This paper investigates the factors influencing credit growth in Kosovo, focusing on the relationship between credit activity and key economic variables, including GDP, FDI, CPI, and interest rates. Its analysis targets loans issued to businesses and households in Kosovo, employing a VAR model integrated into a VEC model to investigate the determinants of credit growth. The findings were validated using OLS regression. Additionally, the study includes a normality test, a model stability test (Inverse Roots AR Characteristic Polynomial), a Granger causality test for short-term relationships, and variance decomposition to analyze variable shocks over time. This research demonstrates that loan growth is primarily driven by its historical values. The VEC model shows that, in the long run, economic growth in Kosovo leads to less credit growth, showing a negative link between it and GDP. Higher interest rates also reduce credit growth, showing another negative link. On the other hand, more foreign direct investment (FDI) increases credit demand, showing a positive link between credit growth and FDI. The results show that loans and inflation (CPI) are positively linked, meaning higher inflation leads to more credit growth. Similarly, more foreign direct investment (FDI) increases credit demand, showing a positive link between FDI and credit growth. In the long term, higher inflation is connected to greater credit growth. In the short term, the VAR model suggests that GDP has a small to moderate effect on loans, while FDI has a slightly negative effect. In the VAR model, interest rates have a mixed effect: one coefficient is positive and the other negative, showing a delayed negative impact on loan growth. CPI has a small and negative effect, indicating little short-term influence on credit growth. The OLS regression supports the VAR results, finding no effect of GDP on loans, a small negative effect from FDI, a strong negative effect from interest rates, and no effect from CPI. This study provides a detailed analysis and adds to the research by showing how macroeconomic factors affect credit growth in Kosovo. The findings offer useful insights for policymakers and researchers about the relationship between these factors and credit activity.

The ongoing dissemination of globalization and digitalization may suggest that personal relationships are becoming less crucial in the context of retail banking and financial services. In Hungary, in addition to private banking, which is associated with high income levels, personal banking also plays an important role. The objective of this study is to develop a model that can identify the factors that determine customer satisfaction and their relative importance. Furthermore, the aim is to incorporate gender and age as moderator variables to identify demographic differences in satisfaction. The analysis was conducted via a questionnaire survey in October to November 2023 employing a purposive sampling approach in a university environment, as the respondents are likely to possess the highest level of existing financial knowledge within this population. The 214 valid responses were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, with the objective of contributing to the development of theory in this field of study. The results demonstrate that perception (β = 0.519) and reliability (β = 0.253) collectively explained 51.8% of the variance in satisfaction. Moreover, the results indicate that perception accounts for 49.2% of the variance in reliability, suggesting the existence of an indirect effect on satisfaction. Therefore, the findings suggest that, despite the advent of digital banking, face to face service remains a pertinent concern in Hungary, and financial institutions should prioritize the factors that shape customer satisfaction. The study contributes to the literature and to the development of customer loyalty strategies for banks based on these findings.

Business organizations use job advertisements to find and attract the high-quality workforce they need. Skillfully crafted job advertisements not only provide job-related information to job seekers but also help develop a strong employer brand in the employee market. Based on signaling theory and person-environment fit theory, we propose that the content and specificity of information provided in job advertisements influence job advertisement effectiveness through various mechanisms. In a scenario-based experiment on 310 young job seekers, we probed the direct and indirect effects of job advertisement informativeness on job pursuit intentions. Using structural equations modelling and multi-group path analysis, the mediating roles of perceived job appropriateness and ad truthfulness, along with the moderating role of previous employment experience, were examined. By manipulating the information content of a hypothetical job advertisement, we demonstrated that: a) both advertisement informativeness and perceived job appropriateness had positive direct effects on application intentions, while the latter had a greater effect; b) perceived job appropriateness mediated the relationship between advertisement informativeness and job pursuit intentions; c) the indirect (mediated) effect of advertisement informativeness on application intentions was moderated by previous employment experience; d) perceived ad truthfulness did not exert any significant effect on application intentions. These findings imply that HR practitioners should provide specific information in job postings to help candidates, especially those with less work experience, evaluate how well the job suits them and increase their motivation to apply.

Many financial crises have occurred in recent decades, such as the International Debt Crisis of 1982, the East Asian Economic Crisis of 1997–2001, the Russian economic crisis of 1992–1997, the Latin American debt Crisis of 1994–2002, the Global Economic Recession of 2007–2009, which had a strong impact on international relations. The aim of this article is to create an econometric model of the indicator for identifying crisis situations arising in stock markets. The approach under consideration includes data for preprocessing and assessing the stability of the trend of time series using higher-order moments. The results obtained are compared with specific practical situations. To test the proposed indicator, real data of the stock indices of the USA, Germany and Hong Kong in the period World Financial Crisis are used. The scientific novelty of the results of the article consists in the analysis of the initial and given initial moments of high order, as well as the central and reduced central moments of high order. The econometric model of the indicator for identifying crisis situations arising considered in the work, based on high-order moments plays a pivotal role in crisis detection in stock markets, influencing financial innovations in managing the national economy. The findings contribute to the resilience and adaptability of the financial system, ultimately shaping the trajectory of the national economy. By facilitating timely crisis detection, the model supports efforts to maintain economic stability, thereby fostering sustainable growth and resilience in the face of financial disruptions. The model's insights can shape the national innovation ecosystem by guiding the development and adoption of monetary and financial innovations that are aligned with the economy's specific needs and challenges.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Indonesia, as a maritime country, has many coastal areas with fishing villages that have significant potential, especially in sociological, economic, and environmental aspects, to be developed as models for sustainable development. Indonesia, with its long-standing fishing traditions, showcases the abundant potential and traditional that could help address global challenges such as climate change, rapid urbanization, and environmental and economic issues. This study aims to develop a conceptual model for sustainable cities and communities based on local potential and Wisdom towards the establishment of a Blue Village in the fishing village of Mundu Pesisir, Cirebon, Indonesia. The urgency of this study lies in the importance of developing sustainable strategies to address these challenges in coastal towns. This study involves an interdisciplinary team, including experts in sociology, social welfare, architecture, law, economics, and information technology. Through the identification of local natural and sociocultural resources, as well as the formulation of sustainable development strategies, this study develops a conceptual Blue Village model that can be applied to other coastal villages. The method employed in this study is qualitative descriptive, involving the steps of conducting a literature review, analyzing local potential, organizing focus group discussions, conducting interviews, and finalizing the conceptual model. The study employed, a purposive sampling technique, involving 110 participants. The results of the study include the modeling of a sustainable city and community development based on local potential and Wisdom aimed at creating Blue Villages in Indonesia, and It is expected to make a significant contribution to the creation of competitive and sustainable coastal areas capable of addressing the challenges of climate change and socioeconomic dynamics in the future.