Information Technology in Economics and Business
Information Technology in Economics and Business (ITEB) is an interdisciplinary academic journal dedicated to advancing research at the intersection of information technology, economics, and business. The journal provides a platform for scholars and researchers to publish high-quality original research articles, reviews, and perspectives on the latest developments, trends, and innovations in the application of information technology within the realms of economics and business. ITEB publishes original research articles, review papers, technical notes, and case studies on a wide range of topics, including but not limited to: - Business Intelligence and Analytics - Economic Forecasting and Simulation Technologies - E-commerce Systems and Digital Marketing - Financial Technologies and Blockchain Applications - Smart Cities and IoT Solutions - Enterprise Resource Planning and Management Information Systems - AI and Machine Learning in Business - Blockchain in Economics - Cybersecurity in Business Environments Our journal aims to bridge the gap between academia and industry by fostering collaborations, facilitating knowledge sharing, and promoting innovation in the rapidly evolving field of economics and business technology. We welcome contributions from researchers and practitioners worldwide and encourage interdisciplinary approaches that address the challenges and opportunities at the nexus of IT, economics and business. ITEB is committed to upholding the highest standards of scholarly excellence and integrity. All submissions undergo a rigorous peer review process by experts in the field to ensure the quality, validity, and significance of the published work. We invite you to explore our journal and become part of our vibrant community dedicated to advancing business technology for global economic development.
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Smart Claims Management in the Insurance Sector from a Digital Transformation Perspective
This study examines digital transformation in the insurance sector within the framework of smart claims management. The aim of the research is to systematically analyze claim-focused digitalization disclosures included in the most recent sustainability reports of insurance companies listed on Borsa Istanbul from the perspectives of production technologies and digital transformation. To this end, a content map and coding framework were developed using content analysis, encompassing five core components: digital claims management, risk and fraud analytics, image processing, scoring systems, and claim cycle time. The codes were graded to reflect the level of evidence intensity and operational concreteness in the reports, and the findings were comparatively evaluated on a company-by-company basis. The results indicate that the extent to which claim-focused digital transformation is represented in sustainability reports is not homogeneous across companies. While some firms provide higher levels of evidence by supporting operational components such as digital claims management and claim cycle time with metrics, analytically intensive and advanced technology-driven components are reported more limitedly and are generally expressed through broad or declarative statements. The study contributes to the literature by conceptualizing smart claims management as an integrated process technology ecosystem that combines operational digitalization, analytical decision support, and advanced automation, and by demonstrating that sustainability reports can serve as an analytical source for the comparative evaluation of digital transformation visibility.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0/
Parametric Discrete Event Simulation for Performance Evaluation and Decision Support in Production Systems
Modern production systems operate under increasing uncertainty due to fluctuating demand, limited resources, and system disruptions such as machine failures and maintenance activities. From an economic and managerial perspective, evaluating the performance of such systems is critical for supporting operational decision-making related to capacity planning, resource utilization, and service efficiency. However, traditional analytical approaches often require restrictive assumptions and fail to capture the dynamic nature of real-world production processes. In this study, a parametric discrete event simulation model is proposed as an a computational/software based decision support tool for the performance evaluation of a representative production service system. The model captures key operational parameters, including arrival rate, service time, failure probability, and maintenance duration, which directly influence system efficiency and economic performance. The model is evaluated through repeated simulation experiments to obtain statistically reliable performance indicators. In particular, the impacts of variations in service time and arrival rate on business-relevant performance metrics such as average waiting time, system availability, resource utilization, number of failures, and the number of serviced entities are systematically analyzed. The results demonstrate that increases in service and arrival intensities lead to performance degradation, highlighting critical trade-offs between system capacity, operational efficiency, and service quality. The proposed approach provides a practical and computationally lightweight framework for preliminary performance analysis and operational decision support in production and service-oriented systems. In addition to its applicability for early-stage economic evaluation, the model also offers educational value by enabling a clear understanding of discrete event simulation principles within an information systems context.
