The Adoption of Citizen Science Apps for Coastal Environment Monitoring
Habit, green self-identity, and empowerment are the strongest drivers of adoption.
By Mariana Cardoso-Andrade
Center for Environmental and Marine Studies (CESAM)
Universidade de Aveiro
By Dr. Frederico Cruz-Jesus
NOVA Information Management School (NOVA IMS)
Universidade Nova de Lisboa
By Dr. Jesus Souza Troncoso
Professor of Ecology and Animal Biology
Laboratorio de Ecoloxía Costeira (ECOCOST)
Universidade de Vigo
By Dr. Henrique Quieroga
Associate Professor, Ecology
Center for Environmental and Marine Studies (CESAM)
Universidade de Aveiro
By Dr. Jorge Manuel dos Santos Gonçalves
Center for Environmental and Marine Studies (CESAM)
Universidade de Aveiro
Environmental and nature conservation authorities are calling for a collective effort to break or reduce the current cycle of environmental degradation. Much of the response depends on scientific knowledge production based on thematically and geographically comprehensive datasets. Citizen science (CS) is a cost-effective support tool for scientific research that provides means for building large and comprehensive datasets and promoting public awareness and participation. One of the greatest challenges of CS is to engage citizens and retain participants in the project. Our work addresses this challenge by (1) defining the role that technological, cultural, and environmental dimensions play in the adoption of CS apps for coastal environment monitoring, and (2) providing base knowledge about the profile of the apps’ most likely users and the functional features they require to be successful. Collectivists and people who assume a green identity are the most likely users of these apps. Drivers of their use are the promotion of citizen empowerment, habit development, provision of facilitating conditions, and proof of environmental performance.
The outcome of this study is a set of guidelines for project managers, app developers, and policymakers for citizens’ engagement and retention in CS coastal environment monitoring projects through their apps.
The unprecedented level of exploitation of our planet’s resources, biosphere pollution, the introduction of non-indigenous species, habitat degradation, and climate change are causing various threats to nature and human lives, health, and well-being (O’Hara et al., 2021). Climate change impacts such as global warming, glacier melting, sea-level rise, extreme weather events (e.g., heat waves, floods, hurricanes, wildfires, droughts), and increasing carbon dioxide in the atmosphere are depleting natural and cultural values and resources and simultaneously putting local, regional, and global economies at risk (McMichael and Lindgren, 2011, Ward et al., 2020). The global community is currently trying to respond to the numerous fronts of environmental degradation, namely the impacts of climate change and the great biodiversity crisis (Fajardo et al., 2021).
During the United Nations (UN) Summit on Biodiversity, the heads of state and governments of 64 countries (today 91) of all World regions, and the President of the European Commission signed the Leaders’ Pledge for Nature in September 2020, committing to a concerted and effective response to current and future global environmental crises (United Nations, 2020; European Commission, 2021). Only a collective effort can break the current cycle of environmental degradation, starting by accomplishing the UN Sustainable Development Goals (SDGs) until the year 2030 (United Nations, 2021).
The UN SDGs depend on the datasets provided by official sources, which have some cost, time, and heterogeneous data availability limitations, undermining the UN SDGs reporting process. Regarding these limitations, Fritz et al. (2019) argued that Citizen Science (CS), or Public Participation in Scientific Research (PPSR), is a promising means for tracking the progress of the UN SDGs by providing new opportunities to (1) acquire data at lower costs, (2) achieve more extensive data coverage and variability across and within nations, and (3) promote data transparency. CS depends on voluntary participation by citizens to contribute to scientific work (e.g., data collection, analysis, interpretation, and dissemination) usually coordinated by a team of professional scientists (Assumpcao et al., 2019, Haklay, 2013). Haklay (2013) identified two types of participants in CS: i) people who enjoy scientific work and see it as a hobby, and ii) people pursuing environmental justice – usually as part of a local community that is conscious of, or affected by, environmental conflicts, who seek a solid evidence base to capture the attention of policymakers and decision-makers.
CS is growing worldwide in marine and coastal ecological matters such as climate change (Marlowe et al., 2021), marine litter and debris (Papakonstantinou et al., 2021, Uhrin et al., 2020), environmental status assessment of communities, and habitats (Johnson et al., 2020, Turicchia et al., 2021), beach erosion (Pucino et al., 2021), early detection of invasive species (Datta et al., 2021, Sullivan and York, 2021), phytoplankton seawater discolorations (Siano et al., 2020), inland and coastal water quality (Malthus et al., 2020, Menon et al., 2021), microplastics (Camins et al., 2020), threatened species (LaRue et al., 2020), noise and air pollution (El-Kholei, 2020), and other areas of concern. Nevertheless, their success may be undermined by one very common setback in CS: the struggle to maintain citizens’ motivation for continuous and sustained long-term participation (Kloetzer et al., 2021, Paul et al., 2020). It is in this critical issue that we seek to contribute by bridging existing gaps in prior research on this subject, as (1) we still need to understand the factors driving CS coastal environment monitoring projects in order to achieve greater success, a problem intrinsic to the gains to be made through their digital platforms (Newman et al., 2012, Tsybulsky, 2020); (2) past literature has mainly studied the motivations to participate in CS environmental monitoring projects but rarely the drivers to use their digital platforms – hereinafter CS apps – as these are primarily mobile app-based (Aitkenhead et al., 2014, Lemmens et al., 2021).
Our work provides two major contributions to this objective: (1) we define the role that technological, cultural, and environmental dimensions (retrieved from prominent models and theories) play in the adoption and use of CS apps for coastal environment monitoring; and (2) we provide base knowledge and guidelines for the effective design and promotion of new CS apps for coastal environment monitoring, including the profile of the most likely participants/app users, the features and services the app must offer to be successful, and the values to be fostered for citizens’ engagement and retention. We address the following research questions:(1)
What are the main drivers of environmental CS apps adoption?(2)
How do individuals’ beliefs and technology characteristics influence environmental CS apps adoption?
Citizen Science for Environmental Monitoring
The concept of Citizen Science (CS) – generically, the participation of citizens in the production of scientific research and its related terminologies, have been recently explored by several authors with expertise in the matter (e.g., Eitzel et al., 2017, Fan and Chen, 2019, Haklay et al., 2021). A consensus exists that the definition of CS may be context-sensitive. Multiple research fields see value in CS, and their actors define it based on different norms, methodologies, values, and expectations around the co-production of knowledge, and according to the different objectives and goals outlined for their CS activities (Haklay et al., 2021). The evolution of the CS concept over time between disciplines and across borders is exposed in detail elsewhere (Eitzel et al., 2017, Fan and Chen, 2019, Haklay et al., 2021, Kullenberg and Kasperowski, 2016).
CS has been described as a participatory approach and support tool for scientific research that allows for continuous and geographically comprehensive data collection and processing at lower costs. It brings potential benefits from the production and dissemination of scientific knowledge to improve literacy, thereby raising citizen awareness and engagement with public interest issues (Eitzel et al., 2017, Johnson et al., 2014). Another strand of CS, originated by Irwin (1995, p. xi) from the social sciences, describes it as a means of science democratization –“ (…) a science which assists the needs and concerns of citizens (…) developed and enacted by citizens themselves (…)” – in which citizens are part of the scientific decision-making processes (Kullenberg & Kasperowski, 2016). Though representing polarized views of CS, the “productivity view” (as a tool for increasing scientific knowledge production) and the “democratization view” defined by Sauermann et al. (2020), can be integrated to meet current sustainability challenges where environmental and socioeconomic issues cross (Sauermann et al., 2020). The exchange of experience among disciplines is encouraged for future developments in CS (Hecker et al., 2018).
CS projects have various purposes, for instance: regional and global environmental justice; modification of individual and collective behaviors; better human health and safety; sustainable management and development; and base knowledge for improved multidisciplinary strategies and policymaking (Johnson et al., 2014). In Europe, by 2016 CS projects of natural and life sciences (e.g., ecology, environmental sciences, and biology) represented>80 % of all CS projects (Hecker et al., 2019).
CS projects are often categorized according to different levels of involvement, as proposed by Bonney et al. (2009): i) contributory projects – representing the lowest level of involvement and encompassing the majority of existing CS projects (Hecker et al., 2019) – in which participants contribute to data collection only using protocols exclusively designed by professional scientists; ii) collaborative projects, in which participants can contribute with their own ideas to adjusting the project’s pre-defined protocols, analyzing data, and disseminating conclusions; iii) co-created projects – representing the highest level of involvement – in which participants are involved in all steps of the project. Subsequently, the “Do-It-Yourself Science” model emerged, in which citizens can be autonomous in all steps of the research, including the definition of the research topic and project goals, without the leadership, coordination, or involvement of professional scientists (Sauermann et al., 2020, Nascimento et al., 2014). As the level of involvement increases, so does social capital, collective knowledge, scientific capacity, and inclusiveness in decision-making processes, making CS projects more sustainable (Robinson et al., 2021). The type of involvement planned for the CS project will determine the level of investment in it (Robinson et al., 2021). CS projects’ budgets are often limited; most CS projects have opted for less involvement of the citizens, which means less investment in their software and, consequently, less motivation for long-term participation (Robinson et al., 2021).
Several studies have identified success drivers of CS participation, and it is well accepted that citizens must feel motivated to volunteer. San Llorente Capdevila et al. (2020) developed a systematic literature review to identify the success factors of CS projects in water quality monitoring, highlighting citizens’ knowledge and experience, environmental awareness, motivation, and socio-economic background, along with institutions’ motivation, type of organization, and adequate funding. Another review on citizen scientists’ motivations, developed by Lee et al. (2018), indicated four compelling motivations on the part of potential participants: contributing to science (the most effective), helping professional scientists, learning about science, and joining a community (the least effective). CS projects require an adequate number of participants to be engaged in their activities over a generous time frame (Luna et al., 2018). However, mobilizing participants and sustaining engagement are some of CS projects’ main challenges (De Rijck et al., 2020). Thus, it is essential to address what drives people to adopt and use CS apps for environmental monitoring projects. Nevertheless, we still lack quantitative data on environmental CS apps’ adoption, making it challenging to assess citizen scientists’ intention to use them (San Llorente Capdevila et al., 2020).
The Role of Technology in CS Projects
The widespread dispersal of internet-capable mobile phones, derived from the significant cost reduction of these devices and their storage systems, paired with the evolution of Global Positioning Systems (GPS), qualify them as scientific instruments for CS initiatives (Ashraf et al., 2021, Haklay, 2013). Besides the innovations in sensor technologies, social networks have also evolved and derived into multiple new tools that can be integrated into mobile or web-based apps (Robinson et al., 2021). CS apps today aim to provide scientists, managers, surveyors, and local authorities the means to closely observe trends and make timely responses to problems that may emerge (De Rijck et al., 2020). Thus, CS apps are useful for environmental monitoring, for instance, to evaluate the impacts and the effects of protection measures inside and outside marine protected areas (Garcia-Soto et al., 2021).
One of the main challenges of CS projects and their apps is to drive motivation for sustained participation, as participants’ motivations, interests, and availability (which are mutable) must correspond somehow to the projects’ motivations and their digital platform’s capacity (Golumbic et al., 2020). Thus, motivations for CS participation and how citizens interact with CS projects and platforms must be carefully examined to choose appropriate citizen engagement tools and to predict behavior intention of citizen participation and retention (Cox et al., 2018, Golumbic et al., 2020). Understanding participants’ motivations to adopt CS apps will also improve the project’s cost-effectiveness, especially in the recruitment and retention processes (Alender, 2016).
Data quality and recognition by the scientific community are also challenges facing CS (Turrini et al., 2018). Data quality may be compromised by poor project design, varying data quality standards, non-experts’ lack of commitment and skills, deliberate provision of fabricated data, and others (Balázs et al., 2021, Weber et al., 2019). Still, project elements and digital platforms can be designed to avoid, detect, and correct data issues (Kosmala et al., 2016; Kasten et al., 2021, San Llorente Capdevila et al., 2020). Relatedly, insufficient funding and resources are often pointed out owing to CS projects’ duration (usually these are long-term projects and funding is temporary), and difficulty in evaluating them following the traditional funding frameworks (Gunnell et al., 2021, Hecker et al., 2018, Turrini et al., 2018). In this regard, CS projects must provide clarity on their necessities and develop evaluation tools that make use of appropriate indicators, and current funding programs must account for the iterative design process of CS (Hecker et al., 2018).
