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    Economic determinants of nonperforming loans in Turkey: Quantile ARDL results
    (Istanbul University Press, 2025) Atılgan Sarıdoğan, Ayşe; Küçükgergerli, Nabi; Yaman, Adem
    In the banking sector, problems in repaying customers’ credits can increase credit risk and fragility. Therefore, it is of great importance for banks to monitor the status of non-performing loans (NPLs) closely. This study analyzes the macroeconomic factors affecting NLPs in the Turkish banking sector. It used ARDL and QARDL approaches and data for 2011M5-2024M9 in the study. According to the long-run estimation results of the ARDL model, inflation and industrial production affect the NLPs in the opposite direction. In contrast, unemployment, the exchange rate, and interest rates affect it in the same direction. The estimation results are consistent with economic theory and the literature. The QARDL estimation results show that lnCPI (τ=0.2 to τ=0.8) has negative and significant coefficients in most quantiles (τ). The coefficients for lnPMI are generally negative and statistically insignificant. The lnUNE variable has positive and significant coefficients at most levels τ (τ=0.1 to τ=0.8). The estimation results for lnEXC show that the overall effect of the variable on NPL is positive and significant. The coefficients of interest rates are generally positive and significant. For the increase in the NLPs to remain at an acceptable threshold level for the banking sector and the Turkish economy, it is critical that the credit risk assessment system at the banking level works effectively and efficiently on the one hand and that macroeconomic indicators in the Turkish economy are supportive of the credit repayment conditions of economic agents on the other.
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    Machine learning insights into nurse retention through job satisfaction and financial incentives
    (Frontiers Media S. A., 2026) Atılgan Sarıdoğan, Ayşe; Küçükgergerli, Nabi; Ertürk, Muzaffer; Emeç, Murat; Yaman, Adem
    The global nursing shortage has reached a critical inflection point, where the financial sustainability of healthcare institutions is increasingly determined by their ability to maintain a stable, high-quality workforce. This study investigates the structural determinants of nurse staffing quality—operationalized as an institutional-level proxy for retention capacity—by integrating financial incentives, workload demands, and job-satisfaction metrics into an advanced machine-learning framework. Using the comprehensive CMS Provider Information dataset (N = 15,640 nursing facilities), we developed and validated a predictive architecture comparing Random Forest, Support Vector Machines, and Histogram-based Gradient Boosting (HGB) models. Our analysis reveals a clear hierarchy of influence: while Financial Incentives and penalties (Total Fines, importance weight: 0.083) and Job Satisfaction Proxies (QM Rating, 0.079) serve as significant secondary drivers, the primary boundaries of staffing stability are governed by Workload and capacity constraints, specifically the Number of Residents (0.309) and Number of Certified Beds (0.287). The Gradient Boosting model emerged as the superior predictive tool (Balanced Accuracy: 0.42; Macro F1: 0.41), demonstrating that institutional scale and patient volume are the dominant predictors of staffing quality ratings. These findings suggest that financial interventions alone are insufficient; sustainable nurse retention requires a dual-strategy that aligns fiscal incentives with rigorous workload management and capacity optimization. By identifying these high-impact variables and explicitly acknowledging the limitations of proxy-based operationalization, this research provides a data-driven roadmap for policymakers and healthcare executives to mitigate turnover and enhance the financial and operational resilience of nursing care systems.