Khazayin of Economic and Administrative Sciences

The research aims to reveal the effect of the strategic architecture variable as an independent variable with its six dimensions (future vision, customer centricity, harmony, knowledge management, dynamic capabilities, and organizational structure on the organizational prestige variable as a dependent variable among a sample of workers in the Baqubah Municipality Directorate. The research raised a fundamental question: What is the impact of strategic architecture on organizational prestige among a sample of employees in the Baqubah Municipality Directorate? The research seeks to prove the existence of an impact of strategic architecture, in its dimensions, on organizational prestige in the Baqubah Municipality Directorate. The tow Researchers relied the descriptive analytical approach and on the basis of this, the questionnaire was used as a tool In order to measure the level of application of the research variables, the study sample was random with (260) workers. To address the research problem, a set of statistical methods were adopted using (26.AMOS.V.24 & SPSS.V). It was found that there was a significant effect of the strategic architecture Its dime

Clustered data are considened one of the most common model in applied studies because of it contrase within-cluster variability. In this regard, A semiparametric Poisson regression model was a dopted to analyze this type of data , throughout mergine the nonparametric procedune to represent the nonlinear relations between independent variables and the parametric procedune that models the random effects specific to each cluster. The two estimation methods were considered. The first method involves simultaneous estimation of both the parametric and nonparametric parts using the Penalized Least Squares (PLS) and. The second method estimates the nonparametric parts first, followed by the parametric parts using the Backfitting algorithm. These two methods were compared with the classical Maximum Likelihood Estimation (MLE) method used to fit the ordinary Poisson regression model, based on the Mean Absolute Percentage Error (MAPE) as the evaluation criterion. Simulation were done with several , considering three different values for the number of clusters: k = 3,5,10. Also Two types of cluster sizes were used:
• In the first, clusters had equal sizes: mₖ = 10,30,50
• In the second, clusters had unequal sizes, drawn from total sample sizes of n = 50,100,250,500 resulting in varying cluster sizes.
The results indicated that the Backfitting algorithm superior the other methods in both experimentaly and practicly.

Alzheimer's disease is a neurological disease caused by several factors affecting brain functions leading to memory loss, which in turn impacts the lives of those affected and those around them, often leading to the death of many elderly people. This study aimed to diagnose this disease and predict whether a person is affected by it. For this purpose, binary logistic regression and linear discriminant analysis were used and compared to each other to arrive at a result that clarifies which of the methods was the best and most powerful in classifying and diagnosing the disease. These methods were applied to data on reached (2149) Alzheimer's patients that were obtained from kaggle. When analyzed using the statistical program SPSS v.25, the results showed the efficiency of both methods in classification and in predicting the affiliation of group elements, as the binary logistic regression analysis method was able to correctly classify (84.8%) while it was (83.2%) for the linear discriminant analysis. As for the comparison between the two models, the results showed that the sensitivity, specificity and AUC of the linear discriminant analysis reached (83.4, 83% and 83.2%) and (74.6%, 90.4% and 82.5%) for the binary logistic regression analysis, which making the LDA model the best for reducing misdiagnosis in patients and the BLR model the best for avoiding misdiagnosis in uninfected people.

This study makes two major contributions: first, it presents a new approach to solving the fuzzy transportation problem (FTP); second, it contrasts two different Parametric methods for analyzing optimal bounds. The first Mechanism is to obtain a fuzzy optimal solution by solving the FTP directly. Then, in order to attain the best crisp outcome, we apply the alpha-cut and parametric function to the fuzzy decision variables and their associated cost values. This produces a sequence of ordinary values for the objective function, and we choose the best one. In the second method, we again employ the alpha-cut and parametric function, but this time we transform all of the Fuzzy transportation problem table components, which are costs, supply, and demand, into crisp numbers. Then we get ordinary Transportation Problem (TP) and solve it to acquire the optimal solution for the objective function. For the values of alpha and beta, we will get a series of crisp solutions this enabling us to determine the best optimal crisp value. This paper offers a thorough analysis of the two approaches, stressing their benefits, drawbacks, and suitability for solving practical issues.

