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- معلومة اضافية
- Alternate Title:
Developing A Conceptual Model of Sustainable Social Housing with Emphasis on the Dimensions of Social Sustainability.
- نبذة مختصرة :
Aims This study aims to develop a conceptual model of sustainable social housing, with a particular focus on the dimensions of social sustainability. Methodology This applied research was carried out in 2025 and used a qualitative approach and employs a meta-synthesis method, due to the vast scope of existing literature on sustainable social housing. Sandlowski and Barroso’s seven-step model was utilized to integrate and extract key concepts from prior studies. A total of 140 scholarly articles and documents were gathered from domestic and international academic databases. After three rounds of screening, 29 documents were selected for the meta-synthesis process. Systematic coding techniques, including open, axial, and selective coding, were used to categorize the findings. To ensure the validity of the coding, experts in the field reviewed the framework, and the reliability was calculated using the percentage agreement method, yielding a Kappa index value of 0.875, indicating high validity. This process ultimately led to the creation of the conceptual model. Findings The research identified 10 sub-categories and 4 main concepts related to sustainable social housing and social sustainability. These were derived by synthesizing the selected scientific documents and linking the concepts, offering a structured overview of the subject matter. Conclusion The resulting conceptual model for sustainable social housing emphasizes four key dimensions including Livability, quality, community orientation, and executive-economic management. [ABSTRACT FROM AUTHOR]
- نبذة مختصرة :
Copyright of Geographical Researches is the property of Afarand Scholarly Publishing Institute and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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