Environmental Responsibility in Urban Forests: A Cognitive Analysis of Visitors’ Behavior

Abstract

The environmentally responsible behavior of urban forest visitors is a key determinant for the conservation of urban forests. Identifying the determinants of individuals’ behavioral intentions and actual behavior in engaging in environmentally responsible actions is a crucial step in promoting such behavior. This research investigates the determinants of environmentally responsible behavior of urban forest visitors in Tehran using Social Cognitive Theory. Data for the study were collected using 456 questionnaires distributed to visitors of urban forests. The data were analyzed using structural equation modelling, which described a 62.9% variance in behavioral intention and 56.6% in environmental behavior of visitors. The socio-structural factors and the observation of others’ behaviors were the most significant predictors of behavioral intentions. Outcome expectations and self-efficacy significantly influence both behavioral intentions and actual behavior. This study demonstrates that while behavioral intention is a key factor, other determinants such as outcome expectations and self-efficacy play a crucial role in shaping actual environmentally responsible actions. These results underscore the importance of increasing awareness and enhancing the skills of urban forest visitors regarding environmental behaviors. Furthermore, this study highlights the need to remove barriers and provide the necessary facilities to promote sustained environmentally responsible behavior among visitors.

1. Introduction

Urbanization has become a defining characteristic of the 21st century, with more than half of the global population now residing in urban areas [1]. The growing urban population has led to various environmental and social challenges, such as exacerbating climate change, increasing the frequency of floods, contributing to air pollution, and affecting the physical and mental well-being of citizens [2,3,4]. Urban managers and policymakers are actively seeking solutions to address the numerous problems arising from urban development. The growing body of literature emphasizes the role of urban green spaces, such as urban forests, in promoting mental and physical health and solving environmental challenges [5,6,7]. Urban forests, comprising trees and other vegetation within urban areas [8], have emerged as vital components of the urban landscape, offering a range of ecological, social, and economic benefits, including reduced stress, lower rates of mental disorders, and higher levels of physical activity [9,10,11], and nature-based solutions to mitigate environmental challenges such as [12,13]. These green spaces play a critical role in enhancing the quality of life for city dwellers while also contributing to the overall sustainability of cities. However, the sustainability and health of these ecosystems depend heavily on the environmentally responsible behavior and participation of citizens who visit them.

The environmentally responsible behavior of urban forest visitors is among the main factors which conserve the ecological integrity of these areas [6]. Environmental behavior is defined as an intentional behavior that considers negative effects on the environment and tries to reduce these effects [14]. By adhering to environmentally responsible behaviors, visitors play a crucial role in maintaining the health and biodiversity of urban forests [15]. The necessity of responsible environmental behavior among visitors also directly influences the preservation and resilience of urban forests in the face of growing urbanization and environmental challenges, and ensures urban forests remain accessible and enjoyable for future generations [6]. Poor environmental practices can lead to the deterioration of these spaces, making them less appealing and less functional for recreational and restorative purposes [16]. Littered pathways, damaged vegetation, and polluted water bodies not only detract from the aesthetic and recreational value of urban forests but also pose health risks to visitors. Environmental behavior in urban forests is also vital for fostering a culture of environmental stewardship and awareness among urban populations. This awareness can lead to increased support for urban forest conservation initiatives, greater community involvement in environmental protection, and a stronger sense of connection between people and nature [6]. Ultimately, this culture of stewardship can extend beyond urban forests, influencing how individuals interact with other natural environments and encouraging broader environmental responsibility in urban areas. Understanding the determinant factors of environmental behaviors is crucial for developing effective strategies to protect and enhance these valuable spaces. One of the primary reasons for studying these influencing factors is the direct impact that visitor behavior can have on the ecological integrity of forests. Another critical reason for studying the environmental behaviors of urban forest visitors is the potential to enhance visitor experiences while simultaneously protecting the environment [17]. Research has shown that individuals are more likely to engage in pro-environmental behaviors when they have a positive connection to nature and a clear understanding of the consequences of their actions [16]. Various factors including psychological and demographic variables might determine urban forests visitors’ behaviors.

