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Volume 38, Issue 3 (2023)
GeoRes 2023, 38(3): 299-310 |
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Received: 2023/06/7 | Accepted: 2023/08/13 | Published: 2023/08/28
Received: 2023/06/7 | Accepted: 2023/08/13 | Published: 2023/08/28
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Hoveizavi H, Saberi H, Azani M, Ahmadi F. Identifying the Drivers Affecting third Places and the Quality of the urban Environment in Ahvaz city. GeoRes 2023; 38 (3) :299-310
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1- Tourism Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2- ourism Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3- Urban Planning Department, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2- ourism Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3- Urban Planning Department, Najafabad Branch, Islamic Azad University, Najafabad, Iran
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Introduction
The acceleration of urbanization and the concentration of industries in cities over recent decades have led to profound transformations [Fang et al., 2021], to the extent that current urban issues and challenges have, in some respects, outpaced the capacity of urban policymakers and planners [Han et al., 2021]. This has rendered attention to the physical quality of urban environments given its impact on public health, citizens’ mental perceptions, social welfare, environmental benefits, and more a critical concern in urban management and planning for improving quality of life in cities [Mouratidis, 2021; Floková et al., 2023]. Since the quality of the urban environment is the result of the interaction between people, activities, and the built form of cities [Rahman et al., 2014], and given the close relationship between the built environment and the social dimension of urban spaces [Sadeghi & Jangjoo, 2022], it can be argued that enhancing the quality of the urban environment substantially contributes to increasing citizens’ satisfaction with urban life, strengthening civic engagement, fostering reliable participatory spaces, instilling a sense of ownership among residents, and reducing social anomalies, among other outcomes [Swayzi et al., 2017].
The notion of “third places,” or desirable spaces for socialization outside the domains of work and home, was introduced in the 1990s by the American sociologist Oldenburg [Davoudi & Modiri, 2015; Williams & Hipp, 2019; Oldenburg, 1989]. As a particular category of urban public space, third places, by encompassing a mixture of public life among diverse citizens, can play an active role in improving urban life [Ding et al., 2023]. They represent a vital component of urban public domains, which, from the city scale to the neighborhood scale, examine social structures and elevate the concept of social interaction from superficial relations to stronger social ties [Behzadpour et al., 2018]. Moreover, they can significantly influence patterns of cohesion and participation in social institutions and processes [Nasehi et al., 2022].
A wide range of studies, both domestic and international, have examined different types of third places such as cafés and stadiums, public spaces, parks and shopping centers, streets and sidewalks, among others. These include works by Bernhardt & Stoll (2010), Kuksa & Childs (2014), Lukito & Xenia (2017), Goosen & Cilliers (2018), Raytcheva et al. (2023), Zamiri (2014), Omidvar & Razmjoe (2018), and Nasehi et al. (2022), among others. The commonality of perspectives and diversity of methods and models in these studies are of particular significance. Distinct from these, the present research employs foresight methodologies to examine the driving forces influencing third places and environmental quality.
Specifically, factors such as the expansion of informal settlements, ethnic and class disparities, high crime rates, lack of collective participation, general dissatisfaction with living conditions, degraded social environments and quality of life, increased urban population density, environmental challenges, special climatic conditions (e.g., dust storms), compact urban fabrics, the mismatch of city forms and images with aesthetic criteria, and weakened sense of identity have contributed to the deterioration of urban environmental quality in Ahvaz. Hence, attention to the role of third places in enhancing urban environmental quality is both unavoidable and crucial. Since these places provide venues for informal life in contrast to formal life, they play a fundamental role in public social life in cities.
In the metropolis of Ahvaz, numerous focal points offering recreational and leisure activities—such as commercial and entertainment centers in the Kianpars district (Tashrifat, Imam Reza Bazaar, Borj, Iran Neguin, Maro Bazaar, the traditional teahouse on 7th Street of Kianpars, and the eastern and western Kianpars riverfront boulevards, as well as the Reef tourism island), Firouzeh City Center in Golestan, Mahziar City Center in Zeytoon Karmandi, Hirad City Center in Padadshahr, the Hyperstar complex in Melli Rah, the revolving restaurant in Pasdaran Boulevard (District 3), Karun Passage in the Naderi pedestrian zone, the Central Library in Kianpars, various cafés and traditional teahouses on Anousheh Street in Lashkarabad, as well as numerous cafés and parks across Ahvaz are recognized as third places. These can significantly contribute to enhancing urban environmental quality, while reciprocally supporting the sustainability of Ahvaz across its social, environmental, economic, and other dimensions.
