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Leveraging collective action and environmental literacy to address complex sustainability challenges

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• Published: 09 August 2022
• Volume 52 , pages 30–44, ( 2023 )

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• Nicole M. Ardoin   ORCID: orcid.org/0000-0002-3290-8211 1 ,
• Alison W. Bowers 2 &
• Mele Wheaton 3  

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Developing and enhancing societal capacity to understand, debate elements of, and take actionable steps toward a sustainable future at a scale beyond the individual are critical when addressing sustainability challenges such as climate change, resource scarcity, biodiversity loss, and zoonotic disease. Although mounting evidence exists for how to facilitate individual action to address sustainability challenges, there is less understanding of how to foster collective action in this realm. To support research and practice promoting collective action to address sustainability issues, we define the term “collective environmental literacy” by delineating four key potent aspects: scale, dynamic processes, shared resources, and synergy. Building on existing collective constructs and thought, we highlight areas where researchers, practitioners, and policymakers can support individuals and communities as they come together to identify, develop, and implement solutions to wicked problems. We close by discussing limitations of this work and future directions in studying collective environmental literacy.

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Introduction

For socio-ecologically intertwined issues—such as climate change, land conversion, biodiversity loss, resource scarcity, and zoonotic diseases—and their associated multi-decadal timeframes, individual action is necessary, yet not sufficient, for systemic, sustained change (Amel et al. 2017 ; Bodin 2017 ; Niemiec et al. 2020 ; Spitzer and Fraser 2020 ). Instead, collective action, or individuals working together toward a common good, is essential for achieving the scope and scale of solutions to current sustainability challenges. To support communities as they engage in policy and action for socio-environmental change, communicators, land managers, policymakers, and other practitioners need an understanding of how communities coalesce and leverage their shared knowledge, skills, connections, and experiences.

Engagement efforts, such as those grounded in behavior-change approaches or community-based social marketing initiatives, that address socio-environmental issues have often emphasized individuals as the pathway to change. Such efforts address a range of domains including, but not limited to, residential energy use, personal transportation choices, and workplace recycling efforts, often doing so in a stepwise fashion, envisioning each setting or suite of behaviors as discrete spheres of action and influence (Heimlich and Ardoin 2008 ; McKenzie-Mohr 2011 ). In this way, specific actions are treated incrementally and linearly, considering first the individual barriers to be removed and then the motivations to be activated (and, sometimes, sustained; Monroe 2003 ; Gifford et al. 2011 ). Once each behavior is successfully instantiated, the next barrier is then addressed. Proceeding methodically from one action to the next, such initiatives often quite successfully alter a series of actions or group of related behaviors (at least initially) by addressing them incrementally, one at a time (Byerly et al. 2018 ). Following this aspirational logic chain, many resources have been channeled into such programs under the assumption that, by raising awareness and knowledge, such information, communication, and educational outreach efforts will shift attitudes and behaviors to an extent that, ultimately, mass-scale change will follow. (See discussion in Wals et al. 2014 .)

Numerous studies have demonstrated, however, that challenges arise with these stepwise approaches, particularly with regard to their ability to address complex issues and persist over time (Heimlich and Ardoin 2008 ; Wals et al. 2014 ). Such approaches place a tremendous—and unrealistic—burden on individuals, ignoring key aspects not only of behavioral science but also of social science more broadly, including the view that humans exist nested within socio-ecological systems and, thus, are most successful at achieving lasting change when it is meaningful, relevant, and undertaken within a supportive context (Swim et al. 2011 ; Feola 2015 ). Individualized approaches often require multiple steps or nudges (Byerly et al. 2018 ), or ongoing reminders to retain their salience (Stern et al. 2008 ). Because of the emphasis on decontextualized action, such approaches can miss, ignore, obfuscate, or minimize the importance of the bigger picture, which includes the sociocultural, biophysical, and political economic contexts (Ardoin 2006 ; Amel et al. 2017 ). Although the tightly trained focus on small, actionable steps and reliance on individual willpower may help in initially achieving success with initial habit formation (Carden and Wood 2018 ), it becomes questionable in terms of bringing about a wave of transformation on larger scales in the longer term. For those decontextualized actions to persist, they require continued prompting, constancy, and support in the social and biophysical context (Schultz 2014 ; Manfredo et al. 2016 ; Wood and Rünger 2016 ).

Less common in practice are theoretically based initiatives that embrace the holistic nature of the human experience, which occurs within complex systems spanning time and space in a multidimensional, weblike fashion (Bronfenbrenner 1979 ; Rogoff 2003 ; Barron 2006 ; DeCaro and Stokes 2008 ; Gould et al. 2019 ; Hovardas 2020 ). These systems-thinking approaches, while varying across disciplines and epistemological perspectives, envision human experiences, including learning and behavior, as occurring within a milieu that include the social, political, cultural, and historical contexts (Rogoff 2003 ; Roth and Lee 2007 ; Swim et al. 2011 ; Gordon 2019 ). In such a view, people’s everyday practices continuously reflect and grow out of past learning and experiences, not only at the individual, but also at the collective level (Lave 1991 ; Gutiérrez and Rogoff 2003 ; Nasir et al. 2020 ; Ardoin and Heimlich 2021 ). The multidimensional context in which we exist—including the broader temporal and spatial ecosystem—both facilitates and constrains our actions.

Scholars across diverse areas of study discuss the need for and power of collective thought and action, using various conceptual frames, models, and terms, such as collective action, behavior, impact, and intelligence; collaborative governance; communities of practice; crowdsourcing; and social movement theory; among many others (Table 1 ). These scholars acknowledge and explore the influence of our multidimensional context on collective thought and action. In this paper, we explore the elements and processes that constitute collective environmental literacy . We draw on the vast, relevant literature and, in so doing, we attempt to invoke the power of the collective: by reviewing and synthesizing ideas from a variety of fields, we strive to leverage existing constructs and perspectives that explore notions of the “collective” (see Table 1 for a summary of constructs and theories reviewed to develop our working definition of collective environmental literacy). A primary goal of this paper is to dialogue with other researchers and practitioners working in this arena who are eager to uncover and further explore related avenues.

First, we present a formal definition of collective environmental literacy. Next, we briefly review the dominant view of environmental literacy at the individual level and, in support of a collective take on environmental literacy, we examine various collective constructs. We then delve more deeply into the definition of collective environmental literacy by outlining four key aspects: scale, dynamic processes, shared resources, and synergy. We conclude by providing suggestions for future directions in studying collective environmental literacy.

Defining collective environmental literacy

Decades of research in political science, economics, anthropology, sociology, psychology, and the learning sciences, among other fields (Chawla and Cushing 2007 ; Ostrom 2009 ; Sawyer 2014 ; Bamberg et al. 2015 ; Chan 2016 ; Jost et al. 2017 ) repeatedly demonstrates the effectiveness, and indeed necessity of, collective action when addressing problems that are inherently social in nature. Yet theoretical frameworks and empirical documentation emphasize that such collective activities rarely arise spontaneously and, when they do, are a result of preconditions that have sown fertile ground (van Zomeren et al. 2008 ; Duncan 2018 ). Persistent and effective collective action then requires scaffolding in the form of institutional, sociocultural, and political economic structure that provides ongoing support. To facilitate discussions of how to effectively support collective action around sustainability issues, we suggest the concept of “collective environmental literacy.” We conceptualize collective environmental literacy as more than collective action; rather, we suggest that the term encapsulates action along with its various supporting structures and resources. Additionally, we employ the word “literacy” as it connotes learning, intention, and the idea that knowledge, skills, attitudes, and behaviors can be enhanced iteratively over time. By using “literacy,” we strive to highlight the efforts, often unseen, that lead to effective collective action in communities. We draw on scholarship in science and health education, areas that have begun over the past two decades to theorize about related areas of collective science literacy (Roth and Lee 2002 , 2004 ; Lee and Roth 2003 ; Feinstein 2018 ) and health literacy (Freedman et al. 2009 ; Papen 2009 ; Chinn 2011 ; Guzys et al. 2015 ). Although these evolving constructs lack consensus definitions, they illuminate affordances and constraints that exist when conceptualizing collective environmental literacy (National Academies of Sciences, Engineering, and Medicine [NASEM] 2016 ).

Some of the key necessary—but not sufficient—conditions that facilitate aligned, collective actions include a common body of decision-making information; shared attitudes, values, and beliefs toward a motivating issue or concern; and efficacy skills that facilitate change-making (Sturmer and Simon 2004 ; van Zomeren et al. 2008 ; Jagers et al. 2020 ). In addition, other contextual factors are essential, such as trust, reciprocity, collective efficacy, and communication among group members and societal-level facilitators, such as social norms, institutions, and technology (Bandura 2000 ; Ostrom 2010 ; McAdam and Boudet 2012 ; Jagers et al. 2020 ). Taken together, we term this body of knowledge, dispositions, skills, and the context in which they flourish collective environmental literacy . More formally, we define collective environmental literacy as: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig.  1 ).

Key elements of collective environmental literacy

Environmental literacy: Historically individual, increasingly collective

Over the past five decades, the term “environmental literacy” has come into increasingly frequent use. Breaking from the traditional association of “literacy” with reading and writing in formal school contexts, environmental literacy emphasizes associations with character and behavior, often in the form of responsible environmental stewardship (Roth 1992 ). Footnote 1 Such perspectives define the concept as including affective (attitudinal), cognitive (knowledge-based), and behavioral domains, emphasizing that environmental literacy is both a process and outcome that develops, builds, and morphs over time (Hollweg et al. 2011 ; Wheaton et al. 2018 ; Clark et al. 2020 ).