Studies have shown that demographical characteristics including gender, age, marital status, education level, and income influence the behavior of visitors [18]. Alongside these characteristics, psychological variables are important in shaping individuals’ environmental behaviors. Studies have used various psychological theories such as the Theory of Planned Behavior (TPB) [19,20,21] and Value-Beliefs-Norm (VBN) theory [22,23,24] to explore the influencing factors on individuals’ behaviors. The findings of these studies confirmed the significant influences of these variables on environmental behaviors, especially in urban forests. Although these models have been used to understand environmental behaviors and can explain a portion of individuals’ intention and behavior, they each focus on distinct pathways to behavior. TPB emphasizes the role of attitudes, subjective norms, and perceived behavioral control in shaping intention [25], while the VBN model highlights the role of values and moral beliefs [26]. However, in the context of urban forests, a research gap exists regarding the determinants of visitors’ environmental intentions and behaviors. Both of the mentioned models offer valuable insights, but Social Cognitive Theory (SCT) introduces a more comprehensive perspective by addressing the dynamic interaction between cognitive, behavioral, and environmental factors [27]. SCT emphasizes the role of self-efficacy, outcome expectations, and social modeling in determining behavior. This approach captures not only individuals’ internal motivations but also how their environment and the behaviors of others influence their choices. This study aims to utilize the SCT model to assess the influence of its behavioral factors in explaining the environmental intentions and behaviors of urban forest visitors. By identifying the factors that encourage or discourage environmentally responsible behaviors, urban forest managers and policymakers can design targeted interventions to promote sustainable use and minimize harmful impacts. Moreover, understanding the factors that shape environmental behaviors is essential for promoting equity and inclusivity in urban forest management. From a broader perspective, studying the influencing factors that shape environmental behaviors in urban forests can contribute to more effective urban sustainability efforts. The objectives of this research were defined as follows:

(1)

Explore explanations for the determinants of the variance in urban forests visitors’ environmental behavior intention.

(2)

Explore explanations for the determinants of the variance in urban forests visitors’ actual environmental behavior.

(3)

Determine the influence of components of SCT on the environmental behavior intention of urban forests visitors.

Theorical Model and Hypotheses of Study

SCT, developed by Albert Bandura [27], is a widely recognized framework for understanding and predicting human behavior. It emphasizes the interplay of personal, behavioral, and environmental factors in shaping an individual’s intentions and actions [28]. SCT provides a valuable lens through which to examine the factors influencing individuals’ intentions and environmental behaviors. The key components of SCT include self-efficacy, observational learning (learning from others’ behavior), outcome expectations, and Socio-Structural Factors [28]. These components play a crucial role in determining how individuals engage with a behavior and, in the case of urban forests’ visitors, their probability of adopting environmentally responsible behaviors.

The concept of outcome expectations describes an individual’s beliefs about the potential results of a particular behavior [27]. The results of research have shown that people’s perception of the outcome of their behavior is a significant determinant of their intention to engage in behaviors such as participating in urban and non-urban forest management [18]. In the context of urban forests, these expectations influence whether individuals believe that their behaviors will lead to positive outcomes, such as a cleaner environment, enhanced biodiversity, or improved public health. When urban forests visitors perceive that their environmental behaviors will have beneficial effects, they are more likely to intend to and engage in these behaviors. Conversely, if the expected outcomes are unclear or perceived as negligible, visitors may be less motivated to act. Therefore, communicating the positive impacts of environmentally responsible behaviors through signs, educational programs, and outreach can strengthen outcome expectations and encourage sustainable actions. The component of self-efficacy in the model refers to the belief of individuals regarding their ability to conduct a specific behavior successfully [27]. In the context of environmental behavior, the perception of self-efficacy by individuals has to do with their abilities to engage in a behavior, and has been determined as a critical determinant of whether a person thinks that they are capable of engaging in pro-environmental behaviors [29,30]. High self-efficacy increases the likelihood that individuals will not only intend to act in environmentally responsible ways but will also follow through on these intentions [31]. Observational learning involves learning behaviors by observing others [27]. This component is particularly important in social settings, where individuals often look to the behavior of others for cues on how to act. If visitors observe peers or community members engaging in environmentally responsible behaviors, they are more likely to imitate these behaviors. This social influence can significantly shape both the intention to behave responsibly, and the actual behavior exhibited in urban forests [27]. Effective environmental management strategies can leverage observational learning by promoting visible examples of positive behavior through volunteer programs, public events, and educational campaigns. Socio-structural factors encompass the broader social and environmental conditions that can facilitate or hinder behavior [32]. These include social norms, economic conditions, infrastructure, policies, and available resources. Socio-structural factors play a significant role in shaping both the opportunities for and the barriers to performing certain behaviors [4]. In the context of urban forests, factors like the availability of opportunities and facilities can either encourage or discourage visitors from engaging in responsible environmental behaviors. Effective management and policy interventions that address these factors can create environments that support positive behaviors. These components of SCT interact to influence an individual’s intention and behavior, particularly in environmental contexts like urban forests. Understanding these factors can help in designing strategies to encourage visitors to engage in sustainable practices, contributing to the conservation and enjoyment of urban green spaces. According to the model’s components, the hypotheses of research were formulated as follows:

H1: 

Outcome expectations of urban forest visitors significantly affect their intention to behave environmentally.

