Al Kut Journal of Economics and Administrative Sciences

The study seeks to investigate the direct and indirect pathways from positive emotional behavior (PEB) to sustainable growth (SG), within private universities in Iraq. This study draws upon the broaden-and-build theory and the dynamic capabilities theory to understand how the two intrinsic forces (emotion and culture) interact to drive organizational sustainability. A quantitative approach was employed, targeting academic staff. A total of 298 valid questionnaires were obtained and analyzed using structural equation modelling (SEM). Results show that PEB conceptualized through optimism, enthusiasm, and empathy positively influences employees’ innovation behavior (EIB), reflected in idea generation, idea promotion, and idea implementations, which in turn significantly promotes SG across its economic, social and environmental aspects. The direct path from PEB to SG was non-significant, suggesting that the effect of positive emotionality on sustainability ambitions is primarily mediated by innovation-driven effects. Additionally, it was established that cultural readiness captured through adaptability, involvement, and mission positively influences the paths from PEB to EIB and from EIB to SG. Theoretically, the study enhances knowledge of the emotional and cultural forces of institutional sustainability. Practically, it advises university leaders and policymakers to establish emotionally-driven and culturally adaptive systems that support sustainable innovation growth.

The research examined how oil price fluctuations affect M2 money supply in Iraq throughout the period from 2004 to 2023 based on monthly statistical records. The research aimed to determine how oil price fluctuations impact monetary supply within nations which export oil such as Iraq. The research employed threshold regression modeling to analyze how this connection evolved through time while showing non-linear patterns. The Phillips-Perron test served our purpose to determine if the data maintained stationary properties. The research findings demonstrated that oil prices maintain stationary behavior in their original form and M2 data becomes stationary after performing its first difference. The threshold model identified three threshold points which established four distinct data segments. The data shows that M2 responds positively to oil price changes in both low and moderate economic environments. The effect becomes negative in high regime settings while the very high regime produces a non-significant weak effect. The model successfully passed all diagnostic tests which checked for autocorrelation and heteroskedasticity and the residual data appears free from major issues. The research findings demonstrate that oil price movements create different effects on money supply based on current monetary conditions. The development of monetary and fiscal policies by policymakers needs to take into account these economic factors which affect nations that heavily rely on oil production.
The research includes the following essential terms: oil prices and money supply and Iraq and threshold model and nonlinearity.

The digital reform in the financial management of the government is one of the core and important aspects of nowadays finance system, as it provides a set technical capacity and supervision support for improving efficiency on public money management, reducing the waste to happen, guaranteeing transparency and accountability. Digital financial systems allow the government to enhance the management of financial information, speed up disbursement processes, ensure control and warrant data driven decision making in finance. The objective of this study is to evaluate the overall effect of digital technologies in financial systems on improvement of government spending's efficiency. Theoretical and Analytical Framework for the variables, Review of related literature and identify research gap, Formulation of scientific hypothesis which can be field tested in further studies

This study aimed to identify the impact of the quality of banking services on financial inclusion through a sample of administrative employees. For this purpose, the questionnaire was used to collect data that will be used to achieve the study objectives, utilizing a randomly selected sample of 242 participants. The descriptive analytical method was applied, and data analysis was conducted using (SPSS). Statistical techniques employed included the arithmetic mean, standard deviation, Pearson correlation, Cronbach's alpha for reliability assessment, and regression analysis.
The study included a basic hypothesis to determine the impact of the quality of banking services on financial inclusion.
The study concluded that there is a significant, statistically significant impact of the quality of banking services on the financial inclusion of the banks that were studied. It also showed that there is a significant, statistically significant impact of the dimensions of the quality of banking services, represented by tangibility, reliability, responsiveness, security, and empathy, on financial inclusion.
This study concluded with a set of recommendations: Among the most important of them is employing distinguished expertise that can be relied upon to provide high-quality services to bank customers, ensuring that services are provided promptly and quickly to commercial bank customers and meeting their needs, and the necessity of providing assistance to commercial bank customers.

Principal Component Analysis (PCA) is one of the most important statistical methods in factor analysis. It is used to reduce data dimensions and extract the underlying factors that represent the underlying structure of variables. This research aims to study the effectiveness of the principal components approach in anal yzing data from a sample of (719) large industrial establishments in Iraq in 2020, using the JASP program .The study relied on a set of basic steps applied to principal components analysis, which included (data preparation and normalization, calculating the variance-covariance matrix, extracting the principal components using singular value decomposition (SVD), and interpreting the extracted factors in light of the explained variance).
The results showed that the principal components analysis method clearly contributed to simplifying the data structure by reducing the number of input variables while retaining most of the total variance, allowing for a clearer and more accurate interpretation of the factors influencing the performance of large industrial establishments. This approach also helped uncover common patterns among variables and obtain a more comprehensive picture of the economic data under study.