The emphasis on defining, measuring, and developing interventions to bring about environmental literacy has primarily remained at the individual scale, as evidenced by frequent descriptions of an environmentally literate person (Roth 1992 ; Hollweg et al. 2011 among others) rather than community or community member. In most understandings, discussions, and manifestations of environmental literacy, the implicit assumption remains that the unit of action, intervention, and therefore analysis occurs at the individual level. Yet instinctively and perhaps by nature, community members often seek information and, as a result, take action collectively, sharing what some scholars call “the hive mind” or “group mind,” relying on each other for distributed knowledge, expertise, motivation, and support (Surowiecki 2005 ; Sunstein 2008 ; Sloman and Fernbach 2017 ; Paul 2021 ).

As with the proverbial elephant (Saxe, n.d.), each person, household, or neighborhood group may understand or “see” a different part of an issue or challenge, bring a novel understanding to the table, and have a certain perspective or skill to contribute. Although some environmental literacy discussions allude to a collective lens (e.g., Hollweg et al. 2011 ; Ardoin et al. 2013 ; Wheaton et al. 2018 ; Bey et al. 2020 ), defining, developing frameworks, and creating measures to assess the efficacy of such collective-scale sustainability-related endeavors has remained elusive. Footnote 2 Looking to related fields and disciplines—such as ecosystem theory, epidemiology and public health, sociology, network theory, and urban planning, among others—can provide insight, theoretical frames, and empirical examples to assist in such conceptualizations (McAdam and Boudet 2012 ; National Research Council 2015 ) (See Table 1 for an overview of some of the many areas of study that informed our conceptualization of collective environmental literacy).

Seeking the essence of the collective: Looking to and learning from others

The social sciences have long focused on “the kinds of activities engaged in by sizable but loosely organized groups of people” (Turner et al. 2020 , para. 1) and addressed various collective constructs, such as collective behavior, action, intelligence, and memory (Table 1 ). Although related constructs in both the social and natural sciences—such as communities of practice (Wenger and Snyder 2000 ), collaborative governance (Ansell and Gash 2008 ; Emerson et al. 2012 ), and the collaboration–coordination continuum (Sadoff and Grey 2005 ; Prager 2015 ), as well as those from social movement theory and related areas (McAdam and Boudet 2012 ; de Moor and Wahlström 2019 )—lack the word “collective” in name, they too leverage the benefits of collectivity. A central tenet connects all of these areas: powerful processes, actions, and outcomes can arise when individuals coalesce around a common purpose or cause. This notion of a dynamic, potent force transcending the individual to enhance the efficacy of outcomes motivates the application of a collective lens to the environmental literacy concept.

Dating to the 1800s, discussions of collective behavior have explored connections to social order, structures, and norms (Park 1927 ; Smelser 2011 /1962; Turner and Killian 1987 ). Initially, the focus emphasized spontaneous, often violent crowd behaviors, such as riots, mobs, and rebellions. More contemporarily, sociologists, political scientists, and others who study social movements and collective behaviors acknowledge that such phenomena may take many forms, including those occurring in natural ecosystems, such as ant colonies, bird flocks, and even the human brain (Gordon 2019 ). In sociology, collective action represents a paradigm shift highlighting coordinated, purposeful pro-social movements, while de-emphasizing aroused emotions and crowd behavior (Miller 2014 ). In political science, Ostrom’s ( 1990 , 2000 , 2010 ) theory of collective action in the context of the management of shared resources extends the concept’s reach to economics and other fields. In education and the learning sciences, social learning and sociocultural theories tap into the idea of learning as a social-cognitive-cultural endeavor (Vygotsky 1980 ; Lave and Wenger 1991 ; Tudge and Winterhoff 1993 ; Rogoff 2003 ; Reed et al. 2010 ).

Collective action, specifically, and collective constructs, generally, have found their way into the research and practice in the fields of conservation, natural resources, and environmental management. Collective action theory has been applied in a range of settings and scenarios, including agriculture (Mills et al. 2011 ), invasive species management (Marshall et al. 2016 ; Sullivan et al. 2017 ; Lubeck et al. 2019 ; Clarke et al. 2021 ), fire management (Canadas et al. 2016 ; Charnley et al. 2020 ), habitat conservation (Raymond 2006 ; Niemiec et al. 2020 ), and water governance (Lopez-Gunn 2003 ; Baldwin et al. 2018 ), among others. Frameworks and methods that emphasize other collective-related ideas—like collaboration, co-production, and group learning—are also ubiquitous in natural resource and environmental management. These constructs include community-based conservation (DeCaro and Stokes 2008 ; Niemiec et al. 2016 ), community natural resource management (Kellert et al. 2000 ; Dale et al. 2020 ), collaboration/coordination (Sadoff and Grey 2005 ; Prager 2015 ), polycentricity (Galaz et al. 2012 ; Heikkila et al. 2018 ), knowledge co-production (Armitage et al. 2011 ; Singh et al. 2021 ), and social learning (Reed et al. 2010 ; Hovardas 2020 ). Many writings on collective efforts in the social sciences broadly, and applied in the area of environment specifically, provide insights into collective action’s necessary preconditions, which prove invaluable to further defining and later operationalizing collective environmental literacy.

Unpacking the definition of collective environmental literacy: Anchoring principles

As described, we propose the following working definition of collective environmental literacy drawing on our analysis of related literatures and informed by scholarly and professional experience in the sustainability and conservation fields: a dynamic, synergistic process that occurs as group members develop and leverage shared resources to undertake individual and aggregate actions over time to address sustainability issues within the multi-scalar context of a socio-environmental system (Fig.  1 ). This definition centers on four core, intertwined ideas: the scale of the group involved; the dynamic nature of the process; shared resources brought by, available to, and needed by the group; and the synergy that arises from group interaction.

Multi-scalar

When transitioning from the focus on individual to collective actions—and, herein, principles of environmental literacy—the most obvious and primary requisite shift is one of scale. Yet, moving to a collective scale does not mean abandoning action at the individual scale; rather, success at the collective level is intrinsically tied to what occurs at an individual level. Such collective-scale impacts leverage the power of the hive, harnessing people’s willingness, ability, and motivation to take action alongside others, share their ideas and resources to build collective ideas and resources, contribute to making a difference in an impactful way, and participate communally in pro-social activities.

Collective environmental literacy is likely dynamic in its orientation to scale, incorporating place-based notions, such as ecoregional or community-level environmental literacy (with an emphasis on geographic boundaries). On the other hand, it may encapsulate environmental literacy of a group or organization united by a common identity (e.g., organizational membership) or cause (e.g., old-growth forests, coastal protection), rather than solely or even primarily by geography. Although shifting scales can make measuring collective environmental literacy more difficult, dynamic levels may be a benefit when addressing planetary boundary issues such as climate change, biodiversity, and ocean acidification (Galaz et al. 2012 ). Some scholars have called for a polycentric approach to these large-scale issues in response to a perceived failure of global-wide, top-down solutions (Ostrom 2010 , 2012 ; Jordan et al. 2018 ). Conceptualizing and consequently supporting collective environmental literacy at multiple scales can facilitate such desired polycentricity.

Rather than representing a static outcome, environmental literacy is a dynamic process that is fluctuating and complex, reflective of iterative interactions among community members, whose discussions and negotiations reflect the changing context of sustainability issues. Footnote 3 Such open-minded processes allow for, and indeed welcome, adaptation in a way that builds social-ecological resilience (Berkes and Jolly 2002 ; Adger et al. 2005 ; Berkes 2007 ). Additionally, this dynamism allows for collective development and maturation, supporting community growth in collective knowledge, attitudes, skills, and actions via new experiences, interactions, and efforts (Berkman et al. 2010 ). With this mindset, and within a sociocultural perspective, collective environmental literacy evolves through drawing on and contributing to the community’s funds of knowledge (González et al. 2006 ). Movement and actions within and among groups impact collective literacy, as members share knowledge and other resources, shifting individuals and the group in the course of their shared practices (Samerski 2019 ).

In a collective mode, effectiveness is heightened as shared resources are streamlined, waste is minimized, and innovation maximized. Rather than each group member developing individual expertise in every matter of concern, the shared knowledge, skills, and behaviors can be distributed, pursued, and amplified among group members efficiently and effectively, with collective literacy emerging from the process of pooling diverse forms of capital and aggregating resources. This perspective builds on ideas of social capital as a collective good (Ostrom 1990 ; Putnam 2020 ), wherein relationships of trust and reciprocity are both inputs and outcomes (Pretty and Ward 2001 ). The shared resources then catalyze and sustain action as they are reassembled and coalesced at the group level for collective impact.

The pooled resources—likely vast—may include, but are not limited to, physical and human resources, funding, time, energy, and space and place (physical or digital). Shared resources may also include forms of theorized capital, such as intellectual and social (Putnam 2020 ). Also of note is the recognition that these resources extend far beyond information and knowledge. Of particular interest when building collective environmental literacy are resources previously ignored or overlooked by those in power in prior sustainability efforts. For example, collective environmental literacy can draw strength from shared resources unique to the community or even subgroups within the larger community. Discussions of Indigenous knowledge (Gadgil et al. 1993 ) and funds of knowledge (González et al. 2006 ; Cruz et al. 2018 ) suggest critical, shared resources that highlight strengths of an individual community and its members. Another dimension of shared resources relates to the strength of institutional connections, such as the benefits that accrue from leveraging the collective knowledge, expertise, and resources of organizational collaborators working in adjacent areas to further and amplify each other’s impact (Wojcik et al. 2021 ).

