AL-Qadisiyah Journal For Administrative and Economic sciences

Abstract: This study develops a Robust Elastic Net regression method based on Density Power Divergence to handle outliers, heavy-tailed noise, and high dimensionality in financial data. By combining divergence-based weighting with elastic regularization, the proposed model achieves robustness, sparsity, and stability simultaneously. Simulation results show that the method performs similarly to the classical Elastic Net under clean data, while providing substantially lower mean squared error and higher resistance to contamination. An application to S&P 500 returns confirms that the proposed approach yields more stable and interpretable coefficient estimates. These findings demonstrate the effectiveness of the Robust Elastic Net for noisy and volatile financial environments.

Abstract: The study aimed to analyze capital risks and financial flexibility, as well as to assess the impact of capital risks on the financial flexibility of the banks in the sample of the study. The study population represented the Iraq Stock Exchange, while the sample consisted of (8) banks listed on the Iraq Stock Exchange, with the objective of measuring the impact of banking risks on the financial flexibility adopted in the research for the banks in the sample for the period (2018-2023). Capital risk was defined as the likelihood of a bank experiencing unplanned or unforeseen losses, leading to fluctuations in cash flows, returns, and market value.This represents a deviation from planned opportunities, which reflects on the bank's financial indicators. Financial flexibility is considered the key indicator in the capital structure of the bank, indicating the bank's ability to generate financial resources in response to future events. Financial flexibility refers to unused or underutilized debt, meaning that a bank with an optimal long-term leverage ratio can utilize these resources when investment opportunities arise. Regarding the role of banking risks in financial flexibility, the study found that as capital risks increase, financial flexibility decreases. This means liquidity declines, and the bank’s ability to meet financial obligations and undertake expansions and investment growth decreases. To analyze and test the hypothesis of the study, the (SPSS 24) and (R-program) software were used, utilizing quarterly data for the period (2018-2023). The study concluded several findings, including a strong inverse correlation between capital risks and financial flexibility. A statistically significant relationship was found between capital risks and the financial flexibility of the banks in the sample, reflecting the relationship between the level of risks faced by the banks and their ability to adapt and respond to financial shocks, including economic crises and the ability to meet obligations without default. The study recommended tightening supervision by the Central Bank of Iraq on banks to ensure their compliance with risk management and the establishment of clear emergency plans to address financial crises. Furthermore, the study suggested developing early warning systems in Iraqi banks to monitor potential risks, such as capital risks, to enhance the financial flexibility of Iraqi banks.

Abstract: Linear regression models are used to describe and estimate the relationship between a response variable and a set of covariates. However, if some of covariates are inactive in the regression, the estimated relationship could be unstable and unpredictable. Many methods have been used over the years to identify the active covariates in the regression. In this paper, we propose Bayesian bridge-randomized expectile regression (BBRER). We compare the proposed method with the traditional model selection methods with Lasso and adaptive Lasso methods. Simulation methods show that all methods are perform comparably, however; Lasso performs the best in 80% of the simulation studies. Real data analyses using prostate cancer data also show that Lasso is the best.

Abstract: This research was undertaken to investigate the effect of Artificial Intelligence (AI) technology on the performance of Islamic banks in Iraq. Digital transformation within the banking industry has resulted in an accelerated integration of AI as a significant factor in improving banking performance (or enhancing) through digital transformation. The study employed a descriptive-analytical research design using primary data collected from 50 respondents via a structured survey (questionnaire) that was distributed to employees working in the Iraqi banking system. Of the 50 respondents, 40 were ultimately analyzed using the SPSS statistical program and the AMOS v23 to test the hypotheses regarding AI applications and their impact upon banking performance using a structural modeling approach. The research results indicate that both the growth of AI applications and the training of employees will contribute to enhancing banking performance within the Islamic banks that operate in Iraq. The results of this research have significant implications for banking managers and policymakers who wish to enhance the performance of banks located within the Iraqi banking system.