The potential of CS apps increases with the near real-time availability of data visualization and processing tools, complemented by public online access to information sources (De Rijck et al., 2020). Furthermore, using these devices seems to increase public engagement and awareness and help solve common CS data quality issues generated by human error (Newman et al., 2012; San Llorente Capdevila et al., 2020). CS is, by definition, an inclusive process seeking to involve a specific or diverse community in a scientific mission. This makes it imperative to study the traits and motivations of that community (Golumbic et al., 2020). Participants should be provided the opportunity to be involved in the design process of the CS app, thereby working together with developers, scientists, and other stakeholders to achieve a more suitable and satisfactory system to increase the likelihood of success in CS projects (Robinson et al., 2021).
Technology Adoption Models
The use of CS apps for coastal environment monitoring depends upon a series of factors, from technical (i.e., those related to the technology itself) to non-technical (i.e., those pertaining to each individual and their inner and external beliefs). Therefore, we must use the right theoretical lenses to understand the main drivers behind it. With this in mind we looked into the published literature in the information systems field for the main theories that could explain such platforms’ adoption and use. In the end we combined four theories in a tailor-made comprehensive conceptual model for investigating the adoption of CS apps. These are:
- UTAUT 2, arguably the most prominent IT adoption theory in the information systems field, which focuses on technological and social factors driving technology adoption and use (Venkatesh et al., 2012, Venkatesh et al., 2003);
- Citizen Empowerment, which evaluates the influence of personal beliefs/ motivations on citizen participation success (Rappaport, 1987, Spreitzer, 1995, Zimmerman and Rappaport, 1988, Zimmerman, 1995);
- Green self-identity, which assesses the effect that environmental awareness and personal values have on pro-environmental behavior (Barbarossa et al., 2017, Sparks and Shepherd, 1992); and.
- Hofstede’s cultural dimensions, which assess cultural variation in an individual’s values, beliefs, and behaviors (Hassan et al., 2011, Srite and Karahanna, 2006).
The reasons for choosing these models are detailed in the following sections. However, considering the unique characteristics of CS apps for coastal environment monitoring, especially the mix between inner (environmental) beliefs and technology aspects, we felt the need to develop a specific model combining different features of those that are already well-established. In other words, we did not find the most important aspects of environmental CS apps covered in earlier theories.
Unified Theory of Adoption and Use of Technology (UTAUT-2)
To assess the drivers for adopting and using information and communication technologies (ICTs), which typically host CS apps, we chose the Unified Theory of Adoption and Use of Technology (UTAUT). The UTAUT model (Venkatesh et al., 2003), including its extension UTAUT-2 (Venkatesh et al., 2012), is arguably the most prominent theory of technology adoption and use (Blut et al., 2022). Venkatesh et al., 2003, Venkatesh et al., 2012 combined eight recognized theories, resulting in a single model with seven constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit, which together provide vital information on both the technological and social factors influencing technology’s potential users. UTAUT-2 has been consistently demonstrated to be useful in explaining technology adoption in a wide variety of settings. For instance, Taghizadeh et al. (2021) used this model to find the determinants of students’ satisfaction and continuous usage intention of online learning during the COVID-19 pandemic. Another example is Li (2021), who used it to understand the factors influencing the intention to adopt E-Government.
Though its representation in CS studies is still sparse, UTAUT-2 constructs are relatable to the technology’s characteristics and motivational factors identified for CS participation. We have adapted this theory’s original constructs to the topic and chose to exclude “price value” – which defines the extent to which users are willing to spend their money in exchange for the benefits brought by the technology (Venkatesh et al., 2012) – as it was considered unsuitable for the nature of the voluntary work proposed by CS apps, which are free.
The key to the success of citizen participation projects, such as CS apps, is motivating individuals, as citizens, to participate in them (hence the term CS). We therefore screened the literature for a theoretical lens focused on understanding the factors influencing people’s intention/motivation to volunteer for public causes. Citizen Empowerment is one of the leading models mentioned in studies assessing citizen participation drivers. In the context of the psychological empowerment theory (Rappaport, 1987, Zimmerman and Rappaport, 1988, Zimmerman, 1995), Citizen Empowerment results from the relationship between the citizen’s perception of their skills and their willingness to apply them in public domain services (Naranjo-Zolotov et al., 2019). It proposes that empowerment, its main dimension, is a second-order construct comprising competence, meaning, impact, and self-determination (Spreitzer, 1995).
Citizen Empowerment has already been integrated with UTAUT-2, with relative success. For instance, Naranjo-Zolotov et al. (2019) used these to design a research model to investigate the adoption drivers of e-participation – a derivation of e-government for citizens’ engagement and participatory governance through ICTs. They found that the Citizen Empowerment constructs of competence, meaning, impact, and self-determination were positively related to e-participation. Interestingly, these constructs are also widely used concepts to characterize individuals’ motivations for initial and continuous long-term participation in CS projects (Geoghegan et al., 2016, Golumbic et al., 2020, Gonçalves et al., 2014, van Noordwijk et al., 2021, Wehn and Almomani, 2019).
Together with individuals’ willingness to act as citizens, another strong driver of CS apps adoption is the extent to which one values the environment and environmental issues. It seems reasonable to assume that people involved in pro-environmental projects do so because they are sensitive to this issue and will very likely adopt environmentally friendly behaviors in other aspects of their lives. We therefore resort to green self-identity, which is a construct determining the adoption of pro-environmental behavior (Barbarossa et al., 2017). If one sees himself/herself as a green individual, he/she will score high on the green self-identity measure (Barbarossa et al., 2017).
Hofstede’s Cultural Dimensions
Finally, in addition to the two previous aspects of CS apps adoption, we also believe that one’s cultural beliefs can play a critical role. Hence, we looked at Hofstede’s cultural dimensions to assess how cultural context can influence participants’ values, beliefs, and behaviors in society. It is one of the most noteworthy theories in studies assessing a national culture’s effects on multiple domains (Kumar et al., 2019). In the 1980 s Hofstede hypothesized that cultural variation drives people’s beliefs and behaviors and initially identified four dimensions to explain it: the power of distance, uncertainty avoidance, individualism versus collectivism, and masculinity versus femininity; later adding a fifth dimension – long-term orientation (Nagy & Molnárné, 2018). In our study’s context we chose to focus on two of these dimensions, which we believe are especially suitable to explain pro-environmental behavior: collectivism (versus individualism) and long-term orientation. The first is the degree to which the society’s identity is shaped from the individuality of its members rather than from a social group (Hofstede, 2001). Individualists cherish their sense of freedom and autonomy, whereas collectivists can more easily concede these values for the greater good of their social group (Kaasa & Andriani, 2021). The second dimension is the degree to which the focus of individuals is on the future and not on the present/past (Nagy & Molnárné, 2018). Long-term-oriented individuals are committed to hard work for a future cause (Sreen et al., 2018), such as reversing or diminishing the regional and global impacts from the environmental crisis in which we are living.
Research Model and Hypotheses
We present the research model in Fig. 1 to investigate the drivers of adoption and use of CS apps for coastal environment monitoring and understand the relationships among users’ perceived values. We have integrated the four theories presented above into a tailor-made, comprehensive research model for the specific case of CS apps.
In this subsection we summarize the definition of each construct within UTAUT-2 (Venkatesh et al., 2012) as well as the rationale for each specific CS apps hypothesis.
Performance expectancy is the degree to which the technology benefits individuals (Venkatesh et al., 2012). Herein we adapted the concept to environmental performance expectancy, i.e., the degree to which the goals and objectives proposed by the environmental CS apps are achieved through the technology. From a logical point of view users must recognize the usefulness of the technology to consider adopting it (Shevchuk & Oinas-Kukkonen, 2020). In this regard, it should be said that performance expectancy is usually indicated to be one of the most important drivers of technology adoption in many areas. Thus, we hypothesize that environmental performance expectancy will also positively affect CS apps adoption.
H1: Environmental performance expectancy (EPE) is positively associated with CS apps behavioral intention (BI).
Effort expectancy is the degree of ease associated with using technology (Venkatesh et al., 2003; Venkatesh et al., 2012). Naturally, the perception of having to face difficulties to use technology will serve as a deterrent for its adoption. We believe that in CS apps this is even more important because not only is their use voluntary, but also the potential CS apps’ benefits are not as much for the individual but for the whole community in the long term (Naranjo-Zolotov et al., 2019). Thus, the perceived lack of time for the voluntary act is even more of a discouragement for participation (West & Pateman, 2016). Main short-time interactions in these platforms include: consulting online open-access information sources, entering/uploading observed data (e.g., text, image, audio, time, GPS coordinates), data processing, and visualization of the uploaded records; while interactions that may take more time are data analysis and validation (usually performed by professional scientists) (Garcia-Soto et al., 2017, Luna et al., 2018).
Effort expectancy is operationalized in terms of perceiving technology to be easy (effortless) to use and we therefore hypothesize:
H2: Effort expectancy (EE) is positively associated with the behavioral intention (BI) of using CS apps for coastal environment monitoring.
Social influence refers to the user’s idea of how a social group perceives his/her identity, personality, or social image as a result of membership (Ashraf et al., 2021). As such, social influence is widely regarded in the literature as a determinant of pro-environmental behavior (Estrada et al., 2017, Göckeritz et al., 2010). Chao et al. (2021) claim that the perceptions and support of significant others might similarly influence pro-environmental behavior, based on testimonials from citizen scientists of CS apps on waterbird refuges. Bouman et al. (2020) also found that individuals who initially underestimate biosphere values (non-humanized environmental concerns) are more likely to increase their environmental engagement if their social group strongly endorse those same values. Hence, we hypothesize:
H3: Social influence (SI) is positively associated with CS apps BI.
Facilitating conditions measure the degree of perceived support, training, and availability of resources to the technology user (Venkatesh et al., 2012). Access to facilitating conditions such as data availability, guidance, and training is paramount in CS apps to promote citizens’ awareness, motivation, and self-efficacy (San Llorente Capdevila et al., 2020). An adequate support system is also essential to ensure input data quality and to foster trust between citizen scientists and project managers (De Rijck et al., 2020; San Llorente Capdevila et al., 2020).
For instance, Kasten et al. (2021) conducted a participatory coastal biodiversity monitoring project with>51 participants. They checked for similarities between their data and data collected by experts and concluded that higher complexity tasks could interfere with data quality and highlighted the importance of identifying participants’ main challenges for developing adequate CS protocols. For these reasons we hypothesize that:
H4: Facilitating conditions (FC) is positively associated with CS apps BI.
Hedonic motivation is the degree of perceived enjoyment derived from using technology (Ashraf et al., 2021). Citizens motivated to use technology due to its perceived enjoyment are more prone to develop the habit of using it (Ashraf et al., 2021). In the context of citizen science, hedonic values such as enjoyment, recreation, and social interaction (especially within a community that shares similar interests) are motivations that improve the chances of citizens’ participation in volunteer activities (Land-Zandstra et al., 2021, van Noordwijk et al., 2021). Therefore, we posit that:
H5: Hedonic motivation (HM) is positively associated with CS apps BI.
Both conscious (intention) and unconscious (habit) decision-making affect human behavior (Ashraf et al., 2021). Habit measures the extent to which an individual believes the behavior to be automatic (Limayem et al., 2007). As CS apps are recent and mostly unknown, we have redirected habit to public participation apps in general. Usually, public e-participation projects are intended to design apps that successfully recruit and engage citizens for active, long-term participation (Tinati et al., 2017; Naranjo-Zolotov et al., 2019). Experts involved in developing technology adoption models and using them widely recognize the variable “habit” to contribute positively to the continuous use of the technology (Venkatesh et al., 2012; Naranjo-Zolotov et al., 2019). Thus, we hypothesize that:
H6: Habit (HA) is positively associated with CS apps BI.
In this subsection we summarize Citizen Empowerment’s four first-order constructs (competence, meaning, impact, and self-determination) as well as the second-order formative-reflective one (empowerment).
Competence is defined as the degree of perceived self-efficacy (or competence) to perform a task with skill (Spreitzer, 1995). Gonçalves et al. (2014) applied the psychological empowerment theory principles to study the motivation drivers for citizen participation. They concluded that perceived competence is one of the main drivers of participation.
Meaning is the degree of the perceived value of a determined goal or purpose, according to the individual’s ideals or standards (Spreitzer, 1995). Regarding community-based environmental monitoring, citizens usually attribute more meaning to a purpose when they feel they are involved at the strategic level, which is associated with self-determination (Wehn & Almomani, 2019). Contributory projects, i.e., projects initiated and regulated by scientists that involve citizens only in pre-determined data collection tasks, present low levels of engagement when compared to inclusive projects, such as co-created CS projects (Golumbic et al., 2020). In co-created CS projects, citizens are involved at all phases of the scientific process, including the choice of research direction. Thus, the perceived value/meaning of the project’s goals and outcomes is/are enhanced compared to contributory or collaborative projects, along with citizens’ willingness to collaborate (Gunnell et al., 2021, Sauermann et al., 2020).