H2: 

Outcome expectations of urban forest visitors significantly determine their actual environmental behaviors.

H3: 

Socio-structural variables significantly affect the intention of urban forest visitors to behave environmentally.

H4: 

Socio-structural variables significantly affect the actual environmental behavior of urban forest visitors.

H5: 

Ecotourists’ perception of other visitors’ behavior affects the intention of individuals to behave environmentally.

H6: 

Ecotourists’ perception of other visitors’ behavior affects their environmental behavior.

H7: 

The self-efficacy of urban forest visitors significantly influences their intention towards environmental behavior.

H8: 

The self-efficacy of urban forest visitors significantly determines their environmental behavior.

H9: 

The intention of urban forest visitors is a significant determinant of their behavior.

The research hypotheses are summarized in Figure 1.

2. Materials and Methods

2.1. Study Area

This study was conducted in Tehran (Figure 2), the capital of Iran and the country’s political and economic center, with a population of approximately 12 million people [33]. Tehran has an urban green space per capita of 6 square meters. The city faces several environmental challenges, including air pollution, urban flooding, and the degradation of urban forests [34]. Therefore, the preservation and maintenance of these urban forests through public participation and environmentally responsible behavior by visitors are of great importance.

2.2. Sampling and Dada Curation

For this study, a multi-stage cluster sampling method was employed. Initially, 5 districts were selected from the 22 urban districts of Tehran as the first cluster. These five districts were selected for two main reasons. The primary reason was their appropriate geographical distribution across Tehran. Additionally, these five urban areas host the most prominent urban forests in Tehran, which attract a large number of visitors. Then, two urban forests from each selected district were chosen as samples. Random sampling was conducted within each urban forest. The Krejcie and Morgan table was applied to determine the sample size [35]. Based on this procedure, 386 samples were determined. For higher accuracy, 456 samples were included in this study. The essential data for the research were collected using a questionnaire, which consisted of seven distinct sections designed to measure theoretical components and gather socio-economic information of the participants. To assess each of the six components of the model, separate questions—regarding outcome expectations (3), self-efficacy (3), behavior of other visitors (3), social-structural variables (3), intention (4), and behavior (4)—were used (Table 1). These questions were evaluated using a five-point Likert scale, a commonly used method for assessing questions in survey questionnaires [36]. The final section of the questionnaire included questions related to the socio-economic characteristics of the study participants. Before the data collection phase, the questionnaire was evaluated by a team of experts in fields related to the research topic. The purpose of this evaluation was to enhance the validity of the questionnaire. Based on the feedback received from the team members, the questionnaire was revised. The revised version was then shared with the team to review and confirm the changes. After the team provided their final approval and before commencing the main sampling, 30 pilot samples were surveyed. The Cronbach’s alpha was calculated above 0.7. Data collection was conducted by face-to-face interviews with participants. Initially, each participant was provided with a detailed explanation of the study’s purpose, the data collection tools, and the research questions. Additionally, the participants were instructed on how to respond to the questions. Ample time was given to each individual to ensure they fully understood the questionnaire and could provide informed responses. Data collection took place between April and June 2024, covering various days of the week and different times of the day to capture a comprehensive sample of urban forest visitors. On average, each questionnaire took 20 min to complete. All participants signed a written consent letter to confirm their willingness to participate in the study. They were also assured of the confidentiality of their responses, with a guarantee that their identities would remain anonymous and that their answers would only be used for the analysis within this research.