Accordingly, investigating third places and strengthening their capacities are regarded as essential strategies for improving environmental quality. The primary objective of this study is to identify the driving forces affecting third places and the quality of the urban environment.
Methodology
This study employed a quantitative approach using a survey method, supported by library–documentary research and the targeted Delphi technique to identify conceptual indicators. The research was conducted in Ahvaz in 2023. According to the official census in 2016 , the population of Ahvaz was 1,302,591. The city, with an area of approximately 18,806.44 hectares, is situated in the Khuzestan plain on flat terrain without significant topographical variation. The Karun River, dividing the city into eastern and western parts, enters from the north and exits in the south. Geographically, the city lies at a crossroads beside the river. Serving as a regional metropolis within the Khuzestan plain, Ahvaz is located at a junction connecting waterways, highways, and railways, with the convergence of routes at the Ahvaz bridge granting the city a unique crossroads position [Ahwaz Municipality, 2023].
Data collection was carried out through a combination of documentary sources and field studies. The primary basis of analysis consisted of expert opinions. First, a systematic and structural review of theoretical literature on third places and urban environmental quality was conducted. Then, based on theoretical foundations and using a two-stage Delphi method, 55 influential indicators and parameters were extracted, categorized into six components for third places (morphological, functional, perceptual, social, visual, and temporal) and four components for environmental quality (activities and land use, image and tranquility, connectivity and accessibility, and social relations).
The MICMAC software was applied to analyze expert opinions within a foresight framework. The panel of experts consisted of 30 specialists selected via the snowball sampling method, including professionals from the Municipality, Housing and Urban Development, the Governor’s Office, related organizations, and academic experts in urban management, urban planning, and related fields.
The general characteristics of the sample population were as follows:
In the second stage, factors were assessed using the cross-impact analysis technique (structural analysis method) within MICMAC. Finally, the key driving forces for third places and environmental quality were identified based on their direct influence and dependence scores.
For the second part of the analysis, structural equation modeling (SEM) was conducted using Smart PLS to examine causal pathways.
Findings
Cross-Impact Analysis through Futures Studies
By placing the factors into a 55×55 matrix, the effect of each factor on others was determined by assigning weights to the factors (from zero to three). All factors involved in third places and environmental quality are considered as a system with intertwined elements, and the relationships among these factors are assessed to identify the most influential ones. The number of iterations for calculating cross-impacts in the matrix was set at two, as recommended by the MICMAC software. The degree of matrix filling was 96.91%, which indicates the dispersion of the parameters affecting third places and environmental quality, reflecting the significant interdependence among the selected factors. Overall, out of 3,039 assessable relationships in the matrix, 97 relationships received a score of zero (no influence), 430 relationships a score of one (low influence), 1,645 relationships a score of two (moderately strong influence), 853 relationships a score of three (very strong key factors), and 111 relationships were assigned a potential relationship (P). Furthermore, after two iterations, the optimization and consistency of the matrix reached 100%, confirming the high validity of the questionnaire and its responses.
Regarding the structural analysis method using MICMAC, two types of dispersions are recognized: stable systems and unstable systems. In stable systems, the dispersion of parameters resembles the English letter “L,” meaning that some parameters exert strong influence while others exhibit high dependence. In contrast, in unstable systems, if the diagram is oriented along the diagonal axis of the plane, the system is considered unstable. Three categories of parameters are identifiable in unstable systems: (a) parameters with very high influence on the system (key factors), (b) independent parameters, and (c) outcome parameters (result parameters) [Godet et al., 2003].
Given the higher concentration of parameters near the diagonal axis in the form of an “L,” it can be concluded that the scatter plot of parameters or driving forces influencing the environmental quality of Ahvaz reflects an “unstable system state.” Other parameters displayed relatively similar conditions, differing only in intensity.
The distribution and dispersion of factors affecting third places and environmental quality in Ahvaz indicate severe system instability. Five types of parameters within the unstable system (influential/key, dual-role, regulatory, dependent, and independent factors) can be identified and distinguished.
The distribution of factors showd that in the northwest of the diagram no parameter with high influence was present, and in the southeast no parameter with high dependence was located. The majority of parameters concentrated in the central region around the centroid of the diagram. Parameters such as night use, sociability, and identity were identified as regulatory factors, which could be promoted to influential or dual-role parameters depending on local development policies.
