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15 April 2025

Evaluating a Community-Based Citizen Science Project: Attitude as a Key Mediator of Behavior Intention Toward Biodiversity Conservation

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1
Biodiversity Division, National Institute for Environmental Studies, Ibaraki 305-8506, Japan
2
Graduate School of Global Environmental Studies, Sophia University, Tokyo 102-8554, Japan
3
Department of Marine Environment and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
4
Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85281, USA
Conservation2025, 5(2), 17;https://doi.org/10.3390/conservation5020017
This article belongs to the Special Issue Social Sciences in Marine Ecology Conservation
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Abstract

Community-based citizen science plays a vital role in biodiversity conservation by engaging the public in scientific research while fostering environmental awareness. This study evaluates a citizen science project conducted in the Taoyuan Algal Reef (TAR) region of Taiwan, focusing on participants’ motivations, learning outcomes, and their relationship with behavioral intentions toward biodiversity conservation. Despite a small sample size, our findings provide valuable insights into the effectiveness of such initiatives. Learning and self-achievement emerged as the primary motivators for participation, with social interaction playing a secondary role. Using the structural equation model (SEM), we confirmed that attitude serves as a critical mediator between knowledge, sense of place, and behavioral intention. This supports the Knowledge-Attitude-Behavior (KAB) model, emphasizing that knowledge acquisition fosters attitudinal shifts that ultimately drive conservation behavior. Moreover, place-based learning was identified as a key component in strengthening participants’ sense of place and ecological awareness. Our findings suggest that aligning citizen science initiatives with participants’ motivations enhances engagement and long-term conservation efforts. Additionally, ongoing community monitoring not only contributes to scientific data collection but also empowers local communities in environmental decision-making. This study highlights the broader educational, social, and ecological benefits of community-based citizen science and underscores the need for systematic evaluations to optimize its impact.

1. Introduction

To achieve nature-positive goals by 2030, understanding biodiversity baselines is essential. Citizen science has emerged as a valuable tool for scientific research, particularly in regions where data collection is challenging or costly. It has contributed to various fields, including ecology, conservation, astronomy, and public health []. Citizen science involves collaboration between the public and scientists in conducting research, monitoring environmental activities, collecting data, interpreting results, and disseminating new findings [,]. This data supports basic scientific research and management purposes [,,].
Moreover, citizen science offers a learning environment for participants [,]. Numerous studies have demonstrated its positive impact on public engagement with the environment and science, enhancing environmental and scientific literacy [,,,]. It has also gained prominence as an environmental education approach in conservation biology []. Ultimately, its key contribution to biodiversity conservation lies in fostering behavior change [,].
Maintaining a citizen science project or community is challenging. Understanding participant motivation is crucial for effectively managing these projects [,]. Insights into participant motivation can also aid in recruiting new members, enhancing satisfaction, and encouraging continued involvement []. However, motivations vary depending on the type of citizen science project. For example, Hsu and Lin (2021) found that participants in a roadkill citizen science project were primarily motivated by learning and self-achievement []. Larson et al. (2020) reported that contributing to science and conservation was the main motivator for participants in the Audubon Christmas Bird Count, with this motivation strengthening as engagement increased []. Motivation is dynamic, evolving with participation levels and project alignment with personal interests [,]. Additionally, motivation is a key driver of learning and can influence learning outcomes [,].
Evaluating learning outcomes has become increasingly important for citizen science program managers to assess participant growth, project impact, and core values []. However, such evaluations are often scarce due to the need for resources, time, and expertise in social science research methods []. Learning outcomes in citizen science vary by project type but commonly include scientific knowledge, inquiry skills, sense of place, attitudes, interests, competencies, and pro-environmental behavior [,,,,]. For biodiversity-focused citizen science, a critical learning outcome is influencing participants’ behavior toward biodiversity conservation, aligning with long-term sustainability goals [,].
Human behavior change toward environmental sustainability is influenced by various factors. Initially, the Knowledge-Attitude-Behavior (KAB) model was widely used to predict behavior [,,]. However, many studies highlight the limitations of linear behavioral predictions, emphasizing the complexity of human behavior [,]. Consequently, more comprehensive models have been developed to explain environmental behavior mechanisms, considering psychological factors such as beliefs, locus of control, norms, and behavioral intentions [,,,]. Nonetheless, Phillips et al. (2018) argued that causal relationships between these factors and behavior are underexplored in citizen science learning outcome research [].
This study focuses on a community-based citizen science project in Taiwan to evaluate participant motivation and learning outcomes. Specifically, we aim to explore the following: (1) the primary motivations for participants in this citizen science project; (2) the main learning outcomes of participants; (3) the relationship between motivations and learning outcomes; and (4) the factors that directly or indirectly influence behavior intention.