Synergistic

Finally, given the inherent complexities related to defining, deploying, implementing, and measuring these dynamic, at-times ephemeral processes, resources, and outcomes at a collective scale, working in such a manner must be clearly advantageous to pressing sustainability issues at hand. Numerous related constructs and approaches from a range of fields emphasize the benefits of diverse collaboration to collective thought and action, including improved solutions, more effective and fair processes, and more socioculturally just outcomes (Klein 1990 ; Jörg 2011 ; Wenger and Snyder 2000 ; Djenontin and Meadow 2018 ). These benefits go beyond efficient aggregation and distribution of resources, invoking an almost magical quality that defines synergy, resulting in robust processes and outcomes that are more than the sum of the parts.

This synergy relies on the diversity of a group across various dimensions, bringing power, strength, and insight to a decision-making process (Bear and Woolley 2011 ; Curşeu and Pluut 2013 ; Freeman and Huang 2015 ; Lu et al. 2017 ; Bendor and Page 2019 ). Individuals are limited not only to singular knowledge-perspectives and skillsets, but also to their own experiences, which influence their self-affirming viewpoints and tendencies to seek out confirmatory information for existing beliefs (Kahan et al. 2011 ). Although the coming together of those from different racial, cultural, social, and economic backgrounds facilitates a collective literacy process that draws on a wider range of resources and equips a gestalt, it also sets up the need to consider issues of power, privilege, voice, and representation (Bäckstrand 2006 ) and the role of social capital, leading to questions related to trust and reciprocity in effective collectives (Pretty and Ward 2001 ; Folke et al. 2005 ).

Leveraging the ‘Hive’: Proceeding with collective environmental literacy

This paper presents one conceptualization of collective environmental literacy, with the understanding that numerous ways exist to envision its definition, formation, deployment, and measurement. Characterized by a collective effort, such literacies at scale offer a way to imagine, measure, and support the synergy that occurs when the emphasis moves from an individual to a larger whole. By expanding the scale and focusing on shared responsibility among actors at the systems level, opportunities arise for inspiring and enabling a broader contribution to a sustainable future. These evolving notions serve to invite ongoing conversation, both in research and practice, about how to enact our collective responsibility toward, as well as vision of, a thriving future.

Emerging from the many discussions of shared and collaborative efforts to address socio-environmental issues, our conceptualization of collective environmental literacy is a first step toward supporting communities as they work to identify, address, and solve sustainability problems. We urge continued discussions on this topic, with the goal of understanding the concept of collective environmental literacy, how to measure it, and the implications of this work for practitioners. The conceptual roots of collective environmental literacy reach into countless fields of study and, as such, a transdisciplinary approach, which includes an eye toward practice, is necessary to fully capture and maximize the tremendous amount of knowledge, wisdom, and experience around this topic. Specifically, next steps to evolve the concept include engaging sustainability researchers and practitioners in discussions of the saliency of the presented definition of collective environmental literacy. These discussions include verifying the completeness of the definition and ensuring a thorough review of relevant research: Are parts of the definition missing or unclear? What are the “blank, blind, bald, and bright spots” in the literature (Reid 2019 p. 158)? Additionally, recognizing and leveraging literacy at a collective scale most certainly is not unique to environmental work, nor is adopting literacy-related language to conceptualize and measure process outcomes, although the former has consistently proven more challenging. Moreover, although we (the authors) appreciate the connotations and structures gained by using a literacy framework, we struggle with whether “environmental literacy” is the most appropriate and useful term for the conceptualizations as described herein; we, thus, welcome lively discussions about the need for new terminology.

Even at this early stage of conceptualization, this work has implications for practitioners. For scientists, communicators, policymakers, land managers, and other professionals desiring to work with communities to address sustainability issues, a primary take-away message concerns the holistic nature of what is needed for effective collective action in the environmental realm. Many previous efforts have focused on conveying information and, while a lack of knowledge and awareness may be a barrier to action in some cases, the need for a more holistic lens is increasingly clear. This move beyond an individually focused, information-deficit model is essential for effective impact (Bolderdijk et al. 2013 ; van der Linden 2014 ; Geiger et al. 2019 ). The concept of collective environmental literacy suggests a role for developing shared resources that can foster effective collective action. When working with communities, a critical early step includes some form of needs assessment—a systematic, in-depth process that allows for meaningfully gauging gaps in shared resources required to tackle sustainability issues (Braus 2011). Following this initial, evaluative step, an understanding of the components of collective environmental literacy, as outlined in this paper, can be used to guide the development of interventions to support communities in their efforts to address those issues.

Growing discussion of collective literacy constructs, and related areas, suggests researchers, practitioners, and policymakers working in pro-social areas recognize and value collective efforts, despite the need for clearer definitions and effective measures. This definitional and measurement work, in both research and practice, is not easy. The ever-changing, dynamic contexts in which collective environmental literacy exists make defining the concept a moving target, compounded by a need to draw upon work in countless, often distinct academic fields of study. Furthermore, the hard-to-see, inner workings of collective constructs make measurement difficult. Yet, the “power of the hive” is intriguing, as the synergism that arises from communities working in an aligned manner toward a unified vision suggests a potency and wave of motivated action essential to coalescing and leveraging individual goodwill, harnessing its power and potential toward effective sustainability solutions.

See Stables and Bishop’s ( 2001 ) idea of defining environmental literacy by viewing the environment as “text.”

The climate change education literature also includes a nascent, but growing, discussion of collective-lens thinking and literacy. See, for example, Waldron et al. ( 2019 ), Mochizuki and Bryan ( 2015 ), and Kopnina ( 2016 ).

This conceptualization is similar to how some scholars describe collective health literacy (Berkman et al., 2010 ; Mårtensson and Hensing, 2012 ).

Adger, W.N. 2003. Social capital, collective action, and adaptation to climate change. Economic Geography 79: 387–404.

Article   Google Scholar  

Adger, W.N., T.P. Hughes, C. Folke, S.R. Carpenter, and J. Rockström. 2005. Social-ecological resilience to coastal disasters. Science 309: 1036–1039. https://doi.org/10.1126/science.1112122 .

Article   CAS   Google Scholar  

Adler, P.S., and S.-W. Kwon. 2002. Social capital: Prospects for a new concept. Academy of Management Review 27: 17–40. https://doi.org/10.5465/amr.2002.5922314 .

Agrawal, A. 1995. Dismantling the divide between Indigenous and scientific knowledge. Development and Change 26: 413–439. https://doi.org/10.1111/j.1467-7660.1995.tb00560.x .

Aguilar, O.M. 2018. Examining the literature to reveal the nature of community EE/ESD programs and research. Environmental Education Research 24: 26–49. https://doi.org/10.1080/13504622.2016.1244658 .

Aguilar, O., A. Price, and M. Krasny. 2015. Perspectives on community environmental education. In M.C. Monroe & M.E. Krasny (Eds.), Across the spectrum: Resources for environmental educators (3rd edn., pp. 235–249). North American Association for Environmental Education.

Aldrich, D.P., and M.A. Meyer. 2015. Social capital and community resilience. American Behavioral Scientist 59: 254–269. https://doi.org/10.1177/0002764214550299 .

Amel, E., C. Manning, B. Scott, and S. Koger. 2017. Beyond the roots of human inaction: Fostering collective effort toward ecosystem conservation. Science 356: 275–279. https://doi.org/10.1126/science.aal1931 .

Ansell, C., and A. Gash. 2008. Collaborative governance in theory and practice. Journal of Public Administration Research and Theory 18: 543–571. https://doi.org/10.1093/jopart/mum032 .

Ardoin, N.M. 2006. Toward an interdisciplinary understanding of place: Lessons for environmental education. Canadian Journal of Environmental Education 11: 112–126.

Google Scholar  

Ardoin, N.M., and J.E. Heimlich. 2021. Environmental learning in everyday life: Foundations of meaning and a context for change. Environmental Education Research 27: 1681–1699. https://doi.org/10.1080/13504622.2021.1992354 .

Ardoin, N.M., C. Clark, and E. Kelsey. 2013. An exploration of future trends in environmental education research. Environmental Education Research 19: 499–520. https://doi.org/10.1080/13504622.2012.709823 .

Armitage, D., F. Berkes, A. Dale, E. Kocho-Schellenberg, and E. Patton. 2011. Co-management and the co-production of knowledge: Learning to adapt in Canada’s Arctic. Global Environmental Change 21: 995–1004. https://doi.org/10.1016/j.gloenvcha.2011.04.006 .

Assis Neto, F.R., and C.A.S. Santos. 2018. Understanding crowdsourcing projects: A systematic review of tendencies, workflow, and quality management. Information Processing & Management 54: 490–506. https://doi.org/10.1016/j.ipm.2018.03.006 .

Bäckstrand, K. 2006. Multi-stakeholder partnerships for sustainable development: Rethinking legitimacy, accountability and effectiveness. European Environment 16: 290–306. https://doi.org/10.1002/eet.425 .

Baldwin, E., P. McCord, J. Dell’Angelo, and T. Evans. 2018. Collective action in a polycentric water governance system. Environmental Policy and Governance 28: 212–222. https://doi.org/10.1002/eet.1810 .

Bamberg, S., J. Rees, and S. Seebauer. 2015. Collective climate action: Determinants of participation intention in community-based pro-environmental initiatives. Journal of Environmental Psychology 43: 155–165. https://doi.org/10.1016/j.jenvp.2015.06.006 .

Bandura, A. 1977. Social learning theory . Englewood Cliffs: Prentice Hall.

Bandura, A. 2000. Exercise of human agency through collective efficacy. Current Directions in Psychological Science 9: 75–78. https://doi.org/10.1111/1467-8721.00064 .

Barron, B. 2006. Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development 49: 193–224. https://doi.org/10.1159/000094368 .

Barry, M.M., M. D’Eath, and J. Sixsmith. 2013. Interventions for improving population health literacy: Insights from a rapid review of the evidence. Journal of Health Communication 18: 1507–1522. https://doi.org/10.1080/10810730.2013.840699 .