2.3. Data Analysis

The research data were analyzed in several sections. The socio-economic characteristics of the participants were examined using descriptive analysis. In this section, the classification of individuals into different categories and the frequency and percentage of individuals in each category were assessed. The data related to the research model were also analyzed in two parts. First, the validity and reliability test of the questionnaire were conducted using various criteria. In this section, Cronbach’s alpha, composite reliability, and average variance extracted (AVE) were utilized as the main criteria. These criteria are key metrics used to assess the reliability and validity of a measurement model in research. Cronbach’s alpha criterion is used to measure the internal consistency of a questionnaire [37]. The result of this analysis shows how closely related a set of statements in a questionnaire are as a group; a higher value suggests that the items consistently reflect the underlying construct. Composite reliability, similar to Cronbach’s alpha, evaluates the reliability of latent constructs, but it accounts for the varying contribution of each item, often providing a more accurate estimate in models with unequal loadings [38]. AVE, on the other hand, assesses the amount of captured variance by a construct of a model in relation to the variance due to measurement error. A higher value of AVE is a standard of stronger convergent validity, meaning that the indicators well represent the intended construct. Together, these metrics provide a comprehensive evaluation of a measurement model’s reliability and validity. The Fornell and Larcker was also used to assess the discriminant validity [39].

After establishing the validity and reliability, the data were analyzed. For this step Structural Equation Modeling (SEM) was chosen to analyze the relationships among the latent variables. SEM is a comprehensive statistical technique that allows for the simultaneous estimation of multiple equations representing the relationships between observed and latent variables [40]. It was chosen due to its ability to model complex relationships and account for measurement errors. The SEM analysis was conducted in a two-stage procedure: the first step was measurement model assessment, and the second step was for structural model evaluation. In the first stage, confirmatory factor analysis (CFA) was performed to further validate the measurement model. This step is carried out to ensure that the observed variables of study are adequate representatives of their respective latent components. In the second stage, the structural model was tested to evaluate the hypothesized relationships among the latent variables. The SEM analysis was conducted using Smart-PLS. The maximum likelihood estimation (MLE) method was employed, as it provides efficient and unbiased parameter estimates under the assumption of multivariate normality. The hypothesized paths in the structural model were tested for significance, and the strength of the relationships between latent variables was evaluated based on standardized regression coefficients. The significance of the paths was determined using p-values, with a significance level set at 0.05.

Table 1. The statements of questionnaire with factor loadings.

Constructs	Statements	ƛ
Outcome expectations	I believe that my efforts to protect the urban forest will have a positive impact on its preservation.	0.795
	If I engage in environmentally friendly behaviors in the urban forest, it will contribute to its sustainability.	0.911
	Protecting the urban forest will improve the local environment and community well-being.	0.904
Socio-structural	Access to facilities affects how responsibly I act in the urban forest.	0.808
	The presence of community programs or events related to urban forest conservation encourages me to participate in environmental activities.	0.890
	Under present conditions, I can consider part of my time to behave environmentally in urban forest visits.	0.848
Others’ behavior	I learn how to behave responsibly in the urban forest by observing other visitors.	0.871
	The actions of other visitors in the urban forest influence my own environmental behavior.	0.779
	Seeing others take care of the urban forest motivates me to do the same.	0.804
Self-efficacy	I am confident that I can take actions to protect the urban forest during my visits.	0.874
	I believe I can minimize my environmental impact when visiting the urban forest.	0.864
	I can easily adopt behaviors that reduce harm to the urban forest.	0.871
Intention	I intend to follow all environmental guidelines during my visits to the urban forest.	0.836
	I will make a conscious effort to reduce my environmental impact when visiting the urban forest.	0.785
	I plan to take activities that help protect the urban forest.	0.814
	I plan to reduce my usage of resources during future visits to the urban forest.	0.798
Behavior	I make sure to stay on designated paths to avoid damaging vegetation in the urban forest.	0.721
	I minimize the use of resources (e.g., water, electricity) when spending time in the urban forest.	0.883
	I ensure that my activities in the urban forest do not harm the natural environment.	0.750
	I consciously reduce waste by bringing reusable items during my visits to the urban forest.	0.873

References: [27,28,29,32,41].

Table 1. The statements of questionnaire with factor loadings.

