2018) found a large effect size in the U.S. (r = .66), where this belief may be more diagnostic of ideology, while Xie et al. (2019) sampled Australians (r = .15), where climate science may be less polarized. Additionally, the meta-analysis conducted by Hornsey et al. (2016) noted that relationships between climate beliefs and all forms of behavioral engagement or policy support were stronger when measuring belief in human-caused climate change as opposed to climate change more generally.
Climate Change Impacts. Xie et al., (2019) also measured beliefs about the impact of climate change, as well as a global evaluation of the extent to which climate change was bad. Unsurprisingly both measures were positively correlated with reported willingness to take action (r =.23 and .53, respectively).
Climate Change Responses. Finally, Xie et al., (2019) noted that, in their sample, the relationship between behavioral willingness and either (a) belief about the existence of climate change and (b) its impacts, were smaller than knowledge about how to effectively respond to it (r = .38).
2.2.2.7 Other Threads
If two or fewer papers reported on a particular theme in relation to collective behavior, I include mention of it below.
Personal Experience. Xie et al., (2019) found a significant, positive correlation between personal experiences of extreme weather and collective behavioral willingness, r = .26.
Value Orientations. Xie et al., (2019) found significant, positive correlations between value orientations and collective behavioral willingness. Biospheric values were correlated at r =.43. Socio-altruistic values were correlated at r =.34. Egoistic values were unrelated.
Perceived Injustice. Furlong & Vignoles (2021) reported a significant, positive correlation between perceived injustice and past actual behavior on behalf of the Extinction Rebellion, r = .28.
Moral Conviction. Furlong & Vignoles (2021) reported a significant, positive correlation between moral conviction and past actual behavior on behalf of the Extinction Rebellion, r = .25. Van Zomeren et al. (2019) also found a significant, positive correlation with collective intention, r = .50.
Organizational Climate. Gulliver et al. (2023) found no correlation between organizational climate (such as clearly specified roles) and reported behaviors. However, in their open-ended responses, participants often cited organizational climate as a reason for taking on more activities.
Pleasantness of Engagement. Bamberg et al. (2015) found a correlation between the perceived pleasantness of engagement and hypothetical intention to participate, r =.47.
Public Figures. Sabherwal et al., (2021) found a significant, positive relationship between awareness of the public figure Greta Thurnburg and public-sphere behavioral intentions, r = .28. The relationship was reduced but still significant after controlling for overall climate supportiveness, and it was stronger among the political left.
Sense of Responsibility. In their qualitative analysis, Bührle & Kimmerle (2021) found that strong feelings of responsibility played an important role in participants’ initial involvement and also grew over time.
2.3 Part III: What is Still Unknown
2.3.1 Chapter Limitations
This chapter will conclude with an overview of salient questions that are still unknown, based on our review of the literature. However, before that, it is worth noting that this chapter itself is limited in many ways, mostly regarding scope.
We purposefully constrained the scope of the chapter to only include research on public-sphere variables related to climate change, as opposed to any other form of collective movement. This excluded much work that focused on environmental activism more broadly, with the rationale that climate change is a unique threat requiring distinct actions within the broader environmental movement. This also excluded many influential papers relevant to the overarching topic of collective action, pertaining to any movement. Yet there is much to learn from work about other kinds of activism, some of which is sourced from other disciplines. It would be worth doing a broader review to see what has been demonstrated in other domains that would be applicable here.
Even within the discipline of psychology, there are fundamental phenomena that may be relevant to the climate problem. Foundational findings on cooperation, group dynamics, social learning, choice architecture, and behavioral change more generally might be applied to the climate crisis in powerful ways, which we have not reviewed here.
Finally, the summaries in this chapter should be taken with the caveat that we do not consider this an exhaustive review of the literature. Although we sought to be as comprehensive as possible, we did not follow meta-analytic standards (e.g., PRISMA) for review, and some studies may have been missed.
2.3.2 Gaps in the Scientific Understanding
2.3.2.1 Behavior Change
With the exception of one meta-analysis that included experimental effects of environmental identity, none of the articles I reviewed provide real information about behavior change. Because the vast number of studies in this literature are cross-sectional, they do not capture change itself, but only relationships between other constructs and someone’s self-reported propensity to engage in action. Framed that way, the findings in this chapter are not particularly surprising or enlightening – it seems natural that these constructs covary together in some fashion. However, these associations don’t necessarily indicate which variables would boost collective climate action, if manipulated. Understanding the directional, causal structure is essential, because only then will we know which levers to pull. This is what is both needed and lacking. Given finite resources to galvanize more people who are not currently engaged, it would be useful to know where, exactly, placing our efforts would have an impact.
Thus, one open question is: which, if any, of these findings work experimentally? Alternatively, scientists could also leverage prospective longitudinal designs to capture shifts in people’s engagement and identify important antecedents.
2.3.2.2 Intention-Behavior Gap
Anyone who has ever resolved in January to create a daily gym habit will know that intentions are a poor substitute for actual behavior. This is evident in our review of the literature, too. For example, data from Furlong & Vignoles (2021) show that among currently active Extinction Rebellion members, the mean level of actual behavior was slightly below the scale midpoint, while the mean level of participation intention is well above the midpoint—a large discrepancy. Moreover, models that predict actual behavior tend to only explain about a third of the variance, while models predicting intentions often explain more than two-thirds (e.g., Gullivan et al. 2023 vs. Bamberg et al., 2015). Therefore, basing our research on intentions could result in an inflated sense of how comprehensively we can understand and predict these dynamics.
