Accounting, Finance and Computational Intelligence

The purpose of this study was to identify and analyze the dimensions of government responsibility in preserving the value of the national currency and controlling currency depreciation with emphasis on monetary policymaking, economic governance, exchange rate management, and public trust. This study was conducted using a qualitative approach based on thematic analysis. The research population consisted of university professors, central bank experts, economic researchers, and executive managers involved in monetary and exchange rate policymaking in Tehran, from whom 21 participants were selected through purposive sampling. Data were collected using in-depth semi-structured interviews, and data analysis was performed according to Braun and Clarke’s six-stage thematic analysis framework. Member checking and intercoder agreement were employed to enhance the credibility and reliability of the findings. The findings revealed that government responsibility in preserving the value of the national currency consists of six major dimensions, including monetary policymaking responsibility, economic governance, exchange rate management, legal and regulatory responsibility, public trust, and real economic development. The results indicated that liquidity control, central bank independence, institutional coordination, and policy transparency are among the most influential factors affecting monetary stability. Furthermore, weak economic governance, rising public debt, inflationary expectations, and ineffective exchange rate management were found to play significant roles in intensifying currency depreciation. In addition, strengthening domestic production, expanding non-oil exports, and enhancing public trust were identified as the most important long-term strategies for controlling exchange rate volatility. The findings suggest that preserving the value of the national currency requires a comprehensive and coordinated approach involving monetary, fiscal, legal, and governance policies. Governments can successfully control currency depreciation only when structural reforms, institutional transparency, economic accountability, and support for productive sectors are pursued simultaneously alongside monetary stabilization policies.

This study aims to identify and explain the key factors influencing the improvement of efficiency and effectiveness in management accounting systems among firms listed on the Tehran Stock Exchange. This research is applied in purpose and descriptive–survey in design with a correlational approach. The statistical population consisted of accountants and financial experts in listed companies, from which a sample of 384 participants was selected using the Morgan table and convenience sampling. Data were collected through a standardized 24-item questionnaire based on a five-point Likert scale, with reliability confirmed via Cronbach’s alpha. Data analysis was conducted using SPSS 24 and AMOS 24 through Structural Equation Modeling (SEM). Confirmatory factor analysis and goodness-of-fit indices were employed to validate the measurement model. The results revealed that all independent variables—decision support and performance control, management accounting processes, information and data quality, reporting and performance analysis, technology and innovation, and cost management and inventory control—have significant positive effects on the efficiency and effectiveness of management accounting systems (P<0.05). The strongest effect was observed for reporting and performance analysis (β=1.490), followed by information and data quality (β=0.840) and cost management (β=0.778). All hypotheses were supported at the 95% confidence level. The findings indicate that strengthening informational, procedural, technological, and control dimensions simultaneously plays a critical role in enhancing the efficiency and effectiveness of management accounting systems, ultimately improving managerial decision-making and organizational performance.

The purpose of this study was to investigate the effect of increasing board independence through a higher proportion of non-executive directors on financial reporting quality and the effort of independent auditors in companies listed on the Tehran Stock Exchange. This applied study adopted a quasi-experimental and ex post facto design using panel data and multivariate regression analysis. The statistical population consisted of firms listed on the Tehran Stock Exchange, and the final sample included 163 companies during the 2011–2021 period. Data were collected from audited financial statements, board reports, the CODAL system, and Rahavard Novin software. Hypotheses were tested using fixed-effects panel regression and GLS estimation. The independent variable was the proportion of non-executive directors on the board, while the dependent variables included financial reporting quality and auditor effort. Control variables included firm size, leverage, sales growth, return on assets, auditor tenure, and audit firm size. The results of the first hypothesis indicated that an increase in the proportion of non-executive directors had no significant effect on financial reporting quality (β=0.0029, p=0.7289); therefore, the first hypothesis was rejected. However, the results of the second hypothesis demonstrated that an increase in board independence had a positive and significant effect on auditor effort (β=0.4436, p=0.0764), supporting the second hypothesis at the 10% significance level. In addition, firm size and audit firm size had positive and significant effects on auditor effort, whereas leverage and sales growth showed significant negative effects. The coefficients of determination also indicated acceptable explanatory power of the estimated models. The findings suggest that merely increasing the number of non-executive directors is insufficient to improve financial reporting quality unless the governance mechanism becomes dynamic, specialized, and effective. Nevertheless, greater board independence significantly increased auditor effort, indicating stronger monitoring demand and a preference for higher-quality audit services. These results imply that board independence may indirectly enhance the quality of financial reporting by strengthening external audit oversight and increasing the intensity of audit procedures.

This study aims to develop and analyze plausible scenarios of shadow banking under the emergence of neurotic structural disruptions in Iran’s economic system. This research adopted an exploratory–developmental mixed-method design conducted in qualitative and quantitative phases; the qualitative phase employed grounded theory through open, axial, and selective coding based on 20 expert interviews, followed by Delphi analysis to assess consensus, while the quantitative phase utilized matrix analysis, MICMAC, and scenario planning techniques to identify key drivers and future outlooks. The findings indicated that the paradigmatic structure of shadow banking consists of causal, contextual, and intervening conditions along with strategies and consequences, where systematic and financial risks emerged as dominant outcomes; scenario analysis revealed four possible futures—Gradual Death, Explosion, Flame Fire, and Ashen Aura—with the Gradual Death scenario identified as the most probable, primarily driven by the paranoid dimension of neurotic structural disruption. The results suggest that neurotic structural disruptions, particularly in their paranoid dimension, significantly intensify the adverse consequences of shadow banking and shape the trajectory of future economic scenarios toward higher systemic risk.

