The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

Since the Industrial Revolution, there has been an evolution in the paradigms under which the industrial worker is perceived and dealt with. These paradigms can be briefly listed in the order of their evolutionary stage as: the food-gatherer, the economic man, the social man, the resourceful man, and the enterprising man. Each of them is a combination of two basic paradigms in different proportions, namely, the outsider paradigm and the partnership paradigm. Obviously, the paradigmatic perspectives of management about their workers will have a significant influence on how they treat their workers, which may become especially conspicuous during recessions and other kinds of hard times. It was in this context that we designed a study to understand the human resource strategies of companies during a period of recession. Data for this study was collected through the content analysis of 46 published cases, wherein we developed the ratings of two sets of variables, namely: the external and internal environments of the company and the strategic actions taken by the respective managements. A surprising finding of the study is that the correlations between the environmental factors and the strategy factors were small and non-significant; moreover, the correlations involving the external environment were smaller than those involving the internal environment. Hence, it may be inferred that strategic actions are influenced primarily by the paradigmatic perspectives of management rather than environmental factors. In order to identify the different types of paradigmatic perspectives, we have further carried out a cluster analysis to develop a taxonomy of paradigms. The results showed that there are five sub-paradigms, which are: (1) Pacifiers, constituting 35% of the sample; (2) Modifiers, constituting 22%; (3) Molders, constituting 17%; (4) Enhancers, constituting 15%; and (5) Exploiters, constituting 11%. The limitations of the study and the implications of the findings are discussed in the concluding part.

In the Indian context, financial planning for salaried individuals has gained increased importance due to economic fluctuations, rising living costs, and the need for robust retirement planning. Despite its importance, there is limited research on the specific factors that influence financial decision-making among salaried employees in India. Understanding these determinants is essential for developing effective strategies to enhance financial well-being among employees. This study explores the key factors influencing financial decision-making among employees, including financial goals, emergency savings, retirement planning, budgeting, financial confidence and literacy, financial stress, use of tax-saving instruments, income level, risk tolerance, and debt levels. A sample of 549 employees from diverse sectors in Uttar Pradesh participated in this research, highlighting the critical aspects of personal financial management that impact financial well-being. The study used a questionnaire-based survey to gather data on factors affecting financial decision-making. Descriptive statistics, correlation, and regression analyses were employed to identify significant predictors. The results reveal that financial literacy, access to resources, attitudes toward retirement planning, and cultural norms significantly influence financial decisions. Additionally, income level, job stability, and social support are crucial in shaping employees’ financial planning. The study recommends enhancing employees’ financial decision-making by offering financial education programs, budgeting tools, retirement planning assistance, debt management programs, tax planning workshops, financial counselling services, and employer match programs for retirement savings. These initiatives aim to boost financial literacy and confidence, enabling employees to make informed financial decisions and improve their financial well-being.

To better analyze the tourist experience of the Jinsha Site Museum, this study adopts a mixed research method, combined with questionnaire surveys, interviews, and online review data, to comprehensively analyze the tourist experience from three dimensions: cognition, emotion, and behavior. After statistical analysis of 223 questionnaire surveys and analysis of 530 online comments, it was found that tourists’ overall satisfaction with the Jinsha Site Museum reached 95.3%. In the feedback on interactive exhibitions, 63.8% of tourists hoped to add more interactive elements and technological applications. The above results indicate that the Jinsha Site Museum has been widely recognized by tourists in providing historical and cultural exhibitions and modern facility services. However, to meet the needs of more tourists, museums should consider innovating and upgrading in interactive exhibitions, adding technological interactive elements, and improving the usability and responsiveness of equipment.

The Public-Private Partnerships management model (PPP) in Portugal was initially applied to the highways sector. Recently, this model began to spread to the health sector for hospital management. The recent growth of patient’s knowledge and expectations regarding the quality of healthcare services is compelling service providers to pursue new ways of delivering this care to meet users’ expectations. One wonders if the increase in patient access to knowledge may indicate a growth in health literacy, particularly regarding PPP Hospitals. This study assesses the Portuguese population’s literacy level regarding the PPP Hospital model, using a quantitative research approach based on a survey of the Portuguese population served by PPP hospitals and a Public Hospital Management (PHM) model. It was found that the Portuguese population has a low literacy concerning the PPP model, which can cause feelings of injustice. It was found that PPP users tend to have a favourable opinion regarding private involvement since they are also more satisfied compared to PMH users. These results may impact political decision-making concerning the renewal of new contracts for private management of public services.

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.