Objective: The rural development planning system in Iran currently faces significant challenges, including limited evaluation practices confined to case reports by select institutions, inadequate attention to monitoring and assessment, insufficient consideration of the potential consequences of decisions and programs, lack of program revision and managerial improvement, absence of comprehensive and specific policy evaluation indicators, limited critical reflection on policy feasibility, and the lack of a structured framework for monitoring rural development implementation. Addressing these shortcomings requires a comprehensive excellence evaluation model for rural development policies. Accordingly, this study aims to propose an appropriate model for evaluating the excellence of rural development policies in Iran.
Method: This study adopts a qualitative, exploratory approach, employing a surveyarchival method for data collection and a cross-sectional design. The research is grounded in pragmatism and follows an inductive, fundamental approach. Data were collected through semi-structured interviews with experts familiar with rural development policies. Thematic analysis was used to analyze the data. The statistical population comprised experts, scholars, university faculty, managers, and executive professionals in rural development, from which 25 individuals were purposively selected.
Results: Through three stages of coding (open, axial, and selective), the study identified seven main categories as the core components of an excellence evaluation model for rural development policies in Iran: Context, Situation Analysis, Input, Activity, Output, Effect, Outcome. Each category is associated with specific indicators, totaling 50 indicators, providing a structured and comprehensive framework for evaluating policy excellence.
Conclusions: This study proposes a comprehensive model for evaluating the excellence of rural development policies in Iran. Excellence evaluation should be considered a continuous and iterative process, requiring regular refinement. The integration of quantitative and qualitative methods, the use of emerging technologies, and the incorporation of global knowledge and best practices can significantly enhance the effectiveness and applicability of the model.