The impact is the degree to which an individual’s behavior or action appears to produce the desired effects for a determined purpose (Spreitzer, 1995). In this study’s context, environmental impact-motivated citizens are those who want to make a difference to science or an ecological issue. Acknowledging the impacts of their actions concerning the outlined objectives has proven to be one of the greatest motivations for their participation in environmental CS projects (van Noordwijk et al., 2021). Therefore, it is essential in these initiatives that the participant is aware of the whole process beyond data collection – how data are treated and used to make a difference – and what results have already been achieved by the project (van Noordwijk et al., 2021). In this regard, one of the Ten Principles of Citizen Science defined by ECSA is that “Citizen scientists are acknowledged in project results and publications”.
Self-determination is the degree to which an individual senses that he/she has the autonomy to make decisions over the work process – initiative and regulation (Spreitzer, 1995). Ryan and Deci (2000) developed the self-determination theory and the cognitive evaluation sub-theory to explain variability in intrinsic motivation – the individual’s motivation to engage in an activity for the simple pleasure of performing it. By contrast, they also explain extrinsic motivation – the individual’s motivation to engage in an activity to achieve an outcome that is external to that activity. In this scope, Tiago et al. (2017) express the importance of nurturing intrinsic motivation in addition to extrinsic motivation instruments (e.g., prizes, certificates, etc.) in encouraging CS. The self-determination theory argues that while intrinsic motivation instruments result in higher achievement, extrinsic motivation instruments can undermine citizens’ engagement and activity performance, especially if they perceive it as a form of external control (Ryan & Deci, 2020).
Empowerment, as a motivational construct, creates conditions for increased motivation for production by expanding the sense of personal efficacy (Sreelakshmi, 2016). In the scope of public participation in scientific research, citizens can get more involved in such initiatives if these are participatory in nature (Wilmsen et al., 2012). If citizens are given the opportunity to use their experience and be part of the decision-making process, and choose to embrace it, they will more likely feel empowered by the process itself and become more interested in its outcomes (Gunnell et al., 2021, Shirk et al., 2012). Deeper involvement in the process and personal contribution to its outcomes will also promote increased awareness, learning, and willingness to change individual and the community’s attitudes (Sauermann et al., 2020).
Naranjo-Zolotov et al. (2019) found that psychological empowerment and its first-order constructs of competence, meaning, impact, and self-determination, positively influence the intention to engage in e-participation. Therefore, based on the evidence, we posit that:
H7a-d: Empowerment (EM) is a second-order formative-reflective construct formed by competence (H7a), meaning (H7b), impact (H7c), and self-determination (H7d) that positively influences CS apps BI.
Participating in CS projects is a form of pro-environmental behavior (Chao et al., 2021). Pro-environmental behaviors may result from altruistic feelings about nature (Chao et al., 2021). In fact, “helping the environment” was widely documented as one of the main motivations for participating in conservation projects (Bruyere and Rappe, 2007, Clary and Snyder, 1999, He et al., 2019). Hart et al. (2011) argued that individuals with strong environmental values are more concerned about how the government is managing environmental issues. Thus, they are also more prone to engage in the management process (Johnson et al., 2014). With this in mind we hypothesize that individuals who recognize a green identity in themselves are more likely to use CS apps. Therefore:
H8a: Green self-identity (GSI) is positively associated with CS apps BI.
We also believe that green self-identity will reinforce the effect of EPE and EMP on BI. It seems reasonable to assume that for someone who score higher on GSI, the eventual performance CS apps will have on monitoring coastal areas, and ultimately protect the environment will be more important than to one who scores lower on GSI, i.e., for whom environmental issues are not that important. The same rationale applies to EMP as the sense of empowerment will be increasingly important as one’s GSI is also higher. Hence, we posit that:
H8b: Green self-identity will moderate the effect of EPE on CS apps BI, such that the relationship will be stronger among individuals with greater green self-identity.
H8c: Green self-identity will moderate the effect of EMP on CS apps BI, such that the relationship will be stronger among individuals with greater green self-identity.
This subsection explains the potential role of Hofstede’s collectivism (versus individualism) and long-term orientation cultural dimensions in influencing CS participants’ values, beliefs, and behaviors in society.
Collectivism versus individualism and long-term orientation are widely accepted in the literature to explain cultural variation in green purchase intention – a “private-sphere” pro-environmental behavior (Mi et al., 2020, Sreen et al., 2018). Mi et al. (2020) classify the act of participation in environmental protection activities as a “public-sphere” pro-environmental behavior if it contributes to the promotion of environmental regulations, policies, and activities. In this study, we consider the act of participating in a CS project for coastal environment monitoring to be a “public-sphere” pro-environmental behavior.
Jakučionytė-Skodienė and Liobikienė (2021) argue that cultural perspective is essential to predict environmental concern and pro-environmental behavior. However, they also posit that environmental concern and pro-environmental behavior are not always positively related. One can find controversial opinions and results in the literature about which societies are more likely to engage in pro-environmental behaviors or environmental performance. If individualists were found to be less concerned and/or more skeptical about climate change than collectivists, they are also more likely to assume personal responsibility and to do their part in pro-environmental actions (Jakučionytė-Skodienė and Liobikienė, 2021, Kaasa and Andriani, 2021). Nevertheless, collectivists also have an acute sense of social responsibility for initiatives fostering pro-environmental sustainable behaviors, as their overarching objectives are to protect the environment and society. Collectivists generally prioritize the goals of their community over their own (Nagy and Molnárné, 2018, Parboteeah et al., 2012, Rotman et al., 2014).
Very few studies related to the CS topic include Hofstede’s cultural dimensions to explain cultural variation in CS participation. Among the exceptions are Beza et al. (2017), who studied the effect of cultural variation in farmers’ motivations to participate in CS projects. They found that farmers’ motivations vary across national cultures, such that in more collectivist societies farmers were more inclined to share information. Rotman et al. (2014) also studied cultural variation in motivations for initial participation and long-term participation in CS apps using the United States (US), India, and Costa Rica as case-studies. They concluded that initial motivation to participate in CS projects depended on the individual’s drive (personal interest, self-promotion, self-efficacy, and social responsibility); social responsibility was observed for only the collectivistic society of Costa Rica. On the other hand, motivation for long-term participation relied on interpersonal interactions (between scientists and participants, and participants and communities) – trust, common goals, acknowledgement, mentorship, education and outreach, and policy and activism (accountability for and acknowledgment of the project’s values beyond participants’ tasks).
Although there is global awareness of the climate change and biodiversity crisis (Secretariat of the Convention on Biological Diversity, 2020), we believe that the willingness to participate in CS apps – aiming to assess coastal habitats and biological communities’ diversity and structure, ecosystem health status, climate change impacts, and other activities – requires individuals to be sensitive to collective/societal goals. We therefore hypothesize:
H9a: Collectivism (CI) is positively associated with CS apps BI.
Moreover, because collectivists are more concerned with the wellbeing of others, it seems also reasonable to hypothesize that those who score higher on CI will value the environmental performance (EPE) of CS Apps more than those who score low in their intention to use them. Hence,
H9b: CI will moderate the effect of EPE on CS apps BI, such that the relationship will be stronger among individuals with greater green self-identity.
Long-term-oriented individuals usually have a positive attitude toward environmental and green products, as they value the future long-term positive effect that their choice represents (Mi et al., 2020, Sreen et al., 2018). Moreover, Mi et al. (2020) found that long-term orientation positively affects public and private-sphere pro-environmental behaviors. Hence, we posit that the positive relationship that collectivism and long-term orientation cultural dimensions have with green purchase intention (Sreen et al., 2018) could be similar to their relationship with citizen participation in environmental CS projects. Although we are already experiencing the effects of climate change and the biodiversity crisis, the reality-check is that worse effects are still to come if we do not act regionally and globally (Bohensky et al., 2011). Thus, long-term orientation will be pivotal to overcoming these issues. Therefore, we hypothesize:
H10: Long-term orientation (LTO) is positively associated with the behavioral intention (BI) of using CS apps for coastal environment monitoring.
We used the control variables age, education, gender, number of days at the beach per year, and distance of respondents’ residences to the beach. While the first three (socio-demographic) control variables are commonly used (see, e.g., Chan et al., 2021), the last two are specific to this study’s context. Note that to use environmental coastal monitoring apps, one needs to live near coastal regions and/or be in these regions often. Hence, we added these two parameters as controls.
Procedure and Data
The research design is presented in Fig. 2. We collected the data using an online survey made available through Qualtrics and shared it through social media. In order to get a more accurate sample of Portuguese and Spanish citizens, we hired a firm, Prolific Academic, to collect the data from respondents in exchange for a small monetary contribution. Prolific is one of the world’s largest crowdsourcing on-demand platforms that enables large-scale data collection by connecting researchers to participants. Participants from Portugal and Spain were recruited from a population of 123,375 eligible people. They were informed that the study was about “Coastal & Marine Environmental Monitoring Digital Platforms”, in a ten-minute online survey. To ensure the best data quality, we have also recorded the participants’ metadata, regarding the platform ID (Prolific ID), as well as the study and session IDs, to prevent issues associated with such crowdsourcing data collection platforms (Kennedy et al., 2022). Because the instrument was administered in Portuguese and Spanish, we hired a professional to translate the original questions from English to both languages, and then another to translate both back into English to assess the instrument’s translation equivalence.
The survey started with an informed consent question, and if the respondent agreed to participate in the study, they proceeded to some basic instructions and a short video explaining CS apps for environmental monitoring, their types, examples, and a glossary of related definitions. The survey then proceeded with the measurement items and subsequently with the sociodemographic questions. The material used in the survey is available upon request.
The instrument was developed to reflect our above-described research model. Every questionnaire item was adapted to CS apps from the original theories, which have all been widely used and tested separately, although not specifically in this context. Every item (question) of the instrument thus has solid theoretical and empirical support. More specifically, the UTAUT constructs (EPE, EE, SI, FC, HM, HA, and BI) were adapted from Venkatesh et al., 2003, Venkatesh et al., 2012; Citizen empowerment (EM, CO, ME, IM, and SD) come from Spreitzer (1995); green self-identity was adapted from Sparks and Shepherd, 1992, Barbarossa et al., 2017; and, finally, Hofstede’s cultural dimensions (CI and LTO) from Srite and Karahanna, 2006, Hassan et al., 2011. All items were measured on a seven-point interval scale anchored between (1) “strongly disagree” to (7) “strongly agree” to guarantee their metric properties. As the original items were not developed for the specific context of CS apps, some slight adjustments were made. The measurement items can be seen in Appendix A.
We started by conducting a pilot test for each country, with 30 respondents each. We assessed if the respondents had complications in answering the questions with the pilots. We also used the pilot to examine the instruments’ reliability and validity. Some questions were adjusted slightly or even deleted in this process if they were ambiguous. Because of these minor adaptations, the pilot responses were not used in the final study.
For the final study we obtained 345 responses (177 from Portugal and 168 from Spain), of which we recorded the IP addresses to avoid duplicates. We employed Harman’s single-factor test to test for common method bias (MacKenzie et al., 2011). As the first factor accounted for only 32.9 % of the covariance among all constructs, well below the threshold of 50 %, we concluded that common method bias was not a risk (MacKenzie and Podsakoff, 2012, Podsakoff et al., 2003). We also added a theoretically irrelevant marker variable in the research model, obtaining<5 % as the maximum shared variance with other variables, a value that can be considered as low (Johnson et al., 2011). The sample’s sociodemographic characteristics can be seen in Table 1.
A partial least squares structural equation modeling (PLS-SEM) was performed supported on the software SmartPLS 3 (Ringle et al., 2015). The analysis of the PLS-SEM was assessed in two parts: the measurement and structural models.
Data Analysis and Results
Following Hair et al. (2017), because our model includes only reflective constructs we evaluated it to assess the internal consistency, and convergent and discriminant validities. Composite reliability was assessed to verify the internal consistency, which, as suggested by Hair et al. (2017), were all above the threshold of 0.70 (see Table 2). Convergent validity was assessed through average variance extracted (AVE) and indicator reliability. The Fornell and Larcker (1981) criterion was met, as the AVE is above 0.50 (see Table2). Moreover, the indicator reliability criterion was also fulfilled as all the loadings were above 0.70 and statistically significant (see Appendix B), thereby demonstrating convergent validity (Fornell and Larcker, 1981, Götz et al., 2010).