Risk and target parameters were located around the diagonal line in the northeast quadrant, indicating their potential to become key actors (main factors) of the system. In the southwest quadrant (independent parameters), factors such as sense of belonging, environmental information, and cultural orientation were positioned. Only Var29 (Visual sense of place) was found to be completely independent of the system. These parameters had the potential to be elevated to influential, determinant, or target/risk parameters.
By analyzing the MICMAC scatter plot and the positions of parameters in the influence–dependence plane, 11 key factors were identified: social comfort, perceptual place identity, social accessibility, property value, visual legibility, perceptual sense of place, social safety and security, retailing, health level, perceptual comfort, and sociability. These factors consistently appeared in both direct and indirect analyses.
Model Fit through Structural Equation Modeling
The tested conceptual model in the standardized mode (PLS algorithm) and path coefficients were calculated. The values displayed on the paths between factors, indicators, and items represent standardized betas in regression or correlations between two indicators and items, evaluated to determine the direct effect of one parameter on another. Values on paths between items and indicators in reflective models represent factor loadings, while the values inside circles indicate the coefficient of determination (R²) of the main indicator, ranging from zero to one.
The calculated R² values for third places (0.352) and environmental quality (0.302) were at desirable levels. Specifically, third places explained 35.2% of the variance in environmental quality, indicating a strong predictive capacity.
The tested conceptual model in the standardized state (PLS algorithm) and path coefficients are presented. Values on the paths indicate standardized betas or correlations between indicators and items, while paths in reflective models denote factor loadings. The coefficients of determination (R²) are shown inside circles, always between zero and one.
A critical issue is whether these coefficients significantly differ from zero. To test the significance of coefficients (regression and determination), significance levels and confidence intervals are required, obtained through bootstrapping.
Bootstrapping of R² and Regression Coefficients
Since variance-based modeling is non-parametric, bootstrapping is employed in SmartPLS to compute error probabilities, test coefficient significance, and estimate confidence intervals. The purpose is to verify whether regression and determination coefficients in the population resemble those observed in the sample, are greater than zero, and, if so, within what range they fall.
The effect of third places on environmental quality was tested using significance levels, showing that the regression and determination coefficients are significantly greater than zero at the 95% confidence level (p=0.044). Thus, the difference of 0.847 from zero is meaningful, confirming that third places significantly affect environmental quality in the sample, and a similar strong positive effect is expected in the population.
For some indicators (e.g., morphology, social relations, accessibility), the effect was not significant, but overall, six dimensions, morphology, function, perception, social, visual, and temporal, significantly influenced third places in Ahvaz. Regarding environmental quality, only two dimensions (activity and use, and mental image and tranquility) had significant effects.
Accordingly, at the 95% confidence level, except for morphology, social relations, and accessibility, the mentioned dimensions showed significant differences with the criterion parameter (environmental quality).
Based on the significance levels, the main research relationship (Third places→ Environmental quality) was validated, as the T-values exceeded the threshold of 1.96, confirming significance. Among third-place dimensions, functionality showed the strongest effect, followed by perception, morphology, social, visual, and temporal dimensions. For environmental quality, activity and land use had the greatest effect (0.422), followed by mental image and tranquility. Conversely, accessibility and social relations had negligible impact. The T-value for third places (2.015) confirmed its strong effect on environmental quality.
Discussion
The purpose of this research was to identify the drivers influencing third places and the quality of the urban environment. By reviewing and compiling indicators of urban environmental quality and third places, and by employing futures studies models such as cross-impact analysis, this study identified the drivers and key factors affecting third places and the environmental quality of Ahvaz city.
The term third place was first introduced by Oldenburg, an American urban sociologist, in his book The Great Good Place in the 1990s [Goosen & Cilliers, 2018; Oldenburg, 1999]. Oldenburg is considered the main theorist of third places, while other scholars such as Carmona [2001], Banerjee [2001], Soja [1998], Cheang [2002], and Lefebvre [1974] have referred to this concept under other terminologies, such as thirdspace, trialectics, or heterotopia.