This study investigates the influence of the leadership and communication component—recognized as a cornerstone in the internal auditors’ professional competency framework—on improving the quality of financial reporting among Iraqi companies listed on the stock exchange. The research aims to address a practical gap in accounting literature by empirically testing a set of hypotheses that examine how internal auditors’ leadership and communication capabilities affect the accuracy, transparency, and reliability of financial disclosures. A combination of advanced statistical methods was employed to analyse the correlation and causal relationships between these competencies and reporting quality indicators. Results demonstrated a statistically significant positive association, indicating that proficient leadership and effective communication within audit teams substantially enhance the integrity of financial reports. Based on these findings, the study recommends implementing structured leadership and communication development initiatives tailored to strengthen the internal audit function. Such programs can contribute to higher reporting standards, better regulatory compliance, and increased stakeholder confidence

This study aims to examine the effect of information literacy on the use of Google educational applications (Google Classroom, Google Docs, Google Scholar, Google Meet, YouTube, Gmail, Google Forms, and Google Spreadsheets) among university students, with a particular focus on the mediating role of self-directed learning. The study adopted a descriptive-analytical approach and was based on a research population of 540 students from the final years of study, from which a sample of 225 students was selected, distributed across the Colleges of Medicine, Administration and Economics, and Science. Final-year students were chosen due to their accumulated experience and continuous use of educational applications throughout their university studies.
The study employed AMOS to analyze the relationships among variables and validate the proposed model. The findings revealed that information literacy has no direct impact on the use of Google educational applications, but exerts an indirect influence through self-directed learning as a mediating variable. Based on these results, the study recommends integrating information literacy programs into university curricula across all disciplines, considering it a fundamental component to support self-directed learning and effectively utilize educational technologies.

The article begins with a review of the concept of job leakage, presenting some of the opinions of researchers who have studied this concept. It is clear that it is a voluntary, unforced, desire-driven abandonment of the organization. The article also highlights the problems and effects that arise for the organization from job leakage, especially if the individual who has left is a competent employee. Among the problems that result from this turnover are the additional costs of attracting and selecting new individuals to fill vacant positions, in addition to the psychological pressure that other individuals in the organization are affected by, weakening their morale and decreasing productivity. The article also focuses on the factors that can contribute to strengthening this situation, the most important of which are economic factors, namely transportation costs and wages that may not be appropriate for the work required of them. Also, turnover may be linked to organizational factors, such as conflict and lack of understanding of job content, or the organizational climate not supporting development and innovation, with the absence of communication and disruption of administrative relations. This is in addition to social factors and the emergence of harmful, unproductive behaviours that will create a state of tension and isolation. Knowing these factors will enable management to play a vital role in improving the work environment and creating an organizational climate. A culture of cooperation, cohesion, and participation in sound decision-making, along with constant encouragement to develop skills and acquire new experiences, will lead to stability within the individual, thus changing their mind and preventing them from leaving their current organization for another that meets their aspirations. The article concluded with some important conclusions, the most important of which is that job leakage is not a spur-of-the-moment issue, but rather the result of a combination of various factors. Furthermore, management must focus on recruiting and selecting human resources to fill vacant positions within the organization, ensuring that the job content matches the job specifications and that there is harmony between them.

هدفت هذه الدراسة إلى تحليل فجوة الكفاءة الرقمية لدى المحاسبين في ظل تطبيق التحول الرقمي في القطاع المصرفي، من خلال دراسة ميدانية استهدفت عينة مكونة من (131) محاسباً في البنوك العراقية.
اعتمدت الدراسة المنهج الوصفي التحليلي، واستخدمت استبانة صممت استناداً إلى نماذج الكفاءة الرقمية العالمية والدراسات السابقة. أظهرت النتائج وجود فجوة ذات دلالة إحصائية بين الكفاءة الرقمية الحالية والمستوى المطلوب، إذ بلغ المتوسط العام للكفاءة الرقمية الحالية (3.28) مقارنة بالمستوى المطلوب البالغ (3.67). كما تبين أن الفجوة تختلف باختلاف المؤهل العلمي، حيث حقق حملة الماجستير والدكتوراه مستويات أعلى من الكفاءة الرقمية مقارنة ببقية المؤهلات.
وأظهرت النتائج أن أبرز أسباب فجوة الكفاءة الرقمية هي ضعف التدريب المستمر على المهارات الرقمية، والمناهج الجامعية التقليدية التي لا تغطي المهارات الحديثة، إضافة إلى سرعة تطور التكنولوجيا. كما أثبتت الدراسة أن فجوة الكفاءة الرقمية تؤثر سلباً على جودة العمل المحاسبي من حيث زيادة الأخطاء المحاسبية، وتأخر إنجاز الأعمال، وضعف القدرة على اكتشاف التلاعب المالي، وارتفاع التكاليف التشغيلية. توصي الدراسة بضرورة تطوير برامج تدريبية متخصصة، وتحديث المناهج الجامعية لتشمل مهارات التحليل الرقمي وأدوات الذكاء الاصطناعي، بهدف تقليص الفجوة الرقمية وتحسين جاهزية المحاسبين لمتطلبات التحول الرقمي في القطاع المصرفي.

