Accounting, Finance and Computational Intelligence

This study examines the factors influencing suboptimal decision-making by auditors in Iran, with particular emphasis on the role of evidential information, time budget pressure, and intolerance of ambiguity in this process. Considering the significance of the behavioral perspective on auditors’ ethics, this research investigates auditors’ suboptimal decision-making when confronted with intolerance of ambiguity, time budget pressure, and evidential information. The present study is applied in purpose and field-based in terms of data collection method. The statistical population consists of audit partners, audit managers, and audit supervisors working in Tehran Province, and the statistical sample includes 15 participants. The results of this research indicate that valid and sufficient evidence, as one of the main pillars of auditors’ evaluations, has a considerable effect on ethical behavior that influences the quality of their decision-making. Time budget pressure is another factor that can affect the quality of auditors’ evaluations. Under time-constrained conditions, auditors are forced to make quicker decisions, which may harm their behavior and the quality of their evaluations. This highlights the necessity of proper time management and the allocation of sufficient resources to the audit process. Intolerance of ambiguity, as a psychological factor, can also affect the decision-making process of auditors. When sufficient information is not available or uncertainty exists, auditors may face challenges in evaluating inventory values. This underscores the importance of developing auditors’ abilities to manage ambiguity and uncertainty.

This study aimed to identify and explain the technical and environmental factors influencing creative accounting practices in Iranian companies and to develop a conceptual model based on the grounded theory approach. This applied, qualitative, and exploratory research collected data through semi-structured interviews with 15 accounting and auditing experts selected via purposive and snowball sampling. Data were analyzed through open, axial, and selective coding using MAXQDA software, and the trustworthiness of findings was ensured through Guba and Lincoln’s criteria and participant feedback validation. Analysis resulted in 289 initial concepts categorized into five main themes: causal factors (e.g., short-term performance pressure, ambiguity of standards), contextual factors (e.g., profit-oriented organizational culture, weak professional ethics), intervening conditions (e.g., weakened auditor independence, corporate governance quality), strategies (accrual-based and real earnings management, disclosure techniques), and consequences (decreased transparency, weakened investor trust, financial crises). The core category identified was “interaction between technical and environmental indicators in shaping creative accounting.” The findings revealed that creative accounting is not solely the outcome of technical deficiencies in accounting standards but emerges from the complex interaction of technical and environmental factors. The proposed conceptual model offers a practical framework for managers, auditors, and regulatory bodies to identify vulnerabilities, strengthen internal controls, enhance transparency, and prevent the emergence of creative accounting.

This study aims to identify and analyze individual, organizational, technological, and social factors influencing organizational decisions to adopt blockchain technology in accounting. This research employed a mixed-methods design. In the qualitative phase, data were collected through semi-structured interviews with five experts in financial management and information technology and analyzed via thematic analysis using NVivo. In the quantitative phase, data from 384 financial managers, accounting experts, and system specialists from firms listed on the Tehran Stock Exchange were analyzed using structural equation modeling in Smart PLS. The instrument’s reliability and validity were confirmed through KMO, Cronbach’s alpha, composite reliability, and convergent and discriminant validity tests. Qualitative findings revealed four main themes—institutional distrust, the role of modern governance tools, enhancement of social capital, and rebuilding trust through transparency—as key influencers of blockchain adoption. Quantitative results showed that perceived ease of use (β=0.948; t=93.533), perceived usefulness (β=0.448; t=4.835), user attitude (β=0.619; t=5.990), managerial self-efficacy (β=0.676; t=7.599), and firm strategic orientation (β=0.812; t=20.764) had significant positive effects on intention and ultimately on blockchain usage. The GOF index value of 0.567 indicated strong model fit. The findings suggest that adopting blockchain in accounting requires simultaneous attention to technical, organizational, individual, and cultural factors. Enhancing perceived usefulness and ease of use, alongside managerial support, positive organizational culture, and human capital development, can facilitate successful adoption, thereby improving transparency, security, and efficiency in accounting processes.

The present article examines the asymmetric effects of government expenditure shocks on the financial cycle of business cycles, with an emphasis on Islamic participation bonds, using the NARDL model. The research method is applied in terms of purpose and descriptive–analytical in nature, falling within the category of post-event studies (using historical data for the period 1987–2023). In this regard, nonlinear NARDL models were employed to investigate short-term and long-term relationships between government expenditures, financial cycle indices, and other macroeconomic variables. The primary focus of the research is on a precise analysis of positive and negative fluctuations in current and capital expenditures and their impact on the performance of the financial cycle and the country’s business cycles. The short-term estimation results indicated that positive shocks in current and capital expenditures increase the financial cycle, whereas negative shocks in these expenditures have opposite and weakening effects. This asymmetry clearly demonstrates the existence of nonlinear relationships between government expenditures and the financial cycle, which holds particular importance in fiscal policymaking. In other words, the financial cycle responds differently and more strongly to increases in government expenditures compared to decreases. Furthermore, the coefficients related to other macroeconomic variables, such as gross domestic product, investment, and interest rates, were negative and statistically significant, indicating that increases in these variables reduce the financial cycle in the short term. In contrast, inflation and consumption showed a positive and significant effect on the financial cycle. The error correction coefficient (ECM) was negative with a relatively high magnitude (approximately –0.72), suggesting that the speed of adjustment from short-term deviations toward long-term equilibrium is considerable and that the model has good predictive capability. Diagnostic tests also confirmed that the model does not suffer from heteroscedasticity, autocorrelation, or specification errors, thereby enhancing the validity of the results. In the long term, the results of the cointegration model confirmed the existence of a stable and significant relationship between government current and capital expenditures, macroeconomic variables, and the financial cycle. The long-term coefficients exhibited patterns similar to the short-term findings, with asymmetric effects of government expenditures on the financial cycle being evident over the long horizon as well. Structural stability tests, including CUSUM and CUSUMQ, confirmed that the model did not experience structural breaks during the study period, thus ensuring the stability and reliability of the results. Based on these findings, it is recommended that fiscal policymakers consider the asymmetric effects of current and capital expenditures and utilize Islamic financial instruments such as participation bonds for government financing. These instruments, while providing sustainable financial resources, can help control asymmetric expenditure fluctuations and improve the efficiency of managing the financial cycle and business cycles of the country.