Lukito and Xenia argue, in contrast to the present study, that the development of information technology has altered certain features of third places [Lukito & Xenia, 2017]. Bernhardt and Stoll, using a descriptive and content analysis method, identified the features of third places. Similar to this study, they outlined the criteria of third places and highlighted the ability to bring social groups together as the most important success factor [Bernhardt & Stoll, 2010]. Kuksa and Childs, by focusing on virtual spaces, studied educational third places and the communications arising from them in a way that differs from the present research [Kuksa & Childs, 2014]. Raytcheva et al. examined the impact of COVID-19 on interactions and collective capacities in third places in France, with a topic and methodology different from this research [Raytcheva et al., 2023]. Zamiri has conducted a review study defining and describing the characteristics of third places, which, methodologically, is entirely different from the current work [Zamiri, 2014]. Omidvar and Razmjooi, through a descriptive approach and by relying on documentary and field methods, investigated the role of Iranian historical baths as third places in shaping social interactions, employing different methods and models [Omidvar & Razmjooi, 2018]. Similarly, Nasehi et al. explored the reciprocal relationship between third places and social sustainability, differing from the present research in terms of dependent parameters and applied models [Nasehi et al., 2022].
All these studies have emphasized the role and significance of third places. Themes such as social sustainability, spatial perceptions, urban flexibility and resilience, information technology, and social interactions have been addressed by various researchers who, by centering their work on third places, confirmed their importance in influencing different dimensions of cities and citizens’ lives across diverse geographical contexts. As Goosen and Cilliers [2018] state, third places enhance residents’ satisfaction and contribute to the improvement of urban environmental quality.
The detailed results of the present study are aligned with the findings of other reviewed research. Moreover, regarding the dimension of environmental quality, the synthesis of prior studies highlights the importance of focusing on third places and their mutual influence on environmental quality. Although research explicitly linking third places to environmental quality has been limited, the available findings indicate a significant reciprocal relationship. Despite the existing body of knowledge across disciplines on the influence of third places, no study has so far addressed the influencing factors in this domain through a futures studies approach.
This study introduces a new perspective by presenting third places as factors influencing the environmental quality of Ahvaz city, employing futures studies methods such as cross-impact analysis, structural equations, and PLS modeling. After identifying the components of third places and environmental quality in Ahvaz using questionnaire analysis, cross-impact analysis, and structural equations, the priorities at various levels of environmental quality in the city were determined. Achieving the comprehensive objectives for the future quality of Ahvaz’s environment depends on the identified factors, taking into account their degree of priority and influence. In this way, it becomes possible to assess the future status of Ahvaz’s environmental quality and to design strategies for maintaining favorable conditions or transforming unfavorable ones into desirable states across multiple dimensions.
Adequate attention by planners, policymakers, and decision-makers to the influential factors shaping the future outlook of third places and environmental quality in this city, along with the prioritization of these factors, will create the necessary foundations for the realization, development, and desirable evolution of third places and, consequently, urban environmental quality. This would ensure balance and long-term sustainability across all dimensions and levels of environmental quality.
Accordingly, considering the 11 key factors (social comfort, perceptual place identity, social accessibility, property value, visual legibility, perceptual sense of place, social safety and security, retailing, health level, perceptual comfort, and sociability) in enhancing the environmental quality of Ahvaz, the following recommendations can be proposed:
Conclusion
Third places and their components have a strong and significant impact on the environmental quality of Ahvaz city. Given the interweaving of the components and indicators of the third place concept, its role in various aspects of environmental quality and the city’s future is undeniable. The quality of Ahvaz’s urban spaces is closely linked to the status of its third places.
Acknowledgments: None reported by the authors.
Ethical Permission: None reported by the authors.
Conflict of interest: None reported by the authors.
Author contributions: Hoveizavi H (first author): Introduction Writer/Methodologist/Main Researcher/Statistical Analyst/Discussion Writer (40%); Saberi H (second author): Introduction Writer/Methodologist/Main Researcher/Statistical Analyst (40%); Azani M (third author): Assistant Researcher (10%); Ahmadi F (fourth author): Assistant Researcher (10%)
Funding: None reported by the authors.
The acceleration of urbanization and the concentration of industries in cities over recent decades have led to profound transformations [Fang et al., 2021], to the extent that current urban issues and challenges have, in some respects, outpaced the capacity of urban policymakers and planners [Han et al., 2021]. This has rendered attention to the physical quality of urban environments given its impact on public health, citizens’ mental perceptions, social welfare, environmental benefits, and more a critical concern in urban management and planning for improving quality of life in cities [Mouratidis, 2021; Floková et al., 2023]. Since the quality of the urban environment is the result of the interaction between people, activities, and the built form of cities [Rahman et al., 2014], and given the close relationship between the built environment and the social dimension of urban spaces [Sadeghi & Jangjoo, 2022], it can be argued that enhancing the quality of the urban environment substantially contributes to increasing citizens’ satisfaction with urban life, strengthening civic engagement, fostering reliable participatory spaces, instilling a sense of ownership among residents, and reducing social anomalies, among other outcomes [Swayzi et al., 2017].