Abstract: The research aims to identify and analyze the evolution of public debt (internal and external) in the Iraqi economy during the period (2004-2023), because it is one of the sources of financing the deficit in the general budget, and it has a direct impact on the Iraqi economy. The Iraqi economy has a deficit in the general budget as a result of increased expenditures on the one hand and decreased public revenues on the other hand. The deficit is addressed by the state borrowing, whether it is internal debt or external debt,The results of the theoretical analysis for the period (2004-2023) showed that the contribution of external public debt to the total public debt is very high compared to the contribution of internal debt. This means that the Iraqi economy is frequently exposed to financial crises, because external debt is denominated in foreign currency, in addition to the accumulation of interest resulting from it, which makes the country burdened with financial obligations towards creditor countries. The increase in the contribution of internal public debt to the total public debt for the years (2014-2017) indicates the government's tendency towards internal debt sources to finance the general budget deficit after the shock of the drop in crude oil prices and the increase in military spending due to the ISIS organization. The study recommended a set of recommendations, most notably working on developing an economic policy or strategy for managing public debt and reducing reliance on internal and external borrowing except in cases of extreme necessity.

Abstract: This research aims to determine the impact of cognitive dissonance on customer experience quality. Cognitive dissonance is a crucial aspect for companies striving for high quality. A state of cognitive dissonance can contribute to a decline in customer experience quality because it affects the customer's evaluation of all stages of the experience, from initial impression to post-use. A sample of 120 managers and marketers was used for the research. A structured questionnaire was used as the primary data collection tool. The collected data was statistically analyzed using descriptive statistical analysis and path analysis, while the formulated hypotheses were tested using Spearman's rank correlation. The research results showed that cognitive dissonance has a positive impact on customer experience quality. The research concluded with several recommendations, the most important of which is that Zain Telecommunications Company in Baghdad should support its current cognitive dissonance approach across its three dimensions to keep pace with rapid and significant developments. Companies need to adopt a cognitive dissonance theory to achieve excellence.

Abstract: This research objectives to investigate the impact of oil fee fluctuations on Iraq's GDP within the framework of sustainable improvement during the period 2004–2023, given the rentier nature of the Iraqi financial system and its heavy reliance on oil sales. The take a look at is primarily based on the basis that oil fee volatility is a vital aspect in figuring out the trajectory of GDP, at once impacting monetary stability and the capacity for reaching sustainable development. The research employs a descriptive-analytical technique the usage of qualitative records evaluation. The findings indicate that the non-stop fluctuation in oil expenses has caused full-size volatility in GDP, contributing to weakened economic sustainability and improved fragility of economic increase in Iraq. The research recommends adopting financial policies aimed toward diversifying profits resources and decreasing dependence on oil, thereby improving the Iraqi financial system's capability to achieve long-time period sustainable improvement.

Abstract: This paper develops a composite quantile regression framework for the analysis of longitudinal count data. Unlike classical regression approaches that assume a continuous response, the proposed method explicitly accounts for the discrete nature of count outcomes while preserving their inherent smooth structure. The model is constructed using a flexible representation based on a growing mixture of asymmetric distributions, which allows the conditional distribution of the response variable to be captured across multiple quantiles. To facilitate Bayesian inference, a structured Gibbs sampling algorithm is derived for parameter estimation. The performance of the proposed approach is carefully evaluated through extensive simulation studies, demonstrating its robustness and efficiency under various data-generating scenarios. Furthermore, the methodology is applied to a real dataset from the field of neurology, illustrating its practical relevance and interpretability. Comparative analysis with existing models highlights the advantages of the proposed composite quantile regression approach for longitudinal count data.

Abstract: In the face of rapid changes in higher education, academic institutions have increasingly adopted high-involvement practices as crucial management strategies to engage faculty and improve performance. High-involvement practices encompass empowerment, information sharing, training and development, and rewards and are posited to strengthen organizational health is measured by seven,dimensions: managerial efficiency, amicable power relations, human resource development orientations and practices, team orientation, organizational values, innovation, and last one is morale.. Meanwhile, organizational health reflects an institution’s capacity to maintain internal balance and adapt to change, characterized by leadership efficiency, amiable power relations, human resource development orientation, collective orientation, organizational values, creativity, and high morale. This study aimed to examine the impact of high-involvement practices on organizational health in three Middle Euphrates region universities (Al-Qadisiyah, Kufa, and Al-Muthanna). A descriptive-analytical methodology was adopted, with data collected via a structured questionnaire administered to 252 faculty members across these universities. The data were analyzed using SPSS (v26) and AMOS (v23). The results revealed a significant positive relationship between high-involvement practices and organizational health. Among the high-involvement dimensions, empowerment and information sharing emerged as the most influential, leading to notable improvements in creativity and faculty morale. These findings underscore the value of participatory, empowerment-based management in fostering a healthier academic work environment.