The research aimed to examine strategic behavior and its impact on enhancing organizational excellence at Iraq National Hospital. The current research relied on the (descriptive-analytical) approach, which is characterized by optimal flexibility, as this approach contributes to a more accurate diagnosis and study of the reality under study. The research used a questionnaire as the primary tool for collecting data and information related to the field aspect of the research. The research community included employees at Iraq National Hospital, while the research sample was limited to a number of employees, totaling (120) workers out of a study community of (172) workers. (110) valid questionnaires were received for statistical analysis. The data were analyzed using appropriate statistical methods (AMOS, SPSS) to test the research hypotheses. The research problem was: What is the impact of strategic behavior on enhancing organizational excellence in the organization under study? The research results showed that strategic behavior patterns enhanced the organizational excellence of the organization under study.

Maternal health risk refers to conditions that endanger a pregnant woman’s well-being and require timely assessment to prevent complications. Early identification of such risks is crucial for effective intervention. In this study, we applied and compared three machine learning algorithms Random Committee (RC), Randomizable Filtered Classifier (RFC), and Nearest Neighbor with Generalization (NNge) to predict maternal health risk levels during pregnancy. The dataset, collected through an IoT-based monitoring system from rural hospitals and maternal healthcare centres in Bangladesh, included six input features: age, systolic blood pressure, diastolic blood pressure, blood sugar, body temperature, and heart rate. The target variable, risk level, was categorized into three classes: low, mild, and high. Results show that RC achieved the highest accuracy (85.21%), with balanced sensitivity and specificity, making it the most effective model for early detection. RFC also performed competitively and can serve as a reliable alternative. By contrast, NNge, although effective in identifying low-risk cases, showed lower overall accuracy and higher misclassification, limiting its suitability for critical maternal health decisions. These findings highlight RC as the most promising approach for supporting healthcare providers in timely maternal risk management.

This study aims to analyze the impact of working capital indicators on the market value of stocks, using a sample of seven industrial companies listed on the Iraq Stock Exchange, These companies comprise 60% of the research sample for the period (2015-2023), The study relied on annual data, To test the study hypothesis, Panel Data technology and the outputs of the statistical program 12.Eviews-V were used, Multiple regression analysis was used to identify the relationship between the independent variable (working capital), the control variables (total assets and financial leverage), and the dependent variable (market value), The study results revealed a statistically significant positive and direct relationship between working capital indicators and the market value of stocks, confirming the importance of effective working capital management in enhancing the value of industrial companies on the Iraq Stock Exchange, Current assets and total assets have a significant impact at the 0.01 level, indicating that asset size and operating liquidity play a crucial role in determining the market value of industrial companies, and that the earnings per share ratio A positive correlation with working capital indicators indicates that effective management of liquidity and operational resources contributes to enhancing company profits and improving shareholder returns. Accordingly, approximately 69% of the change in a company's market value can be explained by the variables in the current study. Therefore, companies should improve working capital management by enhancing the current ratio and net working capital to ensure sufficient liquidity to cover short-term liabilities and support operational activity. This enhances investor confidence and increases the market value of shares