We consider three criteria to assess discriminant validity: the Fornell-Larcker, the cross-loadings, and the Heterotrait-to-Monotrait (HTMT) ratio. The first of these was proposed by Fornell and Larcker (1981) and indicated that the square root of AVE needs to exceed the correlation between all the other constructs (see Table 2). The second criterion states that the cross-loadings should be lower than the loadings of each indicator (Hair et al., 2017) (see Appendix B). Third, HTMT ratios (Table 3) were below the conservative threshold of 0.85 (Hair et al., 2017), thus supporting discriminant validity. We conclude that our measurement model is adequate, as it presents good indicator reliability, constructs reliability, convergent validity, and discriminant validity.
Before assessing the structural model, we examined if multicollinearity was an issue using the variance inflation factor (VIF). The highest VIF is 2.7, well below the threshold of five (Hair et al., 2017). Hence, we conclude that multicollinearity among the independent constructs is not a problem (Lee & Xia, 2010). The results related to the model testing are presented in Fig. 3, which shows that 11 of the 17 hypotheses were supported (please see Table 4).
Looking at the hypotheses derived from UTAUT (H1-H6), EE, SI, and HM were not confirmed (H2, H3, and H6, respectively). Hence, we show that EPE (H1: β = 0.122, p < 0.05), FC (H4: β = 0.139, p < 0.05), and HA (H6: β = 0.317, p < 0.001) have a positive effect on BI. As for the hypotheses developed based on citizen empowerment theory, we see that they are all supported (H7 a-d). These findings demonstrate that empowerment is, in fact, a second-order reflective-formative construct of competence, meaning, impact, and self-determination, and that this is a strong driver of CS apps BI (H7: β = 0.239, p < 0.001). Findings also demonstrate the usefulness of green self-identity, as two of three hypotheses related with it were confirmed. We have thus shown that GSI is positively associated with BI (H8a: β = 0.240, p < 0.001) and that it also positively moderates the relationship between empowerment and BI (H8c: β = 0.093, p < 0.05). However, contrary to our expectations, we did not find evidence that GSI works as a moderator between EPE and BI (H8b: β = -0.042, p > 0.05). Finally, looking at the hypotheses supported by Hofstede’s cultural dimensions, we were able to support only H9a, attesting that collectivism is positively associated with BI (H9a: β = 0.087, p < 0.01). Hence, we were unable to demonstrate that collectivism mediates the relationship between EPE and BI (H9b: β = 0.023, p > 0.05), or that the long-term orientation has any effect on BI (H10: β = 0.028, p > 0.05).
The hypothesized moderation effect that is significant (H8c) is shown in Fig. 4. From its analysis, one can see that, as mentioned above, empowerment has a positive effect on BI (comparing the left with the right side), but that this effect is even more substantial in those with greater levels of green self-identity. This is noticeable in the fact that the slope of the dashed line is steeper than the continuous one (high and low green self-identity, respectively).
Finally, we should note that our model is able to explain two-thirds of the variation in BI (R2 = 66 %), which is a substantial amount. This fact indicates, in our view, the benefits of building tailor-made comprehensive adoption models for the specific technology under study, highlighting the role that the four main theories play in CS apps adoption.
Discussion and Implications
Discussion of Findings and Theoretical Implications
Given the growing importance of CS as a support tool for informing scientists, raising awareness, and encouraging people’s habits to change toward more sustainable behaviors, we developed a multidisciplinary theory to identify the determinants for the adoption and use of CS apps for coastal environment monitoring. The use of CS apps depends on technological, cultural, and environmental dimensions. However, not all of the significant factors are of equal importance. Habit (UTAUT-2), green self-identity, and citizen empowerment are by far the most influential drivers of CS apps BI. Table 4 presents the summary of hypotheses.
Our results suggest that successful CS apps adoption requires habit formation. Habit comes from unconscious and automatic behaviors, and these are developed by repetitive actions (Gardner & Rebar, 2019). As suggested by White et al. (2019), to foster a habitual behavior, the proposed measures should be easy, while participants should be encouraged to comply with their tasks, either through prompts such as notifications (e.g., whenever the participant approaches a beach), or incentives (e.g., direct feedback or acknowledgment of their work’s implications). In this regard, CS project managers should be careful about using rewards as incentives (extrinsic motivation tools) as they can undermine the citizens’ sense of autonomy and competence valued by participants (Ryan & Deci, 2020). Sharma et al. (2019) suggest another possibility to create the habit of using CS apps: to incorporate the monitoring activities into citizens’ daily routines and hobbies (e.g., a walk-in nature).
The use of CS apps is also more likely if participants feel empowered by using them. Fundamental sentiments of empowerment come from internal and external recognition of participants’ contributions to science and their integration during all steps of the process (Froeling et al., 2021, Lee et al., 2018). This behavior is consistent with the fourth, fifth, seventh, and eighth principles laid out by the European Citizen Science Association (ECSA) (Ecsa, 2015, Robinson et al., 2019).
People who identify as having a green self-identity are more inclined to use CS apps. Though green consumption and voluntary participation in CS for coastal environment monitoring are different forms of pro-environmental behavior, both come from self-transcendental values that give rise to environmental concerns (Barbarossa et al., 2017, Steg et al., 2014a). Green self-identity seems to be a cause and effect of CS participation (Dean et al., 2018). CS mobile apps for environmental monitoring provide an opportunity for people to apply their knowledge in close natural settings. This convenience will increase the frequency of contributions, which will improve citizens’ environmental awareness, knowledge, and experience. Sharma et al. (2019) remarked that when citizens closely observe species behavioral patterns, they become more aware of the importance of preserving them and their habitat and the impacts of their actions (e.g., trampling in rocky intertidal ecosystems or littoral dunes). Dean et al. (2018), who studied marine and coastal CS engagement, underlined the benefits of exposing participants to healthy and disturbed environments so that there is greater sensitivity and cooperation with marine conservation. In turn, this sensitiveness promotes pro-environmental behavior and the willingness to share acquired knowledge with others.
Aside from the direct effect that green self-identity has on CS apps BI, we also found that it works as a catalyst for citizen empowerment’s impact on CS apps BI. As hypothesized, we demonstrate that citizen empowerment’s influence on CS apps BI is even more substantial for people who are “green” (see Fig. 4). If CS apps stakeholders can use this joint effect in their efforts, they are more likely to succeed. In this regard, it should be noted that contrary to what we hypothesized, green self-identity does not have this effect on the relationship between environmental performance expectancy and CS apps BI, probably because the first two are independent.
Environmental performance expectancy and facilitating conditions are also technological determinants positively related to the intention to adopt and use CS apps. Surprisingly, effort expectancy, social influence, and hedonic motivation’s effects were not shown to drive CS app BI. This finding contradicts those reported in earlier studies regarding the drivers of pro-environmental behaviors adoption (Estrada et al., 2017, Jakučionytė-Skodienė and Liobikienė, 2021) and the motivations to participate in CS projects (Land-Zandstra et al., 2021). We believe that our study did not observe these potential drivers’ effects because our model comprehends other CS apps determinants (e.g., green self-identity and cultural dimensions) that may have outweighed them. We posit that the non-confirmation of the effort expectancy hypothesis may be justified by the fact that users already know the technology in depth (mobile phone apps) and that the importance of effort expectancy is thus lower when compared to the adoption of more recent technologies (Naranjo-Zolotov et al., 2019). Although respondents were contextualized, another possible explanation could be that they were in part new to the subject and had not yet experienced coastal environmental monitoring and the way it uses technology. An additional explanation regarding effort expectancy is that citizens’ perceptions of effort is a pleasure-seeking activity in the context of CS apps (i.e., walks on the beach and observation of different species and habitats), although this may seem inconsistent with the lack of significance of hedonic motivation. Still, hedonic motivations are developed for “improving one’s feelings and reducing effort” (Steg et al., 2014b, p. 107), and therefore the non-confirmation of effort expectancy is reasonably in line with the non-confirmation of hedonic motivation. Regarding the non-confirmation of social influence, we suggest that internal motivations may overlap social influence when it comes to pro-environmental behaviors, which explains why having a green self-identity is so important (Juaneda-Ayensa et al., 2020). Another possible explanation is the fact that CS is voluntary work and, in this sense, there are no expectations for this behavior nor a social norm to conform with (Perry et al., 2021).
The significant impact of environmental performance expectancy on CS apps BI reveals that it is essential for the participants that the technology be effective – for instance, technical utility, effective monitoring of coastal ecosystems’ health status, improving scientific knowledge, raising people’s environmental awareness, accountability and compliance with environmental policies, and promoting adequate governance and socio-economic response, among other general benefits of CS projects listed by De Rijck et al. (2020). Participants will likely feel more motivated and effective if the app provides the desirable facilitating conditions, i.e., adequate training materials and a user support system to perform the voluntary work (San Llorente Capdevila et al., 2020).
Our results also demonstrate that to some extent culture plays a role in CS apps adoption. We confirmed a positive, although relatively weak, impact of collectivism versus individualism in CS apps BI. This discovery sheds some light upon ambiguity found in the earlier literature. Collectivists seem to be more likely to use these apps, probably because they are usually more concerned about the impacts that climate change and biodiversity crises are having and will have on society. They are also more easily moved by altruistic values than egoistic values. This means they will be more likely to perform selfless acts such as volunteering in CS projects. Long-term participation in CS apps is motivated by education outreach, accountability, and acknowledgment of one’s work in one’s social group (Rotman et al., 2014). Thus, it is essential that CS apps disseminate their results to the public pragmatically, measuring their usefulness for public matters. It may not be enough merely to respond to the CS apps’ specific objectives, but to show how citizens’ effort for this initiative has helped solve current environmental issues and societal concerns and how it has impacted political and socio-economic decision-making.
The effect of long-term orientation was, contrary to our expectations, nonsignificant. In previous studies, long-term orientation was reported to be positively related to pro-environmental behavior (Dangelico et al., 2020, Mi et al., 2020). This is because environmental causes are based primarily on long-term goals, while short-term-oriented persons are less likely to jeopardize their self-interests and goals in favor of community/global causes (Wittmann & Sircova, 2018). CS apps may provide several outcomes: some relatively short-term – improved knowledge, environmental awareness, the opportunity for joining a community – and others long-term – environmental justice, modification of behaviors, enhanced human health and safety, sustainable management and development, and improved political and socio-economic systems (Johnson et al., 2014, Lee et al., 2018). Therefore, a possible explanation for this cultural dimension not being a determining factor could be recognizing the short and long-term benefits of CS apps.
Implications for Practice and Resulting Guidelines
To the best of our knowledge, this research is the first to create a comprehensive, tailor-made, conceptual model for CS apps, thereby addressing the gap identified in the literature regarding what drives citizens’ engagement for participating in CS initiatives within the scope of environmental monitoring (Kasten et al., 2021). Coastal ecosystems are widely affected by climate change and other anthropogenic pressures, as they are subject to land and sea-based activities (He & Silliman, 2019). Thus, CS apps for coastal environment monitoring are different from other CS apps and need direct and custom-made guidelines. This is increasingly urgent and important to achieve effective public participation and trustworthy monitoring datasets of these ecosystems. Intelligent approaches in implementing these apps can also increase awareness and compliance with environmental policies among different stakeholders.
Our study may provide valuable insights to CS stakeholders regarding the profile of the target user groups (i.e., those who are more likely to engage in these initiatives) and adequate functional features to be regarded in the app’s design and development. This information can be beneficial for the rational management of (usually scarce) resources allocated to the citizens’ recruitment and retention processes of CS projects. Hence, we propose the following guidelines (Table 5) to help project managers, app developers, and policymakers to promote CS apps for coastal environment monitoring.
Limitations and Future Work
As with any study, our work also entails some limitations that should be addressed in future research undertakings. First, our data refer to a specific point in time. Therefore, this study does not capture possible changes in the perception of individuals toward CS apps and environmental issues. Hence, future research should seek to capture individuals’ perceptions over time. Second, although we were able to collect data from two countries, cautions should be taken when generalizing our results to other geographical/cultural contexts. Finally, as CS apps for environmental monitoring are widespread, future studies should target post-adoption behaviors, such as outcomes.
