The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

The benefits of information system users are an important topic in research on information system implementation in general as well as in hospital information systems in particular. The study is applying structural equation modelling in determining the factors affecting personal benefits of information system users, with the antecedents being the combination of perspectives, and the outcomes including individual user results of the system in hospitals. The study was conducted in two phases: a preliminary study and a formal study. The preliminary study aimed to adjust and supplement the observed variables to be suitable for the actual conditions in Vietnam by conducting a preliminary survey with a questionnaire involving 55 samples to assess the internal consistency reliability, convergent validity, and discriminant validity of the measurement scales. The formal quantitative study, which employed linear structural analysis with PLS-SEM, was conducted on 215 samples of individuals who had previously used information systems in several hospitals in Vietnam. The proposed model explained 80.6% of the variance in user engagement with the system and 50.6% of the variance in user satisfaction when using the information system. In more detail, for user benefits, it is worth noting that the strongest impact intensity belongs to information quality and the weakest belongs to support structure. In addition, confidence in one’s own abilities also has a high impact on user benefits when using the information system.

This paper aims to investigate the determinants of performance for insurance companies in Tunisia from 2004 to 2017. Namely, we consider three dimensions of determinants; those related to firms’ microenvironment, macroenvironment and meso or industry environment. The performance of insurance companies is measured using three criteria: Return On Assets (ROA), Return On Equity (ROE), and Combined Ratio. The independent variables are categorized into three groups: microeconomic variables (Firm Size, Financial leverage, Capital management risk, Volume of capital, and Age of the firm), meso-economic variables (Concentration ratio and Insurance Sector Size), and macroeconomic variables (Inflation, Unemployment, and Population Growth). The General Least Squares (GLS) regression technique is employed for the analysis. The study reveals that the financial performance of Tunisian insurance companies is positively influenced by firm size, capital amount, and risk capital management. On the other hand, it is negatively influenced by leverage level, industry size, concentration index, inflation, and unemployment. In terms of technical performance, the capital amount of the firm, industry size, age of the firm, and population growth have a positive impact. However, firm size, leverage, concentration index, and risk capital management negatively affect technical performance. This paper contributes to the existing literature by examining the determinants of performance specifically for insurance companies in Tunisia. Besides the classical proxies of performance, this paper has the originality of using the technical performance which is the most suitable for the case of Insurance companies.

This study aims to advance understanding of the factors affecting Generation Z employee commitment in the workplace of the information and technology (IT) companies in Vietnam. A survey of 450 Generation Z employees in IT companies shows that company remuneration, reward and welfare, work environment, colleagues, direct manager, promotion, job characteristics, green initiatives are positively related to Generation Z organizational commitment. More specifically, work environment and direct manager have the highest effect on Generation Z employee commitment to organization while promotion and colleagues have the lowest effect on Generation Z employee commitment to organization. Research results also revealed that green initiatives of the organization have significant effect on Generation Z employee commitment in companies. This finding suggests that including green initiatives in corporate strategy is a valuable approach for improving Generation Z employee commitment to organization. We discuss the implications for theory, practice, limitations, and directions for future research.

This study investigates the influence of perceived value and perceived risk on consumer intentions to purchase counterfeit luxury goods, drawing upon an integrated theoretical framework encompassing perceived value theory, risk perception theory, and consumer behavior models. Through a quantitative research design involving a structured survey and Structural Equation Modeling (SEM), the study examines the relationships among perceived value dimensions (functional, emotional, social, economic), perceived risk factors (financial, social, performance), consumer attitudes, and purchase intentions. The findings reveal that perceived value positively influences purchase intentions, with consumer attitudes acting as a critical mediating mechanism. Conversely, perceived risk negatively impacts purchase intentions, with this relationship also mediated by consumer attitudes. Furthermore, Bayesian Network analysis uncovers the indirect pathways through which perceived risk shapes purchase intentions via its influence on consumer attitudes. By integrating these theoretical frameworks and employing advanced analytical techniques, this study contributes to a comprehensive understanding of the complex decision-making processes underlying counterfeit luxury goods consumption. The findings provide valuable insights for policymakers, luxury brand managers, and consumer protection agencies in devising targeted strategies to address consumer perceptions of value and risk, ultimately mitigating the proliferation of counterfeit luxury goods.