Constructs	Statements	ƛ
Outcome expectations	I believe that my efforts to protect the urban forest will have a positive impact on its preservation.	0.795
	If I engage in environmentally friendly behaviors in the urban forest, it will contribute to its sustainability.	0.911
	Protecting the urban forest will improve the local environment and community well-being.	0.904
Socio-structural	Access to facilities affects how responsibly I act in the urban forest.	0.808
	The presence of community programs or events related to urban forest conservation encourages me to participate in environmental activities.	0.890
	Under present conditions, I can consider part of my time to behave environmentally in urban forest visits.	0.848
Others’ behavior	I learn how to behave responsibly in the urban forest by observing other visitors.	0.871
	The actions of other visitors in the urban forest influence my own environmental behavior.	0.779
	Seeing others take care of the urban forest motivates me to do the same.	0.804
Self-efficacy	I am confident that I can take actions to protect the urban forest during my visits.	0.874
	I believe I can minimize my environmental impact when visiting the urban forest.	0.864
	I can easily adopt behaviors that reduce harm to the urban forest.	0.871
Intention	I intend to follow all environmental guidelines during my visits to the urban forest.	0.836
	I will make a conscious effort to reduce my environmental impact when visiting the urban forest.	0.785
	I plan to take activities that help protect the urban forest.	0.814
	I plan to reduce my usage of resources during future visits to the urban forest.	0.798
Behavior	I make sure to stay on designated paths to avoid damaging vegetation in the urban forest.	0.721
	I minimize the use of resources (e.g., water, electricity) when spending time in the urban forest.	0.883
	I ensure that my activities in the urban forest do not harm the natural environment.	0.750
	I consciously reduce waste by bringing reusable items during my visits to the urban forest.	0.873

References: [27,28,29,32,41].

3. Results

3.1. Participants’ Characteristics

The socio-economic characteristics of the participants are detailed in

Table 2. The participants’ characteristics.

Characteristic	Categories	Frequency	Percentage
Gender	Male	231	50.7
	Female	225	49.3
Marital Status	Single	212	46.3
	Married	254	53.5
Age	<20	31	6.8
	21–30	56	12.3
	31–40	167	36.6
	41–50	141	30.9
	51–60	43	9.43
	>61	18	3.95
Educational status	Illiterate	19	4.17
	School	84	18.4
	Diploma	215	47.1
	University degrees	138	30.3
Monthly Visit	1	127	27.9
	4	113	24.8
	8	124	27
	>8	98	20.4

. The sample included 456 individuals, with a nearly even split between males (50.7%) and females (49.3%). In terms of marital status, 53.5% were married, while 46.3% were single. The age distribution indicated that the largest age group was 31–40 years, making up 36.6% of the participants, followed by those aged 41–50 years at 30.9%. Participants aged 21–30 years accounted for 12.3%, while 6.8% were under 20 years old. The 51–60 age group represented 9.43% of the sample, and those over 61 years made up 3.95%. Regarding education, 47.1% of participants had a diploma, which was the most common level of education. This was followed by 30.3% who had university degrees, 18.4% with school-level education, and 4.17% who were illiterate. In terms of urban forests visits, 27.9% of participants reported visiting urban forests once per month, 24.8% visited four times per month, 27% visited eight times per month, and 20.4% visited more than eight times per month.

3.2. Reliability and Validity of the Constructs

The results of the reliability and validity of the constructs are presented in

Table 3. Reliability and validity of the constructs.

Construct	Cronbach’s Alpha	Composite Reliability	AVE
Behavioral intention	0.823	0.883	0.653
Environmental behavior	0.822	0.883	0.656
Outcome expectations	0.839	0.904	0.759
Other visitors’ behavior	0.754	0.859	0.671
Self-efficacy	0.839	0.903	0.757
Socio-structural variables	0.806	0.886	0.721

. The values of Cronbach’s alpha for all constructs of the model ranged from 0.754 to 0.839, exceeding the commonly accepted threshold of 0.70, indicating good internal consistency [37]. Similarly, values of composite reliability for all constructs ranged from 0.859 to 0.904, surpassing the acceptable threshold of 0.70, further supporting the reliability of the constructs. The AVE values ranged from 0.653 to 0.759, which are all above the 0.50 threshold, indicating adequate convergent validity [38]. These results demonstrate that the items within each construct are well correlated and collectively capture the underlying theoretical concepts. Discriminant validity is illustrated in

Table 4. Discriminant validity (Fornell–Larcker criterion).

	1	2	3	4	5	7
Behavioral intention	0.808
Environmental behavior	0.639	0.81
Other visitors’ behavior	0.706	0.635	0.819
Self-efficacy	0.635	0.64	0.613	0.87
Socio structural variables	0.714	0.598	0.701	0.591	0.849
Outcome expectations	0.64	0.656	0.637	0.633	0.682	0.871

. The square root of the AVE for any specific construct of model is greater than the correlations between that construct and all other constructs in the model, providing evidence of adequate discriminant validity [39]. This indicates that each construct is distinct from the others, confirming that the constructs measure different concepts as intended. The results demonstrate that the measurement model exhibits satisfactory reliability, convergent validity, and discriminant.