Our research provides a model that joins theories of technology adoption and use with the effect of culture in individuals’ values and behaviors, and the motivations for pro-environmental behavior, to provide unique insight about what drives citizens to use CS apps for coastal environment monitoring. This multidisciplinary approach is of utmost importance to understand the various factors that promoters, professional scientists, developers, and other stakeholders must consider when designing and developing successful CS apps for coastal environment monitoring. In turn, successful CS apps for coastal environment monitoring will most likely improve citizens’ attraction and retention in CS projects. Well-designed and developed CS platforms can also intelligently use the project’s resources. In response to our research questions, people who will more likely engage with CS apps in the context of coastal environment monitoring are collectivists, usually more aware and concerned about the environmental crisis and the extended impacts on societies at regional and global scales. They also are assumed to have a green identity, i.e., their behaviors are typically environmentally conscious and sustainable. Drivers of CS apps used for coastal environment monitoring are the sense of empowerment, habit development, provision of facilitating conditions, and environmental performance expectancy.
This study provides clear guidelines for project managers, app developers, and policymakers to improve the chances of sustained public participation in CS projects for coastal environment monitoring through their apps.
M.J. Aitkenhead, D. Donnelly, M.C. Coull, E. Hastings
Innovations in environmental monitoring using mobile phone technology -A review
Int. J. Interact. Mobile Technol., 8 (2) (2014), pp. 42-50
Available at: 10.3991/ijim.v8i2.3645 View PDF
CrossRefView Record in ScopusGoogle ScholarAlender, 2016
Understanding volunteer motivations to participate in citizen science projects: A Deeper look at water quality monitoring
J. Sci. Commun., 15 (3) (2016), p. A04
Available at: 10.22323/2.15030204 View PDF
CrossRefView Record in ScopusGoogle ScholarAshraf et al., 2021
A.R. Ashraf, N. Thongpapanl Tek, A. Anwar, L. Lapa, V. Venkatesh
Perceived values and motivations influencing m-commerce use: A nine-country comparative study
Int. J. Inf. Manage., 59 (2021), Article 102318
Available at: 10.1016/j.ijinfomgt.2021.102318
ArticleDownload PDFView Record in ScopusGoogle ScholarAssumpcao et al., 2019
T.H. Assumpcao, A. Jonoski, I. Theona, C. Tsiakos, M. Krommyda, S. Tamascelli, A. Kallioras, M. Mierla, H. Georgiou, M. Miska, C. Pouliaris, C. Trifanov, K.T. Cimpan, A. Tsertou, E. Marin, M. Diakakis, I. Nichersu, A.J. Amditis, I. Popescu
‘Citizens’ campaigns for environmental water monitoring: Lessons from field experiments’
IEEE Access, 7 (2019), pp. 134601-134620
Available at: 10.1109/ACCESS.2019.2939471 View PDF
CrossRefView Record in ScopusGoogle ScholarBalázs et al., 2021
B. Balázs, P. Mooney, E. Nováková, L. Bastin, J. Jokar Arsanjani
Data Quality in Citizen Science BT – The Science of Citizen Science, Springer International Publishing, Cham (2021), pp. 139-157
Available at: 10.1007/978-3-030-58278-4_8 View PDF
This article is free to access.
CrossRefGoogle ScholarBarbarossa et al., 2017
C. Barbarossa, P. De Pelsmacker, I. Moons
Personal Values, Green Self-identity and Electric Car Adoption
Ecol. Econ., 140 (2017), pp. 190-200
Available at: 10.1016/j.ecolecon.2017.05.015
ArticleDownload PDFView Record in ScopusGoogle ScholarBeza et al., 2017
E. Beza, J. Steinke, J. van Etten, P. Reidsma, C. Fadda, S. Mittra, P. Mathur, L. Kooistra
What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers
PLoS ONE, 12 (5) (2017), p. e0175700
Available at: 10.1371/journal.pone.0175700 View PDF
CrossRefView Record in ScopusGoogle ScholarBlut et al., 2022
M. Blut, A.Y.L. Chong, Z. Tsiga, V. Venkatesh
Meta-Analysis of the Unified Theory of Acceptance and Usage of Technology (UTAUT): Challenging its Validity and Charting a Research Agenda in the Red Ocean
J. Associat. Informat. Syst., 23 (1) (2022), pp. 13-95
Available at: 10.17705/1jais.00719 View PDF
CrossRefView Record in ScopusGoogle ScholarBohensky et al., 2011
E. Bohensky, J.R.A. Butler, R. Costanza, I. Bohnet, A. Delisle, K. Fabricius, M. Gooch, I. Kubiszewski, G. Lukacs, P. Pert, E. Wolanski
Future makers or future takers? A scenario analysis of climate change and the Great Barrier Reef
Global Environ. Change, 21 (3) (2011), pp. 876-893
Available at: 10.1016/j.gloenvcha.2011.03.009
ArticleDownload PDFView Record in ScopusGoogle ScholarBojovic et al., 2021
D. Bojovic, A.L. Clair, I. Christel, M. Terrado, P. Stanzel, P. Gonzalez, E.J. Palin
Engagement, involvement and empowerment: Three realms of a coproduction framework for climate services
Global Environ. Change, 68 (2021), Article 102271
Available at: 10.1016/j.gloenvcha.2021.102271
ArticleDownload PDFView Record in ScopusGoogle ScholarBonney et al., 2009Bonney, R., Ballard, H., Jordan, R., McCallie, E., Phillips, T., Shirk, J., and Wilderman, C.C. (2009) ‘Public participation in scientific research: Defining the field and assessing its potential for informal science education. A CAISE Inquiry Group Report.’, Online submission.
Google ScholarBouman et al., 2020
T. Bouman, L. Steg, S.J. Zawadzki
The value of what others value: When perceived biospheric group values influence individuals’ pro-environmental engagement
J. Environ. Psychol., 71 (2020), Article 101470
Available at: 10.1016/j.jenvp.2020.101470
ArticleDownload PDFView Record in ScopusGoogle ScholarBruyere and Rappe, 2007
B. Bruyere, S. Rappe
Identifying the motivations of environmental volunteers
J. Environ. Plann. Manage., 50 (4) (2007), pp. 503-516
Available at: 10.1080/09640560701402034 View PDF
CrossRefView Record in ScopusGoogle ScholarCamins et al., 2020
E. Camins, W.P. de Haan, V.S. Salvo, M. Canals, A. Raffard, A. Sanchez-Vidal
Paddle surfing for science on microplastic pollution
Sci. Total Environ., 709 (2020), Article 136178
Available at: 10.1016/j.scitotenv.2019.136178
ArticleDownload PDFView Record in ScopusGoogle ScholarChan et al., 2021
F.K.Y. Chan, J.Y.L. Thong, S.A. Brown, V. Venkatesh
Service Design and Citizen Satisfaction with E-Government Services: A Multidimensional Perspective
Public Administr. Rev., 81 (5) (2021), pp. 874-894
Available at: 10.1111/puar.13308 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarChao et al., 2021
S.H. Chao, J.Z. Jiang, K.C. Wei, E. Ng, C.H. Hsu, Y. Chiang, W.T. Fang
Understanding pro-environmental behavior of citizen science: an exploratory study of the bird survey in Taoyuan’s farm ponds project
Sustainability (Switzerland), 13 (9) (2021), p. 5126
Available at: 10.3390/su13095126 View PDF
CrossRefView Record in ScopusGoogle ScholarClary and Snyder, 1999
E.G. Clary, M. Snyder
The motivations to volunteer: Theoretical and practical considerations
Curr. Direct. Psychol. Sci., 8 (5) (1999), pp. 156-159
Available at: 10.1111/1467-8721.00037 View PDF
CrossRefView Record in ScopusGoogle ScholarCox et al., 2018
J. Cox, E.Y. Oh, B. Simmons, G. Graham, A. Greenhill, C. Lintott, K. Masters, J. Woodcock
Doing Good Online: The Changing Relationships Between Motivations, Activity, and Retention Among Online Volunteers
Nonprofit Voluntary Sect. Quart., 47 (5) (2018), pp. 1031-1056
Available at: 10.1177/0899764018783066 View PDF
CrossRefView Record in ScopusGoogle ScholarDangelico et al., 2020
R.M. Dangelico, L. Fraccascia, A. Nastasi
National culture’s influence on environmental performance of countries: A study of direct and indirect effects
Sustain. Dev., 28 (6) (2020), pp. 1773-1786
Available at: 10.1002/sd.2123 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarDatta et al., 2021
A. Datta, S. Maharaj, G.N. Prabhu, D. Bhowmik, A. Marino, V. Akbari, S. Rupavatharam, J.A.R.P. Sujeetha, G.G. Anantrao, V.K. Poduvattil, S. Kumar, A. Kleczkowski
Monitoring the Spread of Water Hyacinth (Pontederia crassipes): Challenges and Future Developments
Front. Ecol. Evolut., 9 (2021), p. 6
Available at: 10.3389/fevo.2021.631338
Google ScholarDe Rijck et al., 2020
K. De Rijck, S. Schade, J-M. Rubio, M. Van Meerloo
Best Practices in Citizen Science for Environmental Monitoring: Commission Staff Working Document., European Commission, Brussels (2020)
Google ScholarDean et al., 2018
A.J. Dean, E.K. Church, J. Loder, K.S. Fielding, K.A. Wilson
How do marine and coastal citizen science experiences foster environmental engagement?
J. Environ. Manage., 213 (2018), pp. 409-416
Available at: 10.1016/j.jenvman.2018.02.080
ArticleDownload PDFView Record in ScopusGoogle ScholarEcsa, 2015ECSA (2015) Ten Principles of Citizen Science. Berlin. Available at: http://doi.org/10.17605/OSF.IO/XPR2N.
Google ScholarEitzel et al., 2017Eitzel, M. V, Cappadonna, J.L., Santos-Lang, C., Duerr, R.E., Virapongse, A., West, S.E., Kyba, C.C.M., Bowser, A., Cooper, C.B., Sforzi, A., Metcalfe, A.N., Harris, E.S., Thiel, M., Haklay, M., Ponciano, L., Roche, J., Ceccaroni, L., Shilling, F.M., Dörler, D., Heigl, F., Kiessling, T., Davis, B.Y., and Jiang, Q. (2017) ‘Citizen Science Terminology Matters: Exploring Key Terms’, Citizen Sci.: Theory Pract., 2(1). Available at: 10.5334/cstp.96.
‘The reliability of citizen science in plan formulation: evidence from Askar, the Kingdom of Bahrain’
Open House Int., 45 (1/2) (2020), pp. 209-222
Available at: 10.1108/OHI-04-2020-0017 View PDF
CrossRefView Record in ScopusGoogle ScholarEstrada et al., 2017
M. Estrada, P.W. Schultz, N. Silva-Send, M.A. Boudrias
The Role of Social Influences on Pro-Environment Behaviors in the San Diego Region
J. Urban Health, 94 (2) (2017), pp. 170-179
Available at: 10.1007/s11524-017-0139-0 View PDF
CrossRefView Record in ScopusGoogle ScholarEuropean Commission, 2021European Commission, 2021. Forging a climate-resilient Europe – the new EU Strategy on Adaptation to Climate Change COM(2021) 82 final. Brussels. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2021:82:FIN.
Google ScholarFajardo et al., 2021
P. Fajardo, D. Beauchesne, A. Carbajal-López, R.M. Daigle, L. Denisse Fierro-Arcos, J. Goldsmit, S. Zajderman, J.I. Valdez-Hernández, M.Y.T. Maigua, R.A. Christofoletti
Aichi Target 18 beyond 2020: mainstreaming Traditional Biodiversity Knowledge in the conservation and sustainable use of marine and coastal ecosystems
PeerJ, 9 (2021), p. e9616
Available at: 10.7717/peerj.9616 View PDF
CrossRefView Record in ScopusGoogle ScholarFan and Chen, 2019
F. Fan, S.L. Chen
Citizen, Science, and Citizen Science
East Asian Sci., Technol. Soc.: Int. J., 13 (2) (2019), pp. 181-193
Available at: 10.1215/18752160-7542643 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarFornell and Larcker, 1981
C. Fornell, D.F. Larcker
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
J. Mark. Res., 18 (1) (1981), pp. 39-50
Available at: 10.2307/3151312
Google ScholarFrensley et al., 2017
T. Frensley, A. Crall, M. Stern, R. Jordan, S. Gray, M. Prysby, G. Newman, C. Hmelo-Silver, D. Mellor, J. Huang
Bridging the Benefits of Online and Community Supported Citizen Science: A Case Study on Motivation and Retention with Conservation-Oriented Volunteers, Citizen Sci.: Theory
Practice, 2 (1) (2017)
Available at: 10.5334/cstp.84
Google ScholarFritz et al., 2019
S. Fritz, L. See, T. Carlson, M. (Muki) Haklay, J.L. Oliver, D. Fraisl, R. Mondardini, M. Brocklehurst, L.A. Shanley, S. Schade, U. Wehn, T. Abrate, J. Anstee, S. Arnold, M. Billot, J. Campbell, J. Espey, M. Gold, G. Hager, S. He, L. Hepburn, A. Hsu, D. Long, J. Masó, I. McCallum, M. Muniafu, I. Moorthy, M. Obersteiner, A.J. Parker, M. Weissplug, S. West
Citizen science and the United Nations Sustainable Development Goals
Nature Sustainability, 2 (10) (2019), pp. 922-930
Available at: 10.1038/s41893-019-0390-3 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarFroeling et al., 2021
F. Froeling, F. Gignac, G. Hoek, R. Vermeulen, M. Nieuwenhuijsen, A. Ficorilli, B. de Marchi, A. Biggeri, D. Kocman, J.A. Robinson, R. Grazuleviciene, S. Andrusaityte, V. Righi, X. Basagaña
Narrative review of citizen science in environmental epidemiology: Setting the stage for co-created research projects in environmental epidemiology
Environ. Int. (2021), Article 106470
Available at: 10.1016/j.envint.2021.106470
ArticleDownload PDFView Record in ScopusGoogle ScholarGarcia-Soto et al., 2017Garcia-Soto, C., van der Meeren, G.I., Busch, J.A., Delany, J., Domegan, C., Dubsky, K., Fauville, G., Gorsky, G., Von, Juterzenka, K., Malfatti, F., Mannaerts, G., McHugh, P., Monestiez, P., Seys, J., Węsławski, J.M., and Zielinski, O. (2017) Advancing Citizen Science for Coastal and Ocean Research. Position Paper 23 of the European Marine Board. Edited by V. French, P. Kellett, J. Delany, and N. McDonough. Ostend, Belgium.