So, adding to the previous open question: which of the associations presented in this chapter would help boost actual behavior, as opposed to mere intentions?
2.3.2.3 Boosting Motivation or Dismantling Obstacles?
Related to the intention-behavior gap, little research in this literature addressed behavioral obstacles in addition to sources of motivation. YSomeone might be highly motivated—whether because of their conviction, negative affect, social norms, environmental identity, or any other channel elucidated here—and still not engage in climate action because of specific barriers in their way. In Gulliver et al. (2023), when allowed to share open-ended responses about reasons for engagement, participants brought up different things when asked why people did engage versus why they didn’t. People reported engaging because of things like passion, self-efficacy, and a good organizational climate. People reported not engaging because of perceived behavioral control: a lack of ability to engage due to family, work, or money, for example.
The open question is: what else might be getting in the way of individuals engaging in collective action, especially among people who would otherwise take part? Psychologically, what might we be missing as hidden obstacles? Is there inertia that stems from lack of awareness about where/how to engage? Are there misperceptions about the amount of time required to do so? What tend to be the competing goals that win-out for people’s time? These are just some of the ideas that might be prompted by considering obstacles, as opposed to only motivation.
2.3.2.4 Sample Heterogeneity
Sample heterogeneity and differential motives have been unexamined as an explanatory factor of behavior
One specific open question is: are the mechanisms of action different for people who are already involved in some climate activism, versus those who are uninvolved? For example, it might be the case that things like climate beliefs, fear, or social norms propel individuals to seek out engagement in the first place, but once they are involved, the well of motivation springs from things like personal or collective efficacy, perceived behavioral control, or identification with the group. Some studies recruit naive samples, while others recruit already engaged citizens, but none of this work has attempted to assess whether there are differential motives.
Additionally, many of the findings presented here are drawn from different countries, which is fantastic from a representation standpoint, but also potentially relevant for practitioners seeking to apply the findings in other countries. Nations vary in both their political environments and affordances for engagement. While the U.S. has one of the largest carbon burdens, it is also one of the most environmentally politicized. Thus, political ideology might be a more important moderator in U.S. samples, while there might be other unexamined variables that are particularly relevant to the political processes in other countries. This has, to our knowledge, not been directly addressed.
2.3.3 Resources for future research
2.3.3.1 List of existing validated scales
Activism Orientation Scale (Corning & Myers, 2002)
- 35-item scale with two subscales: conventional activism and high-risk activism
- Items are geared toward political activism in general but could easily be tailored for climate activism specifically
- Correlates highly with another scale of environmental activism
- Not widely used since its development
Environmental Activism Scale (Seguin et al., 1998)
- 6-item scale that asks respondents to indicate the extent of their current engagement in various environment-related activities
- More widely used, but items are also potentially out-of-date
Climate Change Concern (Chryst et al., 2018)
- 4-item scale used to subdivide samples into 6 levels of climate concern & engagement
- Most widely used
2.4 References
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2.2.2.5 Social norms and networks
Social norms are typically split into injunctive norms and descriptive norms. In this literature, injunctive norms are typically measured as the extent to which ‘important others’ would support or encourage the participant to engage in a particular activity, while descriptive norms are typically measured as the extent to which important others would themselves engage in a particular activity (or already do). Some articles did not differentiate between the two and only presented results for a collapsed measure. Where possible, however, I report on the associations for norm category separately.
Injunctive Norms. Three cross-sectional studies explicitly measured prescriptive norms, with mixed results. Two found significant positive correlations6 with collective engagement (Goldberg et al., 2021; Xie et al., 2019), and one did not (Gulliver et al., 2023). I can think of two salient explanations for these differing results. First, the studies with significant effects measured more passive forms of engagement—policy support (no r value available) and hypothetical behavioral willingness (r = .54)—while the study finding a non-significant effect measured actual past behaviors. A second possibility, however, is that the first two studies sampled the general public, where norms may exert more influence on future engagement, whereas Gulliver et al. (2023) measured already-active citizens. Engaged participants probably consider their participation to be valuable already, so social norms may not have additional predictive power in discriminating between differing levels of behavior among this group.
Descriptive Norms. Two papers also measured descriptive norms, separately from injunctive norms. Xie et al. (2019) found a correlation of r = .41 between descriptive norms and willingness to engage in collective climate action. Goldberg et al. (2021) did not present the raw correlation coefficients but also found a significant, positive relationship between descriptive norms and climate policy support.
Mixed Injunctive/Descriptive Norms. Two papers examined prescriptive and descriptive norms as a composite measure. Both Rees & Bamberg (2014) and Bamberg et al. (2015) found significant positive relationships with collective behavioral intentions (rs = .65 and .57, respectively).
Social Networks. Finally, the role of social and informational networks merits a brief mention here. Bührle & Kimmerle (2021) found that invitation to participate by friends or roommates played a central role in the beginning of activism. This parallels work outside of the climate domain by Snow et al. (1980), who found that the majority of group recruitment happens through one’s social network, by friends and family.