Barton, A.C., and E. Tan. 2009. Funds of knowledge and discourses and hybrid space. Journal of Research in Science Teaching 46: 50–73. https://doi.org/10.1002/tea.20269 .

Bear, J.B., and A.W. Woolley. 2011. The role of gender in team collaboration and performance. Interdisciplinary Science Reviews 36: 146–153. https://doi.org/10.1179/030801811X13013181961473 .

Bendor, J., and S.E. Page. 2019. Optimal team composition for tool-based problem solving. Journal of Economics & Management Strategy 28: 734–764. https://doi.org/10.1111/jems.12295 .

Berkes, F. 2007. Understanding uncertainty and reducing vulnerability: Lessons from resilience thinking. Natural Hazards 41: 283–295. https://doi.org/10.1007/s11069-006-9036-7 .

Berkes, F., and D. Jolly. 2002. Adapting to climate change: Social-ecological resilience in a Canadian western Arctic community. Conservation Ecology 5: 45.

Berkes, F., and H. Ross. 2013. Community resilience: Toward an integrated approach. Society & Natural Resources 26: 5–20. https://doi.org/10.1080/08941920.2012.736605 .

Berkes, F., M.K. Berkes, and H. Fast. 2007. Collaborative integrated management in Canada’s north: The role of local and traditional knowledge and community-based monitoring. Coastal Management 35: 143–162.

Berkman, N.D., T.C. Davis, and L. McCormack. 2010. Health literacy: What is it? Journal of Health Communication 15: 9–19. https://doi.org/10.1080/10810730.2010.499985 .

Bey, G., C. McDougall, and S. Schoedinger. 2020. Report on the NOAA office of education environmental literacy program community resilience education theory of change. National Oceanic and Atmospheric Administration . https://doi.org/10.25923/mh0g-5q69 .

Blumer, H. 1971. Social problems as collective behavior. Social Problems 18: 298–306.

Bodin, Ö. 2017. Collaborative environmental governance: Achieving collective action in social-ecological systems. Science . https://doi.org/10.1126/science.aan1114 .

Bolderdijk, J.W., M. Gorsira, K. Keizer, and L. Steg. 2013. Values determine the (in)effectiveness of informational interventions in promoting pro-environmental behavior. PLoS ONE 8: e83911. https://doi.org/10.1371/journal.pone.0083911 .

Brabham, D.C. 2013. Crowdsourcing . Cambridge: MIT Press.

Book   Google Scholar  

Braus, J. (Ed.). 2011. Tools of engagement: A toolkit for engaging people in conservation. NAAEE/Audubon. https://cdn.naaee.org/sites/default/files/eepro/resource/files/toolsofengagement.pdf .

Brieger, S.A. 2019. Social identity and environmental concern: The importance of contextual effects. Environment and Behavior 51: 828–855. https://doi.org/10.1177/0013916518756988 .

Briggs, J. 2005. The use of Indigenous knowledge in development: Problems and challenges. Progress in Development Studies 5: 99–114. https://doi.org/10.1191/1464993405ps105oa .

Briggs, J., and J. Sharp. 2004. Indigenous knowledges and development: A postcolonial caution. Third World Quarterly 25: 661–676. https://doi.org/10.1080/01436590410001678915 .

Bronfenbrenner, U. 1979. The ecology of human development: Experiments by nature and design . Cambridge: Harvard University Press.

Bruce, C., and P. Chesterton. 2002. Constituting collective consciousness: Information literacy in university curricula. International Journal for Academic Development 7: 31–40. https://doi.org/10.1080/13601440210156457 .

Byerly, H., A. Balmford, P.J. Ferraro, C.H. Wagner, E. Palchak, S. Polasky, T.H. Ricketts, A.J. Schwartz, et al. 2018. Nudging pro-environmental behavior: Evidence and opportunities. Frontiers in Ecology and the Environment 16: 159–168. https://doi.org/10.1002/fee.1777 .

Canadas, M.J., A. Novais, and M. Marques. 2016. Wildfires, forest management and landowners’ collective action: A comparative approach at the local level. Land Use Policy 56: 179–188. https://doi.org/10.1016/j.landusepol.2016.04.035 .

Carden, L., and W. Wood. 2018. Habit formation and change. Current Opinion in Behavioral Sciences 20: 117–122. https://doi.org/10.1016/j.cobeha.2017.12.009 .

Chan, M. 2016. Psychological antecedents and motivational models of collective action: Examining the role of perceived effectiveness in political protest participation. Social Movement Studies 15: 305–321. https://doi.org/10.1080/14742837.2015.1096192 .

Charnley, S., E.C. Kelly, and A.P. Fischer. 2020. Fostering collective action to reduce wildfire risk across property boundaries in the American West. Environmental Research Letters 15: 025007. https://doi.org/10.1088/1748-9326/ab639a .

Chawla, L., and D.F. Cushing. 2007. Education for strategic environmental behavior. Environmental Education Research 13: 437–452. https://doi.org/10.1080/13504620701581539 .

Chinn, D. 2011. Critical health literacy: A review and critical analysis. Social Science & Medicine 73: 60–67. https://doi.org/10.1016/j.socscimed.2011.04.004 .

Clark, C.R., J.E. Heimlich, N.M. Ardoin, and J. Braus. 2020. Using a Delphi study to clarify the landscape and core outcomes in environmental education. Environmental Education Research 26: 381–399. https://doi.org/10.1080/13504622.2020.1727859 .

Clarke, M., Z. Ma, S.A. Snyder, and K. Floress. 2021. Factors influencing family forest owners’ interest in community-led collective invasive plant management. Environmental Management 67: 1088–1099. https://doi.org/10.1007/s00267-021-01454-1 .

Cruz, A.R., S.T. Selby, and W.H. Durham. 2018. Place-based education for environmental behavior: A ‘funds of knowledge’ and social capital approach. Environmental Education Research 24: 627–647. https://doi.org/10.1080/13504622.2017.1311842 .

Curşeu, P.L., and H. Pluut. 2013. Student groups as learning entities: The effect of group diversity and teamwork quality on groups’ cognitive complexity. Studies in Higher Education 38: 87–103. https://doi.org/10.1080/03075079.2011.565122 .

Cutter, S.L., L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, and J. Webb. 2008. A place-based model for understanding community resilience to natural disasters. Global Environmental Change 18: 598–606. https://doi.org/10.1016/j.gloenvcha.2008.07.013 .

Dale, A., K. Vella, S. Ryan, K. Broderick, R. Hill, R. Potts, and T. Brewer. 2020. Governing community-based natural resource management in Australia: International implications. Land 9: 234. https://doi.org/10.3390/land9070234 .

de Moor, J., and M. Wahlström. 2019. Narrating political opportunities: Explaining strategic adaptation in the climate movement. Theory and Society 48: 419–451. https://doi.org/10.1007/s11186-019-09347-3 .

DeCaro, D., and M. Stokes. 2008. Social-psychological principles of community-based conservation and conservancy motivation: Attaining goals within an autonomy-supportive environment. Conservation Biology 22: 1443–1451.

Djenontin, I.N.S., and A.M. Meadow. 2018. The art of co-production of knowledge in environmental sciences and management: Lessons from international practice. Environmental Management 61: 885–903. https://doi.org/10.1007/s00267-018-1028-3 .

Duncan, L.E. 2018. The psychology of collective action. In The Oxford handbook of personality and social psychology , ed. K. Deaux and M. Snyder. Oxford: Oxford University Press.

Edwards, M., F. Wood, M. Davies, and A. Edwards. 2015. ‘Distributed health literacy’: Longitudinal qualitative analysis of the roles of health literacy mediators and social networks of people living with a long-term health condition. Health Expectations 18: 1180–1193. https://doi.org/10.1111/hex.12093 .

Emerson, K., T. Nabatchi, and S. Balogh. 2012. An integrative framework for collaborative governance. Journal of Public Administration Research and Theory 22: 1–29.

Engeström, Y. 2001. Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work 14: 133–156. https://doi.org/10.1080/13639080020028747 .

Ensor, J., and B. Harvey. 2015. Social learning and climate change adaptation: Evidence for international development practice. Wires Climate Change 6: 509–522. https://doi.org/10.1002/wcc.348 .

Fanta, V., M. Šálek, and P. Sklenicka. 2019. How long do floods throughout the millennium remain in the collective memory? Nature Communications 10: 1105. https://doi.org/10.1038/s41467-019-09102-3 .

Feinstein, N.W. 2018. Collective science literacy: A key to community science capacity [Conference session]. American Association for the Advancement of Science Annual Meeting, Austin, TX, USA https://d32ogoqmya1dw8.cloudfront.net/files/earthconnections/collective_science_literacy_key.pdf .

Feola, G. 2015. Societal transformation in response to global environmental change: A review of emerging concepts. Ambio 44: 376–390. https://doi.org/10.2139/ssrn.2689741 .

Fernandez-Gimenez, M.E., H.L. Ballard, and V.E. Sturtevant. 2008. Adaptive management and social learning in collaborative and community-based monitoring: A study of five community-based forestry organizations in the western USA. Ecology and Society 13: 15.

Folke, C., T. Hahn, P. Olsson, and J. Norberg. 2005. Adaptive governance of social-ecological systems. Annual Review of Environment and Resources 30: 441–473. https://doi.org/10.1146/annurev.energy.30.050504.144511 .

Freedman, D.A., K.D. Bess, H.A. Tucker, D.L. Boyd, A.M. Tuchman, and K.A. Wallston. 2009. Public health literacy defined. American Journal of Preventive Medicine 36: 446–451. https://doi.org/10.1016/j.amepre.2009.02.001 .

Freeman, R.B., and W. Huang. 2015. Collaborating with people like me: Ethnic coauthorship within the United States. Journal of Labor Economics 33: S289–S318.

