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Article

The Effects of Experiential Quality on Restorative Experience and Subjective Well-Being: An Assessment of Urban Forest Visitors

by
Nurgül Arısoy
Department of Landscape Architecture, Faculty of Architecture and Design, Selçuk University, Selçuklu, Konya 42130, Turkey
Sustainability 2025, 17(18), 8163; https://doi.org/10.3390/su17188163
Submission received: 7 July 2025 / Revised: 3 September 2025 / Accepted: 5 September 2025 / Published: 10 September 2025
(This article belongs to the Section Sustainable Urban and Rural Development)
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Abstract

Urban green spaces are increasingly recognized for their capacity to support mental health and enhance positive affect through interaction with nature. As a significant component of urban green infrastructure, urban forests provide restorative environments that contribute to individuals’ subjective well-being. This study investigates the effects of quality of experience in urban forest visits on individuals’ restorative experiences and subjective well-being levels. Data were collected via a structured questionnaire from 385 participants who visited an urban forest and analyzed using structural equation modeling (SEM). The findings reveal that quality of experience exerts both direct and indirect positive effects on subjective well-being, with restorative experience serving as a partial mediator in this relationship.

1. Introduction

Urbanization occupies a significant position on the global political agenda and continues to accelerate rapidly. Currently, approximately 55% of the world’s population resides in urban areas, and this figure is projected to exceed 68% by 2050 [1]. The increasing pace of urbanization has led to a range of challenges, including environmental pollution, the densification of living spaces, the reduction in green areas, and a growing disconnection from nature. These factors have contributed to a variety of adverse effects on individuals’ physical and mental health [2,3,4,5]. Inhabitants of large metropolitan areas are increasingly subjected to environmental and social stressors—such as persistent noise exposure, air pollution, social isolation, and chronic psychological strain—all of which are recognized contributors to the prevalence of mental health disorders and symptoms indicative of burnout in urban populations [6,7,8]. Consequently, there is a growing need for accessible and effective strategies to enhance psychological resilience.
In response to these challenges, the World Health Organization (WHO) launched the “Healthy Cities” initiative to promote urban environments that support residents’ physical, mental, and social well-being [9]. This initiative underscores the importance of designing urban spaces that enhance quality of life. Nature-based solutions have emerged as key strategies in achieving this goal. These approaches have been shown to support individuals’ psychological well-being, particularly in the aftermath of stressful life events [10,11,12,13,14,15].
Urban forests serve as critical settings for ecological sustainability and provide opportunities for residents to engage with nature, facilitating mental restoration and psychological healing [16,17,18,19,20]. Research consistently demonstrates that green spaces play a vital role not only in promoting physical health but also in enhancing subjective well-being (SWB) [21,22,23,24]. SWB is a multidimensional construct encompassing life satisfaction, the predominance of positive emotions, and a sense of meaning and purpose in life [25,26,27,28,29,30,31].
In recent years, the concept of quality of experience (QE) has emerged as an important factor in understanding the benefits of green spaces. QE encompasses elements such as environmental aesthetics, accessibility, perceived safety, opportunities for social interaction, and spatial satisfaction. Studies have reported that these components positively influence both restorative experiences (RE) and SWB [32,33,34]. High-quality nature experiences are associated with increased mental clarity, tranquility, and psychological renewal, all of which contribute to improvements in SWB [32,35].
Several studies suggest that spending time in natural settings such as urban forests may significantly enhance life satisfaction and happiness [36,37,38,39,40,41]. However, further empirical evidence is needed to clarify the mechanisms through which these effects occur. In particular, it remains unclear whether the impact of QE on SWB is direct or mediated by RE. Moreover, recent literature highlights that post-COVID-19, individuals’ engagement with outdoor and green spaces has significantly increased, reinforcing the relevance of such research for contemporary urban life [42,43,44].
Therefore, the present study examines the effects of QE in urban forests on RE and SWB. A structural equation model (SEM) is applied to test the potential mediating role of RE in the relationship between QE and SWB. This study seeks to provide evidence-based insights into the psychological benefits and restorative potential of urban green spaces, ultimately contributing to nature-based urban planning and policies that support individual and societal well-being.