2. Materials and Methods

2.1. Background

The citizen science community was established in the Taoyuan Algal Reef (TAR) area of Taiwan, home to the world’s largest intertidal algal reef ecosystem []. The initiative emerged in response to the Taiwanese government’s goal of achieving zero nuclear power generation by 2025, which involved plans to construct two liquefied natural gas (LNG) storage tanks and related infrastructure near TAR. This proposal sparked significant debate within Taiwanese society, with some advocating for sustainable development and others emphasizing environmental preservation. In 2021, a national referendum was held, but the proposal to halt the LNG project was rejected [].
Before and after the referendum, diverse perspectives emerged from the public and nearby communities. Although these viewpoints were valuable, local residents recognized their limited knowledge of the algal reef and its surrounding environment. In response, the government identified the need to enhance local environmental awareness through scientific knowledge. To address this, they commissioned our research team to develop a community-based citizen science program in partnership with the local community, aiming to provide a comprehensive understanding of the TAR.
The development of the citizen science community followed the nine-step framework proposed by Bonney et al. (2009) []. This study focuses on the final step, “evaluation”, to assess the program’s effectiveness. The process of creation, negotiation, and collaboration involved in establishing this citizen science initiative is detailed in Table 1. People who participate in citizen science can be considered part of the citizen science community. Although the participants may change over time, approximately 20 to 30 people can be classified as members of our citizen science project.
Table 1. Development process of community empowerment for the citizen science project in the community surrounding the Taoyuan Algal Reefs.

2.2. Evaluation of Participants in Citizen Science

In this study, we employed a mixed-methods approach to investigate participants’ motivations for participation and their learning outcomes. Quantitative research was utilized for its ability to measure, calculate, and analyze quantifiable aspects and their relationships, thereby providing a systematic understanding of the phenomena under investigation []. Conversely, qualitative research offered in-depth, detailed, and long-term insights through interactive engagement between the researcher and participants, leading to a more comprehensive interpretation of the complexity and richness of their experiences [].

2.2.1. Quantitative Research

The questionnaire was developed based on existing citizen science research, utilizing the framework from Hsu and Lin (2021) [] for the motivation dimension and Hsu and Lin (2023) [] for the learning outcome dimension. Notably, the sense of place subdimension within the learning outcomes was specifically designed for this study. The questionnaire comprised several main dimensions, including basic demographic information, motivation (self-achievement, learning, leisure, physical, and social), and learning outcomes (knowledge, attitude, behavior intention, and sense of place). All dimensions (except for demographic information) were measured using a Likert scale, with “strongly agree” = 5 and “strongly disagree” = 1 as the measurable values. Reliability tests for each dimension were conducted using Cronbach’s alpha, as shown in Table 2.
Table 2. The dimensions and items of the questionnaire used in this study.
Participants were invited to complete the questionnaire through the project’s social media community (Line group). The Line group consisted of 39 members, excluding our research team and documentary filming crew, with an estimated 30 active participants as of 20 May 2023. A total of 18 valid questionnaires were collected, representing 60% of the estimated participant population. Since we did not require everyone to complete the questionnaire, we respected their choice to participate in the survey voluntarily. The demographic information is presented in Table 3.
Table 3. Demographic information of participants, presented as frequencies and percentages.
To explore differences in motivations and learning outcomes, the Kruskal–Wallis rank sum test and Dunn’s post hoc test were employed, as the data did not follow a normal distribution (p < 0.05, Shapiro–Wilk normality test). Given the community-based nature of this citizen science project and the consequently small participant population, the sample size for the questionnaire survey was expected to be low. Therefore, a significance level of p < 0.1 was adopted for this study.
Kendall’s rank correlation was conducted for each subdimension to examine the relationships between motivations and learning outcomes. Due to the limited sample size, a significance level of p < 0.1 was also applied for these analyses.
Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied using the plspm package in R 4.4.3 [] to examine the relationships among latent variables related to learning outcomes and their influence on behavioral intention. Four latent variables were defined: Knowledge, Attitude, Sense of Place, and Behavioral Intention, with the corresponding items listed in Table 2. Due to the limited sample size, bootstrapping with 5000 resamples was conducted to evaluate the significance of the path coefficients, thereby enhancing the robustness of the model’s parameter estimates. The analysis was performed with a maximum of 5000 iterations and a convergence tolerance of 1 × 10−5.
This approach allowed for the exploration of complex relationships among the latent constructs, providing insights into the underlying mechanisms influencing learning outcome dynamics. A path matrix was developed to specify the hypothesized relationships among the latent variables, assuming a lower triangular structure with the following hypotheses (Figure 1):
Figure 1. The structural equation model and the path hypotheses of this study.
H1: 
Knowledge directly influences Attitude.
H2: 
Attitude and Sense of Place are bidirectionally or directly related.
H3: 
Sense of Place and Knowledge are bidirectionally or directly related.
H4: 
Knowledge directly influences Behavioral Intention.
H5: 
Attitude directly influences Behavioral Intention.
H6: 
Sense of Place directly influences Behavioral Intention.
Reflective measurement models were specified for all constructs, with observed variables serving as manifest indicators for their respective latent variables. The inner model path coefficients and outer model weights (loadings) of latent variables on observed variables were estimated. Model fit and predictive accuracy were evaluated using the Goodness-of-Fit (GoF) index, with the following interpretation: GoF ≥ 0.36 indicates high goodness of fit, GoF ≥ 0.25 indicates medium fit, and GoF ≥ 0.10 indicates low fit [].