Abstract: This research proposes a robust discriminant analysis method for classifying genetic sequences related to breast cancer, using real genomic data obtained from GenBank. Traditional discriminant methods such as Fisher’s Linear Discriminant Function and Sherrod’s model are known to be sensitive to outliers, which are common in genomic datasets due to biological variability and sequencing errors. To overcome this, the proposed method integrates robust estimators of location and scale along with a novel reweighting algorithm that reduces the influence of outlying gene expressions. The model’s performance is evaluated through a simulation study under clean and contaminated conditions, showing improved classification accuracy and reduced misclassification rates. For real data application, genetic sequences of breast tissue samples labeled as tumor and normal were analyzed. The robust model achieved superior accuracy in separating cancerous from non-cancerous samples, confirming its practical value in biomedical classification tasks involving noisy or high-variability data.

Abstract: The study aimed to demonstrate the extent to which artificial intelligence models can improve the accuracy of forecasting macroeconomic indicators compared to the traditional model used in analyzing these indicators in Iraq for the period 1990-2024. The variables or economic indicators included (GDP, inflation, oil price, exchange rate). The time series was tested for stationarity to apply the ARIMA model compared to artificial intelligence models LSTM and Transformer. Better results were obtained using artificial intelligence models compared to the traditional ARIMA model for all economic indicators. The study recommends the possibility of adopting the artificial model in forecasting instead of traditional models in decision-making and shaping future policies related to the economic aspect of Iraq.

Abstract: Quantile regression provides a flexible framework for modeling heterogeneous covariate effects across the conditional distribution of a response variable, yet existing Bayesian quantile regression methods often suffer from inefficiency in estimation accuracy and variable selection, particularly under non-Gaussian errors and high-dimensional settings. In this paper, we propose two new Bayesian quantile regression approaches: a Bayesian quantile regression model with a g-prior (NBQRg) and a Bayesian Lasso quantile regression model (NBLQR) based on the epsilon asymmetric Laplace distribution (EALD) as error term for quantile regression model. The proposed methods are evaluated through extensive simulation studies under a variety of scenarios, including sparse, very sparse, and dense coefficient structures, as well as settings with strong predictor correlations. Data are generated under normal, heavy-tailed, and skewed error distributions, and performance is assessed using the median of mean squared error (MMSE) alongside false positive and false negative rates for variable selection. The simulation results demonstrate that NBQRg consistently achieves lower MMSE than classical quantile regression and existing Bayesian counterparts across all quantiles and error distributions considered. Furthermore, NBLQR exhibits superior variable selection performance, yielding lower false negative rates and competitive false positive rates, particularly in sparse and highly correlated designs. Convergence diagnostics confirm stable posterior inference and efficient mixing of the proposed MCMC algorithms. Overall, the proposed Bayesian quantile regression methods offer substantial improvements in estimation accuracy, robustness, and variable selection, making them well suited for complex and high-dimensional regression problems.
Based on the results obtained through simulation applied to real data, the proposed method using the EALD error distribution yields better results compared to the classical method and the BQR method. Furthermore, comparison with the LASSO procedure proved its efficiency in estimating the parameters of the proposed model. Additionally, the Zellner's g-prior model was adopted, as it reduces the variance of the parameter estimates for the proposed regression model, thus enhancing the model's accuracy and increasing its explanatory power.

Abstract: Iraq's environmental crisis, over the past 20 years, has become incomparable. The explosion of dust-affected days — 122 annually in 2000 to 283 by 2022, a rise of 132% — has been recorded by the European Centre for Medium-Range Weather Forecasts. During dust storm events, fine particulate matter (PM2.5) concentrations in the three provinces exceeded WHO air quality guidelines by 14 to 18 times. We obtained hospital admissions spanning three provinces (between January 2000 and December 2024) from 32 governmental hospitals, resulting in a total of 864 months for data collection. These clinical reports were cross-referenced with high-resolution satellite data: MODIS-MAIAC at a 1-km level, TROPOMI at a 3.5-km level, and ERA5 reanalysis datasets.The analytical strategy used a negative binomial count model in a Bayesian hierarchical framework with Markov Chain Monte Carlo simulation (50,000 iterations). The results indicate that for every 10 μg/m³ increase in PM2.5, there was a 3.74% increase in admissions of respiratory cases (95% BCI: 2.86-4.63%). The model had a good fit with the performance results: DIC = 8,342.6 and RMSE = 198.3. The peak health effects of exposure were found to be 2 day in the distributed lags models (coefficie nt=0.0142). The highest level of spatial variation was found between geographical areas; Basra, Baghdad, and Karbala were the most sensitive 0.187, 0.134, and 0.089 respectively.