Mapping Economic Considerations in Clinical Artificial Intelligence Research
Artificial intelligence (AI) has been rapidly adopted in clinical research over the past decade, yet the extent to which economic considerations are integrated into this literature remains unclear. This study presents a large-scale bibliometric analysis of clinical AI research indexed in the Web of Science. Temporal analyses span 2000–2024 (54,219 clinical AI studies), while network mapping, citation overlay, and density analyses focus on the 2024 snapshot (N = 14,995). A ratio-based indicator was used to track the relative prominence of economic considerations over time. The results show a sharp acceleration in clinical AI publications after 2015, while studies explicitly addressing cost, cost-effectiveness, or economic burden remained persistently rare, accounting for less than 1% of annual output in most years. Structural analyses indicate that economic terms are closely linked to modeling and decision-oriented keywords but do not form independent thematic clusters. Although economics-focused studies achieve moderate normalized citation impact when present, their low frequency limits structural influence. The findings reveal a persistent imbalance between rapid methodological innovation and limited economic evaluation in clinical AI research, highlighting the need for more systematic integration of economic perspectives to support sustainable clinical deployment.
Adaptive Multi-Asset Trading Strategy Optimization via Genetic Algorithms with Walk-Forward Robustness Analysis
The stochastic, non-linear, and dynamic nature of financial markets significantly diminishes the effectiveness of traditional trading stratsgies relying on fixed parameters over extended periods. While the Efficient Market Hypothesis (EMH) suggests that asset prices reflect all available information, rendering systematic profit generation impossible, the field of algorithmic trading operates on the premise that temporary market inefficiencies and behavioral anomalies can be exploited. This study presents a comprehensive Genetic Algorithm (GA) framework designed to develop and optimize an adaptive trading strategy for multi-asset portfolios consisting of high-liquidity technology stocks (Apple, Microsoft, Google). Unlike traditional optimization methods that focus solely on parameter tuning for a single indicator, the proposed system introduces a novel "genetic switch" mechanism. This mechanism allows the algorithm to simultaneously optimize the structural components of the strategy determining which combination of indicators (EMA, MACD, RSI, Momentum) yields the best performance and their respective parameters. The model’s fitness function prioritizes risk-adjusted returns by utilizing a Calmar-like ratio, explicitly penalizing excessive drawdowns. To ensure robustness and mitigate the prevalent risk of overfitting (data snooping bias), a rigorous Walk-Forward Optimization (WFO) technique was applied to daily data spanning the 2020-2024 period. The findings demonstrate that the proposed GA framework generates a robust trading system that statistically outperforms the passive "buy-and-hold" strategy, achieving a higher Sortino Ratio (1.98 vs 1.21) and significantly lower maximum drawdown (-18.5% vs -35.1%). The outperformance over the buy-and-hold benchmark is statistically validated across all walk-forward windows, indicating robustness rather than data snooping effects.
Renewable Energy in Türkiye: Principles, Applications and Sustainability Challenges
This study examines the development of renewable energy installed capacity in Türkiye between 2013 and 2023 within the context of resource-based shifts and policy frameworks. Utilizing data from the Ministry of Energy and Natural Resources (ETKB), TEIAS, and IRENA, the analysis evaluates annual growth rates and fluctuations in the share of total capacity. The findings reveal that the total installed capacity, which stood at 64,008 MW in 2013, reached 110,914 MW by the end of 2023; notably, solar energy experienced a growth rate exceeding 1,000% during this period. In alignment with the Paris Agreement targets, the study emphasizes the strategic importance of grid flexibility and storage technologies, providing critical implications for future energy policies.
Study of the Factors Influencing Calorific Consumption of a Vertical Roller Mill and Solution Proposals to Reduce it at a Cement Factory
This paper focuses on the of factors influencing calorific consumption of a vertical roller mill (VRM) and solution proposals to reduce it at a cement factory called X cement factory (for confidentiality reasons). The research aims to identify the main operational, mechanical, and material factors contributing to elevated specific calorific consumption (SCC) and to propose corrective measures for improving energy performance. Data covering a five-month production period are collected from the company’s production reports and analyzes using key performance indicators such as feed rate, moisture content, stoppage frequency, and fuel consumption. Results reveal that increased raw material moisture, frequent operational stoppages, and unstable feed rates are the dominant factors leading to higher SCC. The study concludes by proposing technical and organizational measures such as feed rate stabilization, moisture control, preventive maintenance, and improved combustion management. Implementing these measures can significantly reduce calorific consumption, enhance energy efficiency, and promote sustainable cement production.