The notion of “third places,” or desirable spaces for socialization outside the domains of work and home, was introduced in the 1990s by the American sociologist Oldenburg [Davoudi & Modiri, 2015; Williams & Hipp, 2019; Oldenburg, 1989]. As a particular category of urban public space, third places, by encompassing a mixture of public life among diverse citizens, can play an active role in improving urban life [Ding et al., 2023]. They represent a vital component of urban public domains, which, from the city scale to the neighborhood scale, examine social structures and elevate the concept of social interaction from superficial relations to stronger social ties [Behzadpour et al., 2018]. Moreover, they can significantly influence patterns of cohesion and participation in social institutions and processes [Nasehi et al., 2022].
A wide range of studies, both domestic and international, have examined different types of third places such as cafés and stadiums, public spaces, parks and shopping centers, streets and sidewalks, among others. These include works by Bernhardt & Stoll (2010), Kuksa & Childs (2014), Lukito & Xenia (2017), Goosen & Cilliers (2018), Raytcheva et al. (2023), Zamiri (2014), Omidvar & Razmjoe (2018), and Nasehi et al. (2022), among others. The commonality of perspectives and diversity of methods and models in these studies are of particular significance. Distinct from these, the present research employs foresight methodologies to examine the driving forces influencing third places and environmental quality.
Specifically, factors such as the expansion of informal settlements, ethnic and class disparities, high crime rates, lack of collective participation, general dissatisfaction with living conditions, degraded social environments and quality of life, increased urban population density, environmental challenges, special climatic conditions (e.g., dust storms), compact urban fabrics, the mismatch of city forms and images with aesthetic criteria, and weakened sense of identity have contributed to the deterioration of urban environmental quality in Ahvaz. Hence, attention to the role of third places in enhancing urban environmental quality is both unavoidable and crucial. Since these places provide venues for informal life in contrast to formal life, they play a fundamental role in public social life in cities.
In the metropolis of Ahvaz, numerous focal points offering recreational and leisure activities—such as commercial and entertainment centers in the Kianpars district (Tashrifat, Imam Reza Bazaar, Borj, Iran Neguin, Maro Bazaar, the traditional teahouse on 7th Street of Kianpars, and the eastern and western Kianpars riverfront boulevards, as well as the Reef tourism island), Firouzeh City Center in Golestan, Mahziar City Center in Zeytoon Karmandi, Hirad City Center in Padadshahr, the Hyperstar complex in Melli Rah, the revolving restaurant in Pasdaran Boulevard (District 3), Karun Passage in the Naderi pedestrian zone, the Central Library in Kianpars, various cafés and traditional teahouses on Anousheh Street in Lashkarabad, as well as numerous cafés and parks across Ahvaz are recognized as third places. These can significantly contribute to enhancing urban environmental quality, while reciprocally supporting the sustainability of Ahvaz across its social, environmental, economic, and other dimensions.
Accordingly, investigating third places and strengthening their capacities are regarded as essential strategies for improving environmental quality. The primary objective of this study is to identify the driving forces affecting third places and the quality of the urban environment.
Methodology
This study employed a quantitative approach using a survey method, supported by library–documentary research and the targeted Delphi technique to identify conceptual indicators. The research was conducted in Ahvaz in 2023. According to the official census in 2016 , the population of Ahvaz was 1,302,591. The city, with an area of approximately 18,806.44 hectares, is situated in the Khuzestan plain on flat terrain without significant topographical variation. The Karun River, dividing the city into eastern and western parts, enters from the north and exits in the south. Geographically, the city lies at a crossroads beside the river. Serving as a regional metropolis within the Khuzestan plain, Ahvaz is located at a junction connecting waterways, highways, and railways, with the convergence of routes at the Ahvaz bridge granting the city a unique crossroads position [Ahwaz Municipality, 2023].