2. Theoretical Background and Hypothesis Development

2.1. Experience and Quality of Experience

Experience is generally understood as a perceptual process that involves both cognitive and emotional responses to individuals’ interaction with their environment [45,46,47]. QE, in contrast, refers to the extent to which such experiences are perceived as meaningful, aesthetically pleasing, engaging, and memorable [48,49,50,51]. It is shaped by emotional as well as rational evaluations of the environment, with determinants including social sharing, perceived participation, and psychological responses. Within leisure and visitor experience research, Goulding (2000) and Jennings (2010) [52,53] conceptualize experience as a three-stage process of anticipation, actual experience, and reflection. In natural settings such as urban forests, QE integrates elements that support psychological restoration [54,55,56,57], and recent studies show that high-quality nature-based experiences contribute significantly to subjective well-being [10,11,58,59,60,61,62,63].

2.2. Restorative Experience

RE refers to the process by which individuals recover from mental and emotional fatigue and regain attentional and energy resources. Kaplan’s Attention Restoration Theory (ART) [64] proposes that natural environments help replenish depleted directed attention. Subsequent studies have expanded this framework by highlighting mechanisms such as soft fascination and mental bandwidth that facilitate cognitive recovery [65]. Accordingly, spending time in nature restores attentional capacity, while also reducing stress and anxiety and alleviating mental fatigue, as demonstrated by more recent research [65,66,67,68,69,70].

2.3. Subjective Wellbeing

SWB is a multidimensional construct encompassing overall life satisfaction, the predominance of positive emotions, and the relative absence of negative emotions [28,71]. It integrates both cognitive and affective dimensions, thereby reflecting individuals’ general appraisal of life quality. In the context of global urbanization, SWB has emerged as a critical public health concern [28,72]. Exposure to natural environments has been shown to enhance SWB by replenishing psychological resources and promoting emotional stability [72,73,74,75,76].

2.4. The Relationship Between QE, RE, and SWB

The beneficial effects of nature-based environments on psychological health have been a central focus in environmental psychology and recreation research [77,78,79,80]. Exposure to natural recreational settings such as urban forests is consistently associated with enhanced positive affect, reduced stress, and improved cognitive functioning [38,68]. Within this context, QE captures the perceived meaningfulness, engagement, and satisfaction derived from such environments [81]. Individuals reporting higher levels of QE tend to be more receptive to environmental stimuli and consequently experience greater restorative benefits (RE) [82,83,84].
RE, in turn, involves reduced cognitive fatigue, enhanced attentional capacity, and emotional renewal [85,86]. The relationship between QE and RE has direct implications for SWB, encompassing life satisfaction, emotional balance, and meaning in life [25,28]. Evidence shows that QE influences SWB both directly and indirectly through RE [33,40,87,88]. High-quality experiences in nature strengthen individuals’ emotional connection with their environment, thereby facilitating restoration and enhancing SWB [89,90,91]. Notably, nature-based experiences in urban forests have been shown to play a protective and therapeutic role in fostering psychological well-being [11,92].
Figure 1 illustrates the conceptual model of the hypothesized relationships among the variables. Based on theoretical frameworks and empirical findings, the following hypotheses were formulated:
H1. 
QE positively influences RE.
H2. 
RE positively influences SWB.
H3. 
QE directly and positively affects SWB.
H4. 
RE mediates the relationship between QE and SWB.

3. Methodology

3.1. Research Design

This study employed a quantitative research method to examine the relationships among QE, RE, and SWB. A correlational research design, supported by Structural Equation Modeling (SEM), was adopted for the investigation. The correlational design is a widely used method for examining the strength, direction, and nature of relationships between two or more variables. This model enables the examination of patterns of mutual variation between individuals or objects and is typically structured around correlation and comparison analyses [84,85].
Accordingly, data pertaining to participants’ experiential and psychological states were gathered through the use of validated psychometric instruments, and statistical analyses were conducted to examine the significance of relationships among the study variables. Furthermore, this research design facilitates empirical inferences regarding the associations between latent constructs, albeit without asserting direct causality. The study is situated within the framework of naturalistic, setting-based survey research, offering a context-sensitive approach to understanding how nature-based public environments—such as urban forests—influence subjective well-being in real-life conditions.

