Designing a Pathology Pattern of Public Welfare Governance in Iran

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Abstract:

Introduction: Policymakers' inability to identify and analyze public issues to formulate necessary policies, the weaknesses of agents and executives in implementing the procedures, and the lack of optimal use of existing resources are among the most prominent disadvantages of public welfare governance in various societies. This study aims to provide the pathology of general welfare governance in Iran. Method: The present research is a mixed (quantitative-qualitative) type of research. In order to identify the concepts, dimensions and indicators of the model, theme analysis (qualitative) was used, and structural equation modeling (quantitative) was used to fit the conceptual model of the research. 26 experts, professors and experts in the field of policy and public welfare have been selected for the sociostatistics of the qualitative part. The statistical population of the quantitative part consists of 240 expert experts, managers and senior managers in the field of policy and social welfare. 149 people were considered using Cochran's formula. Findings: in the quality part; 50 initial indicators were identified and after coding, 45 indicators were confirmed by theme analysis. 6 dimensions (management and decision-making; public finance of the government; planning; internal environment; external environment and sectoral governance) and 4 components of causal, contextual, strategy and consequence factors were confirmed. In the quantitative part, the dimensions of management and decision-making and public finance of the government have an effect on the dimensions of the internal and external environment as well as on the dimension of planning, and the dimension of planning also affects the governance of a sector. Discussion: A comprehensive model for identifying public issues enables the country to determine and implement priorities and strategies. The design requirements for the pattern of public welfare pathology in Iran will be six dimensions and 45 indicators extracted in this model.  

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Journal title

volume 23  issue 89

pages  179- 227

publication date 2023-08

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