Google ScholarGarcia-Soto et al., 2021
C. Garcia-Soto, J.J.C. Seys, O. Zielinski, J.A. Busch, S.I. Luna, J.C. Baez, C. Domegan, K. Dubsky, I. Kotynska-Zielinska, P. Loubat, F. Malfatti, G. Mannaerts, P. McHugh, P. Monestiez, G.I. van der Meeren, G. Gorsky
‘Marine Citizen Science: Current State in Europe and New Technological Developments’
Front. Marine Sci. (2021), Article 621472
Available at: 10.3389/fmars.2021.621472 View PDF
View Record in ScopusGoogle ScholarGardner and Rebar, 2019Gardner, B., Rebar, A.L., 2019. Habit Formation and Behavior Change, in Oxford Research Encyclopedia of Psychology. Available at: 10.1093/acrefore/9780190236557.013.129.
Google ScholarGeoghegan et al., 2016
H. Geoghegan, A. Dyke, R. Pateman, S. West, G. Everett
Understanding motivations for citizen science. Final report on behalf of UKEOF, University of Reading, Stockholm Environment Institute (University of York) and University of the West of England (2016)
Google ScholarGöckeritz et al., 2010
S. Göckeritz, P.W. Schultz, T. Rendón, R.B. Cialdini, N.J. Goldstein, V. Griskevicius
Descriptive normative beliefs and conservation behavior: The moderating roles of personal involvement and injunctive normative beliefs
Europ. J. Soc. Psychol., 40 (3) (2010), pp. 514-523
Available at: 10.1002/ejsp.643
View Record in ScopusGoogle ScholarGolumbic et al., 2020
Y. Golumbic, A. Baram-Tsabari, B. Fishbain
Engagement styles in an environmental citizen science project
J. Sci. Commun., 19 (06) (2020), p. A03
Available at: 10.22323/2.19060203 View PDF
CrossRefGoogle ScholarGonçalves et al., 2014
J. Gonçalves, V. Kostakos, E. Karapanos, M. Barreto, T. Camacho, A. Tomasic, J. Zimmerman
Citizen motivation on the go: The role of psychological empowerment
Interact. Comput., 26 (3) (2014), pp. 196-207
Available at: 10.1093/iwc/iwt035 View PDF
CrossRefView Record in ScopusGoogle ScholarGötz et al., 2010Götz, O., Liehr-Gobbers, K., and Krafft, M. (2010) ‘Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach’, in Handbook of Partial Least Squares, pp. 691–711. Available at: 10.1007/978-3-540-32827-8_30.
Google ScholarGunnell et al., 2021
J.L. Gunnell, Y.N. Golumbic, T. Hayes, M. Cooper
Co-created citizen science: Challenging cultures and practice in scientific research
J. Sci. Commun., 20 (5) (2021), p. Y01
Available at: 10.22323/2.20050401 View PDF
CrossRefGoogle ScholarHair et al., 2017
J.F. Hair, G.T.M. Hult, C.M. Ringle, M. Sarstedt
A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
(Second Edition), Sage, California (2017)
Google ScholarHaklay et al., 2021Haklay, M., Dörler, D., Heigl, F., Manzoni, M., Hecker, S., Vohland, K., 2021. What Is Citizen Science? The Challenges of Definition, in The Science of Citizen Science. Available at: 10.1007/978-3-030-58278-4_2.
Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation
D. Sui, S. Elwood, M. Goodchild (Eds.), Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice, Springer, Netherlands, Dordrecht (2013), pp. 105-122
Available at: 10.1007/978-94-007-4587-2_7 View PDF
CrossRefView Record in ScopusGoogle ScholarHart et al., 2011
P.S. Hart, E.C. Nisbet, J.E. Shanahan
Environmental Values and the Social Amplification of Risk: An Examination of How Environmental Values and Media Use Influence Predispositions for Public Engagement in Wildlife Management Decision Making
Soc. Natl. Resour., 24 (3) (2011), pp. 276-291
Available at: 10.1080/08941920802676464 View PDF
CrossRefView Record in ScopusGoogle ScholarHassan et al., 2011
L.M. Hassan, E. Shiu, G. Walsh
A multi-country assessment of the long-term orientation scale
Int. Market. Rev., 28 (1) (2011), 10.1108/02651331111107116 View PDF
Google ScholarHe and Silliman, 2019
Q. He, B.R. Silliman
Climate Change, Human Impacts, and Coastal Ecosystems in the Anthropocene
Curr. Biol., 29 (19) (2019), pp. R1021-R1035
Available at: 10.1016/j.cub.2019.08.042.
ArticleDownload PDFView Record in ScopusGoogle ScholarHe et al., 2019
Y. He, J.K. Parrish, S. Rowe, T. Jones
Evolving interest and sense of self in an environmental citizen science program
Ecol. Soc., 24 (2) (2019)
Available at: 10.5751/ES-10956-240233
Google ScholarHecker et al., 2018
S. Hecker, R. Bonney, M. Haklay, F. Hölker, H. Hofer, C. Goebel, M. Gold, Z. Makuch, M. Ponti, A. Richter, L. Robinson, J.R. Iglesias, R. Owen, T. Peltola, A. Sforzi, J. Shirk, J. Vogel, K. Vohland, T. Witt, A. Bonn
Innovation in Citizen Science – Perspectives on Science-Policy Advances
Citiz. Sci.: Theory Pract., 3 (1) (2018)
Available at: 10.5334/cstp.114
Google ScholarHecker et al., 2019Hecker, S., Garbe, L., and Bonn, A., 2019. The European citizen science landscape – a snapshot, in Citizen Science, pp. 190–200. Available at: 10.2307/j.ctv550cf2.20.
Adoption of communication technologies and national culture
Systèmes d’Information et Management, 6 (3) (2001), pp. 55-74
View Record in ScopusGoogle ScholarIrwin, 1995
Citizen science: a study of people, expertise, and sustainable development
Routledge (Environment and society.), London (1995)
Google ScholarJakučionytė-Skodienė and Liobikienė, 2021
M. Jakučionytė-Skodienė, G. Liobikienė
Climate change concern, personal responsibility and actions related to climate change mitigation in EU countries: Cross-cultural analysis
J. Cleaner Prod., 281 (2021), Article 125189
Available at: 10.1016/j.jclepro.2020.125189
ArticleDownload PDFView Record in ScopusGoogle ScholarJohnson et al., 2014
M.F. Johnson, C. Hannah, L. Acton, R. Popovici, K.K. Karanth, E. Weinthal
Network environmentalism: Citizen scientists as agents for environmental advocacy
Global Environ. Change, 29 (2014), pp. 235-245
Available at: 10.1016/j.gloenvcha.2014.10.006
ArticleDownload PDFView Record in ScopusGoogle ScholarJohnson et al., 2020
J.E. Johnson, E. Hooper, D.J. Welch
Community Marine Monitoring Toolkit: A tool developed in the Pacific to inform community-based marine resource management
Mar. Pollut. Bull., 159 (2020), Article 111498
Available at: 10.1016/j.marpolbul.2020.111498
ArticleDownload PDFView Record in ScopusGoogle ScholarJohnson et al., 2011
R.E. Johnson, C.C. Rosen, E. Djurdjevic
Assessing the Impact of Common Method Variance on Higher Order Multidimensional Constructs
J. Appl. Psychol., 96 (4) (2011), p. 744
Available at: 10.1037/a0021504 View PDF
CrossRefView Record in ScopusGoogle ScholarJuaneda-Ayensa et al., 2020
E. Juaneda-Ayensa, M. Clavel San Emeterio, S. Cirilo-Jordan, L. González-Menorca
Unified Theory of Acceptance and Use of Social Apps (UTAU-SA): The Role of Technology in the Promotion of Recycling Behavior
International Scientific Conference on Innovations in Digital Economy, Springer (2020), pp. 3-22
Available at: 10.1007/978-3-030-84845-3_1
View Record in ScopusGoogle ScholarKaasa and Andriani, 2021
A. Kaasa, L. Andriani
‘Determinants of institutional trust: The role of cultural context’
J Institut. Econom., 18 (1) (2021), pp. 45-65
Available at: 10.1017/S1744137421000199
Google ScholarKasten et al., 2021
P. Kasten, S.R. Jenkins, R.A. Christofoletti
Participatory Monitoring—A Citizen Science Approach for Coastal Environments
Front. Marine Sci., 8 (2021), p. 992
Available at: 10.3389/fmars.2021.681969
Google ScholarKennedy et al., 2022
C. Kennedy, N. Hatley, A. Lau, A. Mercer, S. Keeter, J. Ferno, D. Asare-Marfo
Strategies for Detecting Insincere Respondents in Online Polling
Public Opinion Quar., 85 (4) (2022), pp. 1050-1075
Available at: 10.1093/poq/nfab057 View PDF
CrossRefGoogle ScholarKim and Gupta, 2014
H.W. Kim, S. Gupta
A user empowerment approach to information systems infusion
IEEE Trans. Eng. Manage., 61 (4) (2014), pp. 656-668
Available at: 10.1109/TEM.2014.2354693
View Record in ScopusGoogle ScholarKloetzer et al., 2021
L. Kloetzer, J. Lorke, J. Roche, Y. Golumbic, S. Winter, A. Jõgeva
Learning in Citizen Science
The Science of Citizen Science (2021), p. 283
Available at: 10.1007/978-3-030-58278-4_15 View PDF
This article is free to access.
CrossRefGoogle ScholarKosmala et al., 2016
M. Kosmala, A. Wiggins, A. Swanson, B. Simmons
Assessing data quality in citizen science
Front. Ecol. Environ., 14 (10) (2016), pp. 551-560
Available at: 10.1002/fee.1436 View PDF
CrossRefView Record in ScopusGoogle ScholarKullenberg and Kasperowski, 2016
C. Kullenberg, D. Kasperowski
What is citizen science? – A scientometric meta-analysis
PLoS ONE, 11 (1) (2016), p. e0147152
Available at: 10.1371/journal.pone.0147152 View PDF
CrossRefView Record in ScopusGoogle ScholarKumar et al., 2019
S. Kumar, V. Giridhar, P. Sadarangani
A Cross-national Study of Environmental Performance and Culture: Implications of the Findings and Strategies
Global Busin. Rev., 20 (4) (2019), pp. 1051-1068
Available at: 10.1177/0972150919845260 View PDF
CrossRefView Record in ScopusGoogle ScholarLand-Zandstra et al., 2021Land-Zandstra, A., Agnello, G., and Gültekin, Y.S., 2021. Participants in Citizen Science, in The Science of Citizen Science, p. 243. Available at: 10.1007/978-3-030-58278-4_13.
