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This study aims to fill critical research gaps by providing empirical evidence on the practical application of generative AI in the banking sector. It explores managerial preparedness, regulatory compliance and data privacy challenges in implementing this technology, offering insights into its operational effectiveness and potential in financial services.

The research employs a qualitative approach, conducting in-depth interviews with bank managers and industry experts. These interviews are analysed to identify key factors influencing the integration of generative AI in financial institutions.

The study identifies five critical factors – recognition, requirement, reliability, regulatory and responsiveness – that collectively impact the adoption and operational effectiveness of generative AI in banking. These factors highlight the challenges and opportunities of integrating this technology within the highly regulated financial industry.

The findings have significant theoretical and managerial implications. Theoretically, the research contributes to understanding AI integration in regulated industries, particularly financial services. Managerially, it provides a roadmap for financial institutions to adopt generative AI responsibly, balancing innovation with regulatory compliance and ethical considerations.

This study is among the first to provide empirical data on generative AI’s practical application in the banking sector, addressing the lack of real-world evidence and offering a comprehensive analysis of the factors influencing its successful implementation in a highly regulated environment.

This study aims to investigate scholarly works on higher education management from emerging economies. It investigates how higher education management has evolved in emerging economies.

This study is quantitative, and the authors retrieved the metadata from the Web of Science, an extensive pool of interdisciplinary peer-reviewed literary databases for bibliometric analysis.

The results show the strengths, weaknesses and prospects of higher education institutions (HEIs) in developing countries based on the scholarly output in various journals.

Though the study contributed to the knowledge and expansion of literature in HEIs research, it was only limited to the Web of Science database.

Policymakers and practitioners who want to improve the efficiency and viability of HEIs in developing countries can find a helpful guide in the form of a framework for higher education management in developing countries.

It is important to note that the bibliometric impact of higher education management in developing countries might vary depending on various factors, including the quality and relevance of the research, the level of funding and resources available for research and the level of international collaboration, among others.

This study provides a comprehensive overview of the research landscape in higher education management in developing countries by identifying the most influential authors, institutions and countries and the key research topics and trends. This information can be helpful for researchers, policymakers and practitioners who seek to understand the state of research in the field and identify gaps in knowledge.

We analyze the role of trust as a driver of speculative investment decisions in technology firms.

Structural Equation Modeling analysis in the context of blockchain technology supports our hypotheses.

Our findings indicate that a general propensity to trust technology leads to trusting beliefs in a service based on technology and that trusting beliefs in a technological service leads to a higher propensity to invest in any firm associated with that service. In addition, we show that in a non-technological context, there is no evidence for such an effect of trusting beliefs in a service on investment decisions. These results support the notion that trusting beliefs are facilitators of speculative investment in technology firms.

The research advances knowledge about the influence of trust in technology on investment decisions; its findings can help build new theoretical models regarding investment decisions using Fintech.

For investors, it is important to realize the potential bias identified in this study, so they can actively avoid adhering to it, thus avoiding exposure to unnecessary risk. Further, beyond individual investors, investment firms take active measures to avoid biases in their own decision-making. Banks and investment firms can help guide their clients about trust-based bias when building their investment portfolio.

Although trust in information systems has been studied extensively, research on the relationship between trust in technology and decisions to invest in technology-related firms is limited.

The purpose of this study was to investigate the likely causes of failure of some sections of road pavements in Ajaokuta, Northcentral Nigeria. This was achieved through a geotechnical assessment of subgrade soils in affected areas.

The methods entailed field and laboratory methods and statistical analysis. Subgrade soil samples were retrieved from a depth of 1,000 mm beneath the failed portions using a hang auger. The soils were analyzed for natural moisture content (NMC), Atterberg limit (liquid limit, plastic limit and linear shrinkage), grain size distribution, compaction and California bearing ratio (CBR), respectively.

The results of the geotechnical tests ranged from NMC (12.5%–19.4%), sand (84%–98%), fines (2%–16%), LL (16.0%–32.2%), PL (17%–27.5%), LS (2.7%–6.4%), PI (2.5%–18.4%), maximum dry density (1756 kg/m2–1961 kg/m2), optimum moisture content (13.2%–20.2%), unsoaked CBR (15.5%–30.5%) and soaked CBR (8%–22%), respectively. Pearson’s correlation coefficient performed on the variables showed that some parameters exhibited a strong positive correlation with r2 > 0.5.

Funding was the main limitation.

Comparing the results with Nigerian standards for road construction, and the AASHTO classification scheme, the subgrade soils are competent and possess excellent to good properties. The soils also exhibited very low plasticity, a high percentage of sand, high CBR and low NMC, which implies that it has the strength required for road pavement subgrades. The likely causes of the failures are, therefore, due to the use of poor construction materials, technical incompetence and poor compaction of sub-base materials, respectively.

This study examined the role of artificial intelligence (AI) tools in facilitating the accessibility and usability of electronic resources (e-resources) in academic libraries.

This study employed a quantitative descriptive survey to collect data from library users. The population targeted was sampled using a purposive sampling technique. A total of 427 (58%) participated in this study.

Most respondents preferred electronic journals (e-journals) among the e-resources stored in academic libraries. Chatbots were identified as preferred AI tools for accessing and enhancing the usability of these resources. Strategies mentioned included the potential for integrating AI tools across various e-resources. However, among the challenges reported was the inability to integrate AI tools with the existing library management systems. Improving e-resource discovery and access can significantly enhance the effectiveness of AI tools in academic libraries.

Originality in the context of AI applications in academic libraries refers to the unique approaches, innovative tools and creative solutions that enhance the accessibility and usability of electronic resources. By focusing on unique solutions that enhance the accessibility and usability of e-resources, these libraries can better serve their diverse user populations and adapt to the evolving landscape of information needs.