3.2. Participants

The study sample consisted of 385 participants (55.6% female, 44.4% male). The majority were adults aged 18–64 years (94.3%), with a smaller proportion aged 65 and above (5.7%). Additional demographic characteristics, including marital status, employment, and income levels, are summarized in Table 1.
The demographic profile of the sample shows broad similarities with the population structure of Konya’s metropolitan center (Meram, Selçuklu, and Karatay districts). According to official statistics, the gender distribution in the metropolitan population is balanced (approximately 49% female, 51% male), which is consistent with the sample (55.6% female, 44.4% male). Likewise, the majority of the population falls into the working-age group (18–64 years), aligning with the age distribution of the sample (94.3%). Minor deviations (e.g., slightly higher female participation and a younger concentration) reflect the actual visitor profile of the urban forest.

3.2.1. Sampling and Procedure

A non-probability convenience sampling method was employed due to practical constraints of time and accessibility. This approach enabled direct access to the target population but inherently limits the generalizability of the findings. Inclusion criteria required participants to be adults (18 years or older) who had just completed or were in the process of visiting the urban forest and who voluntarily consented to participate. No exclusion criteria related to gender, education, or occupation were applied.

3.2.2. Ethics

Prior to data collection, all participants were fully informed about the purpose, scope, and voluntary nature of the research, and explicit informed consent was obtained. The study complied with the ethical principles of the Declaration of Helsinki. Data collection was conducted during the spring and summer of 2024, representing typical visitation periods.

3.3. Study Area

This study was conducted in the Konya Urban Forest, located within the boundaries of Meram district in Konya, the sixth most populous city in Türkiye. With a population of approximately 348,000, Meram occupies a strategic position in meeting the city’s overall need for green space.
The Konya Urban Forest was established in 2005 by the Konya Metropolitan Municipality on a forested area covering 5.27 km2. It is a public and freely accessible green space. The site offers a variety of recreational amenities, including walking trails, picnic areas, ponds, gazebos, football and basketball courts, and viewing terraces, providing diverse leisure opportunities for visitors.
Situated approximately 25 km from the city center and along the Konya–Beyşehir highway, the urban forest is easily accessible and attracts significant recreational use throughout the year. Despite high visitor density, particularly on weekends, the site is noted for its quiet and tranquil atmosphere. The area hosts a rich and diverse vegetation structure, composed of approximately 180 native plant species, including dominant tree types such as pine, oak, and cedar, along with numerous endemic species. Additionally, two ponds—measuring 11,000 m2 and 4000 m2, respectively—enhance the ecological richness and visual diversity of the forest.
As one of the officially designated “urban forests” in Türkiye, the Konya Urban Forest is managed under a protocol between the General Directorate of Forestry and the Konya Metropolitan Municipality. The site is officially designated as an urban forest but geographically located in a peri-urban area within Meram District, Konya, Türkiye. Like other urban forests across the country, this area plays a critical role in improving air quality, mitigating the urban heat island effect, ensuring accessible green spaces, and enhancing the physical, mental, and social well-being of urban residents (Figure 2).

3.4. Measures

This study utilized a structured questionnaire comprising both demographic and psychometric instruments to assess the constructs of QE, RE, and SWB in the context of urban nature recreation.

3.4.1. QE Scale

To measure the construct in question, a six-item original measurement tool was developed based on previous research in the field of experiential evaluation. The items reflect dimensions such as meaningfulness, immersion, and aesthetic satisfaction of the experience.

3.4.2. RE Scale

The RE scale was adapted from established instruments developed by [76,86], which are grounded in the Attention Restoration Theory (ART). The items aim to measure cognitive and emotional restoration following exposure to natural environments.

3.4.3. SWB

The SWB construct was assessed through a composite approach including the following subcomponents:
  • Life Satisfaction (LS): Adapted from the Satisfaction With Life Scale (SWLS), 5 items were modified to reflect participants’ satisfaction following the urban forest experience.
  • Positive (PA) and Negative Affect (NA): Based on the Positive and Negative Affect Schedule (PANAS), 6 items (3 positive and 3 reverse-coded negative) were used to evaluate emotional responses after the forest visit. Affect Balance scores were calculated by subtracting the negative affect score from the positive affect score.
  • Meaning and Happiness (MH): Five original items were developed to assess participants’ perceived sense of meaning in life and emotional fulfillment following their forest experience. These items reflect the perceived influence of the experience on life meaning, inner peace, and emotional coherence.
A composite formula was used to compute the total SWB score:
SWB = LS + Affect Balance (AB) + MH
AB= PA − NA
This total score was used in the statistical analyses as the dependent measure of SWB.
All items were rated on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). The reliability of all scales was tested, and Cronbach’s alpha coefficients indicated satisfactory internal consistency. Furthermore, the measurement model was evaluated using Confirmatory Factor Analysis (CFA), and the structural model was tested via Structural Equation Modeling (SEM). The constructs and their supporting literature are summarized in Table 2.

