Investigating the spatial foundations of rural entrepreneurship development using a hybrid method of MCDM, ANN and DTree algorithm

Growing evidence points to rural entrepreneurship as a critical tactic for promoting capacity building, sustainable development, and empowerment in rural communities. This study explored the spatial underpinnings of rural entrepreneurial development and was carried out in Iran. Using a descriptive-analytical methodology, the study surveys 20 experts and 100 active entrepreneurs in the Mochesh district of Kamyaran town. The research uses semi-structured interviews and sources to determine important characteristics for examining the spatial underpinnings. Data were collected using a researcher-made close-ended questionnaire. The validity and reliability of the research instrument were approved by experts opinions and Cronbach’s alpha coefficients, respectively. Analytical techniques used include decision tree data mining models, artificial neural networks (ANN), and Multi-Criteria Decision Making models. The results of the ANN model have shown that social and economic capital had been of the greatest importance in enhancing entrepreneur contexts, with a coefficient of 21 and 22%, respectively. Also, the findings of the data mining analysis show the high impact of environmental capital and structural-spatial components in the development of entrepreneurial activities in the study area. Finally, the entrepreneurs emphasized that one of the most important dimensions affecting the development of entrepreneurial activities and the prosperity of the rural economy is environmental capital and attracting the support of administrative institutions. Therefore, improvement in these components should be duly considered by managers and rural planners.