In order to assess the effects of e-learning integration on university performance and competitiveness, this study uses Oman as a model for the Gulf. Analyzing how e-learning impacts technology integration, diversity, community engagement, infrastructure, financial strength, institutional reputation, student outcomes, research and innovation, and academic quality can reveal whether universities are effectively incorporating digital tools to enhance teaching and learning. By offering a framework for comparable institutions in the Gulf area, this study provides insights into optimizing e-learning techniques to improve university performance and competitiveness. This study uses the Structural Equation Modeling (SEM) with a dataset comprising 424 participants and 55 indicators, analyzed using both measurement and structural models. The results of the hypothesis testing, which indicate that e-learning has a positive effect on factors like student outcomes (B = 0.080, t = 2.859, P = 0.004) and institutional reputation (B = 0.058, t = 2.770, P = 0.005), lend credence to these beliefs. Omani universities need culturally sensitive e-learning, stronger institutional support, and training to enhance diversity (B = 0.002, t = 0.456, P = 0.647) and technology integration (B = −0.009, t = 0.864, P = 0.387). These improvements increase the visibility of Gulf institutions abroad, attracting the best students from all around the world and fostering an inclusive learning atmosphere. Financially speaking, e-learning offers reasonably priced solutions such as digital libraries and virtual laboratories, which are especially beneficial in a region where education plays a major role in socioeconomic development.

This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.

Today’s automation of the audit process increasingly relies on electronic auditing, especially computer-assisted audit techniques (CAATs), and has become a global necessity. Therefore, this study aims to explore the influence of technological, organizational, and environmental (TOE) factors on audit firms’ adoption of CAATs in developing countries, focusing on Ethiopia. The research employed a quantitative approach and gathered 113 valid responses from certified external auditors in Ethiopian audit firms. The data was then analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The findings show that relative advantage and compatibility are the significant technological attributes influencing CAAT adoption in Ethiopian audit firms. Besides, auditors’ information technology (IT) competency was a significant organizational attribute influencing CAAT adoption. Environmental attributes such as the complexity of the client’s accounting information system (AIS) and the professional body support significantly impact the adoption of CAATs. Additionally, the size of an audit firm reduces the impact of clients’ AIS complexity on the adoption of CAATs in Ethiopian audit firms. The findings underscore the significance of CAAT adoption in audit firms and offer valuable insights for policymakers and standard setters in crafting legislation for the Ethiopian audit industry. This study represents the first scholarly effort to provide evidence of CAAT adoption in audit firms in developing countries like Ethiopia.

Climate change is the most important environmental problem of the 21st century. Severe climate changes are caused by changes in the average temperature and rainfall can affect economic sectors. On the other hand, the impact of climate change on countries varies depending on their level of development. Therefore, the aim of this paper is to investigate the relationship between climate changes and economic sectors in developed and developing countries for the period 1990–2021. For this purpose, a novel approach based on wavelet analysis and SUR model has been used. In this case, first all variables are decomposed into different frequencies (short, medium and long terms) using wavelet decomposition and then a SUR model is applied for the examination of climate change effects on agriculture, industry and services sectors in developed and developing countries. The findings indicate that temperature and rainfall have a significant negative and positive relationship with the agriculture, industry and services sectors in developed and developing countries, respectively. But severity of the negative effects is greater in the agricultural and industrial sectors in all frequencies (short, medium and long terms) compared to service sector. Furthermore, the severity of the positive effects is greater in the agricultural sector in all frequencies of developing countries compared to the industrial and services sectors. Finally, developing countries are more vulnerable to climate change in all sectors compared to developed countries.

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

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.