Google ScholarLaRue et al., 2020
M.A. LaRue, D.G. Ainley, J. Pennycook, K. Stamatiou, L. Salas, N. Nur, S. Stammerjohn, L. Barrington
Engaging “the crowd” in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica
Remote Sens. Ecol. Conserv., 6 (1) (2020), pp. 70-78
Available at: 10.1002/rse2.124 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarLee et al., 2018
T.K. Lee, K. Crowston, M. Harandi, C. Østerlund, G. Miller
Appealing to different motivations in a message to recruit citizen scientists: Results of a field experiment
J. Sci. Commun., 17 (1) (2018), p. A02
Available at: 10.22323/2.17010202 View PDF
CrossRefView Record in ScopusGoogle ScholarLee and Xia, 2010
G. Lee, W. Xia
‘Toward agile: An integrated analysis of quantitative and qualitative field data on software development agility’
MIS Quart. Manage. Informat. Syst., 34 (1) (2010), pp. 87-114
Available at: 10.2307/20721416 View PDF
CrossRefView Record in ScopusGoogle ScholarLemmens et al., 2021
R. Lemmens, V. Antoniou, P. Hummer, C. Potsiou
Citizen Science in the Digital World of Apps
The Science of Citizen Science (2021), p. 461
Available at: 10.1007/978-3-030-58278-4_23 View PDF
This article is free to access.
CrossRefGoogle ScholarLi, 2021
The role of trust and risk in Citizens’ E-Government services adoption: A perspective of the extended UTAUT model
Sustainability (Switzerland), 13 (14) (2021), p. 7671
Available at: 10.3390/su13147671 View PDF
CrossRefView Record in ScopusGoogle ScholarLimayem et al., 2007
M. Limayem, S.G. Hirt, C.M.K. Cheung
‘How habit limits the predictive power of intention: The case of information systems continuance’
MIS Quart. Manage. Informat. Syst., 31 (4) (2007), pp. 705-737
Available at: 10.2307/25148817 View PDF
CrossRefView Record in ScopusGoogle ScholarLinder et al., 2022
N. Linder, M. Giusti, K. Samuelsson, S. Barthel
Pro-environmental habits: An underexplored research agenda in sustainability science
Ambio, 51 (3) (2022), pp. 546-556
Available at: 10.1007/s13280-021-01619-6 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarLuna et al., 2018
S. Luna, M. Gold, A. Albert, L. Ceccaroni, B. Claramunt, O. Danylo, M. Haklay, R. Kottmann, C. Kyba, J. Piera, A. Radicchi, S. Schade, U. Sturm
Developing Mobile Applications for Environmental and Biodiversity Citizen Science: Considerations and Recommendations
Multimedia Tool. Appl. Environ. Biodivers. Informat. (2018), pp. 9-30
Available at: 10.1007/978-3-319-76445-0_2 View PDF
CrossRefGoogle ScholarMacKenzie and Podsakoff, 2012
S.B. MacKenzie, P.M. Podsakoff
Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies
J. Retail., 88 (4) (2012), pp. 542-555
Available at: 10.1016/j.jretai.2012.08.001
ArticleDownload PDFView Record in ScopusGoogle ScholarMacKenzie et al., 2011
S.B. MacKenzie, P.M. Podsakoff, N.P. Podsakoff
Construct Measurement and Validation Procedures in MIS and Behavioral Research: Integrating New and Existing Techniques
MIS Quarterly, 35 (2) (2011), pp. 293-334
Available at: 10.2307/23044045 View PDF
CrossRefView Record in ScopusGoogle ScholarMalthus et al., 2020
T.J. Malthus, R. Ohmsen, H.J. van der Woerd
An Evaluation of Citizen Science Smartphone Apps for Inland Water Quality Assessment
Remote Sensing (2020), p. 1578
Available at: 10.3390/rs12101578 View PDF
CrossRefView Record in ScopusGoogle ScholarMantovani and Vergari, 2017
A. Mantovani, C. Vergari
Environmental vs hedonic quality: Which policy can help in lowering pollution emissions?
Environ. Dev. Econ., 22 (3) (2017), pp. 274-304
Available at: 10.1017/S1355770X16000371 View PDF
View Record in ScopusGoogle ScholarMarlowe et al., 2021
C. Marlowe, K. Hyder, M.D.J. Sayer, J. Kaiser
Divers as Citizen Scientists: Response Time, Accuracy and Precision of Water Temperature Measurement Using Dive Computers
Front. Mar. Sci., 8 (2021), p. 114
Available at: 10.3389/fmars.2021.617691
Google ScholarMcMichael and Lindgren, 2011
A.J. McMichael, E. Lindgren
Climate change: present and future risks to health, and necessary responses
J. Intern. Med., 270 (5) (2011), pp. 401-413
Available at: 10.1111/j.1365-2796.2011.02415.x View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarMeasham and Barnett, 2008
T.G. Measham, G.B. Barnett
Environmental Volunteering: Motivations, modes and outcomes
Aust. Geogr., 39 (4) (2008), pp. 537-552
Available at: 10.1080/00049180802419237 View PDF
CrossRefView Record in ScopusGoogle ScholarMenon et al., 2021
N. Menon, G. George, R. Ranith, V. Sajin, S. Murali, A. Abdulaziz, R.J.W. Brewin, S. Sathyendranath
Citizen Science Tools Reveal Changes in Estuarine Water Quality Following Demolition of Buildings
Remote Sens. (2021), p. 1683
Available at: 10.3390/rs13091683 View PDF
CrossRefView Record in ScopusGoogle ScholarMi et al., 2020
L. Mi, L. Qiao, T. Xu, X. Gan, H. Yang, J. Zhao, Y. Qiao, J. Hou
Promoting sustainable development: The impact of differences in cultural values on residents’ pro-environmental behaviors
Sustain. Dev., 28 (6) (2020), pp. 1539-1553
Available at: 10.1002/sd.2103 View PDF
CrossRefView Record in ScopusGoogle ScholarNagy and Molnárné, 2018
S. Nagy, C.K. Molnárné
The Effects of Hofstede’s Cultural Dimensions on Pro-Environmental Behaviour: How Culture Influences Environmentally Conscious Behaviour
Theory Methodol. Pract. (TMP), 14 (01) (2018), pp. 27-36
View Record in ScopusGoogle ScholarNaranjo-Zolotov et al., 2019
M. Naranjo-Zolotov, T. Oliveira, S. Casteleyn
Citizens’ intention to use and recommend e-participation: Drawing upon UTAUT and citizen empowerment
Informat. Technol. People, 32 (2) (2019), pp. 364-386
Available at: 10.1108/ITP-08-2017-0257 View PDF
CrossRefView Record in ScopusGoogle ScholarNascimento et al., 2014Figueiredo Do Nascimento, S., Martinho Guimaraes Pires Pereira, A., and Ghezzi, A. (2014) From Citizen Science to Do It Yourself Science An annotated account of an on-going movement . EUR 27095. Luxembourg: Publications Office of the European Union. Available at: 10.2788/12246.
Google ScholarNewman et al., 2012
G. Newman, A. Wiggins, A. Crall, E. Graham, S. Newman, K. Crowston
The future of Citizen science: Emerging technologies and shifting paradigms
Front. Ecol. Environ. (2012), pp. 298-304
Available at: 10.1890/110294 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarO’Hara et al., 2021
C. O’Hara, M. Frazier, B. Halpern
At-risk marine biodiversity faces extensive, expanding, and intensifying human impacts
Science, 372 (2021), pp. 84-87
Available at: 10.1126/science.abe6731 View PDF
CrossRefView Record in ScopusGoogle ScholarPapakonstantinou et al., 2021
A. Papakonstantinou, M. Batsaris, S. Spondylidis, K. Topouzelis
A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone
Drones, 5 (1) (2021), p. 6
Available at: 10.3390/drones5010006 View PDF
CrossRefGoogle ScholarParboteeah et al., 2012
K.P. Parboteeah, H.M. Addae, J.B. Cullen
Propensity to Support Sustainability Initiatives: A Cross-National Model
J. Bus. Ethics, 105 (3) (2012), pp. 403-413
Available at: 10.1007/s10551-011-0979-6 View PDF
CrossRefView Record in ScopusGoogle ScholarPaul et al., 2020
J.D. Paul, K. Cieslik, N. Sah, P. Shakya, B.P. Parajuli, S. Paudel, A. Dewulf, W. Buytaert
‘Applying Citizen Science for Sustainable Development: Rainfall Monitoring in Western Nepal’
Front. Water, 2 (2020), p. 62
Available at: 10.3389/frwa.2020.581375 View PDF
View Record in ScopusGoogle ScholarPerra and Brinkman, 2021
M. Perra, T. Brinkman
Seeing science: using graphics to communicate research
Ecosphere, 12 (10) (2021), p. e03786
Available at: 10.1002/ecs2.3786 View PDF
This article is free to access.
View Record in ScopusGoogle ScholarPerry et al., 2021
G.L.W. Perry, S.J. Richardson, N. Harré, D. Hodges, P.O.B. Lyver, F.J.F. Maseyk, R. Taylor, J.H. Todd, J.M. Tylianakis, J. Yletyinen, A. Brower
Evaluating the Role of Social Norms in Fostering Pro-Environmental Behaviors
Front. Environ. Sci., 9 (2021), p. 160
Available at: 10.3389/fenvs.2021.620125
Google ScholarPodsakoff et al., 2003
P.M. Podsakoff, S.B. MacKenzie, J.Y. Lee, N.P. Podsakoff
Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies
J. Appl. Psychol. (2003), p. 879
Available at: 10.1037/0021-9010.88.5.879
Google ScholarPucino et al., 2021
N. Pucino, D.M. Kennedy, R.C. Carvalho, B. Allan, D. Ierodiaconou
Citizen science for monitoring seasonal-scale beach erosion and behaviour with aerial drones
Sci. Rep., 11 (1) (2021), p. 3935
Available at: 10.1038/s41598-021-83477-6 View PDF
This article is free to access.
View Record in ScopusGoogle ScholarRappaport, 1987
Terms of empowerment/exemplars of prevention: Toward a theory for community psychology
Am. J. Community Psychol., 15 (2) (1987), pp. 121-148
Available at: 10.1007/BF00919275
View Record in ScopusGoogle ScholarRingle et al., 2015
C. Ringle, S. Wende, J. Becker
SmartPLS GmbH, Bönningstedt (2015)
Available at: http://www.smartpls.com
Google ScholarRobinson et al., 2019
L.D. Robinson, J.L. Cawthray, S.E. West, A. Bonn, J. Ansine
Ten principles of citizen science
Citizen Science: Innovation in Open Science, Society and Policy (2019), pp. 27-40
Available at: 10.2307/j.ctv550cf2.9 View PDF
CrossRefView Record in ScopusGoogle ScholarRobinson et al., 2021
J.A. Robinson, D. Kocman, O. Speyer, E. Gerasopoulos
Meeting volunteer expectations — a review of volunteer motivations in citizen science and best practices for their retention through implementation of functional features in CS tools
J. Environ. Plann. Manage., 64 (12) (2021), pp. 2089-2113
Available at: 10.1080/09640568.2020.1853507 View PDF
This article is free to access.
CrossRefView Record in ScopusGoogle ScholarRotman et al., 2014Rotman, D., Hammock, J., Preece, J., Hansen, D., Boston, C., Bowser, A., He, Y., 2014. Motivations Affecting Initial and Long-Term Participation in Citizen Science Projects in Three Countries, in, pp. 110–124. Available at: 10.9776/14054.
Google ScholarRyan and Deci, 2000
R.M. Ryan, E.L. Deci
Self-Determination Theory and the Facilitation of Instrinsic Motivation, Social Development, and Well-Being
Am. Psychol., 55 (1) (2000), pp. 68-78
Available at: 10.1037110003-066X.55.1.68
Google ScholarRyan and Deci, 2020
R.M. Ryan, E.L. Deci
Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions
Contempor. Educat. Psychol., 61 (2020), Article 101860
Available at: 10.1016/j.cedpsych.2020.101860
ArticleDownload PDFView Record in ScopusGoogle ScholarSan Llorente Capdevila et al., 2020
A. San Llorente Capdevila, A. Kokimova, S. Sinha Ray, T. Avellán, J. Kim, S. Kirschke
Success factors for citizen science projects in water quality monitoring
Sci. Total Environ., 728 (2020), Article 137843
Available at: 10.1016/j.scitotenv.2020.137843
ArticleDownload PDFView Record in ScopusGoogle ScholarSauermann et al., 2020
H. Sauermann, K. Vohland, V. Antoniou, B. Balázs, C. Göbel, K. Karatzas, P. Mooney, J. Perelló, M. Ponti, R. Samson, S. Winter
Citizen science and sustainability transitions
Res. Policy, 49 (5) (2020), Article 103978
Available at: 10.1016/j.respol.2020.103978
ArticleDownload PDFView Record in ScopusGoogle ScholarSchade et al., 2017Schade, S., Manzoni, M., Tsinaraki, C., Kotsev, A., Fullerton, K.T., Sgnaolin, R., Spinelli, F., and Mitton, I., 2017. Using new data sources for policymaking. Luxembourg: EUR 28940 EN, Publications Office of the European Union, JRC109472. Available at: 10.2760/739266.
