An Effective Decision-Making Model in Social Security Branches (Case Study: Kerman Branches)
Keywords:
Effective decision-making, Social Security Organization, Kerman branches, exploratory factor analysis, thematic analysis, public administration, data-driven management, organizational participation, adaptabilityAbstract
This study was conducted with the aim of designing an effective decision-making model for the branches of the Social Security Organization in Kerman Province, intending to provide a localized framework for improving decision-making processes, enhancing organizational accountability, and increasing stakeholder satisfaction under the region’s complex conditions. A sequential mixed-methods approach (qualitative and quantitative) was employed. In the qualitative phase, thematic analysis of semi-structured interviews with 12 experts (branch and headquarters managers and specialists with a minimum of 10 years of experience) was carried out to identify the dimensions and components of effective decision-making. In the quantitative phase, exploratory factor analysis (EFA) was applied to a sample of 384 branch employees selected through multistage cluster sampling, using a questionnaire based on a five-point Likert scale. Thematic analysis of the interviews revealed that decision-making in the Kerman branches is often unsystematic, based on individual experience, and confronted with challenges such as lack of accurate data, insufficient transparency, and weak documentation practices. Key themes included the need for precise problem definition, strengthening of informational infrastructure, enhancement of employee participation, and continuous monitoring of decision outcomes. The exploratory factor analysis extracted eight key dimensions: strategic and policy-making, operational and executive, information and technology, data-driven analysis, organizational participation, organizational culture, evaluation and learning, and adaptability and innovation. These dimensions were validated using indicators such as financial sustainability rate with a factor loading of 0.82, clarity of SMART decision-making goals with a factor loading of 0.80, data accuracy rate with a factor loading of 0.80, and employee participation percentage with a factor loading of 0.79, altogether explaining 89.79% of the total variance.
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Copyright (c) 2025 Zahra Negari (Author); Abbas Babaeinejad (Translator); Alireza Sanatkha, Soheila Shamsadini (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