3.5. Data Collection

Data were collected between April and June 2024 during participants’ visits to the Konya Urban Forest. A structured, face-to-face questionnaire was administered on-site using paper-based survey forms. The data collection was carried out by a trained research assistant (undergraduate student) under the supervision of the lead researcher. Surveys were conducted between 9:00 a.m. and 7:00 p.m. on both weekdays and weekends to capture a diverse sample of visitors.
Prior to participation, individuals were fully informed about the purpose and scope of the study, the voluntary nature of their involvement, and their right to withdraw at any stage without penalty. Verbal informed consent was obtained in line with ethical research standards. Anonymity and confidentiality were strictly maintained throughout the process.
The questionnaire comprised items assessing participants’ demographic characteristics, QE, RE, and SWB (Table 3).

4. Results

The findings of the study support the theoretical framework and reveal significant associations among QE, RE, and SWB. The adequacy of model fit indices and the statistically significant results for the hypothesized paths provide a robust basis for further discussion of the effects of urban green space experiences on individual well-being. This section presents descriptive statistics, correlation analyses, regression models, and SEM results, followed by their theoretical and practical implications.

4.1. Descriptive Statistics and Correlations

Descriptive statistics for the main study variables are summarized in Table 4 (see also Table 1 for demographic details of participants). Item-level means, standard deviations (SD), skewness, and kurtosis values indicated that all skewness values fell within the acceptable range of ±1, and kurtosis values were within ±2. These results satisfy the criteria for univariate normality [100], confirming that the data were suitable for subsequent parametric analyses.
Table 5 presents the Pearson correlation coefficients among the latent constructs. Statistically significant correlations at the 0.01 level were observed, providing empirical support for the proposed framework. For example, LS and PA were strongly and positively correlated (r = 0.65), while both were negatively correlated with NA (r = −0.60 and −0.58, respectively). QE showed positive associations with both RE (r = 0.82) and SWB indicators, suggesting meaningful interrelationships among these constructs.

4.2. Hierarchical Regression Analysis Results

The hierarchical regression analysis showed that the control variables (income, profession, and visit frequency) accounted for 15.6% of the variance in Subjective Well-Being (SWB). When Quality of Experience (QE) was included in the second step, the proportion of explained variance increased to 19.5% (ΔR2 = 0.039, p < 0.001; see Table 6).
As shown in Table 7, income was statistically significant in Model 1 (β = 0.13, p < 0.05), but this association became non-significant after Quality of Experience (QE) was entered in Model 2 (β = 0.10, p = 0.074). Visit frequency remained the strongest correlate across both models (β = 0.33, p < 0.001). Importantly, Quality of Experience (QE) was also positively associated with Subjective Well-Being (SWB) (β = 0.20, p < 0.001), indicating its unique contribution beyond socioeconomic variables and visit frequency.

4.3. Measurement Model

Confirmatory Factor Analysis (CFA) was conducted to evaluate the measurement properties of the study constructs. All standardized factor loadings exceeded the recommended threshold of 0.70 (see Table 8), supporting indicator reliability and convergent validity. In addition, internal consistency and convergent validity were further confirmed by Cronbach’s alpha (α), McDonald’s omega (ω), and Average Variance Extracted (AVE) values, all of which met or exceeded the recommended cut-off criteria (α, ω ≥ 0.70; AVE ≥ 0.50). These findings provide strong evidence of the reliability and construct validity of the scales used in this study. Detailed results are presented in Table 8 (factor loadings) and Table 9 (reliability and validity indices).
Table 9 presents internal consistency and convergent validity indicators for each latent construct. Cronbach’s Alpha (α), McDonald’s Omega coefficients (ω1, ω2, ω3), and Average Variance Extracted (AVE) values are reported. All constructs meet the recommended thresholds (α, ω ≥ 0.70; AVE ≥ 0.50), indicating satisfactory reliability and convergent validity [101,102,103].

