Fitting the Antifragility Model in Iranian Financial Organizations Through Confirmatory Factor Analysis - Ghadim Ghobadi, Reza; Hashemi, Seyed Hamed; Feizi Zengir , Mohammad; Sourati , Pouran (Management Strategies and Engineering Sciences, 2025)
This study was conducted to fit the antifragility model in Iranian financial organizations. The research method was quantitative, utilizing a correlational and descriptive approach. The statistical population comprised managers and experts from financial organizations in the northwest of Iran, totaling 488 individuals. Using Cochran’s formula, a sample size of 208 participants was selected. The validity of the research instrument was confirmed through consultation and guidance from academic advisors, while its reliability was verified with a Cronbach’s alpha coefficient above 0.7. Descriptive statistics were employed to describe demographic indicators, and inferential statistics were used for quantitative model analysis through confirmatory factor analysis, utilizing SPSS 25 and Lisrel 8.8 software. Statistical analysis was conducted on 19 components categorized into four groups: (1) the random and environmental category, which included the components of flexibility, financial crisis, resilient performance, agility, and adaptability; (2) the financial performance category, which included the components of cost control, institutional financial performance, central bank independence, inflation volatility, and cash flow management; (3) the managerial category, which included the components of risk management, economic growth improvement, capital management, financial policies, and government relations; and (4) the organizational category, which included the components of innovation and creativity, adaptability, resilience and resistance, integration, and service empowerment. The results of the confirmatory factor analysis, with observed factor loadings for all model components exceeding 0.4 within the acceptable range and a significant T-value test greater than 1.96, indicated that the theoretical model was well-fitted. Additionally, structural equation modeling results demonstrated that the research variables had a positive and significant impact on each other, as evidenced by path coefficients and significant T-values.