This study conducts research on retailers’ behavioral intentions and behavior in adopting e-commerce platforms (ECPs) and uses the unified theory of acceptance and use of technology (UTAUT2) model as well as add other factors such as Personalization Platform, Seamless Interaction. The findings show that Effort Expectancy, Social Influence, Hedonic Motivation, Retailers’ Capacity, Integration Strategies have a positive impact on retailers’ behavioral intention of adopting ECPs and Performance Expectancy has a negative impact on retailers’ behavioral intention of adopting ECPs. At the same time, Behavioral Intention, Facilitating Conditions have a positive impact on retailers’ behavior adopting ECPs and Seamless Interaction has a negative impact on retailers’ behavior adopting ECPs. With important implications, these findings are proposed to relevant parties, helping retailers and ECPs suppliers identify factors affecting retailers’ behavioral intention and behavior in adopting ECPs in Vietnam.

The flipped classroom (FC) model has long brought significant benefits to higher education, secondary, and elementary education, particularly in improving the quality and effectiveness of learning. However, the implementation of FC model to support elementary students in developing self-learning skills (autonomous learning, independent study, self-directed learning) through technology still faces numerous challenges in Vietnam due to various influencing factors. Data for the study were collected through direct questionnaires and online surveys from 517 teachers at elementary schools in Da Nang, Vietnam. Based on SEM analysis, the study identified factors such as perceived usefulness, accessibility, desire, teaching style, and facilitating conditions. The research findings indicate that factors like the perceived effectiveness of the model, teaching style, and facilitating conditions have a positive correlation with the decision to adopt the FC model. Therefore, to encourage the use of the FC model in teaching, it is essential to raise awareness of the model’s effectiveness, improve teaching styles, and create favorable conditions for implementation.

Research networks organized around a particular topic are built as knowledge is produced and socialized. These are parts of a seminal or initial production, to which new authors and subtopics are added until research and knowledge networks are formed around a particular area. The purpose of the research was to find this type of relationship or network between authors, institutions, and countries that have contributed to the issue of the circular economy and specifically its relationship with sustainability. This allows those interested in the said object of study to know the research advances of the network, enter their research lines, or create new networks according to their interests or needs. The study used a bibliometric-type descriptive quantitative approach using the Scopus scientific database, the R Studio data analytics application, and the Bibliometrix library. The results were found to determine a relationship building from 2006, which makes it an emerging topic. However, the growth it has achieved in recent years of more than 31% shows a strong interest in the subject. Of the subtopics that have been addressed, sustainability, recycling, solid waste, wastewater, and renewable energy. Similarly, sectors such as construction, the automotive industry, tourism, cities, the agricultural sector, the chemical industry, and the implementation of technologies 4.0 and 5.0 in their processes stood out. The most prominent country in the scientific approach to this area is Italy. The most prominent author for his citations is Molina-Moreno, the source of knowledge that stands out for his contributions is the University of Granada and different networks have been built around their knowledge.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

Business intelligence is crucial for businesses, from start-ups to multinationals. Examining the role and efficacy of business intelligence (BI) technologies in gathering, processing, and evaluating data to assist responsible management practices and decision-making is crucial in the modern age, especially for educational institutions. This study investigates the impact of Business Intelligence (BI) tools on Knowledge Management (KM) stages and their subsequent influence on Responsible Business Practices Outcomes in the educational sector of the United Arab Emirates. Using a quantitative research design, the study collected data from 406 faculty and staff members across various UAE universities via a structured survey. It analyzed the data using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results revealed a significant positive relationship between the use of BI Tools and the implementation of KM Stages, indicating that the utilization of BI tools is instrumental in enhancing knowledge management processes. However, the direct effect of BI Tools’ usage on responsible business practices’ outcomes was insignificant, suggesting the need for a mediating factor. KM Stages Implementation emerged as a significant mediator, indicating that the benefits of BI tools on responsible business practices are realized through their influence on KM processes. Moderation analyses showed that Institutional Culture, Training, and Expertise significantly moderated the relationship between BI Tools Usage and KM stage implementation, while Support from Management did not have a significant moderating effect. These findings highlight the importance of fostering an enabling institutional culture and investing in training and expertise to leverage the full potential of BI tools in promoting responsible business practices in educational settings. The study contributes to the literature on technology adoption in education and provides practical implications for educational administrators and policymakers seeking to integrate BI tools into their institutional practices.