Gadgil, M., F. Berkes, and C. Folke. 1993. Indigenous knowledge for biodiversity conservation. Ambio 22: 151–156.

Galaz, V., B. Crona, H. Österblom, P. Olsson, and C. Folke. 2012. Polycentric systems and interacting planetary boundaries—Emerging governance of climate change–ocean acidification–marine biodiversity. Ecological Economics 81: 21–32. https://doi.org/10.1016/j.ecolecon.2011.11.012 .

Geiger, S.M., M. Geiger, and O. Wilhelm. 2019. Environment-specific vs general knowledge and their role in pro-environmental behavior. Frontiers in Psychology 10: 718. https://doi.org/10.3389/fpsyg.2019.00718 .

Gifford, R., C. Kormos, and A. McIntyre. 2011. Behavioral dimensions of climate change: Drivers, responses, barriers, and interventions. Wires Climate Change 2: 801–827. https://doi.org/10.1002/wcc.143 .

González, N., L.C. Moll, and C. Amanti. 2006. Funds of knowledge: Theorizing practices in households, communities, and classrooms . New York: Routledge.

Gordon, D.M. 2019. Measuring collective behavior: An ecological approach. Theory in Biosciences . https://doi.org/10.1007/s12064-019-00302-5 .

Gould, R.K., N.M. Ardoin, J.M. Thomsen, and N. Wyman Roth. 2019. Exploring connections between environmental learning and behavior through four everyday-life case studies. Environmental Education Research 25: 314–340.

Graham, S., A.L. Metcalf, N. Gill, R. Niemiec, C. Moreno, T. Bach, V. Ikutegbe, L. Hallstrom, et al. 2019. Opportunities for better use of collective action theory in research and governance for invasive species management. Conservation Biology 33: 275–287. https://doi.org/10.1111/cobi.13266 .

Granovetter, M. 1978. Threshold models of collective behavior. American Journal of Sociology 83: 1420–1443.

Groulx, M., M.C. Brisbois, C.J. Lemieux, A. Winegardner, and L. Fishback. 2017. A role for nature-based citizen science in promoting individual and collective climate change action? A systematic review of learning outcomes. Science Communication 39: 45–76. https://doi.org/10.1177/1075547016688324 .

Gutiérrez, K.D., and B. Rogoff. 2003. Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher 32: 19–25. https://doi.org/10.3102/0013189X032005019 .

Guzys, D., A. Kenny, V. Dickson-Swift, and G. Threlkeld. 2015. A critical review of population health literacy assessment. BMC Public Health 15: 1–7. https://doi.org/10.1186/s12889-015-1551-6 .

Halbwachs, M. 1992. On collective memory (L. A. Coser, Ed. & Trans.). University of Chicago Press. (Original works published 1941 and 1952).

Heikkila, T., S. Villamayor-Tomas, and D. Garrick. 2018. Bringing polycentric systems into focus for environmental governance. Environmental Policy and Governance 28: 207–211. https://doi.org/10.1002/eet.1809 .

Heimlich, J.E., and N.M. Ardoin. 2008. Understanding behavior to understand behavior change: A literature review. Environmental Education Research 14: 215–237. https://doi.org/10.1080/13504620802148881 .

Hill, R., F.J. Walsh, J. Davies, A. Sparrow, M. Mooney, R.M. Wise, and M. Tengö. 2020. Knowledge co-production for Indigenous adaptation pathways: Transform post-colonial articulation complexes to empower local decision-making. Global Environmental Change 65: 102161. https://doi.org/10.1016/j.gloenvcha.2020.102161 .

Hollweg, K.S., J. Taylor, R.W. Bybee, T.J. Marcinkowski, W.C. McBeth, and P. Zoido. 2011. Developing a framework for assessing environmental literacy: Executive summary . North American Association for Environmental Education. https://cdn.naaee.org/sites/default/files/envliteracyexesummary.pdf .

Hovardas, T. 2020. A social learning approach for stakeholder engagement in large carnivore conservation and management. Frontiers in Ecology and Evolution 8: 436. https://doi.org/10.3389/fevo.2020.525278 .

Jagers, S.C., N. Harring, Å. Löfgren, M. Sjöstedt, F. Alpizar, B. Brülde, D. Langlet, A. Nilsson, et al. 2020. On the preconditions for large-scale collective action. Ambio 49: 1282–1296. https://doi.org/10.1007/s13280-019-01284-w .

Jordan, A., D. Huitema, H. van Asselt, and J. Forster. 2018. Governing climate change: Polycentricity in action? Cambridge: Cambridge University Press.

Jörg, T. 2011. New thinking in complexity for the social sciences and humanities: A generative, transdisciplinary approach . New York: Springer Science & Business Media.

Jost, J.T., J. Becker, D. Osborne, and V. Badaan. 2017. Missing in (collective) action: Ideology, system justification, and the motivational antecedents of two types of protest behavior. Current Directions in Psychological Science 26: 99–108. https://doi.org/10.1177/0963721417690633 .

Jull, J., A. Giles, and I.D. Graham. 2017. Community-based participatory research and integrated knowledge translation: Advancing the co-creation of knowledge. Implementation Science 12: 150. https://doi.org/10.1186/s13012-017-0696-3 .

Kahan, D.M., H. Jenkins-Smith, and D. Braman. 2011. Cultural cognition of scientific consensus. Journal of Risk Research 14: 147–174. https://doi.org/10.1080/13669877.2010.511246 .

Kania, J., and M. Kramer. 2011. Collective impact. Stanford Social Innovation Review 9: 36–41.

Karachiwalla, R., and F. Pinkow. 2021. Understanding crowdsourcing projects: A review on the key design elements of a crowdsourcing initiative. Creativity and Innovation Management 30: 563–584. https://doi.org/10.1111/caim.12454 .

Kellert, S.R., J.N. Mehta, S.A. Ebbin, and L.L. Lichtenfeld. 2000. Community natural resource management: Promise, rhetoric, and reality. Society & Natural Resources 13: 705–715.

Klein, J.T. 1990. Interdisciplinarity: History, theory, and practice . Detroit: Wayne State University Press.

Knapp, C.N., R.S. Reid, M.E. Fernández-Giménez, J.A. Klein, and K.A. Galvin. 2019. Placing transdisciplinarity in context: A review of approaches to connect scholars, society and action. Sustainability 11: 4899. https://doi.org/10.3390/su11184899 .

Koliou, M., J.W. van de Lindt, T.P. McAllister, B.R. Ellingwood, M. Dillard, and H. Cutler. 2020. State of the research in community resilience: Progress and challenges. Sustainable and Resilient Infrastructure 5: 131–151. https://doi.org/10.1080/23789689.2017.1418547 .

Kopnina, H. 2016. Of big hegemonies and little tigers: Ecocentrism and environmental justice. The Journal of Environmental Education 47: 139–150. https://doi.org/10.1080/00958964.2015.1048502 .

Krasny, M.E., M. Mukute, O. Aguilar, M.P. Masilela, and L. Olvitt. 2017. Community environmental education. In Urban environmental education review , ed. A. Russ and M.E. Krasny, 124–132. Ithaca: Cornell University Press.

Chapter   Google Scholar  

Lave, J. 1991. Situating learning in communities of practice.

Lave, J., and E. Wenger. 1991. Situated learning: Legitimate peripheral participation . Cambridge: Cambridge University Press.

Lee, S., and W.-M. Roth. 2003. Science and the “good citizen”: Community-based scientific literacy. Science, Technology, & Human Values 28: 403–424. https://doi.org/10.1177/0162243903028003003 .

Lévy, P., and R. Bononno. 1997. Collective intelligence: Mankind’s emerging world in cyberspace . New York: Perseus Books.

Lloyd, A. 2005. No man (or woman) is an island: Information literacy, affordances and communities of practice. The Australian Library Journal 54: 230–237. https://doi.org/10.1080/00049670.2005.10721760 .

Lopez-Gunn, E. 2003. The role of collective action in water governance: A comparative study of groundwater user associations in La Mancha aquifers in Spain. Water International 28: 367–378. https://doi.org/10.1080/02508060308691711 .

Lu, J.G., A.C. Hafenbrack, P.W. Eastwick, D.J. Wang, W.W. Maddux, and A.D. Galinsky. 2017. “Going out” of the box: Close intercultural friendships and romantic relationships spark creativity, workplace innovation, and entrepreneurship. Journal of Applied Psychology 102: 1091–1108. https://doi.org/10.1037/apl0000212 .

Lubeck, A., A. Metcalf, C. Beckman, L. Yung, and J. Angle. 2019. Collective factors drive individual invasive species control behaviors: Evidence from private lands in Montana, USA. Ecology and Society . https://doi.org/10.5751/ES-10897-240232 .

Mackay, C.M.L., M.T. Schmitt, A.E. Lutz, and J. Mendel. 2021. Recent developments in the social identity approach to the psychology of climate change. Current Opinion in Psychology 42: 95–101. https://doi.org/10.1016/j.copsyc.2021.04.009 .

Magis, K. 2010. Community resilience: An indicator of social sustainability. Society & Natural Resources 23: 401–416. https://doi.org/10.1080/08941920903305674 .

Manfredo, M.J., T.L. Teel, and A.M. Dietsch. 2016. Implications of human value shift and persistence for biodiversity conservation. Conservation Biology 30: 287–296. https://doi.org/10.1111/cobi.12619 .

Marshall, G.R., M.J. Coleman, B.M. Sindel, I.J. Reeve, and P.J. Berney. 2016. Collective action in invasive species control, and prospects for community-based governance: The case of serrated tussock ( Nassella trichotoma ) in New South Wales, Australia. Land Use Policy 56: 100–111. https://doi.org/10.1016/j.landusepol.2016.04.028 .