2.2.2. Qualitative Approach: Semi-Structured Interview

After completing the surveys for all four seasons, we conducted semi-structured interviews with the participants. A total of 10 individuals (33.3% of participants) took part in the interviews. Their demographic information is presented in Table 4. The primary purpose of these interviews was to explore participants’ motivations for participation and their learning outcomes. Additionally, the insights gathered will guide future adjustments to the project, ensuring its sustainable operation.
Table 4. Demographic information of interviewees from semi-structured interviews.
Although the semi-structured interviews followed a general outline, the researchers probed deeper when interviewees provided meaningful insights related to the research theme. The interview outline for this study included the following topics:
1.
Personal Information Collection
a.
Could you please provide your full name?
b.
Which age range do you fall into?
c.
What is your educational background?
d.
What is your current occupation?
2.
Motivation for Participation
a.
How did you first learn about this project?
b.
What initially motivated you to participate?
3.
Learning Outcomes
a.
What did you learn the most from participating in this project? Why?
For qualitative analysis, we employed an open coding approach, known for its flexibility in describing data compared to structured methods. This was followed by axial coding to organize the data into categories related to motivation and learning outcomes. Furthermore, we enhanced the quantitative findings by including detailed insights into participants’ motivations and learning outcomes throughout the project.

3. Results

3.1. Motivation

3.1.1. Quantitative Analysis of Motivation

The results indicated a significant difference in participants’ motivations (p < 0.001, Kruskal–Wallis test). Self-achievement and learning emerged as the strongest motivations (Figure 2), followed by social motivation, leisure, and finally physical motivation as the lowest (Figure 2). The p-values for the pairwise comparisons between these dimensions, obtained from Dunn’s test, are presented in Table 5.
Figure 2. Differences in motivation across dimensions. Different letters indicate significant differences.
Table 5. Post hoc comparison results between dimensions using Dunn’s test (* < 0.05; ** < 0.01; *** < 0.001).

3.1.2. Qualitative Analysis of Motivation

The results of the qualitative analysis on motivations for participation revealed four main categories: ‘Learning’, ‘Self-achievement’, ‘Social mission’, and ‘Social motivation’ (Table 6).
Table 6. Qualitative analysis of participants’ motivations for participation.
Among these, “Learning” and “Self-achievement” were the most prevalent, with nine participants (75%) citing each of these motivations and eight participants (66.6%) mentioning both. This was followed by “Social mission”, cited by five participants (41.6%). These findings align with the results from the quantitative analysis.
The “Social mission” category was not explicitly classified in the quantitative result. However, during the interviews, several participants expressed concerns about the environment and a desire to “do something” for the community or the environment. Therefore, this category was specifically highlighted. These participants were motivated not only by personal learning but also by a purpose beyond themselves—they wanted to contribute to improving the environment and society.