Data collection was carried out through a combination of documentary sources and field studies. The primary basis of analysis consisted of expert opinions. First, a systematic and structural review of theoretical literature on third places and urban environmental quality was conducted. Then, based on theoretical foundations and using a two-stage Delphi method, 55 influential indicators and parameters were extracted, categorized into six components for third places (morphological, functional, perceptual, social, visual, and temporal) and four components for environmental quality (activities and land use, image and tranquility, connectivity and accessibility, and social relations).
The MICMAC software was applied to analyze expert opinions within a foresight framework. The panel of experts consisted of 30 specialists selected via the snowball sampling method, including professionals from the Municipality, Housing and Urban Development, the Governor’s Office, related organizations, and academic experts in urban management, urban planning, and related fields.
The general characteristics of the sample population were as follows:
- Field of study: 50% urban planning, 18.8% urban design–architecture and civil engineering, 21.9% geography, 9.3% urban management.
- Education level: 50% Master’s degree, 50% PhD.
- Organization of employment: 21.9% universities, 62.5% municipality, 9.4% housing and urban development, 6.2% Khuzestan Governor’s Office.
- Organizational position: 31.2% managers/mayors, 34.4% deputies, 18.8% experts and staff, 15.6% faculty members.
- Work experience: 9.4% less than 10 years, 59.4% between 10–20 years, 31.2% more than 20 years.
In the second stage, factors were assessed using the cross-impact analysis technique (structural analysis method) within MICMAC. Finally, the key driving forces for third places and environmental quality were identified based on their direct influence and dependence scores.
For the second part of the analysis, structural equation modeling (SEM) was conducted using Smart PLS to examine causal pathways.
Findings
Cross-Impact Analysis through Futures Studies
By placing the factors into a 55×55 matrix, the effect of each factor on others was determined by assigning weights to the factors (from zero to three). All factors involved in third places and environmental quality are considered as a system with intertwined elements, and the relationships among these factors are assessed to identify the most influential ones. The number of iterations for calculating cross-impacts in the matrix was set at two, as recommended by the MICMAC software. The degree of matrix filling was 96.91%, which indicates the dispersion of the parameters affecting third places and environmental quality, reflecting the significant interdependence among the selected factors. Overall, out of 3,039 assessable relationships in the matrix, 97 relationships received a score of zero (no influence), 430 relationships a score of one (low influence), 1,645 relationships a score of two (moderately strong influence), 853 relationships a score of three (very strong key factors), and 111 relationships were assigned a potential relationship (P). Furthermore, after two iterations, the optimization and consistency of the matrix reached 100%, confirming the high validity of the questionnaire and its responses.
Regarding the structural analysis method using MICMAC, two types of dispersions are recognized: stable systems and unstable systems. In stable systems, the dispersion of parameters resembles the English letter “L,” meaning that some parameters exert strong influence while others exhibit high dependence. In contrast, in unstable systems, if the diagram is oriented along the diagonal axis of the plane, the system is considered unstable. Three categories of parameters are identifiable in unstable systems: (a) parameters with very high influence on the system (key factors), (b) independent parameters, and (c) outcome parameters (result parameters) [Godet et al., 2003].
Given the higher concentration of parameters near the diagonal axis in the form of an “L,” it can be concluded that the scatter plot of parameters or driving forces influencing the environmental quality of Ahvaz reflects an “unstable system state.” Other parameters displayed relatively similar conditions, differing only in intensity.
The distribution and dispersion of factors affecting third places and environmental quality in Ahvaz indicate severe system instability. Five types of parameters within the unstable system (influential/key, dual-role, regulatory, dependent, and independent factors) can be identified and distinguished.
The distribution of factors showd that in the northwest of the diagram no parameter with high influence was present, and in the southeast no parameter with high dependence was located. The majority of parameters concentrated in the central region around the centroid of the diagram. Parameters such as night use, sociability, and identity were identified as regulatory factors, which could be promoted to influential or dual-role parameters depending on local development policies.
Risk and target parameters were located around the diagonal line in the northeast quadrant, indicating their potential to become key actors (main factors) of the system. In the southwest quadrant (independent parameters), factors such as sense of belonging, environmental information, and cultural orientation were positioned. Only Var29 (Visual sense of place) was found to be completely independent of the system. These parameters had the potential to be elevated to influential, determinant, or target/risk parameters.
By analyzing the MICMAC scatter plot and the positions of parameters in the influence–dependence plane, 11 key factors were identified: social comfort, perceptual place identity, social accessibility, property value, visual legibility, perceptual sense of place, social safety and security, retailing, health level, perceptual comfort, and sociability. These factors consistently appeared in both direct and indirect analyses.
