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.

This study investigates the optimization of ride-sharing services (RSS) on the ride-hailing service (RHS) providers in Bangladesh. This study employed an explanatory sequential mixed method research design- a qualitative study followed by a quantitative one. Qualitative data were collected through focus group discussions and in-depth interviews with twenty (20) riders and drivers in Bangladesh, and quantitative data were collected from 300 respondents consisting of riders and drivers using a convenience sampling technique. Factor analysis and hierarchical cluster analysis were applied to the data analysis. The qualitative analysis reveals several significant factors associated with RSS and RHS, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, business growth, and security. The quantitative results indicate that using RSS is associated with more significant benefits than RHS in various aspects, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, and expansion of the automobile industry. The findings may assist policymakers in understanding how RSS can yield more incredible economic, environmental, and social benefits than RHS by analyzing fare sharing among passengers, carbon emissions, fuel consumption, and the expansion of the vehicle markets etc. Therefore, the government can formulate distinct policies for RSS holders due to their contributions to economic, social, and environmental concerns. While RHS services are available in many cities in Bangladesh, this study considered only Dhaka and Sylhet cities. Thus, future studies can consider more respondents from other cities for a holistic understanding.

This research aims to examine the role of learning leadership on teacher performance in elementary schools, analyze the influence of digital literacy on teacher performance, analyze the role of emotional intelligence on teacher performance and analyze the role of intellectual intelligence on teacher performance. In this digital era, digital literacy plays an important role in education. The application of digital literacy in education is still not optimal and there is no previous research that discusses the variables of instructional leadership, teacher performance, digital literacy, emotional intelligence and intellectual intelligence. The research method used is quantitative, the population of this research is all teachers who have used e-learning methods, and the analysis of this research uses structural equation modelling (SEM), the respondents for this research are 675 Indonesian teachers. The sampling method is simple random sampling. Research data was obtained from distributing online questionnaires designed using a 5-point Likert scale, namely scale 1 is strongly disagree, scale 2 is disagree, scale 3 is neutral, scale 4 is agree and scale 5 is strongly agree. Data processing uses SmartPLS 3.0 software tools. The SEM test stages in this research are the outer model test, namely convergent validity, discriminant validity and composite reliability, and then the inner model test, namely hypothesis testing. The results of the analysis using SEM are that the Instructional leadership variable has a positive and significant relationship to teacher performance, the Digital literacy variable has a positive and significant relationship to teacher performance, the Emotional intelligence variable has a positive and significant relationship to teacher performance and Intellectual intelligence has a positive and significant relationship to teacher performance. The novelty of this research is the discovery of a model of the relationship between instructional leadership variables, digital literacy variables, emotional intelligence variables, and intellectual intelligence variables on teacher performance which did not exist in previous research studies. This research has a novelty, namely a model analyzed using SEM-PLS in the digital era. The principal must be able to determine and set learning objectives in his school, in his implementation the principal always involves teachers in developing and implementing learning goals and objectives and the principal also refers to the curriculum set by the government in developing learning. The dimensions of instructional leadership are defining school goals, managing learning programs, and creating a positive learning climate. In other words, the principal has implemented Instructional Leadership with indicators of setting learning goals, indicators of being a resource for staff, indicators of creating a school culture and climate that is conducive to learning, indicators of communicating the school’s vision and mission to staff, indicators of conditioning staff to achieve their goals.

The objective is to determine the impact of economic growth on the externalities of infrastructure investments for the Peruvian case for the periods from 2000 to 2022. The methodologies used are descriptive, explanatory and correlational, analyzing qualitative and mainly quantitative methods. Econometric software was used, and correlations of variables were created for each proposed hypothesis. The estimated model shows that all the independent variables have a significant t-statistic greater than 2 and a probability of less than 5%, which indicates that they are significant and explains the model. The R² is 98.02% which indicates that there is a high level of explanation by the independent variables to the LOG(RGDP). The results of the estimated models demonstrate the existence of a positive and significant relationship of investments in infrastructure and externalities on the growth of the non-deterministic component of real GDP, therefore, in a practical way, private and public investment has a positive effect on the non-deterministic growth of real GDP.

In the contemporary landscape characterized by technological advancements and a progressive economic environment, the utilization of currency has undergone a paradigm shift. Despite the growing prevalence of digital currency, its adoption among the Vietnamese population faces several challenges, including limited financial literacy, concerns over security, and resistance to change from traditional cash-based transactions. This research aims to identify these challenges and propose solutions to encourage the widespread use of digital currency in Vietnam. This research adopts a quantitative approach, utilizing Likert scale questionnaires, with a dataset of 330 records. The interrelationships among variables are analyzed using partial least squares structural equation modeling (PLS-SEM). The analysis results substantiate the viability of the research model, confirming the hypotheses. The findings demonstrate a positive relationship and the significance impact of factors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived trust (PT), social influence (SI), openness to innovation (OI), and financial knowledge (FK) to intention to use digital currency (IUDC). Thereby aiming to inform policymakers, industry stakeholders, and the wider community, fostering a deeper understanding of consumer behavior and providing solutions to enhance the adoption of digital currency in the evolving landscape of digital finance.