Building upon the cognition-affect-behavior model, this research aims to investigate the effect of greenwashing on consumers purchase intentions by testing the mediating effect of perceived betrayal and the moderating effect of brand loyalty. Data was collected through a questionnaire from 339 Chinese consumers involved in the fashion industry. AMOS 24 was employed to test the fitness of the proposed model, and the proposed hypotheses were tested through hierarchical regression analysis using SPSS 23. The results indicate that greenwashing has a negative impact on consumer purchase intentions, with perceived betrayal partially mediating this relationship. Additionally, the findings show that brand loyalty can mitigate the adverse effects of greenwashing on consumer purchase intentions. The findings of this study enhance the existing knowledge on the relationship between greenwashing and consumer purchase intentions, particularly in the fashion industry, and have significant practical implications for companies in this sector aiming to increase consumer purchase intentions.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

The increasing use of social media has played a prominent role in shaping opinions and forming attitudes, especially among university students. They use them increasingly to transfer information, exchange data, and disseminate topics among students and all members of society. Therefore, this study aims to examine these networks and their role in public life, especially in shaping public opinion among university students. The study adopted a descriptive survey approach to achieve its objectives. The study was conducted on a sample of undergraduate students from four Jordanian universities, totaling 832 participants selected through purposive sampling and using the equal distribution method according to variables (gender, university, specialization). The study relied on a questionnaire as a method of data collection and filling out the data from the respondents in the questionnaire. The study found that social media plays a significant role in shaping opinions, beliefs, and ideas, and that its role is unparalleled. Also, the study showed that social media had a significant impact on shaping public opinion in Jordan among university students who use social media extensively and exchange opinions, ideas, and information, contributing to shaping a series of opinions among young people and contributing to their adoption of new ideas or changing their old ones through the dialogue facilitated by these networks, as users exchange and adopt ideas, contributing to shaping a public opinion on an issue. These findings underscore the importance of understanding and leveraging social media and online platforms to effectively communicate with and engage students.

Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

China’s Belt and Road Initiative (BRI) hopes to deliver trillions of dollars in infrastructure financing to Asia, Europe, and Africa. If the initiative follows Chinese practices to date for infrastructure financing, which often entail lending to sovereign borrowers, then BRI raises the risk of debt distress in some borrower countries. This paper assesses the likelihood of debt problems in the 68 countries identified as potential BRI borrowers. We conclude that eight countries are at particular risk of debt distress based on an identified pipeline of project lending associated with BRI.

Because this indebtedness also suggests a higher concentration in debt owed to official and quasi-official Chinese creditors, we examine Chinese policies and practices related to sustainable financing and the management of debt problems in borrower countries. Based on this evidence, we offer recommendations to improve Chinese policy in these areas. The recommendations are offered to Chinese policymakers directly, as well as to BRI’s bilateral and multilateral partners, including the IMF and World Bank. 

This study explores the critical role of the retail sector in the global economy and the importance of working capital management within retail businesses. Recognizing retail’s influence beyond just income generation, the research examines its impact on economic stability, job creation, and national GDP, and how it links industries such as manufacturing and logistics. Employing a blended-methods approach, the study integrates quantitative analysis using AMOS software with qualitative insights from interviews with financial managers and retail experts. Key focus areas include cash flow management, market demand, and supplier relationship management in the context of working capital management. Findings highlight the necessity of effective working capital management in maintaining financial stability, optimizing shareholder wealth, and ensuring long-term business viability in the retail sector. Strategies for enhancing profitability, such as improving supplier relationships and adapting to market demands, are identified. This research contributes to understanding the economic impact of the retail sector and the intricacies of working capital management. It offers insights for policymakers, retail managers, and academics, emphasizing the need for supportive retail industry measures and effective financial management practices. The study fills a gap in literature and sets a foundation for future research in this critical area of economic studies and retail management.