Abstract: It is through accounting and auditing Using artificial intelligence (AI) techniques such as expert systems, Knowledge representation and reasoning, machine learning to achieve that in Iraqi commercial bank this paper aim to affects. Research methodology The research adopted a descriptive–analytical method, and it used the primary data, as collected using structured questionnaire form to (a) be examined in 12 commercial banks on ISE. Data analysis Data are analysed using simple linear regression to examine the impact of AI applications on enhancing audit effectiveness. The results indicate that the expert systems have beneficial effects on bettering auditors accuracy and reliability of their judgments and knowledge representation, reasoning enhance analytical, cognitive skills in an auditing environment. Furthermore, by automating the identification of anomalies, machine learning enhances audit efficiency. 10 The broad ideas expressed in this paper are that: (i) AI systems would result in great benefits regarding quality, accuracy and timeliness of auditing whilst reducing human errors as well as assisting managements in making decisions – informed Ones I. buffalo.com/fishing last access on June 24th 2008. Embracing the AI for Iraqi Banking against money laundering can help to strenght their internal control systems, decrease the losses from being exposed in bad credit grants and thus increase financial reports reputation. The study recommend to advocate digitalization in audit office at the firms, generating training systems for auditors and AI tools designing frameworks about ethics and governance when using responsible AI encourage cooperation between IT personnel and auditors so as to increase both audit quality and professional independence.

Abstract: The current study aims to test the impact of the (DMAIC) model in enhancing the quality of service in Zain Iraq Telecommunications Company, and to identify the reality of the company's situation as a study community, in order to help it solve the problems it faces and develop appropriate solutions to overcome these problems and deal with the changing environment effectively and efficiently.
The study adopted the comprehensive enumeration method, meaning that the study community is the same as the study sample, and on this basis, an electronic form was adopted to be distributed to the target sample through agreement with the company's public relations department, and accordingly, the study sample included (282) valid answers for analysis from a community of (290), which means that the actual sample response rate reached (97%), and the descriptive analytical approach was relied upon in this study.
In order to analyze and interpret the collected data, some statistical methods were used, such as the arithmetic mean, standard deviation, coefficient of variation, relative importance, as well as the structural modeling equation (SEM) and the simple correlation coefficient (Pearson), and impact analysis using statistical analysis programs (SPSS & AMOS.V.29). Several results were reached, the most important of which was the existence of a correlation and influence relationship between the (DMAIC) model on the one hand, and enhancing service quality. This indicates that it is a system that derives its interactions from the reciprocity of the relationship between them on the one hand, and the integration in the structure on the other hand.
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Abstract: The current research sought to test the level of contribution of strategic renewal in its dimensions (exploring opportunities, entrepreneurship, and investing opportunities) in achieving organizational survival in its dimensions (adaptability, maintaining high performance standards, and achieving job performance goals). it adopted strategic renewal as an independent variable, while organizational survival came as a dependent variable. The questionnaire was used as a main tool for collecting data and information. A random sample consisting of (50) administrative leaders in the private colleges was selected. The descriptive analytical approach was used in presenting and analyzing the study information. For data processing, the researcher used a number of statistical methods, namely the weighted arithmetic mean, the intensity of the answer, the standard deviation, the coefficient of variation, the simple correlation coefficient (Pearson) and the simple and multiple regression coefficient.
One of the most prominent conclusions reached by the study is that the colleges of the research sample seek to enhance their interest in exploring new opportunities and entrepreneurship to invest in those opportunities, in addition to adopting a distinct structural design based on clear and streamlined organizational procedures for the flow of information within the leadership participation to exchange information to achieve its organizational survival.