3.2. Learning Outcome

3.2.1. Quantitative Analysis of Learning Outcomes

The quantitative analysis revealed no significant differences among the dimensions of learning outcomes (p = 0.81, Kruskal–Wallis test) (Figure 3). Although no single dimension stood out as the highest, the median values for all dimensions were above 3, indicating that most participants perceived positive learning outcomes across all dimensions through their involvement in this citizen science project (Figure 3).
Figure 3. Differences in learning outcomes across dimensions. Different letters indicate significant differences.

3.2.2. Qualitative Analysis of Learning Outcomes

The quantitative questionnaire analysis revealed significant learning outcomes in the dimensions of “Knowledge”, “Attitude”, “Behavioral intention”, and “Sense of place”. From the qualitative interviews, the learning outcomes in the “Knowledge” dimension were particularly prominent.
Participants primarily acquired “Biological Knowledge” and “Survey Methods”, with eight participants (66.6%) gaining biological knowledge and seven participants (58%) learning survey methods. Notably, six participants (50%) reported learning both. Additionally, “Scientific Perspectives” and “Project Design” were mentioned by two participants (16.6%) each.
Regarding “Survey Methods,” participants’ learning experiences could be categorized into two aspects: (1) the operation of the “Survey Process” and (2) “Sampling Methods.” Some participants were particularly impressed by the quadrat sampling technique (Table 7).
Table 7. Qualitative analysis of participants’ learning outcomes.
As for “Scientific perspectives” and “Project design”, these outcomes went beyond the initially anticipated learning objectives of the project, reflecting more personal growth for the participants. In terms of “Scientific Perspectives”, some participants were inspired by one researcher’s view on the unity of humans and nature, which gave them new insights and feelings about the natural environment (F-2, F-3). For “Project Design”, two student participants (S-2, S-3), who are interested in environmental fields and engaged in environmental education, observed and learned about project design methods through their involvement in the study (Table 7).

3.3. Relationship Between Motivation and Learning Outcome Dimensions

According to the results of Kendall’s rank correlation analysis, out of 20 correlations (5 dimensions of motivation × 4 dimensions of learning outcomes), only 3 showed significant positive associations: learning with sense of place (p = 0.06 < 0.1), leisure with behavioral intention (p < 0.05), and social motivation with behavioral intention (p = 0.08 < 0.1) (Figure 4).
Figure 4. Kendall’s rank correlation analysis between motivation and learning outcome dimensions. Solid circles indicate significant correlations, with blue representing positive and orange representing negative associations. The size of the circles reflects the relative correlation coefficients (Tau). The circles with white bullets indicate that there is no significant relationship between the variables.

3.4. Structural Equation Model Revealing Influential Factors for Behavioral Intention

The result revealed that our model’s goodness of fit is 0.5, which indicates high goodness of fit (≥0.36). In the path, knowledge and sense of place significantly influenced attitude. For the behavior intention, only attitude significantly influenced behavior intention, indicating attitude was the crucial mediator between knowledge, sense of place, and behavior intention toward conservation (Figure 5).
Figure 5. Structural equation model for behavioral intention developed in this study. The number on each path represents the path coefficient, while the value in brackets indicates the 2.5th and 97.5th percentiles of the confidence interval. If the interval includes 0, the path is not significant. Solid arrows denote significant paths, whereas dashed arrows indicate non-significant paths.

