Statement of the Problem: In the last two decades, the concept of social capital has been emphasized through its relationship to fundamental social components including awareness, participation, trust, cohesion and social networking for sustainable development of communities, particularly rural communities. Therefore, social capital is essential for achieving sustainable rural development.
The purpose of this study was to evaluate the impact of social capital in sustainable development of rural settlements and its spatial analysis in Bojnourd County.
The research method in this study is descriptive-analytical and its purpose is fundamental. Documentary and field methods have been used to collect the data. The sample population is 22 villages with over 20 households in Bojnourd County. From a total of 4849 households in rural areas of the sample, using Cochran formula, the sample size of 298 households were selected by random sampling. To test the conceptual model of research and to investigate the impact of social capital on sustainable development of rural settlements, partial least squares technique and Smart PLS software and Geographically Weighted Regression model were used.
The coefficients of T among the main variables of the study were above 2.58, meaning a significant and indirect relationship; thus, social capital has a positive and significant effect on sustainable development of rural settlements. According to total coefficients, social network with coefficient of 0.575 has the highest and social cohesion with coefficient of 0.046 has the least effect on sustainable development of rural settlements. The results of spatial analysis using GWR model showed that social capital impact factor on sustainable rural development in Izman-e-Paieen and Miyanzou villages was highest and in total 36.6% of villages and 41% of rural population of Bojnourd County had an impact factor between 0.871 up to 0.885. Innovation: This study was the first attempt to discuss the spatial analysis of the impact of social capital on sustainable development through Geographically Weighted Regression.
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