3.3. Structural Model Evaluation and Hypotheses Testing

The structural model was assessed to determine the relationships between the latent variables, with the results depicted in Figure 3. This model accounted for 62.9% of the variance in behavioral intention and 56.6% of the variance in environmental behavior. The significance of these relationships was further substantiated through hypotheses testing (Figure 4 and

Table 5. The hypothesis test.

Hypotheses	Description	Confidence Interval Bias Corrected		t-Statistic	p-Values	Result
H1	Outcome expectations > Behavioral intention	0.02	0.218	2.153	0.032	+
H2	Outcome expectations > Environmental behavior	0.165	0.364	5.116	0.000	+
H3	Socio-structural variables > Behavioral intention	0.213	0.213	5.866	0.000	+
H4	Socio-structural variables > Environmental behavior	0.071	0.071	0.612	0.541	-
H5	Other visitors’ behavior > Behavioral intention	0.218	0.382	6.753	0.000	+
H6	Other visitors’ behavior > Environmental behavior	0.072	0.282	3.255	0.000	+
H7	Self-efficacy > Behavioral intention	0.112	0.277	5.042	0.000	+
H8	Self-efficacy > Environmental behavior	0.129	0.334	4.765	0.000	+
H9	Behavioral intention > Environmental behavior	0.07	0.261	3.142	0.002	+

), as detailed below.

The influence of outcome expectations on behavioral intention was significant (H1), with a standardized coefficient of 0.260 (t-value = 2.153, p-value = 0.032). This finding suggests that individuals’ expectations regarding the outcomes of their actions positively influence their intention to engage in the environmental behavior during their visit to urban forests. A direct and significant relationship was also observed between outcome expectations and environmental behavior (β = 0.114, t-value = 5.116, p-value < 0.001). This indicates that outcome expectations not only shape intentions but also have a direct impact on the actual environmental behaviors of urban forests’ visitors. Socio-structural variable demonstrated a robust positive effect on behavioral intention (β = 0.314, t-value = 5.866, p-value < 0.001), underscoring the critical role of socio-structural factors in shaping individuals’ intentions to act environmentally when they visit urban forests. However, the direct influence of socio-structural variables on environmental behavior was not statistically significant (β = 0.030, t-value = 0.612, p -value = 0.541), indicating that these variables might exert their influence on behavior predominantly through their effect on intentions rather than through direct pathways. The behavior of other visitors was found to significantly influence behavioral intention, with a coefficient of 0.182 (t-value = 6.753, p-value < 0.001). This suggests that social cues and the observed behavior of others play a substantial role in shaping an individual’s intentions. Additionally, the path from other visitors’ behavior to environmental behavior was significant (β = 0.199, t-value = 3.255, p-value = 0.001), highlighting the role of observational learning in fostering the environmentally responsible behavior of urban forests’ visitors. Self-efficacy emerged as a strong predictor of behavioral intention (β = 0.236, t-value = 5.042, p-value < 0.001), indicating that confidence in one’s ability to perform a behavior is a critical driver of intention. The influence of self-efficacy on environmental behavior was also significant (β = 0.236, t-value = 4.765, p-value < 0.001), demonstrating that individuals with higher levels of self-efficacy are more likely to translate their intentions into actual environmental behavior. Finally, behavioral intention was found to significantly predict environmental behavior (β = 0.173, t-value = 3.142, p-value = 0.002), affirming the central role of intention in guiding actual behavior. The results of the path analysis and testing of the hypotheses provide robust support for the proposed model, demonstrating that outcome expectations, socio-structural variables, and self-efficacy are key determinants of behavioral intention, while both behavioral intention and self-efficacy are crucial in predicting environmental behavior. The model exhibits strong explanatory power, particularly for behavioral intention (R² = 0.629) and environmental behavior (R² = 0.566), validating the effectiveness of the theoretical framework in predicting individuals’ environmental actions. The significance of most hypothesized paths reinforces the model’s theoretical underpinnings and its applicability to understanding environmental behavior.