Nonparametric regression is considered a suitable alternative to parametric regression, as it does not require assuming a specific functional form of the relationship between variables. However, traditional nonparametric methods suffer from high sensitivity to fluctuations and noise, as well as weak performance when dealing with non-stationary data. To overcome these challenges, signal processing techniques such as wavelet transforms and the Short-Time Fourier Transform (STFT) can be employed, as they provide a more accurate representation of the temporal and frequency characteristics of the data. This integration enhances estimation accuracy and reduces both bias and variance, granting this advanced methodology a clear advantage over classical nonparametric regression approaches, particularly in the analysis of complex economic, financial, and natural data.
In this study, the Discrete Wavelet Transform (DWT) is employed, along with flexible thresholding values that handle coefficients at each level separately, unlike universal thresholds that process all levels simultaneously. Furthermore, the Short-Time Fourier Transform (STFT) and the Adaptive Short-Time Fourier Transform (ASTFT) are utilized with newly proposed thresholding rules. Through simulation experiments using test functions, sample sizes (128, 256, 512), and noise ratios (5%, 10%), the findings reveal that the most efficient estimation method is the DWT combined with the Fast Fourier Transform (FFT-DWT), followed by the DWT combined with the Adaptive Fast Fourier Transform (AFFT-DWT), as evaluated by the Average Mean Squared Error (AMSE) criterion.

The accountancy world is not what it used to be a few years back and all of this because E-Accounting entered the accounting scene and how! The study aims to explore the role of the E-Accounting component of CI's role in offering to modern business entities and how it enhances the AIS unit. The study seeks to examine and explain how E- Accounting assist AIS in the realization of the financial reporting, decision making and the control. It also covers challenges thought by the companies while implementing E-Accounting system, security issues and the role of new technology i.e., Artificial Intelligence ( AI), blockchain and cloud computing. Methodologies of use) real evidences in real life and patterns in practice where it is visible a) what is the result of e-Accounting to the small / big business? To achieve the research objective, a questionnaire was created and information was collected from a sample size of (200). The findings emphasise the role of E-Accounting as an element of AIS for improving performance and for empowering informed strategic decision making.

The research aims to study the role of external audit quality in improving the financial performance of Iraqi economic units by testing the relationship between the dimensions of external audit quality, which are represented by (independence, auditor experience and competence, adherence to professional standards) and the dimensions of financial performance, which are represented by (profitability, operational efficiency, reducing financial risks), as well as studying the factors affecting the quality of external audit. The researchers followed the descriptive analytical approach and relied on the questionnaire to collect and analyze data, as the research community included external auditing companies, auditors, and Iraqi profit-oriented economic units who possess sufficient experience and academic qualifications. The sample included (118) individuals. After analyzing the results, it was concluded that there is a direct relationship with significant moral significance between the quality of external audit and the financial performance of economic units, indicating that the high level of audit quality contributes to improving various aspects of financial performance. The results also concluded that the impact of external audit quality was more evident in the profitability dimension, followed by the financial risk reduction dimension and then operational efficiency, indicating that the role of external audit quality is not limited to the supervisory aspect, but extends to influence administrative and strategic decisions. The study concluded with a set of recommendations, the most important of which is the need to enhance the independence of external auditors through legislation and regulatory controls that reduce the impact of personal relationships or administrative pressures on their professional work, and to call on regulatory and supervisory bodies to adopt mechanisms for periodic evaluation of the effectiveness of external auditing within the framework of improving the efficiency of the financial market in general

This study aims to analyze the impact of artificial intelligence (AI) technologies on enhancing the performance of the banking sector by evaluating the effects of various AI tools on operational efficiency and service quality. It further explores the extent to which AI contributes to strengthening banking innovation and achieving competitive advantage through its application in administrative, service, and digital functions within the banking industry. The study employs monthly data for the period 2021–2024 and adopts the Autoregressive Distributed Lag (ARDL) model to examine the relationships among the study variables.
The findings indicate that AI serves as a key driver in improving the operational efficiency of banks in the long run, although the strength of its impact varies depending on the specific technologies used. Machine Learning (ML), Process Robotics Automation (PRA), and Smart Customer Service (CS) demonstrated a positive and statistically significant effect on operational efficiency. In contrast, Predictive Risk Analysis (PR) showed a positive but statistically insignificant relationship. The study concludes that a long-run equilibrium relationship exists between AI technologies and the operational efficiency of the banking sector