The Dynamic Impact of Gold and Oil Uncertainty on XU100, CDS, and Exchange Rate in Türkiye: A Wavelet Analysis
Geopolitical tensions and macroeconomic fluctuations in global markets have significantly influenced investor behavior and the direction of financial markets. In this turbulent environment, strategic commodities such as gold and oil have emerged as prominent safe-haven assets. The price volatility of these assets is considered a key indicator of market uncertainty and is measured through implied volatility indices, namely the GVZ (Gold Volatility Index) and OVX (Oil Volatility Index). In this respect the relationship between these indices and financial indicators becomes particularly critical during periods of economic distress. This study examines the effects of GVZ and OVX indices on the main financial variables in Türkiye, namely XU100 index, USD/TRY exchange rate and CDS spreads, in the time-frequency domain using wavelet analysis method. The analyses are conducted through wavelet power spectrum, wavelet coherence, and phase difference techniques. The findings reveal that GVZ exerts strong and persistent influences on exchange rates and CDS spreads, particularly over medium to long term horizons, often acting as a leading indicator. OVX also demonstrates a leading role, with more pronounced effects in the short to medium term. In contrast, the XU100 index exhibits a weaker and more fragmented response to these uncertainties, mostly limited to short-term episodes. In conclusion, implied volatility indices represent significant indicators for both investment decisions and macroeconomic policymaking, particularly in economies like Türkiye that are vulnerable to external shocks. This study underscores the necessity of analyzing uncertainty financial market interactions within a time frequency framework and offers meaningful policy implications for uncertainty management.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Bibliometric Analysis of the Impact of Big Data Technology on Business and Management
Businesses are established with the aim of generating more profit and remaining in operation for longer periods of time. Various practices contribute to ensuring the sustainability of businesses. One of these practices is big data technology. The application of big data technology, which has taken the whole world by storm and affected all sectors, is based on digital transformation. As a technology-based development, Industry 4.0 applications interact with many scientific fields such as sociology, economics, biological systems, and computer systems. Industry 4.0 applications bring about strategic transformations and paradigm shifts in all scientific fields and sectors. A bibliometric study is being conducted to observe and analyze the development of Industry 4.0 applications in the field of business, which enables important developments. The data for studies related to big data technology in the field of business and management was obtained from the Web of Science database, consisting of 2011 studies. The data obtained was analyzed using the Bibliometrix software in the R Studio program via the Biblioshiny database. The results show that 5,568 authors conducted research between 2020 and 2025. Additionally, it was found that the most frequently used keywords are “big data,” “big data analysis,” “digital transformation,” and “artificial intelligence.” Another important finding is that Bag and Papa are the authors who have conducted the most research in this field. Big data technology will make significant contributions to business and management, especially in 2024. The journal Technological Forecasting and Social Change contains the most studies related to this research topic. This research output serves as a guide for researchers interested in this field.
An Assessment of the Concept of Competitiveness through Postgraduate Thesis Studies in Türkiye
The understanding of the concept of competitiveness and its transformation into a subject of research has become an interesting field both in practice and in theoretical frameworks. As emphasised in academic articles, textbooks, and papers, competitiveness also emerges as an evaluation and comparison tool in reports prepared for the analysis of local and regional areas, as well as in comparisons made at the global and national levels. In this study, we sought to investigate how the concept is approached in postgraduate programs, in which areas studies on competitiveness are conducted, whether the concept is addressed with a correct positioning, and what the method-output relationship is for the results obtained. It was observed that the concept is used in place of concepts such as ‘competitive advantage’ and ‘competitive superiority,’ and that the Porter Five Forces Model is predominantly used for the outputs obtained. It was also noted that the outputs are included in the recommendations related to the thesis topic within the context of competitiveness, and that these are mostly encountered in doctoral theses, while certain gaps and evaluations regarding competitiveness are not made.
Medical Imaging Technologies and Healthcare Infrastructure: Artificial Intelligence-Based Analysis of Global Trends
Medical imaging technologies have a critical role in improving healthcare efficiency, diagnostic accuracy, and patient outcomes. This study investigates the global distribution of advanced medical imaging devices such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and mammography across OECD countries between 2015 and 2023 using OECD health data. Using correlation and regression analyses, this research explores the relationships between imaging device density, healthcare infrastructure capacity, population size, and healthcare expenditures. The analysis reveals a strong positive correlation between imaging device availability and healthcare infrastructure capacity ($\rho = 0.77$), as well as a robust association with population size ($\rho = 0.87$). In contrast, healthcare expenditures demonstrate a weaker relationship with these variables ($\rho \approx 0.41 - 0.55$), indicating that strategic planning is essential beyond mere budget increases. K-Means clustering and Principal Component Analysis (PCA) categorize countries into distinct groups according to imaging technology availability and infrastructure capacity. Integration of artificial intelligence (AI) within medical imaging is highlighted as a promising approach for enhancing early diagnosis, reducing unnecessary healthcare utilization, and improving operational efficiency. Findings emphasize that effective healthcare policies should focus not only on increasing budgets but also on targeted resource allocation, infrastructure optimization, and adoption of advanced AI technologies.
