درخت حوزه‌های تخصصی

This research investigates the relationship between Economic Policy Uncertainty (EPU), credit risk, and lending decisions using the Generalized Method of Moments (GMMs)over the period from2019 to 2023 for12banks listed on the Tehran Stock Exchange. The study employs three regression models to analyze the dynamics of Non-Performing Loans (NPLs), Loan-To-Deposit Ratios(LTDRs), and Return onAssets (ROAs)within the banking sector. Findings reveal a significant persistence in NPLs, indicating that banks with higher past NPLs face ongoing challenges that adversely affect their financial health. A notable negative relationship between Leverage(Lev) and Non-Performing Loan Ratio (NPLR)suggests that more leveraged banks may implement effective risk management strategies, reducing their exposure toNPLs. Additionally, capital adequacy emerges as a critical factor, with higher capital ratios correlating with lower NPLs. The analysis of LTDR indicates thatLevand capital adequacy significantly influence lending practices, while a marginally significant relationship between EPU and LTDR suggests external uncertainties may slightly impact lending decisions. Model results further demonstrate strong persistence in profitability, with historical ROA positively predicting current ROA. Overall, this study underscores the importance of effective risk management practices in banking and highlights ongoing challenges posedbyNPLs, particularly for larger institutions. Recommendations include prioritizing capital buffers and monitoring lending practices to mitigate risks while fostering sustainable profitability growth. Future research should explore additional variables to elucidate the complexities of banking performance metrics

As changes in information technology have a significant impact on the structure of companies, integration within the supply chain, which exists directly or indirectly in the structure of any company, can be beneficial for company performance. This study examines the indirect impact of customer responsiveness integration within the supply chain on the economic sustainability of the supply chain through the adoption of blockchain technology as a dynamic capability component in the automotive industry using a Bayesian Structural Equation Modeling (BSEM)approach. The statistical population of the study consists of experts, supervisors, and managers of automotive companies, with 123 individuals selected through non-probability sampling. To assess the validity of the questionnaire, opinions of experts weresought, and Cronbach's alpha was used to determine the reliability of the questionnaires. The SEM-AMOS24 software was used to analyze the research hypotheses and examine the validity of the model usingBSEM. Thefindings of the study indicate that internal integration, including customer and supplier integration, and external integration within the supply chain, have a significant impact on the relationship between blockchain technology adoption and the economic sustainability of the supply chain. Additionally, customer responsiveness about Supply Chain Integration (SCI) and economic sustainability is achieved by attracting desirable domestic and foreign investors.