Mårtensson, L., and G. Hensing. 2012. Health literacy: A heterogeneous phenomenon: A literature review. Scandinavian Journal of Caring Sciences 26: 151–160. https://doi.org/10.1111/j.1471-6712.2011.00900.x .

Martin, C., and C. Steinkuehler. 2010. Collective information literacy in massively multiplayer online games. E-Learning and Digital Media 7: 355–365. https://doi.org/10.2304/elea.2010.7.4.355 .

Masson, T., and I. Fritsche. 2021. We need climate change mitigation and climate change mitigation needs the ‘We’: A state-of-the-art review of social identity effects motivating climate change action. Current Opinion in Behavioral Sciences 42: 89–96. https://doi.org/10.1016/j.cobeha.2021.04.006 .

Massung, E., D. Coyle, K.F. Cater, M. Jay, and C. Preist. 2013. Using crowdsourcing to support pro-environmental community activism. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . https://doi.org/10.1145/2470654.2470708 .

McAdam, D. 2017. Social movement theory and the prospects for climate change activism in the United States. Annual Review of Political Science 20: 189–208. https://doi.org/10.1146/annurev-polisci-052615-025801 .

McAdam, D., and H. Boudet. 2012. Putting social movements in their place: Explaining opposition to energy projects in the United States, 2000–2005 . Cambridge University Press.

McKenzie-Mohr, D. 2011. Fostering sustainable behavior: An introduction to community-based social marketing (3rd edn.). New Society Publishers.

McKinley, D.C., A.J. Miller-Rushing, H.L. Ballard, R. Bonney, H. Brown, S.C. Cook-Patton, D.M. Evans, R.A. French, et al. 2017. Citizen science can improve conservation science, natural resource management, and environmental protection. Biological Conservation 208: 15–28.

Miller, D.L. 2014. Introduction to collective behavior and collective action (3rd ed.). Waveland Press.

Mills, J., D. Gibbon, J. Ingram, M. Reed, C. Short, and J. Dwyer. 2011. Organising collective action for effective environmental management and social learning in Wales. The Journal of Agricultural Education and Extension 17: 69–83. https://doi.org/10.1080/1389224X.2011.536356 .

Mistry, J., and A. Berardi. 2016. Bridging Indigenous and scientific knowledge. Science 352: 1274–1275. https://doi.org/10.1126/science.aaf1160 .

Mochizuki, Y., and A. Bryan. 2015. Climate change education in the context of education for sustainable development: Rationale and principles. Journal of Education for Sustainable Development 9: 4–26. https://doi.org/10.1177/0973408215569109 .

Monroe, M.C. 2003. Two avenues for encouraging conservation behaviors. Human Ecology Review 10: 113–125.

Nasir, N.S., M.M. de Royston, B. Barron, P. Bell, R. Pea, R. Stevens, and S. Goldman. 2020. Learning pathways: How learning is culturally organized. In Handbook of the cultural foundations of learning , ed. N.S. Nasir, C.D. Lee, R. Pea, and M.M. de Royston, 195–211. Routledge.

National Academies of Sciences, Engineering, and Medicine. 2016. Science literacy: Concepts, contexts, and consequences . https://doi.org/10.17226/23595

National Research Council. 2015. Collective behavior: From cells to societies: Interdisciplinary research team summaries . National Academies Press. https://doi.org/10.17226/21737

Niemiec, R.M., N.M. Ardoin, C.B. Wharton, and G.P. Asner G.P. 2016. Motivating residents to combat invasive species on private lands: Social norms and community reciprocity. Ecology and Society , 21. https://doi.org/10.5751/ES-08362-210230

Niemiec, R.M., S. McCaffrey, and M.S. Jones. 2020. Clarifying the degree and type of public good collective action problem posed by natural resource management challenges. Ecology and Society 25: 30. https://doi.org/10.5751/ES-11483-250130 .

Norström, A.V., C. Cvitanovic, M.F. Löf, S. West, C. Wyborn, P. Balvanera, A.T. Bednarek, E.M. Bennett, et al. 2020. Principles for knowledge co-production in sustainability research. Nature Sustainability 3: 182–190. https://doi.org/10.1038/s41893-019-0448-2 .

Olick, J.K. 1999. Collective memory: The two cultures. Sociological Theory 17: 333–348. https://doi.org/10.1111/0735-2751.00083 .

Ostrom, E. 1990. Governing the commons: The evolution of institutions for collective action . Cambridge University Press.

Ostrom, E. 2000. Collective action and the evolution of social norms. Journal of Economic Perspectives 14: 137–158. https://doi.org/10.1257/jep.14.3.137 .

Ostrom, E. 2009. A general framework for analyzing sustainability of social-ecological systems. Science 325: 419–422. https://doi.org/10.1126/science.1172133 .

Ostrom, E. 2010. Polycentric systems for coping with collective action and global environmental change. Global Environmental Change 20: 550–557. https://doi.org/10.1016/j.gloenvcha.2010.07.004 .

Ostrom, E. 2012. Nested externalities and polycentric institutions: Must we wait for global solutions to climate change before taking actions at other scales? Economic Theory 49: 353–369. https://doi.org/10.1007/s00199-010-0558-6 .

Ostrom, E., and T.K. Ahn. 2009. The meaning of social capital and its link to collective action. In Handbook of social capital: The troika of sociology, political science and economics , ed. G.T. Svendsen and G.L.H. Svendsen, 17–35. Edward Elgar Publishing.

Papen, U. 2009. Literacy, learning and health: A social practices view of health literacy. Literacy and Numeracy Studies . https://doi.org/10.5130/lns.v0i0.1275 .

Park, R.E. 1927. Human nature and collective behavior. American Journal of Sociology 32: 733–741.

Paul, A.M. 2021. The extended mind: The power of thinking outside the brain . Boston: Mariner Books.

Pawilen, G.T. 2021. Integrating Indigenous knowledge in the Philippine elementary science curriculum: Integrating Indigenous knowledge. International Journal of Curriculum and Instruction 13: 1148–1160.

Prager, K. 2015. Agri-environmental collaboratives for landscape management in Europe. Current Opinion in Environmental Sustainability 12: 59–66. https://doi.org/10.1016/j.cosust.2014.10.009 .

Pretty, J., and H. Ward. 2001. Social capital and the environment. World Development 29: 209–227. https://doi.org/10.1016/S0305-750X(00)00098-X .

Putnam, R.D. 2020. Bowling alone: Revised and updated: The collapse and revival of American community . Anniversary. New York: Simon & Schuster.

Raymond, L. 2006. Cooperation without trust: Overcoming collective action barriers to endangered species protection. Policy Studies Journal 34: 37–57. https://doi.org/10.1111/j.1541-0072.2006.00144.x .

Reed, M.S., A.C. Evely, G. Cundill, I. Fazey, J. Glass, A. Laing, J. Newig, B. Parrish, et al. 2010. What is social learning? Ecology and Society 15: 12.

Reicher, S., R. Spears, and S.A. Haslam. 2010. The social identity approach in social psychology. In The SAGE handbook of identities (pp. 45–62). SAGE. https://doi.org/10.4135/9781446200889

Reid, A. 2019. Blank, blind, bald and bright spots in environmental education research. Environmental Education Research 25: 157–171. https://doi.org/10.1080/13504622.2019.1615735 .

Rogoff, B. 2003. The cultural nature of human development (Reprint edition) . Oxford: Oxford University Press.

Roth, C.E. 1992. Environmental literacy: Its roots, evolution and directions in the 1990s . http://eric.ed.gov/?id=ED348235

Roth, W.-M. 2003. Scientific literacy as an emergent feature of collective human praxis. Journal of Curriculum Studies 35: 9–23. https://doi.org/10.1080/00220270210134600 .

Roth, W.-M., and A.C. Barton. 2004. Rethinking scientific literacy . London: Psychology Press.

Roth, W.-M., and S. Lee. 2002. Scientific literacy as collective praxis. Public Understanding of Science 11: 33–56. https://doi.org/10.1088/0963-6625/11/1/302 .

Roth, W.-M., and S. Lee. 2004. Science education as/for participation in the community. Science Education 88: 263–291.

Roth, W.-M., and Y.-J. Lee. 2007. “Vygotsky’s neglected legacy”: Cultural-historical activity theory. Review of Educational Research 77: 186–232.

Sadoff, C.W., and D. Grey. 2005. Cooperation on international rivers: A continuum for securing and sharing benefits. Water International 30: 420–427.

Samerski, S. 2019. Health literacy as a social practice: Social and empirical dimensions of knowledge on health and healthcare. Social Science & Medicine 226: 1–8. https://doi.org/10.1016/j.socscimed.2019.02.024 .

Sawyer, R.K. 2014. The future of learning: Grounding educational innovation in the learning sciences. In The Cambridge handbook of the learning sciences , ed. R.K. Sawyer, 726–746. Cambridge: Cambridge University Press.

Saxe, J.G. n.d.. The blind man and the elephant . All Poetry. Retrieved October 6, 2020, from https://allpoetry.com/The-Blind-Man-And-The-Elephant .

Scheepers, D., and N. Ellemers. 2019. Social identity theory. In Social psychology in action: Evidence-based interventions from theory to practice , ed. K. Sassenberg and M.L.W. Vliek, 129–143. New York: Springer International Publishing.

Schipper, E.L.F., N.K. Dubash, and Y. Mulugetta. 2021. Climate change research and the search for solutions: Rethinking interdisciplinarity. Climatic Change 168: 18. https://doi.org/10.1007/s10584-021-03237-3 .

Schoerning, E. 2018. A no-conflict approach to informal science education increases community science literacy and engagement. Journal of Science Communication, Doi 10: 17030205.

Schultz, P.W. 2014. Strategies for promoting proenvironmental behavior: Lots of tools but few instructions. European Psychologist 19: 107–117. https://doi.org/10.1027/1016-9040/a000163 .