Abstract: The research aims to develop an accurate model for the prediction of Bitcoin and Ethereum values using deep learning methods. These are significant digital assets in today's financial markets. Daily data collected from 2016 to 2023 was analyzed by LSTM and CNN and the combined LSTM-CNN model in order to assess their performance in handling complex time patterns and price shifts, usually characterizing cryptocurrency markets. The statistical results revealed that the LSTM model performed best according to performance metrics such as RMSE and MAE and MAPE and R². Moreover, strong generalization with precision in predictions while avoiding overfitting was depicted. These findings give enhanced statistical and economic performance that involves enhanced prediction accuracy and therefore helps investment and risk management in an evolving financial scenario. It highlights the usage of AI in analyzing digital markets, giving better insight than previous studies dealing either with traditional models or less integrated models. The findings mark progress toward more reliable economic models capable of handling complex dynamic financial data.

Abstract: This paper develops the Factorial Bayesian LASSO (FBLASSO), a hierarchical shrinkage method designed for high-dimensional factorial experiment designs where numerous main and interaction effects must be estimated simultaneously. The model incorporates distinct shrinkage parameters for main and interaction terms, enabling adaptive penalization and improving estimation stability in the presence of multicollinearity. A controlled Monte Carlo simulation, reflecting realistic agricultural conditions, demonstrates that FBLASSO achieves lower mean squared error and higher true positive rates compared with Ordinary Least Squares, classical LASSO, and Ridge regression. The method is further applied to a real wheat field experiment conducted in Al-Qadisiyah Governcy, Iraq, involving fertilizer levels, irrigation regimes, and cultivar types. Results show that fertilizer and irrigation exert the strongest main effects on yield, while only a limited subset of interactions is retained by the model. These findings highlight the effectiveness of FBLASSO in recovering influential factorial effects and producing interpretable results in complex agricultural experiments.

ABSTRACT:This study investigates the impact of macroeconomic factors on credit risk management in Iraqi commercial banks, addressing the limited empirical evidence on how macroeconomic instability affects banking credit portfolios in emerging economies such as Iraq. Using a panel data approach for 10 Iraqi commercial banks over the period 2015–2024, the research measures how variables such as GDP growth, inflation, and capital adequacy influence Non-Performing Loans (NPLs). The descriptive analysis focuses on the 2020–2024 period to capture recent economic shifts. The results, based on Fixed Effects estimation, show that the model is statistically significant (Prob > F = 0.000), leading to the rejection of the null hypothesis. Findings indicate that GDP growth and capital adequacy significantly reduce credit risk, while inflation and interest rates exacerbate it. The study recommends strengthening internal bank capital and enhancing macroeconomic stability to mitigate credit defaults.

Abstract: The impact study of economic variables varies among countries depending on the nature of their economic activities and their macroeconomic policies. For example, the rentier nature of the Iraqi economy has resulted in structural fragility, which has clearly affected the behavior of economic variables. This research focuses on two key monetary policy variables (monetary liquidity and interest rates) and examines their impact on bank credit, the primary channel for transmitting monetary policy effects to the real sector. The research also attempts to provide an applied model for the effectiveness of monetary tools in an unstable economic environment. Furthermore, the research employs an advanced econometric methodology using both time series and panel data for seven commercial banks for the period 2004-2024. Panel ARDL (Auto-Regressive Distributed Lag) models are applied, allowing for the measurement of both short-term and long-term effects. Three sub-models are designed to estimate the relationship: one for total bank credit, one for cash credit (direct financing), and one for contractual credit (guarantees and letters of credit). The assessment revealed varying relationships between the monetary policy indicators under investigation and both types of bank credit. In the long term, the results confirmed that bank liquidity plays a pivotal role in supporting banks' lending capacity. In the short term, the results demonstrated the existence of corrective mechanisms for imbalances, with adjustment times ranging from 0.67 to 1.22 years to return to long-term equilibrium. As is well known, monetary policy Quantitatively, the Panel ARDL estimation revealed that in the long run, a 1% increase in bank liquidity (cash-to-assets ratio) leads to an expansion in total bank credit by approximately 0.66%. Conversely, the interest rate exhibits a significant negative long-term impact, where a 1% increase is associated with an 11.23% contraction in total credit. In the short run, the error correction mechanism (ECM) coefficients, ranging from -0.67 to -1.22, confirm a statistically significant speed of adjustment towards long-run equilibrium. Empirical results further indicate a differentiated impact across credit types; cash credit is more sensitive to liquidity changes, while contractual credit shows greater inertia and is less directly influenced by short-term interest rate fluctuations in Iraq faces structural challenges that hinder the efficient transmission of its effects to the credit sector. Therefore, the research suggests the necessity of adopting balanced monetary policies that take into account the specific nature of the Iraqi economy, while emphasizing the importance of strengthening the bank's independence The central bank and the development of unconventional monetary policy tools. The research also suggests the need to address structural imbalances in the macroeconomy to support the effectiveness of monetary policy in achieving its objectives.

