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.

The contraction of manufacturing economic activity in Latin American countries has been affected by the health crisis in the last few years. This phenomenon has negatively impacted the Latin American countries’ economies. In order to evaluate the impact of the manufacturing economy, this research integrates the impact of Foreign Direct Investment (FDI) on the growth of the Ecuadorian manufacturing sector, from 1981 to 2019, considering the role of the state through public spending using cointegration. The results are not consistent considering the empirical framework used; thus, FDI has a negative and significant influence on the manufacturing sector. Also, the manufacturing sector has a strong relationship with FDI in the short run and a less significant one in the long run. The results presented in this research suggest promoting domestic and FDI in the manufacturing sector, not only towards overexploited and monopolized sectors such as mining and telecommunications.

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.

Background: People who are financially literate are able to make sound decisions regarding their money since they have a firm grasp of the fundamentals of money and financial products. The significance of financial literacy has been acknowledged by numerous nations, prompting the formation of task teams to assess their populations and develop educational and outreach programs. The requirement to make educated decisions about ever-increasing financial goods necessitates a higher level of financial literacy. Aim: Being able to make sense of one’s personal financial situation is becoming an increasingly valuable skill in today’s world. One of the most essential components for making sure and successful decisions is having a good grip on one’s financial status. By contrast, financial literacy refers to an individual’s level of knowledge and awareness regarding financial matters, whereas investors’ decision-making is characterised by their understanding, prediction, investigation, and assessment of the various stages and transactions involved in making an investment decision. Risk, a decision-making framework and process, and investing itself are all components of investing. Method: Researchers will conduct a cross-sectional survey of Saudi Arabian investors. We used a structured questionnaire to gather data. Using “Cronbach’s a and confirmatory factors” analysis, we checked whether the data is reliable. The links between financial literacy and investment decisions was demonstrated using structural equation modeling (SEM) in IBM-SPSS and SmartPLS. Purpose: The purpose of this research is to look at how the investment choices of Saudi Arabians are correlated with their degree of financial literacy. Consequently, research on the connection between financial literacy, knowledge, behaviour, and investment choices is lacking. Researchers on this subject have already acknowledged the problem’s importance and intended to devote substantial time and energy to solving it. Findings: The study concluded that there was a significant relationship between financial literacy and financial knowledge with respect of investment decision of investors. Similarly, there was a significant relationship between financial behaviour and financial knowledge with respect of investment decision of investors. The discovery of the outcomes will enable regulatory authorities to aid investors in preventing financial losses by furnishing them with sufficient financial information.

This financial modelling case study describes the development of the 3-statement financial model for a large-scale transportation infrastructure business dealing with truck (and some rail) modalities. The financial modelling challenges in this area, especially for large-scale transport infrastructure operators, lie in automatically linking the operating activity volumes with the investment volumes. The aim of the paper is to address these challenges: The proposed model has an innovative retirement/reinvestment schedule that automates the estimation of the investment needs for the Business based on the designated age-cohort matrix analysis and controlling for the maximum service ceiling for trucks as well as the possibility of truck retirements due to the reduced scope of tracking operations in the future. The investment schedule thus automated has a few calibrating parameters that help match it to the current stock of trucks/rolling stock in the fleet, making it to be a flexible tool in financial modelling for diverse transport infrastructure enterprises employing truck, bus and/or rail fleets for the carriage of bulk cargo quantifiable by weight (or fare-paying passengers) on a network of set, but modifiable, routes.