Sharifi, A. 2016. A critical review of selected tools for assessing community resilience. Ecological Indicators 69: 629–647. https://doi.org/10.1016/j.ecolind.2016.05.023 .

Sherrieb, K., F.H. Norris, and S. Galea. 2010. Measuring capacities for community resilience. Social Indicators Research 99: 227–247. https://doi.org/10.1007/s11205-010-9576-9 .

Singh, R.K., A. Singh, K.K. Zander, S. Mathew, and A. Kumar. 2021. Measuring successful processes of knowledge co-production for managing climate change and associated environmental stressors: Adaptation policies and practices to support Indian farmers. Journal of Environmental Management 282: 111679. https://doi.org/10.1016/j.jenvman.2020.111679 .

Sloman, S., and P. Fernbach. 2017. The knowledge illusion: Why we never think alone . New York: Riverhead Books.

Smelser, N.J. 2011. Theory of collective behavior . Quid Pro Books. (Original work published 1962).

Sørensen, K., S. Van den Broucke, J. Fullam, G. Doyle, J. Pelikan, Z. Slonska, H. Brand, and (HLS-EU) Consortium Health Literacy Project European. 2012. Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health 12: 80. https://doi.org/10.1186/1471-2458-12-80 .

Spitzer, W., and J. Fraser. 2020. Advancing community science literacy. Journal of Museum Education 45: 5–15. https://doi.org/10.1080/10598650.2020.1720403 .

Stables, A., and K. Bishop. 2001. Weak and strong conceptions of environmental literacy: Implications for environmental education. Environmental Education Research 7: 89. https://doi.org/10.1080/13504620125643 .

Stern, M.J., R.B. Powell, and N.M. Ardoin. 2008. What difference does it make? Assessing outcomes from participation in a residential environmental education program. The Journal of Environmental Education 39: 31–43. https://doi.org/10.3200/JOEE.39.4.31-43 .

Stets, J.E., and P.J. Burke. 2000. Identity theory and social identity theory. Social Psychology Quarterly 63: 224–237. https://doi.org/10.2307/2695870 .

Sturmer, S., and B. Simon. 2004. Collective action: Towards a dual-pathway model. European Review of Social Psychology 15: 59–99. https://doi.org/10.1080/10463280340000117 .

Sullivan, A., A. York, D. White, S. Hall, and S. Yabiku. 2017. De jure versus de facto institutions: Trust, information, and collective efforts to manage the invasive mile-a-minute weed (Mikania micrantha). International Journal of the Commons 11: 171–199. https://doi.org/10.18352/ijc.676 .

Sunstein, C.R. 2008. Infotopia: How many minds produce knowledge . Oxford: Oxford University Press.

Surowiecki, J. 2005. The wisdom of crowds . New York: Anchor.

Swim, J.K., S. Clayton, and G.S. Howard. 2011. Human behavioral contributions to climate change: Psychological and contextual drivers. American Psychologist 66: 251–264.

Thaker, J., P. Howe, A. Leiserowitz, and E. Maibach. 2019. Perceived collective efficacy and trust in government influence public engagement with climate change-related water conservation policies. Environmental Communication 13: 681–699. https://doi.org/10.1080/17524032.2018.1438302 .

Tudge, J.R.H., and P.A. Winterhoff. 1993. Vygotsky, Piaget, and Bandura: Perspectives on the relations between the social world and cognitive development. Human Development 36: 61–81. https://doi.org/10.1159/000277297 .

Turner, R.H., and L.M. Killian. 1987. Collective behavior , 3rd ed. Englewood Cliffs: Prentice Hall.

Turner, R.H., N.J. Smelser, and L.M. Killian. 2020. Collective behaviour. In Encyclopedia Britannica . Encyclopedia Britannica, Inc. https://www.britannica.com/science/collective-behaviour .

van der Linden, S. 2014. Towards a new model for communicating climate change. In Understanding and governing sustainable tourism mobility , ed. S. Cohen, J. Higham, P. Peeters, and S. Gössling, 263–295. Milton Park: Routledge.

van Zomeren, M., T. Postmes, and R. Spears. 2008. Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives. Psychological Bulletin 134: 504–535. https://doi.org/10.1037/0033-2909.134.4.504 .

Vygotsky, L.S. 1980. Mind in society: The development of higher psychological processes . Cambridge: Harvard University Press.

Waldron, F., B. Ruane, R. Oberman, and S. Morris. 2019. Geographical process or global injustice? Contrasting educational perspectives on climate change. Environmental Education Research 25: 895–911. https://doi.org/10.1080/13504622.2016.1255876 .

Wals, A.E.J., M. Brody, J. Dillon, and R.B. Stevenson. 2014. Convergence between science and environmental education. Science 344: 583–584.

Wenger, E.C., and W.M. Snyder. 2000. Communities of practice: The organizational frontier. Harvard Business Review 78: 139–146.

Weschsler, D. 1971. Concept of collective intelligence. American Psychologist 26: 904–907. https://doi.org/10.1037/h0032223 .

Wheaton, M., A. Kannan, and N.M. Ardoin. 2018. Environmental literacy: Setting the stage (Environmental Literacy Brief, Vol. 1). Social Ecology Lab, Stanford University. https://ed.stanford.edu/sites/default/files/news/images/stanfordsocialecologylab-brief-1.pdf .

Wojcik, D.J., N.M. Ardoin, and R.K. Gould. 2021. Using social network analysis to explore and expand our understanding of a robust environmental learning landscape. Environmental Education Research 27: 1263–1283.

Wood, W., and D. Rünger. 2016. Psychology of habit. Annual Review of Psychology 67: 289–314. https://doi.org/10.1146/annurev-psych-122414-033417 .

Woolley, A.W., C.F. Chabris, A. Pentland, N. Hashmi, and T.W. Malone. 2010. Evidence for a collective intelligence factor in the performance of human groups. Science 330: 686–688. https://doi.org/10.1126/science.1193147 .

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Acknowledgements

We are grateful to Maria DiGiano, Anna Lee, and Becca Shareff for their feedback and contributions to early drafts of this paper. We appreciate the research and writing assistance supporting this paper provided by various members of the Stanford Social Ecology Lab, especially: Brennecke Gale, Pari Ghorbani, Regina Kong, Naomi Ray, and Austin Stack.

This work was supported by a grant from the Pisces Foundation.

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Ardoin, N.M., Bowers, A.W. & Wheaton, M. Leveraging collective action and environmental literacy to address complex sustainability challenges. Ambio 52 , 30–44 (2023). https://doi.org/10.1007/s13280-022-01764-6

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The ultimate guide to quantitative data analysis

Numbers help us make sense of the world. We collect quantitative data on our speed and distance as we drive, the number of hours we spend on our cell phones, and how much we save at the grocery store.

Our businesses run on numbers, too. We spend hours poring over key performance indicators (KPIs) like lead-to-client conversions, net profit margins, and bounce and churn rates.

But all of this quantitative data can feel overwhelming and confusing. Lists and spreadsheets of numbers don’t tell you much on their own—you have to conduct quantitative data analysis to understand them and make informed decisions.

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This guide explains what quantitative data analysis is and why it’s important, and gives you a four-step process to conduct a quantitative data analysis, so you know exactly what’s happening in your business and what your users need .

Collect quantitative customer data with Hotjar

Use Hotjar’s tools to gather the customer insights you need to make quantitative data analysis a breeze.

What is quantitative data analysis? 

Quantitative data analysis is the process of analyzing and interpreting numerical data. It helps you make sense of information by identifying patterns, trends, and relationships between variables through mathematical calculations and statistical tests. 

With quantitative data analysis, you turn spreadsheets of individual data points into meaningful insights to drive informed decisions. Columns of numbers from an experiment or survey transform into useful insights—like which marketing campaign asset your average customer prefers or which website factors are most closely connected to your bounce rate. 

Without analytics, data is just noise. Analyzing data helps you make decisions which are informed and free from bias.

What quantitative data analysis is not

But as powerful as quantitative data analysis is, it’s not without its limitations. It only gives you the what, not the why . For example, it can tell you how many website visitors or conversions you have on an average day, but it can’t tell you why users visited your site or made a purchase.

For the why behind user behavior, you need qualitative data analysis , a process for making sense of qualitative research like open-ended survey responses, interview clips, or behavioral observations. By analyzing non-numerical data, you gain useful contextual insights to shape your strategy, product, and messaging. 

Quantitative data analysis vs. qualitative data analysis 

Let’s take an even deeper dive into the differences between quantitative data analysis and qualitative data analysis to explore what they do and when you need them.

The bottom line: quantitative data analysis and qualitative data analysis are complementary processes. They work hand-in-hand to tell you what’s happening in your business and why.  

💡 Pro tip: easily toggle between quantitative and qualitative data analysis with Hotjar Funnels . 

The Funnels tool helps you visualize quantitative metrics like drop-off and conversion rates in your sales or conversion funnel to understand when and where users leave your website. You can break down your data even further to compare conversion performance by user segment.

Spot a potential issue? A single click takes you to relevant session recordings , where you see user behaviors like mouse movements, scrolls, and clicks. With this qualitative data to provide context, you'll better understand what you need to optimize to streamline the user experience (UX) and increase conversions .

Hotjar Funnels lets you quickly explore the story behind the quantitative data

4 benefits of quantitative data analysis

There’s a reason product, web design, and marketing teams take time to analyze metrics: the process pays off big time. 

Four major benefits of quantitative data analysis include:

1. Make confident decisions 

With quantitative data analysis, you know you’ve got data-driven insights to back up your decisions . For example, if you launch a concept testing survey to gauge user reactions to a new logo design, and 92% of users rate it ‘very good’—you'll feel certain when you give the designer the green light. 