Location-Based Technology for Real-Time Artifact Recognition in Businesses
The recognition of historical artifacts play a crucial role in sustaining cultural heritage and advancing tourism. Despite advancements in object detection technologies, accurately identifying artifacts in diverse geographical and environmental contexts remains a significant challenge. Existing models often struggle to adapt to region-specific features and the complexity of historical artifacts, limiting their practical applications. To address these limitations, this study evaluates the potential of YOLOv4, YOLOv7-X, and YOLOv9c models for historical artifact recognition, with a particular focus on location-based segmentation. Geographically distinct datasets were utilized for training and evaluation, enabling the models to achieve higher accuracy in region-specific artifact detection. Among the tested models, YOLOv9c demonstrated superior performance, achieving the highest metrics across accuracy (96%), precision (93%), recall (95%), and mean average precision (mAP, 71%), making it the best-performing model. These results highlight YOLOv9c’s robustness and adaptability to complex datasets and diverse artifact characteristics. A user-friendly application interface was also developed, allowing real-time detection and providing detailed historical information about the artifacts. However, challenges such as the high computational cost of training YOLOv9c on high-resolution datasets were observed, particularly when compared to YOLOv4, which was computationally efficient but less accurate. YOLOv7-X offered a balance between performance and computational efficiency. The results demonstrate that location-based segmentation significantly enhances detection accuracy, making this approach highly effective for real-world applications in cultural heritage preservation and tourism.
Artificial intelligence object detection
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0/
This article comprehensively examines the economic impacts of the Internet of Things (IoT) on smart cities. Smart cities aim to enhance sustainability, efficiency, and quality of life through the integration of IoT technologies. The economic benefits of IoT in various domains, such as infrastructure management, energy conservation, transportation optimization, and the improvement of public services, are elaborated. Furthermore, potential costs and challenges, including high initial investments, data security concerns, and workforce transformation, are discussed. Supported by a literature review and current case analyses, the article presents findings that highlight IoT's contributions to the economic structure of smart cities and the barriers encountered. In conclusion, it emphasizes that IoT technologies have the potential to stimulate economic growth in smart cities through proper strategic planning and policy support.
Decision Making in Bank Personnel Selection Using the Analytical Hierarchy Process
Banks, like in many other areas, must make the right decisions in personnel selection. Incorrect personnel choices can lead to customer loss and increase internal organizational problems. Therefore, it is crucial for banks to make the right personnel selections and use objective criteria when making these decisions. Selecting the most suitable employee who can contribute to increasing the value of the organization from numerous applications requires a multi-stage process. Recent studies have observed that multi-criteria decision-making methods are frequently used in evaluating recruitment processes. In this regard, the Analytical Hierarchy Process (AHP) method is a tool that can assist banks in making accurate personnel decisions. The aim of the study is to identify the criteria used in personnel selection in banks and prioritize these criteria. In the research, key factors in the recruitment process include personal characteristics, organizational expectations, and experience as the main criteria. Using the AHP method, priority rankings of these factors have been made, and the most important criteria have been identified. The results highlight that determining the right criteria is crucial for enhancing the quality of the workforce and designing efficient recruitment processes in the banking sector. Furthermore, the AHP model provides a concrete tool for recruitment processes in the sector, helping decision-makers to make more informed and objective decisions.
Transparency in Decision-Making: The Role of Explainable AI (XAI) in Customer Churn Analysis
In many industries, such as the telecommunications industry, identifying the causes of customer churn is a primary challenge. In the telecommunications industry, it is of great importance to predict which customers will abandon or continue their subscriptions. Machine learning and data science offer numerous solutions to this problem. These proposed solutions have an important place in decision making processes in various sectors. This study aims to predict lost customers using machine learning algorithms and explain the reasons behind them. The dataset used used Linear Regression, Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, XGBoost (eXtreme Gradient Boosting), LightGBM, AdaBoost and CatBoost to find the classification with the best performance. Contains algorithms. model. Performance metrics such as R2 score, Mean Square Error, Mean Absolute Error, Root Mean Square Error and Accuracy are used in this process. Finally, the LightGBMmodel, which gives the highest accuracy value (73.085%), is explained using explainable artificial intelligence (XAI) algorithms.