Abstract: This research aims to study the impact of the level of accounting conservatism on the performance indicators of a sample of Iraqi banks listed on the Iraq Stock Exchange during the period 2019-2023. The research seeks to examine the extent to which accounting conservatism contributes to improving the quality of earnings and, consequently, enhancing the value and sustainability of the banks. To achieve the research objectives, a sample of five commercial banks was selected. The Givoly & Hayn (2000) Total Negative Benefits model was used to measure the accounting conservatism variable, while the dependent variable, financial performance, was measured through profitability indicators, namely return on assets (ROA) and return on equity (ROE). To analyze the data and test the hypotheses, the research used several specialized statistical programs, including SPSS version 26, to test correlations and statistical significance. The results showed a low level of accounting conservatism applied in the banks included in the study sample .The lack of impact of accounting conservatism on either return on assets (ROA) or return on equity (ROE) indicates that the level of accounting conservatism applied does not directly affect performance indicators. . This may be attributed to the influence of other operational and structural factors on performance indicators, which dilutes the impact of accounting conservatism on both ROA and ROE.One of the most important recommendations of the research is to encourage banks to apply balanced accounting reservations that ensures improving the quality of measuring profits without affecting the financial performance indicators in order to achieve financial stability as well as to improve the quality of accounting information available in the financial statements.

Abstract: This study investigates heterogeneity in the reported effectiveness of the Bacillus Calmette Guérin (BCG) vaccine against tuberculosis (TB) using a meta-regression framework. Despite its long history, the BCG vaccine has shown wide variability in protection levels across studies, which necessitates a systematic quantitative assessment. Meta-regression was employed to identify study-level factors influencing vaccine effectiveness and to compare two classical estimators of between-study variance: DerSimonian–Laird (DL) and Restricted Maximum Likelihood (REML). A simulation study with 100 replications and a real-data analysis using 30 published clinical trials were conducted. Performance was evaluated using bias, average mean squared error (AMSE), and coverage probability. The results demonstrate that the REML estimator consistently provides lower bias, smaller AMSE, and more accurate coverage probabilities than DL, both in simulated and real settings. The real data analysis further revealed that geographic latitude, study design, and diagnostic methods significantly contribute to heterogeneity in BCG effectiveness. Overall, REML is recommended as a more reliable and efficient estimator for modeling heterogeneity in classical meta-regression applications.

Abstract: This study aimed to analyze the impact of digital transformation on business growth from the perspective of a sample of senior and middle management in private banks in DhiQar Governorate. The study population consisted of employees of private banks listed on the Iraq Stock Exchange in the province, namely: Al-Ahli Iraqi Bank, International Development Bank, and Iraqi Commercial Bank, with a sample size of 50 individuals. The study relied on a descriptive analytical approach, and data were collected through a questionnaire distributed to the sample individuals, then analyzed using appropriate statistical methods. The study findings revealed a statistically significant correlation between digital transformation dimensions and business economics, indicating that increased interest in implementing digital transformation contributes positively to enhancing business economics growth in the banks surveyed. The results also showed a significant causal effect of digital transformation as a total variable in business economics, confirming the pivotal role of digital transformation in improving banking performance, increasing operational efficiency, and enhancing competitiveness. In light of the findings, the study recommended that private banks adopt comprehensive digital transformation strategies, intensify investment in digital infrastructure, and develop the digital skills and capabilities of human resources to ensure sustainable growth and enhance competitiveness in the banking market.

