Search Results for operational-efficiency
Abstract
The aim of the research is to identify the external determinants of the performance of the banking sector, represented by growth rate in gross domestic product (GGDP) and inflation (I), and the internal determinants represented by size (S), operational efficiency (OE) and financial intermediation (FM) for the period (1996 to 2017), in some of the Arabic countries represented by (Egypt, Jordan, Saudi Arabia, the Emirates). To reach this goal, cross-sectional time-series models (Pooled regression model, fixed effects model, random effects model) were used. These models were compared using the restricted F test and Hausman's test, and it was found that the random effects model is the appropriate model to represent the relationship between the research variables.The results of the research revealed that there was no effect of the external determinants (growth in GDP and inflation) and financial intermediation as one of the internal determinants on the performance of banking sectors, expressed in return on capital (ROE), because the relationship between these variables was not significant, in addition to the existence of a negative impact of a significant indication of the volume on the performance of the banking sectors and a positive impact with a significant effect of the operational efficiency on the performance of that sector. The research recommended the necessity for the supervisory and supervisory authorities to pay special attention to size and operational efficiency for their clear impact on the performance of the banking sectors, the research sample.
Abstract
Given the challenges facing Iraqi industry—such as limited government support and the high cost of production compared to imported goods—industrial organizations are in urgent need of adopting effective technologies to improve their operational efficiency. Supply chain analysis stands out as one of the most significant of these technologies, as it helps optimize the flow of materials and information, reduce costs, and enhance the accuracy of accounting data.
This study aims to highlight the impact of supply chain analysis on strengthening the effectiveness of the accounting information system through a field application at the National Company for Chemical and Plastic Industries. The findings indicate that integrating supply chain activities with the accounting system contributes to improving the quality of financial information, controlling costs, and supporting managerial decision-making. The study further recommends the development of digital systems that integrate logistical and accounting functions, along with training personnel on modern technologies.
Keywords: supply chain, accounting information system, operational efficiency, cost reduction , effective and ineffective activities.
Abstract
Rapid evolution of the Internet of Things (IoT) has transformed the urban infrastructure because it is possible to interconnect systems that can improve transportation, healthcare, security, energy management, and public services. Implementation of IoT network in Iraqi cities faces numerous constraints including lack of infrastructural development, network saturation, unstable connections, poor cybersecurity and lack of resource management. These factors largely affect dependability and functionality of smart city applications. This paper examined how Artificial Intelligence (AI) methods applied to improve IoT network performance in Iraqi cities through effective data routing, predictive maintenance, anomaly detection, bandwidth optimization, and intelligent resource management. Machine learning, Deep Learning and reinforcement learning methods were commonly used methods to address the conditions of dynamic networks and maximize operational efficiency. The study aims at the application of AI techniques to strengthen current IoT systems to enhance scalability, minimize latency, enhance security, and energy efficiency. The research, based on analytical modeling and comparative performance analysis, shows that AI-based IoT systems significantly improve technological resilience of cities and assist Iraq in the development of smart cities. The results offer viable advice to policy makers, network engineers and urban planners who wish to develop modern cities in Iraq using intelligent technology.
Abstract
This study aimed to measure the impact of digital transformation on the financial performance of the Iraqi banking sector during the period (2020–2025). To achieve the objectives of the study, the descriptive-analytical approach was adopted through the analysis of financial data obtained from a sample of Iraqi banks under investigation. In parallel, a quantitative approach was employed using a questionnaire administered to a sample of (777) respondents. The results revealed a statistically significant statistically significant association between digital transformation variables, represented by digital services, artificial intelligence, and technological platforms, and financial performance indicators, namely Return on Assets (ROA) and Return on Equity (ROE). The findings also indicated that digital transformation contributes effectively to reducing operating costs and enhancing the operational efficiency of the surveyed banks. Based on these findings, the study proposed a set of recommendations focusing on the necessity of strengthening digital infrastructure, intensifying specialized training programs for human resources, and developing cybersecurity frameworks to ensure the sustainability of financial performance within the rapidly evolving digital environment.
Abstract
The research problem stems from the increasing challenges facing the telecommunications sector amidst a highly competitive and rapidly evolving technological environment. Organizations in this sector are striving to adopt Enterprise Resource Planning (ERP) systems to enhance their performance; however, there remains a knowledge gap regarding the actual and tangible impact of these systems on organizational performance indicators within the local context. Therefore, this research aims to investigate the impact of implementing ERP system dimensions (Information Integration, Information Quality, and Process Efficiency) on enhancing organizational performance efficiency dimensions (Operational Efficiency, Performance Quality, and Speed of Completion) within telecommunications companies operating in the Middle Euphrates provinces (Babylon, Karbala, and Al-Najaf Al-Ashraf). The research adopted the descriptive-analytical approach. The results revealed a statistically significant positive correlation between the implementation of ERP systems and organizational performance efficiency. Regression analysis indicated that ERP implementation explains 52% of the variance in organizational performance efficiency. Detailed findings showed that the "Process Efficiency" dimension was the most influential on "Operational Efficiency" and "Speed of Completion," while the "Information Quality" dimension was the strongest predictor of "Performance Quality." Based on these results, the research recommends that companies invest in improving data quality, re-engineering processes to align with system requirements, providing continuous training programs for employees, and ensuring top management support. The research also suggests conducting future studies that incorporate mediating variables, such as organizational culture, and expanding the scope to include other economic sectors using diverse research methodologies to deepen the understanding of this relationship.
