Currently, there is a unique situation in the global economy, industrial eras coexist together, there is interaction and transformation of financial systems simultaneously within the framework of Industry 4.0 and Industry 5.0. New, digital resources are entering the economy, intellectual capital is becoming virtual, artificial intelligence is increasingly finding its application in the structure of financial support. Financial intermediation in developing countries is also subject to global trends, the active development of new instruments for developing economies is especially important. The aim of the study is to identify effective ways to develop financial intermediation in Industry 5.0 for the economies of developing countries. Based on the results of the study on the development of financial institutions mediation revealed a problem related to the lack of reasonable tools that could be used to improving the efficiency of the financial intermediaries market, proposed the main directions of such a process: mobilization of savings, distribution financial assets, payment system, risk management and control over market agents involved in financial operations.

Relational database models offer a pathway for the storage, standardization, and analysis of factors influencing national sports development. While existing research delves into the factors linked with sporting success, there remains an unexplored avenue for the design of databases that seamlessly integrate quantitative analyses of these factors. This study aims to design a relational database to store and analyse quantitative sport development data by employing information technology tools. The database design was carried out in three phases: (i) exploratory study for context analysis, identification, and delimitation of the data scope; (ii) data extraction from primary sources and cataloguing; (iii) database design to allow an integrated analysis of different dimensions and production of quantitative indicators. An entity-relationship diagram and an entity-relationship model were built to organize and store information relating to sports, organizations, people, investments, venues, facilities, materials, events, and sports results, enabling the sharing of data across tables and avoiding redundancies. This strategy demonstrated potential for future knowledge advancement by including the establishment of perpetual data updates through coding and web scraping. This, in turn, empowers the continuous evaluation and vigilance of organizational performance metrics and sports development policies, aligning seamlessly with the journal’s focus on cutting-edge methodologies in the realm of digital technology.

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.

Enhancing the emphasis on incorporating sustainable practices reinforces a linear transition towards a circular economy by organizations. Nevertheless, although studies on circular economy demonstrate an increasing trend, the drivers that support circular economy practices towards sustainable business performance in the Small and Medium-Sized Enterprise (SME) sector, especially in developing nations, demand exploration. Accordingly, the study examines circular economy drivers, i.e., green human resource management, in establishing sustainability performance and environmental dynamism as moderating variables. The study engaged 207 SMEs and 621 respondents who were analyzed utilizing structural equation modeling. The analysis indicated that sustainable business performance was affected by green human resource management and a circular economy. Subsequently, the circular economy mediated the linkage between green human resources management and sustainable business performance. The environmental dynamism moderated the linkage between green human resources management and the circular economy.

Purpose: This research examines the intricate interplay between Business Intelligence (BI), Big Data Analytics (BDA), and Artificial Intelligence (AI) within the realm of Supply Chain Management (SCM). While the integration of these technologies has promised improved operational efficiency and decision-making capabilities, concerns about complexities and potential overreliance on technology persist. The study aims to provide insights into achieving a balance between data-driven insights and qualitative factors in SCM for sustained competitiveness. Design/methodology/approach: The research executed interviews with ten Arab Gulf-based consulting firms. These companies’ ability to successfully complete BI projects is well recognised. Findings: Through examining the interplay of human judgement and data-driven strategies, addressing integration challenges, and understanding the risks of excessive data reliance, the research enhances comprehension of the modern SCM landscape. It underscores BI’s foundational role, the necessity of balanced human input, and the significance of customer-centric strategies for lasting competitive advantage and relationships. Practical implications: The research provided information for organizations seeking to effectively navigate the complexities of integrating data-driven technologies in SCM. The research is a foundation for future studies to delve deeper into quantitative measurement methodologies and effective data security strategies in the SCM context. Originality: The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.