Abstract: This study aimed to conduct a comprehensive analytical assessment of childbirth patterns in government hospitals across three major Iraqi governorates: Baghdad, Basra, and Erbil. These governorates were selected due to their demographic, geographic, and health-system variation. The study employed a quantitative research approach using secondary analysis of data from the 2018 Multiple Indicator Cluster Survey (MICS), one of the most important and nationally representative health surveys in Iraq.
The final analytical sample consisted of 1,293 births after excluding, 128 cases with missing or undefined information regarding the place of delivery, amongst women of reproductive age (15-49 years) from the three governorates, with sample weights carried out to ensure the representativeness of the results. researchers conducted descriptive and inferential statistical analyses, inclusive of descriptive statistics, statistical significance assessments, and logistic regression models, using the Stata software model 16. The findings revealed significant regional variations (P = 0.0008) within the prices of utilisation of presidency hospitals for childbirth. Basra Governorate recorded the very first rate percent at 85.1% (95% CI: 80.9%–88.6%), observed This study aimed to conduct a comprehensive analytical assessment of childbirth patterns in government hospitals across three major Iraqi governorates: Baghdad, Basra, and Erbil. These governorates were selected due to their demographic, geographic, and health-system variation. The study employed a quantitative research approach using secondary analysis of data from the 2018 Multiple Indicator Cluster Survey (MICS), one of the most important and nationally representative health surveys in Iraq.
The final analytical sample consisted of 1,293 births after excluding, 128 cases with missing or undefined information regarding the place of delivery, amongst women of reproductive age (15-49 years) from the three governorates, with sample weights carried out to ensure the representativeness of the results. researchers conducted descriptive and inferential statistical analyses, inclusive of descriptive statistics, statistical significance assessments, and logistic regression models, using the Stata software model 16. The findings revealed significant regional variations (P = 0.0008) within the prices of utilisation of presidency hospitals for childbirth. Basra Governorate recorded the very first rate percent at 85.1% (95% CI: 80.9%–88.6%), observed with the useful resource of Baghdad Governorate at 63.2% (95% CI: 58.2%-67.9%), after which Erbil Governorate at 57.2% (90 5% CI: 41.5%-71.7%).
Multivariate logistic regression evaluation confirmed that financial elements play a pivotal position in determining the selection of birthplace, with wealthier individuals decreasing their chances of the usage of government hospitals by way of 63% (odds ratio: 0.37, 95% CI: 0.16-0.84).
The study concludes that there may be an urgent need for targeted fitness guidelines that do not forget regional specificities and paintings towards equalising health offerings throughout governorates. It also recommends further qualitative research to recognise the underlying elements at the back of those disparities.

Abstract: Understanding the impact of credit risk on the profitability of Iraqi commercial banks is the central question of our study. The study aimed to determine the impact of credit risks on the profitability ratios of commercial banks and to work on reducing this impact on bank profits by developing safe and low-risk investment methods. The Eviews statistical software was used, and the statistical methods employed included descriptive statistics, correlation analysis using Pearson's correlation coefficient, and linear regression analysis to assess the reliability of the study indicators. The study sample consisted of eight Iraqi commercial banks (Bank of Baghdad, Gulf Commercial Bank, Commercial Bank of Iraq, Basra International Investment Bank, United Investment Bank, Iraqi Credit Bank, Iraqi Investment Bank, and Middle East Investment Bank). The study period was 10 years (2010-2020). The results confirmed that credit risk plays an effective role in the banking profits of the banks in the study sample, and that an increase in credit risk leads to a decrease in banking profitability ratios. The results also showed that there is a statistically significant negative effect of loan-to-deposit ratios, loan-to-total-loans ratio, and leverage-to-total-assets ratios on return on assets (ROA) and return on equity (ROE). The results also showed a statistically significant negative impact of the ratio of non-performing loans to total deposits (CDCTR) and the ratio of loan losses to total loans (PLLR) on return on assets (ROA) and return on equity (ROE). The study recommended focusing on developing credit management in commercial banks to achieve lower credit risk levels and effectively control them.

Abstract: Despite the growing interest in metaverse as a promising three-dimensional digital environment in marketing, capable of transforming the entire marketing landscape through immersive and engaging experiences, the literature still suffers from a clear lack of empirical studies explaining the mechanisms by which metaverse marketing influences consumer purchasing intention, particularly among online platform users. Addressing this research gap, this study aims to analyze the impact of metaverse marketing on purchasing intention within a theoretical framework based on consumer behavior in immersive digital environments. This framework explains the relationship through innovative marketing dimensions that reflect interaction and immersion. The study employed a quantitative approach using an online questionnaire distributed to 200 online platform users, representing the youth demographic. The data were analyzed using SmartPLS. The study offers a scientific contribution by testing an empirical explanatory model that elucidates the role of metaverse marketing in shaping purchasing intention. The results demonstrated a significant positive impact of all metaverse marketing dimensions on purchasing intention, providing important theoretical and practical implications.