4.4. Structural Model

Structural equation modeling (SEM) results revealed significant relationships among the study constructs. Quality of Experience (QE) had a strong positive effect on Restorative Experience (RE), and both QE and RE significantly predicted Subjective Well-Being (SWB). Moreover, RE was found to partially mediate the relationship between QE and SWB. These findings highlight the critical role of restorative processes as psychological mechanisms linking environmental experiences to well-being outcomes.
The structural model demonstrated an acceptable fit to the data, with all indices exceeding recommended thresholds (CFI = 0.948, TLI = 0.943, RMSEA = 0.055, SRMR = 0.040) (Table 10). Standardized path coefficients (Table 11), explained variances (R2 values, Table 12), and hypothesis testing results (Table 13) further confirm the robustness of the proposed model.
Table 11 presents the standardized path coefficients (β) estimated from the structural equation model. All path coefficients were statistically significant at p < 0.001, indicating meaningful relationships among the constructs. For example, Experience Quality had a strong direct effect on Restorative Experience (β = 0.54), and both Experience Quality and Restorative Experience significantly predicted Subjective Well-Being. Negative Affect was negatively associated with SWB. These results provide support for the hypothesized model structure [104], as illustrated in Figure 3.
This SEM illustrates the direct and indirect relationships between QE, RE, and SWB among users of the urban forest. In this model, SWB is conceptualized as a latent construct composed of four subdimensions: LS, PA, NA, and MH. Each latent variable is represented by its respective observed indicators (M1–M31), enabling a comprehensive measurement of the underlying psychological constructs.
The coefficient of determination (R2) values provide insight into the proportion of variance in each endogenous (dependent) variable that is accounted for by the structural model. Specifically, the model explains 70.1% of the variance in SWB, indicating a substantial explanatory capacity. RE construct demonstrated a notably high R2 value of 0.913, reflecting strong predictive power. Additionally, the observed R2 values for the SWB subdimensions were also substantial: MH = 0.903, LS = 0.832, PA = 0.762, and NA = 0.709. These values suggest that the latent constructs are well-represented in the model and that the measurement model possesses strong construct validity and explanatory adequacy.
The findings of the structural equation model reveal that QE significantly predicts both RE and SWB. RE also has a significant positive effect on SWB. Furthermore, the results indicate that RE partially mediates the relationship between QE and SWB. All hypothesized paths were found to be statistically significant and thus supported (Table 13).

5. Discussion

This study employed SEM to examine the relationships between QE, RE, and SWB in the context of urban forest recreation. The findings indicate that QE significantly influences both RE and SWB, aligning with a growing body of literature on the psychological benefits of natural environments [14,17,19]. By fostering environmental aesthetics, engagement, and perceived meaning, QE promotes a positive internal state that enhances indicators of well-being such as life satisfaction [15].
The correlation results further support the theoretical framework. Strong positive associations were observed between LS and PA (r = 0.65), while both variables were negatively related to NA (r = −0.60 and −0.58). These findings corroborate the dual-structure model of SWB emphasized in the literature and are consistent with theoretical approaches proposed by Diener (2000) [26]. Moreover, the very strong correlation between QE and RE (r = 0.82) highlights that more meaningful and engaging experiences in urban forests are closely linked with higher levels of psychological restoration [105].
SEM results revealed that QE had a strong effect on RE (β = 0.893, p < 0.001), confirming that aesthetically pleasing and engaging natural environments promote restoration [31]. QE functions as a comprehensive evaluative process, operating at both cognitive and emotional levels, and thereby directly enhancing SWB. In line with Kaplan’s ART and subsequent studies [19,31], natural settings provide mental relaxation and sensory coherence, reinforcing that urban forests are not only ecological spaces but also restorative environments. These findings support Hypothesis 1 (H1) and align with prior frameworks [81,84].
RE was also found to have a significant positive effect on SWB (β = 0.200, p < 0.001), supporting the proposition that nature interaction facilitates mental recovery [17,76,80]. Similarly, the direct effect of QE on SWB was significant (β = 0.596, p < 0.001), echoing prior evidence on the psychological impacts of environmental quality [15,106]. Thus, QE emerges not merely as an aesthetic factor but as a structural determinant of psychological well-being. These findings strongly support Hypothesis 3 (H3). Furthermore, RE partially mediated the QE–SWB relationship (β = 0.178, p = 0.028), consistent with mediation models suggested in earlier research [18,20,107]. However, the stronger direct effect of QE underscores its central role in shaping well-being. This supports Hypothesis 4 (H4) and aligns with nature-based mediation mechanisms proposed by Bratman et al. (2019) [6].
Overall, the results demonstrate that QE strongly predicts RE and exerts both direct and indirect effects on SWB. While RE plays a meaningful mediating role, the influence of QE on SWB remains stronger, suggesting that experiential quality directly enhances well-being while restorative processes provide additional, secondary benefits.
These findings highlight the importance of prioritizing experiential quality in urban forest planning, alongside accessibility and infrastructure. Features such as safety, aesthetic coherence, and opportunities for social interaction should be regarded as essential in maximizing psychological benefits.
Beyond individual outcomes, the Konya Urban Forest must also be understood within the broader metropolitan recreational landscape. As a peri-urban green space, it not only serves local residents but also attracts visitors across the Konya metropolitan region. Similarly to other rapidly urbanizing Anatolian cities, forest parks act as substitutes for limited urban parks, offering affordable, family-oriented, and accessible leisure opportunities. In this sense, the Konya Urban Forest represents a hybrid recreational pattern where traditional activities such as picnicking coexist with more individualistic pursuits such as walking, sports, and nature appreciation. Recognizing these dynamics underscores the forest’s disproportionate importance in shaping subjective well-being compared to typical urban green spaces.