4. Discussion

This study was designed to examine the role of behavioral factors on the environmental intentions and behaviors of urban forest visitors. SCT was employed as the research model for this investigation. Data were collected through questionnaires distributed to urban forest visitors. This study’s findings indicated that the variables within the model can explain 62.9% of the variance in the visitors’ behavioral intentions to engage in environmentally responsible behaviors during their visits to urban forests. Additionally, data analysis revealed that the model can account for 56.6% of the variance in the visitors’ actual environmental behaviors. Other studies have also demonstrated the model’s ability to predict individuals’ intentions and behaviors across various topics, such as soil and water resource conservation [29,32]. The results confirmed that the model’s variables are capable of partially predicting environmental intentions and behaviors, making it a valuable tool for investigating determinants of environmental behavior in the context of urban forests.

The impact of different components of the model on individuals’ environmental intentions and actual behaviors was assessed through hypothesis testing, with the results discussed subsequently. Hypothesis testing revealed that outcome expectations have a significant and positive impact on both environmental intentions (H1) and actual environmental behaviors (H2). The influence of this variable on intentions and behaviors has been confirmed in various studies [28,29], indicating that individuals’ confidence or lack thereof in the outcomes of their behaviors can significantly affect their participation in those behaviors. Given the importance of this variable as a determining factor, it is essential to educate urban forest visitors of the environmental positive and negative outcomes of their actions and provide education in this regard. Through such education, visitors can evaluate their behaviors and the expected outcomes, leading them to engage in more environmentally responsible actions.

The impact of socio-structural factors on environmental intentions (H3) and behaviors (H4) yielded conflicting results. While these factors were found to be a significant and positive determinant of intentions, they did not have a significant influence on actual environmental behavior. Previous research has identified the negative influence of social and structural constraints on environmental and forest-related behaviors [29,42]. However, the findings of this study differ. Although individuals’ behavioral intentions to engage in environmental behaviors are influenced by these factors, their actual behaviors are not. This suggests that while socio-structural factors can shape intentions, individuals with strong behavioral intentions to engage in environmentally responsible behavior are affected by determinants other than these factors. Nevertheless, addressing social and structural barriers could be a crucial step in fostering behavioral intentions towards responsible environmental behavior during visits to urban forests.

The influence of learning through observing others’ behavior was confirmed in hypotheses (H5) and (H6). Specifically, observing the environmentally responsible behavior of other urban forest visitors has a significant and positive impact on individuals’ intentions to engage in similar behavior. Moreover, this factor directly influences individuals’ actual behavior. Learning through observation is a core principle of the theory, and its impact has been validated in other studies as well [32,42]. The significant positive effect of this factor clearly illustrates how the environment and the behavior of others can shape individuals’ intentions and actions. This factor may create a form of social expectation or pressure through learning. When individuals observe the environmentally responsible behavior of other visitors and the outcomes of such actions, they may feel compelled to engage in similar behavior themselves. Social expectations serve as an important determinant of behavioral intention in models such as TPB. Research findings indicate that this variable can have contrasting effects on individuals’ intentions. While some studies confirmed positive significant influence of social pressures [43,44], research suggests that social expectations may exert a positive influence on the behavioral intentions of older generations, while having little to no effect [19]. These differences may be attributed to social background, generational gaps, or conflicts between personal and societal attitudes. This reaction can be seen as an effort to align with socially approved positive behavior in order to gain social acceptance.

The significant positive impact of self-efficacy on the environmental intentions and behaviors of urban forest visitors was confirmed in hypotheses (H7) and (H8). This factor refers to individuals’ perception of their own ability to perform a behavior. The findings are consistent with previous research, indicating that as individuals’ confidence in their ability to perform a behavior increases, or as the ease of performing the behavior improves, their intention to engage in that behavior and the likelihood of actually doing so also increase [20,43]. The influence of individuals’ perceived control over behavior execution is recognized as a key determinant of intention in other behavioral models as well, and it directly affects behavior itself. Thus, there are two ways to enhance the likelihood of urban forest visitors engaging in environmentally responsible behavior by influencing this variable. First, through education, visitors can be informed about environmentally responsible practices during their visits to urban forests. This includes providing necessary training on behaviors such as tree care, preventing pollution of water and soil resources, optimizing energy use, and managing waste effectively. Second, by providing the necessary facilities and amenities within urban forests, the conditions for performing environmentally responsible behaviors can be improved. Amenities such as camping and accommodation sites, adequate waste collection and separation facilities, and recreational services are among the provisions that can facilitate environmentally responsible behavior. The influence of behavioral intention on actual behavior (H9) was confirmed, showing that this variable has a significant and positive effect on the environmentally responsible behavior of urban forest visitors. Behavioral intention is often regarded as the most important determinant of actual behavior in various behavioral models and studies [22,45]. However, in this study, behavioral intention was not the most significant factor influencing actual behavior. Instead, factors such as outcome expectations, self-efficacy, and observing others’ behavior played a more substantial role in determining behavior compared to intention. This discrepancy may stem from the fact that individuals sometimes engage in environmentally responsible behaviors spontaneously, without a predetermined intention or plan. However, the sustainability of these behaviors requires intention and planning. Therefore, educating individuals on planned environmentally responsible behavior can lead to more deliberate actions with the aim of positively impacting the urban forest environment. Despite these novel findings, this research had some limitations. First, it was geographically limited, so caution should be exercised when generalizing the results to other regions. Additionally, this study relied on self-reported data, which may have led participants to present themselves as more environmentally conscious than they actually are, possibly to gain social approval. Therefore, it is recommended that future research be conducted using behavioral observation methods to gain a more accurate understanding of the factors influencing behavior. Furthermore, a multi-stage cluster sampling method was used in this study, which may have introduced bias in the results. Replicating this study using other sampling methods is necessary to obtain a more comprehensive understanding of the population. This study is specifically focused on urban forest visitors and cannot be generalized to other visitor behaviors. It is essential that similar research be conducted in relation to other forests or natural resources to gain a more comprehensive understanding of the determinants of pro-environmental behavior.