Since you’re relying less on intuition and more on facts, you reduce the risks of making the wrong decision. (You’ll also find it way easier to get buy-in from team members and stakeholders for your next proposed project. 🙌)

2. Reduce costs

By crunching the numbers, you can spot opportunities to reduce spend . For example, if an ad campaign has lower-than-average click-through rates , you might decide to cut your losses and invest your budget elsewhere. 

Or, by analyzing ecommerce metrics , like website traffic by source, you may find you’re getting very little return on investment from a certain social media channel—and scale back spending in that area.

3. Personalize the user experience

Quantitative data analysis helps you map the customer journey , so you get a better sense of customers’ demographics, what page elements they interact with on your site, and where they drop off or convert . 

These insights let you better personalize your website, product, or communication, so you can segment ads, emails, and website content for specific user personas or target groups.

4. Improve user satisfaction and delight

Quantitative data analysis lets you see where your website or product is doing well—and where it falls short for your users . For example, you might see stellar results from KPIs like time on page, but conversion rates for that page are low. 

These quantitative insights encourage you to dive deeper into qualitative data to see why that’s happening—looking for moments of confusion or frustration on session recordings, for example—so you can make adjustments and optimize your conversions by improving customer satisfaction and delight.

💡Pro tip: use Net Promoter Score® (NPS) surveys to capture quantifiable customer satisfaction data that’s easy for you to analyze and interpret. 

With an NPS tool like Hotjar, you can create an on-page survey to ask users how likely they are to recommend you to others on a scale from 0 to 10. (And for added context, you can ask follow-up questions about why customers selected the rating they did—rich qualitative data is always a bonus!)

Hotjar graphs your quantitative NPS data to show changes over time

4 steps to effective quantitative data analysis 

Quantitative data analysis sounds way more intimidating than it actually is. Here’s how to make sense of your company’s numbers in just four steps:

1. Collect data

Before you can actually start the analysis process, you need data to analyze. This involves conducting quantitative research and collecting numerical data from various sources, including: 

Interviews or focus groups 

Website analytics

Observations, from tools like heatmaps or session recordings

Questionnaires, like surveys or on-page feedback widgets

Just ensure the questions you ask in your surveys are close-ended questions—providing respondents with select choices to choose from instead of open-ended questions that allow for free responses.

Hotjar’s pricing plans survey template provides close-ended questions

 2. Clean data

Once you’ve collected your data, it’s time to clean it up. Look through your results to find errors, duplicates, and omissions. Keep an eye out for outliers, too. Outliers are data points that differ significantly from the rest of the set—and they can skew your results if you don’t remove them.

By taking the time to clean your data set, you ensure your data is accurate, consistent, and relevant before it’s time to analyze. 

3. Analyze and interpret data

At this point, your data’s all cleaned up and ready for the main event. This step involves crunching the numbers to find patterns and trends via mathematical and statistical methods. 

Two main branches of quantitative data analysis exist: 

Descriptive analysis : methods to summarize or describe attributes of your data set. For example, you may calculate key stats like distribution and frequency, or mean, median, and mode.

Inferential analysis : methods that let you draw conclusions from statistics—like analyzing the relationship between variables or making predictions. These methods include t-tests, cross-tabulation, and factor analysis. (For more detailed explanations and how-tos, head to our guide on quantitative data analysis methods.)

Then, interpret your data to determine the best course of action. What does the data suggest you do ? For example, if your analysis shows a strong correlation between email open rate and time sent, you may explore optimal send times for each user segment.

4. Visualize and share data

Once you’ve analyzed and interpreted your data, create easy-to-read, engaging data visualizations—like charts, graphs, and tables—to present your results to team members and stakeholders. Data visualizations highlight similarities and differences between data sets and show the relationships between variables.

Software can do this part for you. For example, the Hotjar Dashboard shows all of your key metrics in one place—and automatically creates bar graphs to show how your top pages’ performance compares. And with just one click, you can navigate to the Trends tool to analyze product metrics for different segments on a single chart. 

Hotjar Trends lets you compare metrics across segments

Discover rich user insights with quantitative data analysis

Conducting quantitative data analysis takes a little bit of time and know-how, but it’s much more manageable than you might think. 

By choosing the right methods and following clear steps, you gain insights into product performance and customer experience —and you’ll be well on your way to making better decisions and creating more customer satisfaction and loyalty.

FAQs about quantitative data analysis

What is quantitative data analysis.

Quantitative data analysis is the process of making sense of numerical data through mathematical calculations and statistical tests. It helps you identify patterns, relationships, and trends to make better decisions.

How is quantitative data analysis different from qualitative data analysis?

Quantitative and qualitative data analysis are both essential processes for making sense of quantitative and qualitative research .

Quantitative data analysis helps you summarize and interpret numerical results from close-ended questions to understand what is happening. Qualitative data analysis helps you summarize and interpret non-numerical results, like opinions or behavior, to understand why the numbers look like they do.

 If you want to make strong data-driven decisions, you need both.

What are some benefits of quantitative data analysis?

Quantitative data analysis turns numbers into rich insights. Some benefits of this process include: 

Making more confident decisions

Identifying ways to cut costs

Personalizing the user experience

Improving customer satisfaction

What methods can I use to analyze quantitative data?

Quantitative data analysis has two branches: descriptive statistics and inferential statistics. 

Descriptive statistics provide a snapshot of the data’s features by calculating measures like mean, median, and mode. 

Inferential statistics , as the name implies, involves making inferences about what the data means. Dozens of methods exist for this branch of quantitative data analysis, but three commonly used techniques are: 

Cross tabulation

Factor analysis

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BTPI will research relationship between Bitcoin and financial freedom

By giles morris cornell jeb e. brooks school of public policy.

The Brooks School Tech Policy Institute (BTPI) has announced a $1M project to study financial freedom in countries with authoritarian governments. Led by BTPI Director Sarah Kreps , John L. Wetherill Professor in the Department of Government in the College of Arts & Sciences and the Cornell Jeb E. Brooks School of Public Policy, the research will employ quantitative and qualitative approaches to understanding the use of Bitcoin and stablecoins by individuals around the world.  

“If you live in a place where the government silences its critics by threatening their assets or where you cannot trust the local banking system, you understand the importance of financial freedom to democracy,” said Kreps. “We want to study how people in these countries are using Bitcoin and stablecoins in the pursuit of their financial security.”

BTPI Director Dr. Sarah Kreps

The innovative approach to purpose-driven research is designed to build a framework for the creation of a Bitcoin and stablecoins adoption index that would map and analyze uptake globally. Supported by funding from Human Rights Foundation– a nonpartisan, nonprofit organization that promotes and protects human rights globally, with a focus on closed societies– and the Reynolds Foundation– a family foundation dedicated to supporting medical and spinal cord research and treatment, education, and democracy and freedom– the project is scheduled to kick off in July 2024 and conclude in 2026. 

Reynolds Foundation president and CEO Dr. Álvaro Salas Castro MPA '14 explained the multimodal and collaborative nature of the inquiry would lead to wider access to knowledge at a critical moment in global policy.

"The Tech Policy Institute at Cornell University's Brooks School of Public Policy stands as a crucial center for investigating the intricate relationship between emerging technology and global politics. Through a dedicated focus on critical issues such as artificial intelligence, cybersecurity, and digital privacy regulations, the Institute's interdisciplinary research aims to shed light on the complexities of our rapidly evolving technological landscape," said Dr. Salas Castro. "As Bitcoin adoption continues to exert influence over the global economic system, the Institute's steadfast commitment to fostering collaboration among scholars, policymakers, and industry leaders aligns seamlessly with the Reynolds Foundation's vision to support a network of knowledge-sharing that harnesses this technology for societal benefit."

According to Professor Kreps, the project will focus on about 12 countries, including India, Nigeria, El Salvador, Indonesia, and Turkey, and investigate Bitcoin and stablecoin uptake.  Kreps and her research team will partner with a major global research firm to develop and deliver surveys to 1,000 participants in each country that query not just adoption behaviors on the basis of demographic within each country but also perceptions and attitudes toward these digital currencies. A research team that includes undergraduate students will then conduct interviews to dig deeper into the basis for use.

According to HRF chief strategy officer Alex Gladstein, the combination of survey data and qualitative research in countries where relatively little is known about the use of Bitcoin and stablecoins represents a new approach to creating a baseline of understanding about their potential to enhance financial freedom around the world. 

"Bitcoin continues to grow into a bigger part of the global economic system. Today we have nation-state adoption, widespread mining operations on all continents, spot Exchange Traded Funds (ETFs) trading in the United States, and recent all-time-highs in price. The Human Rights Foundation has also observed that human rights groups and nonprofits are adopting Bitcoin in order to challenge financial repression from authoritarian regimes," said Gladstein. "This research under Professor Kreps will illuminate exactly who out there in the world is using Bitcoin and dollar-substitute stable coins, and why."

The goals of the financial freedom research project include:

• Increasing shared understanding of the factors driving cryptocurrency adoption, usage patterns, and the broader implications for financial inclusion and technological innovation.
• Assessing the ability of policymakers, industry stakeholders, and community organizations to make informed decisions regarding regulatory frameworks, product development, and educational initiatives related to cryptocurrencies.
• Expanding access to financial services and economic opportunities for individuals who are underserved or excluded by traditional banking systems.
• Raising public awareness and understanding of cryptocurrency technology, its potential benefits, risks, and implications for individuals and society.

“Researchers have posited a number of different reasons why citizens in these countries might use crypto and our research will probe these mechanisms of uptake to understand motivations and goals and whether these vary depending on region or type of government,” said Kreps. 

Giles Morris is assistant dean for communications in the Cornell Jeb E. Brooks School of Public Policy.

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