5.1. Limitations

Despite providing valuable insights, this study is subject to limitations. First, the sample was limited to visitors of a single urban forest, reducing generalizability to other cultural or socioeconomic contexts. Second, its cross-sectional design prevents causal inference. Third, reliance on self-report measures introduces potential biases such as social desirability and recall errors. Fourth, environmental variables (e.g., noise, air quality, visit duration) were not controlled and may have influenced experiences. Fifth, data were collected in spring and summer, so seasonal variations were not captured. Finally, because data were collected immediately after the visit, the duration of restorative effects could not be evaluated. Longitudinal studies are needed to examine whether these effects persist over time.

5.2. Future Research Directions

Future studies should address these limitations by employing longitudinal designs to clarify causal pathways and test whether the effects of QE and RE on SWB endure over time. Cross-cultural and multi-site studies would enhance external validity and allow comparative analyses, especially in high-density metropolitan contexts. Supporting evidence for such cultural validation is provided by multi-country research showing consistent well-being outcomes across populations [108] and studies highlighting geographical and cultural variations in perceptions of urban green spaces [109].
In addition, incorporating socioeconomic variables and visit frequency as covariates would yield a more nuanced understanding of SWB. Expanding data collection across different seasons could capture seasonal variations in experiential quality and restoration.
Another promising direction is integrating objective physiological or behavioral indicators (e.g., heart rate variability, cortisol, or activity tracking) alongside self-reports. Environmental features such as biodiversity, vegetation density, and soundscapes should also be considered, as they may differentially contribute to restoration. Future research could also use control groups, experimental designs, and therapeutic nature-based interventions to provide stronger causal evidence.
Finally, economic perspectives should be integrated, such as estimating potential healthcare cost reductions associated with additional hectares of urban green space, thereby providing more persuasive evidence for policymakers and urban planners.

6. Conclusions

This study explored the relationships between QE, RE, and SWB in the context of a single urban forest by employing SEM. The findings provide evidence that QE may contribute to SWB both directly and indirectly through RE, with RE acting as a partial mediator. These results suggest that high-quality experiences in nature can foster subjective well-being not only via restorative mechanisms but also through meaningful, aesthetically rich, and engaging interactions with the environment.
The study highlights the critical role of urban forests not only as ecological assets but also as spaces that enhance psychological and social health. Accordingly, urban planning and policy efforts should prioritize not only the quantity of green areas but also their qualitative features—such as safety, accessibility, aesthetic coherence, and opportunities for social interaction—that enable emotionally fulfilling and restorative experiences.
Future studies should expand on these results by including multiple study sites, diverse demographic groups, and different sociocultural contexts, as well as adopting longitudinal or experimental designs, to further validate and generalize the observed relationships.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Scientific Ethics Review Board of the Faculty of Architecture and Design at Selçuk University (Approval Code: 05/04, Approval Date: 24 June 2023).

Informed Consent Statement

Verbal informed consent was obtained from all subjects involved in the study. Participation was voluntary, and anonymity and confidentiality were assured.

Data Availability Statement

The survey data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.

Conflicts of Interest

The author declares no conflicts of interest.