Theorical and Empirical Implications

This paper presents a novel study in examining the determinants of behavioral intentions and behavior of urban visitors using SCT, offering significant theoretical implications. First, this study identified that learning from others is a key determinant of visitors’ pro-environmental behavior. This finding clearly highlights the influence of the environment and other visitors on individuals’ behavior. Additionally, this paper demonstrated that self-efficacy and outcome expectations are also crucial for behavioral intentions and actions. The direct impact of these variables on intention and behavior among urban forest visitors had not been previously explored, and the findings of this study suggest that engaging in pro-environmental behavior requires individuals to be aware of both the positive and negative consequences of their actions, as well as having the ability to perform such behavior. As the results indicate, alongside personal abilities, the presence of appropriate social and infrastructural conditions is a strong factor that can either encourage or hinder individuals from engaging in such behavior.

This study also offers important policy and practical implications for promoting environmentally responsible behavior among urban forest visitors. The results demonstrated that the research factors had a significant and positive impact on individuals’ behavioral intentions to engage in environmentally responsible actions. Therefore, these variables should be prioritized in educational and promotional programs aimed at fostering environmentally responsible behavior among urban forest visitors. Key actions include raising awareness about the environmental impact of their behaviors, empowering them by enhancing their skills for engaging in environmentally responsible practices, and removing barriers while providing the necessary facilities to support such behaviors. The study also revealed that behavioral intention was not the strongest determinant of actual environmentally responsible behavior among visitors. This indicates that visitors’ environmental actions are often not carried out with clear intention or planning. While this highlights the potential for promoting environmentally responsible behaviors, it also underscores the need for sustained, intentional actions through targeted awareness efforts. For future research, several recommendations are provided. First, studies should examine the impact of awareness-raising and skill-building programs on individuals’ self-efficacy and their perception of outcomes of behaviors. Second, the influence of socio-economic factors on individuals’ environmental behaviors should be investigated, as this would provide better insight into the determinants of environmentally responsible behavior and help identify target groups for educational interventions. Additionally, it is recommended that the determinants of citizens’ environmentally responsible behavior be explored using other behavioral models such as stimulus–organism–response or norm activation theory to gain a more comprehensive understanding of the factors influencing behavioral intentions and actions.

5. Conclusions

This study utilized SCT to examine the determinants of environmentally responsible intention and behavior among urban forest visitors. The findings revealed that socio-structural factors and the observation of other visitors’ behaviors had the most significant influence on individuals’ behavioral intentions. Additionally, outcome expectations and self-efficacy were also found to have a significant impact on behavioral intentions. Except for socio-structural factors, these variables also played a positive and significant role in determining individuals’ actual behavior. The results highlight the importance of enhancing visitors’ awareness and skills regarding environmentally responsible behaviors. Furthermore, this study suggests that removing barriers and providing the necessary facilities for citizens to engage in environmentally responsible behaviors can have a decisive impact. Therefore, raising visitors’ awareness about the consequences of their actions, educating them on the necessary skills for engaging in pro-environmental behavior, and removing barriers that prevent such behavior are essential steps to enhance the likelihood of pro-environmental actions.