درخت حوزه‌های تخصصی

The financial markets are encountering uncertain conditions that are heightening their tail risk. This study analyzed eight stock market indices employing a neural network quantile regression methodology from 24 July 2017 to 22 August 2023. The findings demonstrated that the proposed model effectively estimated the tail risk by VaR and CoVaR of the sample indices of the Iranian stock market while considering oil and gold price fluctuations as risk factors. The results showed that the global crisis of the COVID-19 pandemic, which began in China in 2020, had significant impacts on global indices. However, the shock was relatively worse in the Iranian stock market, particularly in some industries such as Metals, Metal ores, and Chemicals, and the Overall indices had greater vulnerability than the rest of the indices. During the global crisis in 2022, which was triggered by the war in Ukraine, the Iranian capital market experienced a significant shock.

This research aims to investigate the effect of risk management on the speed of adjustment of commercial credit by considering the role of structural characteristics of companies' management. The statistical population is all the companies listed on the Tehran Stock Exchange, and using the systematic elimination sampling method, 124 companies were selected as the research sample. They were examined in the ten years between 2014 and 2023. The results of the research hypotheses test showed that risk management has a direct and significant effect on the adjustment speed of trade receivables and payables. Also, the interaction of management history with risk management directly affects the speed of commercial credit payable and receivable adjustment. The interaction of management independence and risk management has a direct and significant effect on the speed of adjustment of trade credit payable, the interaction of these two variables has an inverse impact on the speed of adjustment of trade credit receivable, and finally, the interaction of the position of management and risk management influences the speed of adjustment of trade credit payable and is not receivable. In summary, this research indicates that risk management plays a significant role in the speed of trade credit adjustment, and this relationship is influenced by the company’s managerial structural characteristics. The findings emphasize the importance of risk management strategies and managerial structure in enhancing financial transparency and efficiency.

Background: This study was conducted with the aim of designing the architectural model of human resources of Tax Administration in Southeastern Provinces of Iran with the intelligent approach. Methods: This study was of a mixed type and the statistical population in the qualitative part was 20 experts of Tax Administration managers in the southeastern provinces of Iran (TASEPI), namely Sistan and Baluchistan, Kerman, Hormozgan and South Khorasan. 264 people were selected using G*Power. Sampling of the qualitative part was purposeful and the quantitative part was random cluster sampling. The data collection tool was a researcher-made questionnaire containing 77 items that included six dimensions of intelligent human resources architecture and six dimensions of intelligentization. The software used was Smart-PLS and SPSS-16. Results: The results showed that the dimensions of human resource architecture were effective in the way of intelligentization as follows: intelligent human resource system (0.965), intelligent human resource management (0.960), intelligent organizational learning (0.955), intelligent organizational architecture strategy (0.953). Technology-oriented (0.945) and smart knowledge management (0.451). The dimensions of intelligentization are also from the dimension of intelligentizing human resources (0.974), intelligent participation of employees (0.965), human resource maintenance activities (0.962), forming a talent fund (0.949), advanced functional activities (0.927) and the dimension of creating new roles of human resources (0.895).

Given the growing demand for electricity in Iraq and the significant contribution of gas-fired power plants to electricity generation, evaluating and improving the efficiency of these units from economic, environmental, and social perspectives is an imperative. This study aims to develop an integrated framework for identifying inputs and outputs for employing Data Envelopment Analysis (DEA) to assess the performance of gas-fired power plants in southern Iraq. To this end, Soft Systems Methodology (SSM) was employed to identify and structure problematic factors and challenges through expert interviews. The challenges of electricity generation and influencing factors were structured as inputs and outputs. The findings revealed that the inputs and outputs of gas-fired power plants in Iraq can be defined within seven subsystems: economic, environmental, supply, human resources, technology and infrastructure, social, and managerial. The integration of SSM and DEA provides an effective framework for multifaceted performance analysis, enabling root definitions and conceptual modeling that support evidence-based policymaking, efficient resource allocation, and strategic planning for structural reform and transition toward sustainable energy within Iraq's power sector.

The material interests of personnel significantly affect the productivity and quality of services provided. Understanding these interests is very important for organizations that aim to increase employee motivation and performance. This research aims to design a human resource productivity model based on the material interests of personnel. The research is exploratory-applied in terms of its purpose and survey-correlation in terms of its method. The research was conducted in a qualitative-quantitative manner. The statistical population in the qualitative part is university professors and fire department managers in Iraq. The statistical population in the quantitative part consists of all fire department employees in Iraq, which is considered to be an unlimited number, so the sample size is 384 people based on the Cochran formula. The research tool is a researcher-made questionnaire. Data analysis in the qualitative part is the theme method and in the quantitative part is structural equations in the PLS software. The results of the study indicate that in the qualitative part, 5 main dimensions have been extracted, which include fair and competitive salaries and wages, bonuses and fringe benefits, job security and financial stability, financial growth opportunities through performance, and support for work-life balance with economic benefits. In the quantitative part, the overall fit of the research model based on the GOF formula was obtained as 0.65, which indicates a strong fit. The first and second-order factor loadings have been confirmed with 99% confidence.