Abstract
This research aims to examine the role of time-directed resource consumption accounting (TD-RCA) techniques in achieving competitive advantage for economic units by improving costing accuracy, rationalizing resource consumption, and enhancing product quality. TD-RCA relies on analyzing resource consumption based on the actual time of activities, which helps determine the fair cost of products, reduce waste, and achieve higher operational efficiency.
Process reengineering also contributes to the redesign of manufacturing activities with the aim of eliminating unnecessary processes and enhancing customer value, leading to reduced costs and improved product quality. The research focuses on the application of these two techniques in a ready-made garment factory in Najaf. The results showed that combining the two techniques helps reduce overall costs, increase the factory's competitiveness, and enhance its responsiveness to market demands.
The research recommends the adoption of these modern techniques in cost management, given their positive impact on achieving a sustainable competitive advantage, achieving optimal resource utilization, and enhancing production efficiency.
Abstract
Given the swift digital changes occurring in the Banking industry, the purpose of this paper is to examine how well artificial intelligence systems can forecast and protect against future disasters. By utilizing its skills in big data analytics, forecasting financial behavior, and more accurately and effectively managing risks, artificial intelligence (AI) is increasingly regarded as a crucial component in the development of banking systems and improving their operational efficiency.
By enhancing client satisfaction, tailoring banking services to meet the demands of each individual, and cutting down on operational errors and administrative expenses, banks hope to gain a competitive edge by utilizing these technologies. AI also helps to speed up credit decisions, make it possible to identify financial crime early, and create clever marketing plans based on forecasts of future market trends.
In order to ensure financial sustainability and achieve integration between digital transformation and the demands of banking innovation, studies show that the future of AI encompasses strategic, cultural, human, technological, and organizational dimensions in addition to technical ones.
The paper also examined a number of anticipated long-term effects of AI applications, such as increased forecasting precision, lower operating expenses, better customer satisfaction, increased worker productivity, and assistance with investment choices. The findings show that implementing AI applications in the banking sector is a strategic requirement to guarantee long-term growth and competitiveness in the digital era, not a technical luxury.
In order to enhance lending decisions and lower default risks, the paper also assesses how well a number of categorization algorithms work in assessing loan applicants' creditworthiness. Using a dataset that represented the traits and financial activities of clients, seven machine learning techniques were used: Gradient Boosting, Random Forest, Extra Trees, Gaussian Naive Bayes, Logistic Regression, SVC-RBF, and KNN.
The paper used a database of 21 variables for loan applicants. Numerical variables included (age, income, credit score, debt-to-income ratio, and loan amount). Descriptive variables included (loan purpose, region, marital status, employer, educational level, and application channel). Binary variables included (whether or not the applicant had a history of default). These variables were used to predict the approval or rejection decision, with the dependent variable being represented by two values: 0 for rejection and 1 for approval.
The models were evaluated using the following six key performance indicators: Accuracy, Precision, Recall, F1 Score, Receiver Operating Characteristic Area Under the Curve (ROC AUC), and Brier Score. The findings demonstrated that the Gradient Boosting algorithm performed best overall in both probability prediction quality and customer differentiation across different risk levels. The Random Forest algorithm, which showed stability and balanced metrics, came next. On the other hand, despite its moderate performance, Logistic Regression provided great interpretability, while the Gaussian Naive Bayes algorithm demonstrated high sensitivity in identifying high-risk customers. In terms of overall accuracy and probability quality, some models—like SVC-RBF and KNN—performed worse.
Abstract
The research aimed to determine the extent to which tourism training strategies, with their dimensions (analyzing tourism training needs, setting training objectives, implementing training, evaluating training), contribute to improving organizational performance with its dimensions (operational efficiency, financial performance, customer satisfaction) through sustainable development with its dimensions (economic dimension, social dimension, environmental dimension) The research problem was embodied in identifying the challenges facing the companies under study in designing their training programs while integrating the dimensions of sustainable development to achieve outstanding organizational performance. A questionnaire was distributed to (60) employees and (5) were excluded for scientific and methodological reasons in a General Authority for Tourismin Baghdad Governorate. The descriptive and analytical approach was used to present and analyze the data. The (SPSS v21) program was used to analyze the data using statistical methods and the use of the correlation coefficient (Pearson), the simple regression analysis method, Multiple linear regression، and the F, t test. One of the most important conclusions reached by the research is that there is a strong correlation between tourism training strategy and organizational performance in the tourism companies studied, and that sustainable development plays an important mediating role in the relationship between tourism training strategy and organizational performance. This means that the effectiveness of training increases when it is integrated with sustainability practices to achieve outstanding organizational performance. The most important recommendations that the research provides are: adopting an integrated strategic framework for tourism training and moving Iraqi tourism companies from the traditional method of implementing courses to a strategic training management model based on linking needs analysis, long-term company plans, and organizational performance indicators, to ensure the sustainability of the training impact.