4. Discussion

Our research evaluated a community-based citizen science project in the Taoyuan Algal Reef (TAR) region of Taiwan. Although the sample size was relatively small, it provided valuable insights into biodiversity conservation. Therefore, we encourage all small citizen science communities to conduct evaluations to ensure alignment with project goals and to provide feedback for management []. This study specifically focused on evaluating participants’ experiences, while evaluations of the generated data and its ecological significance have also been completed and are currently under journal review. MacPhail and Colla (2020) identified delays in disseminating outcomes as a major barrier to citizen science development []. Given that our participants recently completed a two-year continuous survey, we believe our evaluation is timely and relevant for publication.
Motivation plays a crucial role in retaining participants’ engagement in citizen science initiatives for biodiversity conservation [,]. Regarding participation motivation, most participants cited self-achievement and learning as their primary reasons for joining the project. Learning appears to be a particularly strong motivator for participation in citizen science projects. For instance, Domroese and Johnson (2017) found that the primary motivation for participants in the Great Pollinator Project was their interest in learning about bees, followed by their desire to contribute to science []. Similarly, Hsu and Lin (2021) reported that participants in a roadkill citizen science project joined primarily to learn how to identify animals and understand the mechanisms for preventing roadkill [].
In this study, we define self-achievement as the desire to contribute to nature and fulfill personal interests. This dimension was also a strong motivator for participation in some citizen science projects, particularly those related to scientific curiosity or conservation efforts []. Although social interaction was not as important as learning and self-achievement in our study, it still received a score above 3 on the rating scale, indicating that participants generally acknowledged its significance. This finding aligns with results from an avian citizen science project in South Africa, where social interaction was also found to be an important motivator []. In contrast, motivations related to leisure (recreation) and tangible rewards (such as gaining reputation or receiving gifts) were not as prominent in this project, though they have been identified as key motivators in other studies [,].
Participation in citizen science projects can be considered a form of informal education []. Regarding learning outcomes, our study did not find significant statistical differences among dimensions. However, all dimensions received a median score above 3, indicating that participants generally perceived gains in knowledge across multiple aspects. Qualitative analysis further revealed that understanding scientific processes and species identification were particularly important learning outcomes (Table 7). This is consistent with findings from Perry et al. (2021) [] and Peter et al. (2021) [], who reported that species identification knowledge and skills were major learning outcomes for participants in biological citizen science projects. Although our quantitative analysis did not show significant differences among learning dimensions, the qualitative data suggest that knowledge gain was a predominant outcome. We attribute this to the fact that dimensions such as attitude and sense of place are more abstract and therefore less frequently mentioned by interviewees.
In terms of the correlation between motivations and learning outcomes, three significant relationships emerged. A strong motivation for learning was associated with a higher sense of place, suggesting that individuals who were eager to learn also sought a deeper understanding of their local environment. Our project can be regarded as an example of place-based learning, which involves not only acquiring locally relevant knowledge and skills but also deepening one’s connection to a place []. Another correlation was observed between leisure motivation and behavioral intention; we suggest that participants’ relaxed and stable emotional states while engaging in the project facilitated pro-environmental behavior change []. Additionally, high motivation for social interaction was correlated with strong behavioral intentions, which can be linked to subjective norms—the idea that peer influence exerts social pressure, encouraging behavior change []. While correlation does not imply causation, these relationships suggest interdependencies rather than mutual exclusivity [,].
One of our key findings is that structural equation modeling (SEM) confirmed that attitude serves as a mediator between knowledge, sense of place, and behavioral intention toward biodiversity conservation (Figure 5). Our results support the classic Knowledge-Attitude-Behavior (KAB) model, which posits that knowledge influences attitudes, which in turn shapes behavior []. However, we found that sense of place did not directly influence behavioral intention; instead, attitude mediated this relationship (Figure 5). Similarly, Lo et al. (2019) found that satisfaction mediated the relationship between sense of place and pro-environmental behavior, with satisfaction functioning as an attitudinal factor, aligning with our findings []. Based on our results, we conclude that attitude change is the most critical factor in fostering behavioral change.

Author Contributions

Conceptualization, C.-H.H., H.-L.H. and C.-P.C.; methodology, C.-H.H., J.K. and L.-Y.Y.; software, C.-H.H. and J.K; validation, C.-H.H., J.K. and L.-Y.Y.; formal analysis, C.-H.H., J.K. and L.-Y.Y.; investigation, C.-H.H. and L.-Y.Y.; resources, C.-P.C., H.-L.H. and H.-J.L.; data curation, C.-H.H., J.K. and L.-Y.Y.; writing—original draft preparation, C.-H.H., W.-C.K. and L.C.; writing—review and editing, C.-H.H.; visualization, C.-H.H. and J.K.; supervision, C.-P.C., H.-L.H. and H.-J.L.; project administration, C.-H.H. and H.-J.L.; funding acquisition, C.-H.H. and H.-J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Office of Coast and Resource Circulation Construction, Taoyuan.

Institutional Review Board Statement

The research methodology of this study was approved by the Institutional Review Board of National Taiwan University (Approval No. 201806HS082).

Data Availability Statement

Data will be made available on request.

Acknowledgments

We sincerely thank all the citizen scientists who participated in this survey and the interviewees who generously shared their insights. We are especially grateful to the Yongxing Community Development Association for their collaboration and ongoing support. Our appreciation also extends to the assistants from Innovation and Development Center of Sustainable Agriculture of National Chung Hsing University and to Zhe-Yu Lin for their invaluable help in facilitating the research. Finally, we thank the Office of Coast and Resource Circulation Construction, Taoyuan, Taiwan, for their support of this project.

Conflicts of Interest

The authors declare no conflicts of interest.

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