The study was carried out to investigate effects of ChatGPT on 21st century skills of university students. Since there are several 21st century skills, the study focused on only five (i.e. critical thinking, communication, collaboration, creativity, and problem-solving skills). The study employed systematic review of literature in which PRISMA framework was employed to facilitate selection of studies included in the review. Findings revealed that ChatGPT poses effects to students on each of the 21st century skills. However, the type and magnitude of effects depends on the type of skill. ChatGPT was found to bring more positive impacts to university students’ communication skills while more negative impacts on collaboration skills. While that was the case, the study found that ChatGPT can bring either positive or negative impacts on critical thinking, creativity and problem-solving skills. The effects depend mainly on the magnitude of students’ dependency on it. Those that depend more on it are likely to be more negatively affected. For instance, the more the students depend on ChatGPT, the less the ability for them to think critically and solve problems. The study recommends, therefore, that universities should embrace ChatGPT instead of avoiding it. However, they should find ways to reduce students’ over-reliance on it, among other recommendations.

This study aimed to identify the aspects affecting elite retention in organizations using a metasynthesis-based qualitative method by investigating 682 scientific-research papers conducted on elite retention. Once the metasynthesis stages were completed, a sample of 23 papers was finally selected according to inclusion criteria. Results of the selected qualitative studies were reviewed systematically, the components and contents were identified and classified using the content analysis method. The validity of the research results was verified using a CASP checklist and confirmed by experts, and the reliability of the data was calculated using the Cohen's kappa coefficient. Findings of the present research indicated 304 identified initial concepts classified into 20 sub-concepts and ultimately 10 main concepts. Based on the research findings, organizations are progressing and developing in a galloping manner. Therefore, the presence of elites in scientific fields and production of underlying knowledge could be highly effective. The employment of elites' capabilities could be effective in creating and boosting their motivation to promote and develop organizations.

The aim of this research is to analyze and map international scientific publications related to Customer Lifetime Value (CLTV). This study adopts an interpretive paradigm and employs a descriptive approach using a systematic review method. By utilizing specific search terms in the Web of Science database, covering the period from 1985 to 2024, and after thorough screening and qualitative assessment of the studies, the final analysis was conducted on 639 articles. An in-depth examination of the selected articles revealed a notable increase in international research in this field, particularly during the last twenty years. However, there have been periods of decreased research activity in years such as 2008, 2017, and 2023. The primary focus of this research has been on customer lifetime value and customer segmentation, with a significant association to the keyword "data mining," highlighting the importance of this technique in the discipline. Moreover, it was found that countries like Iran, Canada, and Turkey have lower average citation rates, whereas the United States, France, and Germany exhibit higher average citation rates. This suggests different patterns of co-authorship among these countries. By examining the most and least productive countries and researchers through scientometrics, new research opportunities in the field of customer lifetime value can be identified, providing insights for Iranian researchers to enhance the visibility of their findings on an international scale.

The motivations driving cash holdings have a profound influence on corporate decision-making and performance. Exploring the dynamics between risk-taking behaviour, cash reserves, and their adjustment pace provides valuable insights into effective financial resource management. This study examines the impact of corporate risk-taking on the adjustment speed of cash holdings, with a focus on the moderating effect of CEO tenure. A sample of 151 firms listed on the Tehran Stock Exchange from 2011 to 2023 (1,963 firm-year observations) was analysed using multiple regression and the Generalized Method of Moments (GMM) estimator. Results indicate that the adjustment speed of cash holdings is 49.5%. A significant negative relationship exists between corporate risk-taking and the speed of cash holdings adjustment, suggesting that elevated risk-taking decelerates the alignment of cash reserves with optimal levels. Moreover, the findings highlight the moderating role of CEO tenure in the relationship between corporate risk-taking and the speed of cash holdings adjustment; in other words, in firms with longer-tenured CEOs, the negative association between corporate risk-taking and cash holdings adjustment speed is weaker than in firms with shorter-tenured CEOs. These findings suggest that risk-taking hinders swift cash adjustment, necessitating a precise determination of optimal cash levels to prevent liquidity shortages in high-risk scenarios. Additionally, the experience of long-tenured CEOs appears to facilitate better liquidity management, aligning corporate interests with strategic financial goals.