Google ScholarSchaefer et al., 2021
T. Schaefer, B. Kieslinger, M. Brandt, V. van den Bogaert
Evaluation in Citizen Science: The Art of Tracing a Moving Target
The Science of Citizen Science (2021), p. 495
Available at: 10.1007/978-3-030-58278-4_25 View PDF
This article is free to access.
CrossRefGoogle ScholarSecretariat of the Convention on Biological Diversity, 2020
Secretariat of the Convention on Biological Diversity
Global Biodiversity Outlook 5
Secretariat of the Convention on Biological Diversity, Montreal, Canada: Montreal, Canada (2020), p. 208
Google ScholarSenabre Hidalgo et al., 2021
E. Senabre Hidalgo, J. Perelló, F. Becker, I. Bonhoure, M. Legris, A. Cigarini
Participation and Co-creation in Citizen Science
The Science of Citizen Science (2021), pp. 199-218
Available at: 10.1007/978-3-030-58278-4_11 View PDF
This article is free to access.
CrossRefGoogle ScholarSharma et al., 2019
N. Sharma, S. Greaves, A. Siddharthan, H.B. Anderson, A. Robinson, L. Colucci-Gray, A.T. Wibowo, H. Bostock, A. Salisbury, S. Roberts, D. Slawson, R. van der Wal
From citizen science to citizen action: Analysing the potential for a digital platform to cultivate attachments to nature
J. Sci. Commun., 18 (1) (2019), pp. 1-35
Available at: 10.22323/2.18010207 View PDF
CrossRefGoogle ScholarShevchuk and Oinas-Kukkonen, 2020
N. Shevchuk, H. Oinas-Kukkonen
‘Aiding Users in Green IS Adoption with Persuasive Systems Design’
Urban Science, 4 (4) (2020), p. 52
Available at: 10.3390/urbansci4040052 View PDF
CrossRefView Record in ScopusGoogle ScholarShirk et al., 2012
J.L. Shirk, H.L. Ballard, C.C. Wilderman, T. Phillips, A. Wiggins, R. Jordan, E. McCallie, M. Minarchek, B.V. Lewenstein, M.E. Krasny, R. Bonney
Public participation in scientific research: A framework for deliberate design
Ecol. Soc., 17 (2) (2012)
Available at: 10.5751/ES-04705-170229
Google ScholarSiano et al., 2020
R. Siano, A. Chapelle, V. Antoine, E. Michel-Guillou, F. Rigaut-Jalabert, L. Guillou, H. Hégaret, A. Leynaert, A. Curd
Citizen participation in monitoring phytoplankton seawater discolorations
Marine Policy, 117 (2020), Article 103039
Available at: 10.1016/j.marpol.2018.01.022
ArticleDownload PDFGoogle ScholarSparks and Shepherd, 1992
P. Sparks, R. Shepherd
Self-Identity and the Theory of Planned Behavior: Assesing the Role of Identification with “Green Consumerism”
Soc. Psychol. Quart., 55 (4) (1992), pp. 388-399
Available at: 10.2307/2786955 View PDF
CrossRefGoogle ScholarSpreitzer, 1995
Psychological Empowerment in the Workplace: Dimensions, Measurement, and Validation
Acad. Manage. J., 38 (5) (1995), pp. 1442-1465
Available at: 10.2307/256865
Google ScholarSreelakshmi, 2016
Empowerment Constructs : An Implementation Model-A Case Study
J. Business Manage., 18 (6) (2016), pp. 37-42
View Record in ScopusGoogle ScholarSreen et al., 2018
N. Sreen, S. Purbey, P. Sadarangani
Impact of culture, behavior and gender on green purchase intention
J. Retail. Consum. Serv., 41 (2018), pp. 177-189
Available at: 10.1016/j.jretconser.2017.12.002
ArticleDownload PDFView Record in ScopusGoogle ScholarSrite and Karahanna, 2006
M. Srite, E. Karahanna
‘The role of espoused national cultural values in technology acceptance’
MIS Quart. Manage. Informat. Syst., 30 (3) (2006), pp. 679-704
Available at: 10.2307/25148745 View PDF
CrossRefView Record in ScopusGoogle ScholarSteg et al., 2014a
L. Steg, J.W. Bolderdijk, K. Keizer, G. Perlaviciute
An Integrated Framework for Encouraging Pro-environmental Behaviour: The role of values, situational factors and goals
J. Environ. Psychol., 38 (2014), pp. 104-115
Available at: 10.1016/j.jenvp.2014.01.002
ArticleDownload PDFView Record in ScopusGoogle ScholarSteg et al., 2014b
L. Steg, G. Perlaviciute, E. van der Werff, J. Lurvink
The Significance of Hedonic Values for Environmentally Relevant Attitudes, Preferences, and Actions
Environ. Behav., 46 (2) (2014), pp. 163-192
Available at: 10.1177/0013916512454730 View PDF
CrossRefView Record in ScopusGoogle ScholarSullivan and York, 2021
A. Sullivan, A.M. York
Collective action for changing forests: A spatial, social-ecological approach to assessing participation in invasive plant management
Global Environ. Change, 71 (2021), Article 102366
Available at: 10.1016/j.gloenvcha.2021.102366
ArticleDownload PDFView Record in ScopusGoogle ScholarTaghizadeh et al., 2021
S.K. Taghizadeh, S.A. Rahman, D. Nikbin, M.M.D. Alam, L. Alexa, C. Ling Suan, S. Taghizadeh
Factors influencing students’ continuance usage intention with online learning during the pandemic: a cross-country analysis
Behav. Informat. Technol., 41 (9) (2021), pp. 1998-2017
Available at: 10.1080/0144929X.2021.1912181
Google ScholarTiago et al., 2017
P. Tiago, M.J. Gouveia, C. Capinha, M. Santos-Reis, H.M. Pereira
The influence of motivational factors on the frequency of participation in citizen science activities
Nat. Conservat. (2017), pp. 61-78
Available at: 10.3897/natureconservation.18.13429 View PDF
CrossRefView Record in ScopusGoogle ScholarTinati et al., 2017
R. Tinati, M. Luczak-Roesch, E. Simperl, W. Hall
An investigation of player motivations in Eyewire, a gamified citizen science project
Comput. Hum. Behav., 73 (2017), pp. 527-540
Available at: 10.1016/j.chb.2016.12.074
ArticleDownload PDFView Record in ScopusGoogle ScholarTsybulsky, 2020Tsybulsky, D., 2020. Self-Reported Reasons for Participating in Pro-environmental Citizen Science Activities: A Case Study of Butterfly Monitoring in Israel, Front. Educat., 5. Available at: 10.3389/feduc.2020.00116.
Google ScholarTuricchia et al., 2021
E. Turicchia, C. Cerrano, M. Ghetta, M. Abbiati, M. Ponti
MedSens index: The bridge between marine citizen science and coastal management
Ecol. Ind., 122 (2021), Article 107296
Available at: 10.1016/j.ecolind.2020.107296
ArticleDownload PDFView Record in ScopusGoogle ScholarTurrini et al., 2018
T. Turrini, D. Dörler, A. Richter, F. Heigl, A. Bonn
The threefold potential of environmental citizen science – Generating knowledge, creating learning opportunities and enabling civic participation
Biol. Conserv., 225 (2018), pp. 176-186
Available at: 10.1016/j.biocon.2018.03.024
ArticleDownload PDFView Record in ScopusGoogle ScholarUhrin et al., 2020
A.V. Uhrin, S. Lippiatt, C.E. Herring, K. Dettloff, K. Bimrose, C. Butler-Minor
‘Temporal Trends and Potential Drivers of Stranded Marine Debris on Beaches Within Two US National Marine Sanctuaries Using Citizen Science Data’
Front. Environ. Sci. (2020), p. 232
Google ScholarUnited Nations, 2021
The Sustainable Development Goals Report 2021
United Nations, New York, USA (2021)
Google ScholarUnited Nations, 2020
Leaders’ pledge for nature: United to Reverse Biodiversity Loss by 2030 for Sustainable Development, United Nations Summit on Biodiversity [Preprint] (2020)
Google Scholarvan Noordwijk et al., 2021
T. van Noordwijk, I. Bishop, S. Staunton-Lamb, A. Oldfield, S. Loiselle, H. Geoghegan, L. Ceccaroni
Creating Positive Environmental Impact Through Citizen Science
The Science of Citizen Science (2021), pp. 373-395
Available at: 10.1007/978-3-030-58278-4_19 View PDF
This article is free to access.
CrossRefGoogle ScholarVenkatesh et al., 2012
V. Venkatesh, J.Y.L. Thong, X. Xu
Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology
MIS Quart. Manage. Informat. Syst., 36 (1) (2012), pp. 157-178
Available at: 10.2307/41410412 View PDF
CrossRefGoogle ScholarWard et al., 2020
P.J. Ward, M.C. de Ruiter, J. Mård, K. Schröter, A. van Loon, T. Veldkamp, N. von Uexkull, N. Wanders, A. AghaKouchak, K. Arnbjerg-Nielsen, L. Capewell, M. Carmen Llasat, R. Day, B. Dewals, G. di Baldassarre, L.S. Huning, H. Kreibich, M. Mazzoleni, E. Savelli, C. Teutschbein, H. van den Berg, A. van der Heijden, J.M.R. Vincken, M.J. Waterloo, M. Wens
‘The need to integrate flood and drought disaster risk reduction strategies
Water Security, 11 (2020), Article 100070
Available at: 10.1016/j.wasec.2020.100070
ArticleDownload PDFView Record in ScopusGoogle ScholarVenkatesh et al., 2003
V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis
User acceptance of information technology: Toward a unified view, MIS Quart
Manage. Informat. Syst., 27 (3) (2003), pp. 425-478, 10.2307/30036540 View PDF
Google ScholarWeber et al., 2019
K. Weber, F. Pallas, M.R. Ulbricht
Challenges of Citizen Science: Commons, Incentives, Organizations, and Regulations
Am. J. Bioeth. (2019), pp. 52-54
Available at: 10.1080/15265161.2019.1619862 View PDF
CrossRefView Record in ScopusGoogle ScholarWehn and Almomani, 2019
U. Wehn, A. Almomani
Incentives and barriers for participation in community-based environmental monitoring and information systems: A critical analysis and integration of the literature
Environ. Sci. Policy, 101 (2019), pp. 341-357
Available at: 10.1016/j.envsci.2019.09.002
ArticleDownload PDFView Record in ScopusGoogle ScholarWest and Pateman, 2016
S. West, R. Pateman
Recruiting and Retaining Participants in Citizen Science: What Can Be Learned from the Volunteering Literature?
Citizen Sci.: Theory Pract., 1 (2) (2016)
Available at: 10.5334/cstp.8
Google ScholarWhite et al., 2019
K. White, R. Habib, D.J. Hardisty
How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework
J. Market., 83 (3) (2019), pp. 22-49
Available at: 10.1177/0022242919825649 View PDF
CrossRefView Record in ScopusGoogle ScholarWilmsen et al., 2012Wilmsen, C., Elmendorf, W.F., Fisher, L., Ross, J., Sarathy, B., and Wells, G., 2012. Partnerships for empowerment: Participatory research for community-based natural resource management, Partnerships for Empowerment: Participatory Research for Community-Based Natural Resource Management. Routledge. Available at: 10.4324/9781849772143.
Google ScholarWittmann and Sircova, 2018
M. Wittmann, A. Sircova
Dispositional orientation to the present and future and its role in pro-environmental behavior and sustainability
Heliyon (2018), p. e00882
Available at: 10.1016/j.heliyon.2018.e00882
ArticleDownload PDFView Record in ScopusGoogle ScholarZimmerman, 1995
Psychological empowerment: Issues and illustrations
Am. J. Community Psychol., 23 (5) (1995), pp. 581-599
Available at: 10.1007/BF02506983
View Record in ScopusGoogle ScholarZimmerman and Rappaport, 1988
M.A. Zimmerman, J. Rappaport
Citizen participation, perceived control, and psychological empowerment
Am. J. Community Psychol., 16 (5) (1988), pp. 725-750
Available at: 10.1007/BF00930023
View Record in ScopusGoogle Scholar
Originally published by Global Environmental Change 77 (November 2022, 102606) under the terms of a Creative Commons license.