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Figure 1. Conceptual model of the hypothesized relationships among the variables.
Figure 1. Conceptual model of the hypothesized relationships among the variables.
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Figure 2. Location of the study area (Konya Urban Forest, Meram District, Konya, Türkiye).
Figure 2. Location of the study area (Konya Urban Forest, Meram District, Konya, Türkiye).
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Figure 3. Structural Equation Model: Relationships Among QE, RE, and SWB.
Figure 3. Structural Equation Model: Relationships Among QE, RE, and SWB.
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Table 1. Demographic characteristics of the sample population.
Table 1. Demographic characteristics of the sample population.
Dimensions FrequencyFrequency Percentage
GenderFemale21455.6
Male17144.4
AgeAdults (18–64)36394.3
Older Adults (65+)225.7
Marital StatusMarried20553.3
Single18046.8
Working StatusPublic institution7318.96
Private sector13535.07
Unemployed17745.97
IncomeLow 215.45
Medium26969.87
High9524.67
Table 2. Construct and supporting literature.
Table 2. Construct and supporting literature.
ConstructLiterature
QE[93,94]
RE[19,85,95,96]
SWB[25,71,97]
LS[25,71]
AB[97]
MH[27,98,99]
Table 3. Measurement Items by Construct.
Table 3. Measurement Items by Construct.
DimensionItem CodeMeasurement Item
LSLS1I feel more satisfied with my life in general after spending time in the urban forest.
LS2This experience has contributed positively to my life.
LS3Spending time in the urban forest has increased my daily life satisfaction.
LS4I now feel more positive about my current living conditions.
LS5Being in the urban forest has positively influenced my overall life satisfaction.
PAPA1After spending time in the urban forest, I felt energetic.
PA2This experience relaxed me and lifted my mood.
PA3After the forest visit, I felt peaceful and balanced.
NANA1I still feel stressed after the urban forest experience.
NA2My mental fatigue persisted after leaving the forest.
NA3I felt unrested after the urban forest experience.
MHMH1The time I spent in the urban forest made me happy.
MH2This experience added meaning to my life.
MH3Spending time in the forest nourished me spiritually.
MH4Thanks to this experience, I feel more peaceful and balanced.
MH5The urban forest visit made a positive difference in my life.
QEQE1The urban forest experience was meaningful for me.
QE2The time I spent in the forest fully captured my attention.
QE3Being in the natural environment was emotionally satisfying.
QE4I found this experience aesthetically enjoyable.
QE5The forest experience exceeded my expectations.
QE6I didn’t notice how time passed during the visit.
RERE1The time I spent in the urban forest cleared my mind.
RE2This experience helped reduce my stress.
RE3I felt mentally refreshed.
RE4Being in this environment was spiritually calming.
RE5The urban forest experience gave me a sense of inner balance.
RE6I felt distanced from the chaos of daily life.
Table 4. Item-Level Descriptive Statistics.
Table 4. Item-Level Descriptive Statistics.
ItemMeanSDSkewnessKurtosis
Life Satisfaction 4.060.95−0.69−0.29
Positive Affect 3.650.96−0.610.17
Negative Affect 2.891.260.26−1.08
Meaning and Happiness 3.510.97−0.510.07
Quality Experience 4.040.9−1.131.63
Restorative Experience 4.030.91−1.091.61
Table 5. Correlation Matrix of Constructs.
Table 5. Correlation Matrix of Constructs.
VariableLSPANAMHQERE
LS1
PA0.651
NA−0.60−0.581
MH0.700.68−0.621
QE0.660.64−0.550.711
RE0.680.66−0.530.720.821
Table 6. Model Summary of Hierarchical Regression.
Table 6. Model Summary of Hierarchical Regression.
ModelR2ΔR2Fp
Model 10.156-23.497<0.001
Model 20.1950.03922.984<0.001
Note. R2 = Coefficient of determination. ΔR2 = Change in explained variance. p-values are based on the F change statistics.