With the advancements in information technology and the emergence of artificial intelligence, the automation of accounting processes has been highlighted as one of the most significant transformations in the field of financial management and auditing. This study, employing a qualitative analysis approach, examines the various components influencing the automation of accounting processes and identifies and analyzes the role of causal conditions, contextual conditions, intervening factors, core phenomena, strategies, and outcomes of this technological transformation. The findings indicate that artificial intelligence, while enhancing the accuracy, speed, and transparency of financial reports, transforms the role of accountants and improves the quality of financial decision-making. However, challenges such as data security, ethical issues, the need for specialized skills, and cultural resistance remain major barriers to the adoption of this technology. The present study emphasizes that the success of accounting process automation requires comprehensive planning in areas such as training, development of technological infrastructure, formulation of legal and ethical frameworks, and organizational change management. By integrating diverse domestic and international perspectives, this research provides a practical framework for managers and policymakers to effectively harness the potential of artificial intelligence in accounting.

This study aimed to compare the performance of different neural networks in predicting cryptocurrency prices, with a focus on Bitcoin, under risk conditions. The research was applied and descriptive-analytical in nature. Historical data of Bitcoin, Ethereum, and Ripple from 2018 to 2023 were collected from reliable exchanges and financial platforms. After preprocessing, the data were divided into training and testing sets. Two neural network models, Multilayer Perceptron (MLP) and Radial Basis Function (RBF), were designed and implemented. Model performance was evaluated using error measurement indices including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicated that the MLP model outperformed the RBF model in prediction accuracy. The coefficient of determination reached 99.5% for MLP and 98.9% for RBF. Comparative analysis of error metrics showed significant reductions in RMSE and MAE in the MLP model compared to RBF. Moreover, the proposed Multi-Input Deep Learning (MICDL) model surpassed comparative CNN-LSTM models in classifying short-term price movements, particularly in directional prediction accuracy. Neural networks, especially the MLP model, demonstrated high accuracy and flexibility in forecasting cryptocurrency price fluctuations. These models can serve as effective tools for investors, analysts, and policymakers in risk management and informed decision-making. However, incorporating more diverse and real-time data along with hybrid deep learning techniques could further enhance predictive performance in future studies.

Technological advancements in recent decades have confronted accounting systems with fundamental transformations. Digital accounting, as a modern approach, emphasizes the use of emerging technologies such as artificial intelligence, blockchain, big data, and automation of financial processes to enhance the quality of financial reports. This study was conducted with the aim of developing a forward-looking model for the implementation of digital accounting in organizations. In doing so, causal, contextual, and intervening factors, as well as strategies and outcomes arising from this transformation, were identified to provide organizations with a more precise roadmap for execution. The research method was qualitative with a grounded theory approach, and data were collected and analyzed through content analysis of relevant documents and records available both domestically and internationally. The findings from open, axial, and selective coding revealed that components such as technological infrastructure, organizational support, digital skills of human resources, and macro-environmental requirements play a decisive role in the successful implementation of digital accounting. Ultimately, the designed paradigm model can serve as a practical roadmap for organizations to achieve transparency, accountability, efficiency, and quality in financial reporting within a digital context.

The aim of the present study was to design a model of factors affecting capital structure based on the balanced scorecard, considering the role of financial resilience. This research, using a qualitative approach and grounded theory methodology, was conducted through semi-structured interviews with 8 experts and senior managers of banks listed on the Tehran Stock Exchange. The study provided an in-depth identification of the conceptual model of factors influencing capital structure through a combined approach. The findings indicated that components such as liquidity, credit risk, asset quality, statutory reserves, and shareholders’ equity play causal roles in the formation of capital structure. At the contextual level, factors such as the internal control system and the asset–liability management system provide the executive and operational basis for these decisions. Factors such as customer satisfaction, information sharing, and innovation, as intervening variables, can act as facilitators or constraints, influencing the way capital structure interacts with its surrounding environment. Strategies such as establishing an integrated risk management system and enhancing transparency and stakeholder engagement, as overarching orientations, guide capital structure toward resilience. Ultimately, consequences such as financial structure sustainability, improvement of financial resilience, deepening of public trust, and enhancement of bank performance represent the tangible outcomes of this structure within a resilient and forward-looking framework. The results of this study demonstrate that the capital structure of banks is the product of systematic interactions among various factors, and that the balanced scorecard approach, combined with financial resilience, provides an effective framework for strategic decision-making. This model can strengthen financial stability and public trust in the banking system.