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.

This systematic literature review examines data saturation in qualitative research within the context of entrepreneurship studies from 2004 to 2024. Data saturation, a critical concept in ensuring the rigor of qualitative research, remains inadequately defined in terms of sample size and assessment criteria across various studies. This review synthesizes 11 empirical studies, focusing on strategies such as stopping criterion, code frequency counts, and comparative methods for determining saturation. It identifies sample sizes ranging from 7 to 39 interviews, with an average saturation occurring between 10 and 12 interviews. Furthermore, the study explores the influence of different sampling methods and homogeneity of study populations on saturation outcomes. Despite the reliability of existing methods, the findings underscore the need for greater transparency and consistency in reporting saturation criteria. The review offers valuable insights for entrepreneurial researchers aiming to design qualitative studies, emphasizing the importance of tailored saturation standards based on research objectives and methodologies. This research contributes to a clearer understanding of data saturation in entrepreneurial studies and highlights the necessity for further empirical investigation into saturation across diverse qualitative methods.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. the research uses advanced econometric methods, including the autoregressive distributed lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. the findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. oil revenues also play a critical role, reflecting the country’s economic reliance on oil. the study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. these findings also support the importance of consistent and predictable income sources for sustaining consumption. additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

The nexus between foreign direct investment, natural resource endowment, and their impact on sustained economic growth, is contentious. This study investigates the resource curse hypothesis and the effects of FDI on economic growth in Kazakhstan. The study covers the period from 1990 to 2022 and employs the Autoregressive Distributed Lag (ARDL) model and Toda-Yamamoto causality methods. The Bounds cointegration results reveal the existence of long-term equilibria between per capita GDP and the predictors. The findings reveal a significant impact of oil rents on economic growth, contradicting the resource curse hypothesis and suggesting a resource boon instead. In stark contrast, the impact of FDI on Kazakhstan’s economic growth is found to be insignificant, despite the presence of a causal nexus. Furthermore, economic freedom and export diversification have a positive significant impact on economic growth, while inflation exhibits a negative but significant impact. Although governance has a direct impact on GDP per capita, it is deemed insignificant, as the negative average governance index implies poor governance. Expectedly, the result establishes a causal effect between export diversification, economic freedom, governance, oil rents, and economic growth. This underscores the fundamental role played by the interplay of diversification, economic freedom, governance, and oil rents in fostering sustainable economic growth. In addition, economic freedom stimulates gross fixed capital formation, indicating that it enhances domestic investment. Notably, the findings refute the crowding-out effect of FDI on domestic investment in Kazakhstan. Consequently, to escape the resource curse and the Dutch disease syndrome, the study advocates for enhancing good governance capabilities in Kazakhstan. Thus, we recommend that good governance could reconcile the twin goals of economic diversification and deriving benefits from oil resources, ultimately transforming oil wealth into a boon in Kazakhstan.

This research explores the necessity and the effect of job resources for undergraduates’ career satisfaction during work experience in an apprenticeship program. Additionally, we examine the extent to which a supportive environment enhances apprentice career satisfaction by providing access to valuable learning experiences. We propose PLS equation modelling with a sample of 81 students who completed a dual apprenticeship degree in Business Administration and Management at Spanish University. The study finds that all three workplace job resources are necessary for career satisfaction among apprentices. Learning opportunities and social relations have significant effects, while job control contributes only marginally. It highlights that learning opportunities enhance social relations, emphasizing the importance of feedback. The study extends job resource research to university level apprenticeships, showing that without these resources, apprentices lack career satisfaction. It highlights that learning opportunities are crucial for satisfaction through social relations and offers guidance for designing effective workplace training programs.