The purpose of this study is a comparison between classical approaches and machine learning models in forecasting the parallel and official Iraqi dinar exchange rate. The study utilized the creation of models ARIMA and NNAR on monthly bases for the time span (2004-2024). The exchange rate is an essential mechanism connecting the local economy with the global economic system. Through understanding the pattern of the exchange rate and exploring the variables that assume roles in influencing it, it becomes feasible to design future estimations on its movement, which assists economic units in having appropriate decision-making on how to handle potential risks and dangers. The results indicated that the NNAR model with the neural network surpassed the ARIMA model in accurately forecasting exchange rates, particularly in terms of the auction price. The root mean square error (RMSE) in the NNAR auction model decreased from 14.18 in the ARIMA model to 11.15, and the mean absolute percentage error (MAPE) also dropped from 0.2837 in the ARIMA model to 0.2396.

this research aims to study the impact of applying the principles of banking governance and financial disclosure indicators in enhancing the confidence of depositors and investors, considering that trust is the main pillar for the stability of the banking sector and increasing the flow of capital. The importance of the research is to show the fundamental relationship between transparency and disclosure on the one hand, and the decisions of depositors and investors on the other hand, especially in light of economic challenges and the fluctuations of financial markets. The research relied on the descriptive-analytical method, with the use of statistical methods to measure the impact of financial disclosure and governance indicators in a sample of commercial banks, during the period (2019-2024). The research found that the application of banking governance rules and raising the level of disclosure leads to reducing the information gap and enhancing customer confidence, which reflects positively on the stability of banks and increases their ability to attract deposits and investments

High-dimensional data represent one of the major challenges in statistical modeling, as the inflation in the number of predictors often leads to multicollinearity and instability in the estimates of multiple regression coefficients. Principal Component Analysis (PCA) is considered an effective statistical tool to address this issue by transforming correlated variables into a set of uncorrelated components while retaining the maximum possible variance in the data. This study aims to evaluate the role of PCA in improving the performance of multiple regression in high-dimensional settings, by applying it to synthetic simulated data (300–600 observations, 50–300 predictors) and to real-world data (Coffee Quality Dataset, 1,339 observations, 43 variables). The results of Principal Component Regression (PCR) were compared with Ordinary Least Squares (OLS) and regularized models such as Ridge, Lasso, and Elastic Net. The models were evaluated using common statistical measures, including the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (R²), in addition to cross-validation. The findings revealed that applying PCA prior to regression effectively reduced the impact of multicollinearity and improved the accuracy and stability of the models, emphasizing PCA as a practical and efficient approach for handling high-dimensional data and enhancing predictive modeling.

This research aims to demonstrate the importance of lean green auditing and its role in achieving sustainable development, through an analytical study of the impact of applying modern auditing methods that combine flexibility (agility) with environmental responsibility (green). The research relied on a descriptive analytical approach, and the study population included a group of administrative competencies and employees in regulatory and accounting bodies in government institutions. (150) questionnaires were distributed, and (130) valid questionnaires were retrieved for statistical analysis. The results showed a strong significant impact and positive correlation between lean green auditing and achieving sustainable development goals. This indicates that implementing this type of audit effectively contributes to raising the efficiency of regulatory performance and improving the quality of reports, as well as reducing waste of time and resources and enhancing adherence to environmental, social, and institutional standards. The research emphasized a set of fundamental recommendations, most notably: The necessity of adopting lean and green methods in auditing work, given their impact on improving efficiency and effectiveness and reducing red tape. Organizing specialized training courses for those working in the regulatory field to enhance their skills in using modern technologies and environmentally friendly practices. Encouraging institutions to integrate environmental and social performance indicators into their periodic audit plans to ensure their alignment with sustainable development goals. Emphasizing rapid response to environmental and economic changes and adopting a culture of continuous improvement in audit work. Thus, the research demonstrates that agile green auditing is an effective strategic tool for improving institutional performance and achieving a balance between economic, environmental, and social objectives within the framework of sustainable development