This study aims to develop and validate an intelligent hybrid model for credit risk prediction with early warning capability to support proactive decision-making in banking systems. This data-driven, ex-post facto study was conducted on corporate clients of Bank Sepah. The population included 2,847 firms (2018–2022), from which 340 were selected using proportional stratified sampling. Data were collected from audited financial statements, credit records, and behavioral indicators, and preprocessed through normalization, outlier treatment, and class balancing. Analytical approaches included classical models (logistic regression and Cox survival analysis) and machine learning models (SVM and neural networks). A genetic algorithm was employed for hyperparameter optimization and model aggregation. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and AUC . Results indicated that liquidity, profitability, cash flow, and relationship duration significantly reduce default probability and hazard, whereas leverage increases risk. Nonlinear models outperformed linear approaches. The hybrid metaheuristic model achieved the highest AUC and lowest Type II error, demonstrating superior predictive performance and robustness. The survival model also showed strong capability in predicting time-to-default. The proposed framework enhances credit risk prediction accuracy and enables dynamic monitoring and early warning, facilitating more effective and proactive risk management in banking.

This study aims to develop a comprehensive conceptual model of investor behavior in interaction with financial technologies (fintech) in Iranian private banks. This research is qualitative in nature and employs a thematic analysis approach. Data were collected through in-depth semi-structured interviews with 17 experts in banking and fintech, selected using purposive (snowball) sampling. The data analysis process involved open, axial, and selective coding and was conducted using MAXQDA software. Data credibility was ensured through theoretical saturation, expert validation, and iterative analysis. The findings indicate that five core themes—digital transformation in banking services, enhancement of trust and data security, digital investor behavior analysis, data-driven decision-making, and development of digital financial human capital—significantly shape investor behavior. Results further reveal that the interaction between big data analytics and behavioral biases critically determines financial decision quality, while trust in technology functions as a mediating variable enhancing the impact of other factors. The study concludes that successful fintech implementation in private banks requires an integrated approach combining technological, behavioral, and institutional dimensions; beyond digital infrastructure development, reengineering technological culture and improving digital financial literacy are essential prerequisites for enhancing investor decision-making.

This study aims to develop a serendipity-based approach to identify and prioritize the drivers of learning flows in accounting knowledge. This research employed a mixed-methods design. In the qualitative phase, grounded theory was used through 14 semi-structured interviews with academic experts in accounting, and data were analyzed via open, axial, and selective coding. The Delphi method was then applied to validate the findings. In the quantitative phase, matrix analysis and pairwise comparisons were used to prioritize the identified core components. The quantitative sample consisted of 25 certified accountants selected purposively. The findings indicate that learning in accounting knowledge, within a serendipity framework, is multilayered and shaped by the interaction of cognitive, structural, and social factors. Six core components were identified: perceptual, thinking, educational, executive, cultural, and integrative capabilities. Quantitative results reveal that perceptual capabilities are the most influential driver, playing a central role in guiding discovery, interpretation, and meaning-making processes in accounting. Enhancing perceptual capabilities and fostering exploratory learning environments can significantly improve learning quality and decision-making effectiveness in accounting, ultimately supporting innovation and professional development in the field.

The present study aimed to develop a productivity model for auditing firms based on the Theory of Constraints (TOC) and to identify the dimensions, components, and bottlenecks affecting the efficiency and quality of audit services from the perspective of auditing and management accounting experts. This study was applied in purpose and qualitative in methodology, using the grounded theory approach. The statistical population consisted of audit firm managers, senior auditors, university faculty members in auditing, and experts in management accounting who were selected through snowball sampling. In-depth semi-structured interviews were conducted with 14 experts in 2026. Data were analyzed using Atlas.ti version 9 through open, axial, and selective coding procedures. During the open coding stage, 326 initial codes were extracted and subsequently categorized into major dimensions and components. To assess reliability, Cohen’s Kappa coefficient was calculated, yielding a value of 0.75 with a significance level of 0.00, confirming the reliability of the proposed model. The findings indicated that contextual factors included human resource management and limited team capacity, coordination and information flow, internal control and supervision, organizational policies and structure, and corporate governance. Causal factors consisted of staff training and skill development, workload and project scheduling pressure, psychological stress and employee motivation, as well as risk management and financial analysis. Intervening factors included the use of technology and management tools, employee resistance to change, compliance with regulations, and financial flexibility. The core category of the model involved audit quality and accuracy, operational efficiency, reduction of rework, and bottleneck management. The main strategies identified were workflow monitoring and improvement, process optimization and management, and proper task standardization and division. The outcomes of implementing the model included reduced errors and post-report corrections, enhanced professional credibility of audit firms, and improved audit operational flow. The findings demonstrated that the productivity of auditing firms is influenced by a complex interaction of human, structural, managerial, and technological factors, and that the Theory of Constraints provides an effective framework for identifying and managing operational bottlenecks in auditing firms. Implementing strategies based on process standardization, workflow management, professional skill development, and advanced technologies can improve audit quality, reduce rework, enhance operational efficiency, and strengthen the competitive advantage of auditing firms.