Model Fit through Structural Equation Modeling
The tested conceptual model in the standardized mode (PLS algorithm) and path coefficients were calculated. The values displayed on the paths between factors, indicators, and items represent standardized betas in regression or correlations between two indicators and items, evaluated to determine the direct effect of one parameter on another. Values on paths between items and indicators in reflective models represent factor loadings, while the values inside circles indicate the coefficient of determination (R²) of the main indicator, ranging from zero to one.
The calculated R² values for third places (0.352) and environmental quality (0.302) were at desirable levels. Specifically, third places explained 35.2% of the variance in environmental quality, indicating a strong predictive capacity.
The tested conceptual model in the standardized state (PLS algorithm) and path coefficients are presented. Values on the paths indicate standardized betas or correlations between indicators and items, while paths in reflective models denote factor loadings. The coefficients of determination (R²) are shown inside circles, always between zero and one.
A critical issue is whether these coefficients significantly differ from zero. To test the significance of coefficients (regression and determination), significance levels and confidence intervals are required, obtained through bootstrapping.
Bootstrapping of R² and Regression Coefficients
Since variance-based modeling is non-parametric, bootstrapping is employed in SmartPLS to compute error probabilities, test coefficient significance, and estimate confidence intervals. The purpose is to verify whether regression and determination coefficients in the population resemble those observed in the sample, are greater than zero, and, if so, within what range they fall.
The effect of third places on environmental quality was tested using significance levels, showing that the regression and determination coefficients are significantly greater than zero at the 95% confidence level (p=0.044). Thus, the difference of 0.847 from zero is meaningful, confirming that third places significantly affect environmental quality in the sample, and a similar strong positive effect is expected in the population.
For some indicators (e.g., morphology, social relations, accessibility), the effect was not significant, but overall, six dimensions, morphology, function, perception, social, visual, and temporal, significantly influenced third places in Ahvaz. Regarding environmental quality, only two dimensions (activity and use, and mental image and tranquility) had significant effects.
Accordingly, at the 95% confidence level, except for morphology, social relations, and accessibility, the mentioned dimensions showed significant differences with the criterion parameter (environmental quality).
Based on the significance levels, the main research relationship (Third places→ Environmental quality) was validated, as the T-values exceeded the threshold of 1.96, confirming significance. Among third-place dimensions, functionality showed the strongest effect, followed by perception, morphology, social, visual, and temporal dimensions. For environmental quality, activity and land use had the greatest effect (0.422), followed by mental image and tranquility. Conversely, accessibility and social relations had negligible impact. The T-value for third places (2.015) confirmed its strong effect on environmental quality.
Discussion
The purpose of this research was to identify the drivers influencing third places and the quality of the urban environment. By reviewing and compiling indicators of urban environmental quality and third places, and by employing futures studies models such as cross-impact analysis, this study identified the drivers and key factors affecting third places and the environmental quality of Ahvaz city.
The term third place was first introduced by Oldenburg, an American urban sociologist, in his book The Great Good Place in the 1990s [Goosen & Cilliers, 2018; Oldenburg, 1999]. Oldenburg is considered the main theorist of third places, while other scholars such as Carmona [2001], Banerjee [2001], Soja [1998], Cheang [2002], and Lefebvre [1974] have referred to this concept under other terminologies, such as thirdspace, trialectics, or heterotopia.
Lukito and Xenia argue, in contrast to the present study, that the development of information technology has altered certain features of third places [Lukito & Xenia, 2017]. Bernhardt and Stoll, using a descriptive and content analysis method, identified the features of third places. Similar to this study, they outlined the criteria of third places and highlighted the ability to bring social groups together as the most important success factor [Bernhardt & Stoll, 2010]. Kuksa and Childs, by focusing on virtual spaces, studied educational third places and the communications arising from them in a way that differs from the present research [Kuksa & Childs, 2014]. Raytcheva et al. examined the impact of COVID-19 on interactions and collective capacities in third places in France, with a topic and methodology different from this research [Raytcheva et al., 2023]. Zamiri has conducted a review study defining and describing the characteristics of third places, which, methodologically, is entirely different from the current work [Zamiri, 2014]. Omidvar and Razmjooi, through a descriptive approach and by relying on documentary and field methods, investigated the role of Iranian historical baths as third places in shaping social interactions, employing different methods and models [Omidvar & Razmjooi, 2018]. Similarly, Nasehi et al. explored the reciprocal relationship between third places and social sustainability, differing from the present research in terms of dependent parameters and applied models [Nasehi et al., 2022].