In data-driven marketing, customer behavior analysis plays a crucial role in developing targeted marketing strategies aimed at increasing return on investment, enhancing profitability, and gaining a larger market share. In this study, four clustering methods- including K-means, density-based clustering, principal component analysis, and hierarchical clustering- as well as four classification methods- including Support Vector Machine, XGBoost, Random Forest, and Gradient Boosting- are examined for customer behavior analysis. The data for this study was extracted from the "Amazon Customer Behavior Survey" dataset, which includes 23 features from 602 customers. Initially, the data was preprocessed, and then, using clustering methods, customers were divided into different groups. The performance of these methods was evaluated based on criteria such as the silhouette index, and ultimately, appropriate marketing strategies for each cluster were proposed. Additionally, to examine the possibility of predicting customer membership in the extracted clusters, the aforementioned classification models were implemented and compared. The results indicate that the K-means method performed the best in clustering, while the XGBoost model performed the best in classification. The innovation of this research lies in combining clustering and classification methods to provide targeted marketing strategies and comprehensively comparing these methods on real customer data. This study demonstrates that combining clustering and classification methods can help businesses better understand customer behavior and make more optimal marketing decisions.

Forecasting financial variables, especially the returns of debt instruments, plays a vital role in economic decision-making and risk management. Although the forecasting literature in financial markets is extensive, few studies have focused on predicting the returns of Islamic Treasury Bonds with unconventional structures. Moreover, despite the importance of these bonds, very limited work has been done using machine learning in the debt market. This study aims to predict the returns of Islamic Treasury Bonds using three models: Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLP), and Radial Basis Function Neural Network (RBF). Monthly data from 2018 to 2023 were collected using Excel and Python. The training and evaluation of the models were carried out in MATLAB. Eleven influential variables were selected based on previous studies and expert opinions. The models' performance was evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The findings indicate that the Multilayer Perceptron Neural Network model has higher accuracy in predicting the returns of Islamic Treasury Bonds compared to Multiple Linear Regression and Radial Basis Function models. These results suggest that neural network models can serve as more effective tools in financial and economic analyses, significantly enhancing forecasting accuracy.

The Neighborhood Rough Set (NRST) method is a valuable approach for selecting a subset of features from a complete dataset, enabling us to preserve the essential information that the entire feature set provides. In financial datasets, which often contain high-dimensional input features, effective feature selection techniques are crucial to identify the features that yield the most predictable results. In this work, we use neighborhood concepts to discover data dependencies and reduce the number of features in a financial dataset based solely on the data itself, without relying on additional information. This process also includes removing extra features. To facilitate a simple algorithm, we use the properties of neighbourhood rough sets to formulate a Binary Integer Linear Programming (BILP) model. Optimal solutions to these problems are obtained using genetic algorithms. Our approach allows for feature reduction from minimum to maximum cardinality. We demonstrate the efficiency of our proposed method compared to other techniques through various tables showing the results on several benchmark datasets characterized by unbalanced class distributions. The financial dataset used in the present study is taken from the UCI Machine Learning Repository.

In this research, first the financial criteria used in capital decision-making were identified and refined, then the most effective criteria were selected based on the deep learning algorithms including: RF, XGBoost, and LightGBM. In this stage, 11 factors were selected from the 35 factors found in previous research. In the next stage, based on the Forensic-Based Investigation algorithm (FBI), feasible investment options were identified and the internal rate of return was calculated over a 5-year period, and 42 companies that had an internal rate of return higher than the risk-free investment were selected as feasible investment options. During the next stage, different random combinations were used as investment portfolios using three methods: equal weight allocation, mean-variance model, and hierarchical risk preference model. Investment weights were determined for each invested share (combination) and investment returns were evaluated using different metrics. Finally, in order to validate the findings, the feasible investment options were divided into two categories of companies active in the financial industry and others, and the superiority of decision-making (higher returns) in a dynamic process was accepted.

In this study, in which a new business model was constructed to examine the reflections of digitalization on the field of authentication due to the increasing importance of digitalization day by day. This paper aims to design a business model for digital transformation using Near-Field Communication (NFC) technology to develop foreign markets for original products by creating a product authentication database. Given the global use of NFC technology for authenticity checks and market development, this research is pioneering in proposing a business model for applying this approach to authenticate original products in Iran. Beyond product authentication, this approach can facilitate extensive market research, particularly in international markets, where many handicraft and clothing products are highly successful but often overlooked by industry owners. Following a description of Osterwalder’s business model, a business model canvas is developed, and service and sales revenue models are presented. The service revenue model includes two business strategies: "service provision per tap" and "annual subscription strategies (Bronze, Silver, and Gold)".