Table 7. Hierarchical Regression Coefficients Predicting Subjective Well-Being.
Table 7. Hierarchical Regression Coefficients Predicting Subjective Well-Being.
PredictorB (M1)SE B (M1)β (M1)t
(M1)		p (M1)B (M2)SE B (M2)β (M2)T
(M2)		p (M2)
Income0.510.2060.1332.4780.0140.3660.2040.0961.7930.074
Profession−0.0360.059−0.033−0.6140.54−0.0750.058−0.068−1.2910.198
Visit Frequency0.5330.0730.3457.2640.00.5160.0720.3347.1720.0
Quality Experience—————0.5510.1290.2014.2720.0
Note. SWB = Subjective Well-Being. Unstandardized coefficients (B), standard errors (SE B), standardized coefficients (β), t values, and p values are reported.
Table 8. Standardized Factor Loadings from Confirmatory Factor Analysis.
Table 8. Standardized Factor Loadings from Confirmatory Factor Analysis.
Latent VariableItemβ (Standardized Loading)
LSM10.832
LSM20.770
LSM30.843
LSM40.766
LSM50.841
PAM60.752
PAM70.804
PAM80.786
NAM290.864
NAM300.841
NAM310.809
MHM120.814
MHM130.795
MHM140.803
MHM150.845
MHM160.832
QEM170.899
QEM180.754
QEM190.807
QEM200.769
QEM210.795
QEM220.901
REM230.904
REM240.726
REM250.900
REM260.704
REM270.912
REM280.775
SWBLS0.912
SWBPA0.873
SWBN0.842
SWBMH0.950
Table 9. Reliability and Convergent Validity Indices for Latent Constructs.
Table 9. Reliability and Convergent Validity Indices for Latent Constructs.
Construct (F)α (Cronbach’s Alpha)ω1ω2ω3AVE
SWLS0.8990.8990.8990.8970.640
PA0.8330.8350.8350.8360.628
NA0.8840.8910.8910.8910.733
MH0.9200.9200.9200.9190.698
QE0.9170.9180.9180.9110.653
RE0.9030.9040.9040.8940.617
Table 10. Model Fit Indices.
Table 10. Model Fit Indices.
Fit IndexValue
SRMR0.040
RMSEA0.060 (95% CI [0.055, 0.066], p < 0.001)
CFI0.948
TLI0.943
NFI0.915
IFI0.948
RFI0.906
PNFI0.830
Table 11. Standardized Path Coefficients from Structural Equation Modeling.
Table 11. Standardized Path Coefficients from Structural Equation Modeling.
PathStandardized Coefficient (β)p-Value
QE → RE0.54<0.001
QE → SWB0.36<0.001
RE → SWB0.42<0.001
MH → SWB0.51<0.001
LS → SWB0.47<0.001
PA → SWB0.44<0.001
NA → SWB−0.39<0.001
Table 12. R2 Values for Observed and Latent Variables.
Table 12. R2 Values for Observed and Latent Variables.
VariableR2
SWB0.701
RE0.913
MH0.903
LS0.832
PA0.762
NA0.709
Table 13. Hypothesis Testing Results Based on the Structural Equation Model.
Table 13. Hypothesis Testing Results Based on the Structural Equation Model.
HypothesisPathβ (Standardized)z-Value/p-ValueResult
H1QE → RE0.893z = 36.64
p < 0.001		Supported
H2QE → SWB0.596z = 6.63
p < 0.001		Supported
H3RE → SWB0.200z = 2.24
p = 0.025		Supported
H4QE → RE → SWB (Indirect)0.178z = 2.20
p = 0.028		Supported
Note. All standardized path coefficients (β) are statistically significant at p < 0.05. Indirect effect significance was tested using bootstrapping with 2000 samples and bias-corrected 95% confidence intervals (CI).
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Arısoy, N. The Effects of Experiential Quality on Restorative Experience and Subjective Well-Being: An Assessment of Urban Forest Visitors. Sustainability 2025, 17, 8163. https://doi.org/10.3390/su17188163

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Arısoy N. The Effects of Experiential Quality on Restorative Experience and Subjective Well-Being: An Assessment of Urban Forest Visitors. Sustainability. 2025; 17(18):8163. https://doi.org/10.3390/su17188163

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Arısoy, Nurgül. 2025. "The Effects of Experiential Quality on Restorative Experience and Subjective Well-Being: An Assessment of Urban Forest Visitors" Sustainability 17, no. 18: 8163. https://doi.org/10.3390/su17188163

APA Style

Arısoy, N. (2025). The Effects of Experiential Quality on Restorative Experience and Subjective Well-Being: An Assessment of Urban Forest Visitors. Sustainability, 17(18), 8163. https://doi.org/10.3390/su17188163

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