All these studies have emphasized the role and significance of third places. Themes such as social sustainability, spatial perceptions, urban flexibility and resilience, information technology, and social interactions have been addressed by various researchers who, by centering their work on third places, confirmed their importance in influencing different dimensions of cities and citizens’ lives across diverse geographical contexts. As Goosen and Cilliers [2018] state, third places enhance residents’ satisfaction and contribute to the improvement of urban environmental quality.
The detailed results of the present study are aligned with the findings of other reviewed research. Moreover, regarding the dimension of environmental quality, the synthesis of prior studies highlights the importance of focusing on third places and their mutual influence on environmental quality. Although research explicitly linking third places to environmental quality has been limited, the available findings indicate a significant reciprocal relationship. Despite the existing body of knowledge across disciplines on the influence of third places, no study has so far addressed the influencing factors in this domain through a futures studies approach.
This study introduces a new perspective by presenting third places as factors influencing the environmental quality of Ahvaz city, employing futures studies methods such as cross-impact analysis, structural equations, and PLS modeling. After identifying the components of third places and environmental quality in Ahvaz using questionnaire analysis, cross-impact analysis, and structural equations, the priorities at various levels of environmental quality in the city were determined. Achieving the comprehensive objectives for the future quality of Ahvaz’s environment depends on the identified factors, taking into account their degree of priority and influence. In this way, it becomes possible to assess the future status of Ahvaz’s environmental quality and to design strategies for maintaining favorable conditions or transforming unfavorable ones into desirable states across multiple dimensions.
Adequate attention by planners, policymakers, and decision-makers to the influential factors shaping the future outlook of third places and environmental quality in this city, along with the prioritization of these factors, will create the necessary foundations for the realization, development, and desirable evolution of third places and, consequently, urban environmental quality. This would ensure balance and long-term sustainability across all dimensions and levels of environmental quality.
Accordingly, considering the 11 key factors (social comfort, perceptual place identity, social accessibility, property value, visual legibility, perceptual sense of place, social safety and security, retailing, health level, perceptual comfort, and sociability) in enhancing the environmental quality of Ahvaz, the following recommendations can be proposed:
- Upgrading and creating valuable third places, such as existing commercial–recreational centers in the Kianpars district (Tashrifat, Imam Reza Bazaar, Borj, Iran Negin, Marv Bazaar, the traditional teahouse on 7th Kianpars Street, the eastern and western Kianpars coastal boulevards, and Reef tourist island), Firoozeh City Center in Golestan district, Mahziar City Center in Zeytoon Karmandi, Hyperstar Holding in Melli Rah district, the revolving restaurant on Pasdaran Boulevard (District 3), Karun Mall on Naderi pedestrian street, the Central Library in Kianpars, traditional cafés and teahouses on Anousheh Street in Lashkarabad, as well as coffee shops and urban parks throughout Ahvaz.
- Organizing identity-building activities, such as festivals, street music, and similar events in urban third places and local neighborhoods of Ahvaz, to strengthen the sense of place and belonging among citizens.
- Improving the objectivity of space through spatial design and the organization of visual aesthetics in major streets and busy areas, including Imam Street (Naderi), Kianpars main street (Chamran), Golestan main street (Farvardin), and Zeytoon Karmandi main street.
- Enhancing the subjectivity of space by introducing semantic and perceptual qualities within third places across Ahvaz.
Conclusion
Third places and their components have a strong and significant impact on the environmental quality of Ahvaz city. Given the interweaving of the components and indicators of the third place concept, its role in various aspects of environmental quality and the city’s future is undeniable. The quality of Ahvaz’s urban spaces is closely linked to the status of its third places.
Acknowledgments: None reported by the authors.
Ethical Permission: None reported by the authors.
Conflict of interest: None reported by the authors.
Author contributions: Hoveizavi H (first author): Introduction Writer/Methodologist/Main Researcher/Statistical Analyst/Discussion Writer (40%); Saberi H (second author): Introduction Writer/Methodologist/Main Researcher/Statistical Analyst (40%); Azani M (third author): Assistant Researcher (10%); Ahmadi F (fourth author): Assistant Researcher (10%)
Funding: None reported by the authors.
Keywords:
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