eXplainable artificial intelligence (XAI)
The Effects of Islamic Economy and Islamic Finance on Social Life
Besides the advantages of living together, there may also be disadvantages. When people live together, they benefit from each other in meeting their needs. In other words, people can buy goods and services that they cannot produce themselves from other members of the society. At the same time, people living together harm each other by usurping each other's rights. The religion of Islam has emerged with radical changes compared to the past in terms of ensuring justice in social life. In addition to the rules it brought to the social life of the society, Islam also introduced innovations and rules in the field of economy and finance. When these rules brought by the Islamic religion are followed, justice is achieved in social life and social welfare increases. In the study, Islamic economy and Islamic finance will be mentioned first. Subsequently, the effects of Islamic economy and Islamic finance on social life will be examined.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
Enhancing Financial Decision-Making: Predictive Modeling for Personal Loan Eligibility with Gradient Boosting, XGBoost, and AdaBoost
This study aims to improve the prediction of personal loan eligibility through the application of advanced machine learning techniques. Accurate prediction of creditworthiness is crucial for financial institutions to mitigate risks and optimize their lending processes. We evaluated three algorithms Gradient Boosting, XGBoost, and AdaBoost using a comprehensive dataset containing demographic and banking information. Among these, XGBoost proved to be the most effective model, achieving an accuracy of 0.95, precision of 0.95, recall of 0.95, and an F1 score of 0.95. These results demonstrate XGBoost's superior ability to accurately identify individuals likely to repay loans, making it an invaluable tool for enhancing decision-making in loan approvals. By leveraging XGBoost, banks can reduce the risk of defaults, streamline their operations, and provide better customer service, ultimately leading to more efficient and reliable lending strategies.
Predicting Customer Purchase Behavior Using Machine Learning Models
In this study, we aim to predict customer purchase behavior using various machine learning models to better understand customer tendencies and enhance marketing strategies. We use a dataset containing demographic and behavioral data, including age, gender, annual income, number of purchases, product category, time spent on the website, loyalty program membership, and discounts availed. Our analysis involves data preprocessing, exploratory data analysis (EDA), and feature engineering. We then train and evaluate six different machine learning models: Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The models are assessed using metrics such as accuracy, precision, recall, F1-score, and ROC AUC. Results indicate that ensemble models, specifically Random Forest and Gradient Boosting, outperform the other models in terms of accuracy and ROC AUC. The study concludes that ensemble models are highly effective for predicting customer purchase behavior, providing valuable insights for businesses to tailor their marketing efforts. Future research could explore additional features, more advanced models, and real-time prediction capabilities.
Waqf Culture in the Ottoman Empire and Reflections of the Activities of Cash Waqfs on Today
It is known that waqf culture was quite widespread in the Ottoman state. Certain needs in society were met through waqfs. Cash waqfs were also established for financial needs. In this study, in which the prevalence of waqf culture in the Ottoman Empire and its reflections on society were tried to be discussed, the reflections of some of the debates that came with the emergence of cash waqfs to the present day were mentioned. In addition, the study investigated the activities of cash waqfs, what kind of benefits they provide to society, and how their continuity is ensured over time. Comments are included about the reason why cash waqfs, which are seen to be more common in the geography of Rumelia, are relatively less in the southern and eastern provinces. The parts of the discussions on cash waqfs that are misunderstood today are emphasized. In the conclusion of the study, the similar and different aspects of today's Islamic financial institutions and cash waqfs are compared.
New Specializations in Accounting Profession
Humans have innumerable needs, and every economic unit that engages in activities to meet these needs is called a business. As a result of the activities of businesses, transactions must be documented and recorded. The concept of accounting has emerged for this purpose. Accordingly, the profession of accountancy has developed and people who do this job have been trained. Currently, professionals such as Certified Public Accountant and Sworn-in Certified Public Accountant have emerged. The field of accounting has been significantly impacted by the rapid advancement of information technologies and economies on a global scale, the phenomenon of globalization, and the intensifying competitive landscape. In response to the increasingly complex business activities, the content of transactions carried out by accountants has also become of paramount importance. Significant alterations have been observed in domains such as corporate governance, auditing, and financial reporting. In consequence of these developments, new areas of specialisation in accounting have emerged. This study begins with an examination of the concept of accounting and the evolution of the accounting profession. It also considers the emergence of new specializations within the accounting profession and selected topics within these specializations.