Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

Contract workers are the direct victims of casualization but beyond that, the effects they suffer transcend to their families and the larger society. The study examined the effects of casualization on the contract workers of banks in Sokoto, Nigeria. The primary methods of gathering data for the study were in-depth and key informant interviews, with sixty individuals who were specifically chosen. Following content analysis, the gathered data were presented narratively with verbatim quotations. According to the study, there are a number of negative effects of casualization, such as low wages that contribute to a low standard of living and the inability of employees and their families to adequately meet their basic needs, the arbitrary termination of casual employees without cause, and the lack of a claim for work-related injuries or diseases in the event of an accident or death. The overall inference is that the temporary employees are working in appallingly subpar conditions. The study suggests that in order to raise the living standards of their temporary employees, banks should provide welfare packages. Additionally, because inflation is on the rise, contract employees’ compensation should be reviewed upward.

While extensive research has explored interconnectedness, volatility spillovers, and risk transmission across financial systems, the comparative dynamics between Islamic and conventional banks during crises, particularly in specific regions such as Saudi Arabia, are underexplored. This study investigates risk transmissions and contagion among banks operating in Islamic and conventional modes in the Kingdom of Saudi Arabia. Daily banking stock data spanning November 2018 to November 2023, encompassing two major crises—COVID-19 and the Russian-Ukraine war—were analyzed. Using the frequency TVP-VAR approach, the study reveals that average total connectedness for both banking groups exceeds 50%, with short-run risk transmission dominating over long-term effects. Graphical visualizations highlight time-varying connectedness, driven predominantly by short-run spillovers, with similar patterns observed in both Islamic and conventional banking networks. The main contribution of this paper is the insight that long-term investment strategies are crucial for mitigating potential risks in the Saudi banking system, given its limited diversification opportunities.

Since the Reform and Opening up, GDP of the cities on eastern bank of the Pearl River Estuary in Guangdong Province were higher than the eastern bank cities. Therefore, this article aims to modify the urban gravity model combines it with the entropy weight method to calculate urban quality and applies it to measure the degree of connectivity between cities over the past decades. The research aims to explore whether cities with higher economic output have a greater attraction for surrounding cities, and whether the eastern bank cities can also promote the development of the west. Through detailed data collection and analysis, this essay reveals the dynamic changes of the gravity among cities and its influence factors such as economic, transportation and urban development. The research results indicate that the strongest gravitational force between cities on the east and west banks is between Dongguan and Zhongshan, rather than between Shenzhen and cities on the west bank. This demonstrates that the connection between cities on the east and west banks is primarily constrained by geographical factors, and the geographical location of a city influences on surrounding cities significantly. In particular, Dongguan and Zhongshan play a key role in connecting the eastern and western bank of the Pearl River Estuary, rather than Shenzhen, which is traditionally considered to have the highest economic aggregate. In addition, the study also found that the COVID-19 epidemic has had a significant impact on inter-city communication, resulting in a decline in inter-city gravity in recent years.

ESG (environmental, social and governance, a framework used to assess an organisation’s business practices and performance on various sustainability and ethical issues) and Digital Transformation (the process of using digital technologies to change a business’s operations, products and services by integrating digital solutions into all areas of the business, which can lead to cultural and technological changes) are emerging issues across different industries, including the banking field. There has been limited research focusing on exploring the linkages between ESG, Digital Transformation and Customer Behaviour in the banking area, especially within developing countries such as Vietnam. Based on this gap, this study analyses and assesses the role of Digital Transformation and ESG on customer behaviour towards brands in the banking sector in Ho Chi Minh City. The research employed the quantitative research methods with the combination of fundamental analytical methods such as statistics, Cronbach’s alpha reliability, Exploratory Factor Analysis (EFA), measurement models and Partial Least Squares Structural Equation Modelling (PLS-SEM). The analysis was based on survey data from 550 customers who are the commercial banks’ current customers and live in Ho Chi Minh City, yielding 514 valid responses. Using SPSS and SMART PLS software, the study provided notable results. Specifically: (1) The component factors of ESG, including Environmental Issues (EN), Social Issues (SO), Government Issues (GO) and Digital Transformation (DT), positively influence Customer Behaviour (CB); (2) The component factors of ESG, including Environmental Issues (EN), Social Issues (SO) and Government Issues (GO), play a mediating role in the relationship between Digital Transformation (DT) and Customer Behaviour (CB).

Burnout and workaholism, known to decrease efficiency and lead to depersonalisation, have negative effects on both employees and organisations. The survey aimed to analyse the correlation between burnout and workaholism among selected employees in the public and private sectors in Slovenia. The goal was to compare the results obtained in the administrative units and at the two selected banks. Various methodological approaches were employed, including statistical tests such as multivariate analysis, the Kolmogorov-Smirnov and Shapiro-Wilk tests, Spearman’s rank-order correlation coefficient, and the Mann-Whitney U test. Survey results indicate statistically significant differences between employees in administrative units and those in two banks. Workaholism is higher among employees in administrative units than at banks. Positive correlations were found between workaholism and the two dimensions of burnout, both among employees in administrative units and in the selected banks.