The study aims to explain the effect of perceived responsible marketing on customer trust and brand patronage with the mediating role of consumer social responsibility in Al-Rafidain Bank, Baghdad Branch. The study was applied in terms of purpose and descriptive-correlational in terms of method and was conducted with a quantitative approach. The statistical population included customers of Al-Rafidain Bank in Baghdad, and given the unlimited nature of the population, the sample size was determined as 384 people based on the Cochran formula. The data collection tool was a standard questionnaire with a five-point Likert scale that measured the four main research constructs: perceived responsible marketing, consumer social responsibility, customer trust, and brand patronage. The validity and reliability of the tool were confirmed using Cronbach's alpha, composite reliability, and average variance extracted (AVE). The data were analyzed with Smart PLS version 4 software and structural equation modeling. The findings revealed that perceived responsible marketing has a positive and significant impact on both variables of customer trust and willingness to support the brand. From the point of view of Al-Rafidain Bank, which has faced long periods of bureaucracy and economic change, responsible marketing as a social capital-building approach is very important in establishing brand loyalty and legitimacy

The study addresses the classical transportation problem, which is central to operational research and logistics decision-making. Traditional transportation models often focus on minimizing costs under supply and demand constraints but tend to overlook the efficiency of network structures and alternative optimization strategies. This research seeks to determine whether transforming conventional transportation models into a multi-objective linear programming framework, while incorporating shortest path analysis, can yield more practical and cost-effective solutions. The research applies multi-objective linear programming techniques to three case studies from Union Food Industries Company, where commodities are transported from production sites to demand centers. Conventional allocation methods (Northwest Corner, Least-Cost, Vogel’s Approximation) were first applied to generate feasible initial solutions. These were then tested for optimality using the Modified Distribution Method. In parallel, the transportation models were reformulated as network optimization problems and solved using the shortest path method, allowing direct comparison between classical and network-based solution. The analysis demonstrated that shortest path optimization consistently reduced transportation costs compared to conventional methods. For example, in one case, the optimal cost decreased from $440 to $360 when applying shortest path modeling. The results also showed that while shortest path solutions sometimes initially violated feasibility conditions, minor adjustments restored balance without sacrificing optimality. Overall, the approach proved efficient, reliable, and computationally less complex than traditional iterative methods. Practically, the study highlights the potential of shortest path models to improve cost efficiency in transportation planning for industrial firms. Academically, it contributes to the field of operations research by integrating classical transportation theory with network-based optimization, offering a framework that balances multiple objectives while simplifying application. This work underlines the importance of modernizing transportation analysis to better support organizational decision-making.

Adopting DFT technology, therefore, can promote the insurance industry to shift towards more operation simplification. This paper examines the impact of DFT on the operational simplification of investment management at six large international insurers from 2016 to 2025: Allianz (Germany), AXA (France), Zurich Insurance Group (Switzerland), Prudential Financial (USA), MetLife (USA), and Ping An Insurance (China). Based on panel data, this paper examines the effect of different KPIs or operational simplifications on investment return, portfolio diversification, operating cost, and risk-adjusted performance. It identifies a statistically significant positive association at both local and regional levels between the DFT efforts and the operational simplification of investment management. It helps the practitioners of the sector fully exploit the digital innovations to serve their invested strategy.

This research shows the role of the net public budget in shaping the official and parallel exchange rates in Iraq from 1990 to 2024. It employs a threshold regression model that allows the budget effects to differ across regimes. The linear correlation between the budget and the two exchange rates is small, while the nonlinear correlation is considerably larger, which explains the choice of the threshold regression model. The Bai and Perron search algorithm finds two significant thresholds for each market, which are -1,372,342 and 44,022 for the official exchange rate, and -173,783 and 44,022 for the parallel exchange rate. The models are well-specified and satisfy tests of normality, autocorrelation, and heteroskedasticity. In the official exchange rate equation, the budget coefficient is positive and significant in the deep and moderate deficit regimes, while it becomes negative in the high surplus regimes.
For the parallel rate, the coefficient is large and positive in deep deficits, negative in the middle regime, and negative again in high surplus, as expected to ease market pressure as fiscal conditions tighten. These results suggest that exchange rate responses are asymmetric and regime-dependent, with larger and quicker responses in the parallel market. Policy should focus on budget settings that achieve outcomes above the 44,022 threshold if possible, develop buffers to stabilize oil revenue fluctuations, and eschew money financing of deficits that would boost prices and foreign currency demand The findings offer practical advice on budget execution coordination with foreign exchange management and tracking regime changes that signal increased pressure in the parallel market