This study examines the key factors influencing investment decisions through decision tree regression, grounded in behavioral finance theory. By analyzing a comprehensive dataset incorporating behavioral, demographic, and financial variables—including investment attitudes, decision-making behaviors, financial education, age, income, and education—this study identifies significant predictors of investment outcomes. While the model shows moderate predictive performance (R² = 0.185; MAPE = 172.96%), it identifies hierarchical relationships among behavioral, cognitive, and demographic predictors. These results highlight the complexity of investment decisions and the need for integrative, behavioral-driven approaches in predictive modeling. Investment attitudes (25.88%), decision-making behaviors (19.53%), and financial education (16.68%) emerge as the most influential variables, while traditional demographic factors such as income and age have a lower impact. The hierarchical structure of the decision tree highlights critical decision-making patterns, particularly regarding speculative behaviors and investment attitudes. These findings challenge classical models of rationality by emphasizing the dominant role of behavioral factors in investment decision making. This study contributes to bridging computational modeling with financial economics, demonstrating the utility of decision tree regression in uncovering complex investor behavior. Practical implications include enhancing personalized financial advisory services and designing targeted financial literacy programs to improve decision-making efficiency. These insights, while exploratory, can guide future research and decision-support systems in behavioral finance.

Demographic trends play an important role in the development of workforce diversity. Population expansion and immigration are two examples of demographic changes that have created more diverse environments and made it crucial for organizations to be able to adapt and manage change. Organizations constantly need to reassess their methods and come up with new, original ideas for managing people as a result of demographic and global changes. Positive adjustments could boost an organization’s performance in matters of innovation and strengthen its ties to its customers as well as to the entire community. The research purpose of our paper is to provide an analysis of various viewpoints on the perceived image of the organization while taking into account the influence of three aspects: workforce diversity, creativity and innovation capacity, and corporate social responsibility. The perception of these three important organizational aspects was examined among employees from organizations of all sizes and in a range of activities from Romania. A questionnaire was used as an information gathering tool. The study is based on the quantitative approach, which considers the statistical analysis of numerical data. Based on the study findings and due to the inductive reasoning, today’s leaders are responsible both for managing employees, but also for meeting the needs of customers who are increasingly ethnically and culturally diverse. It is necessary to pay attention to aspects related to diversity. Whether the goal is to be a valued employer, to provide excellent products and services to customers, or to maintain a competitive advantage, diversity is recognized and used as an important organizational resource.

In an era of escalating digital threats and growing dependence on online systems, cybersecurity and user data protection have become critical global priorities. This study aims to identify cutting-edge discoveries and emerging trends in the field through a bibliometric analysis of 708 articles indexed in the Web of Science database. The first part of the analysis presents publication trends, while a semantic analysis of the scientific output highlights the main research directions within the field. For each research direction, emerging terms were identified and used to map the key trends shaping current developments. To fully capture the nuances of the relationships among emerging terms, the identified trends were discussed and contextualized to enhance their understanding. The results provide a comprehensive overview of the evolving dynamics of cybersecurity and user data protection research, and they underscore the dynamic areas of interest driving innovation in this domain.

Europe’s labor markets and organizational practices are changing due to the rapid spread of artificial intelligence and the increasing importance of sustainable finance. While both artificial intelligence and sustainable finance are increasingly seen as key drivers of long-term economic growth, we still know little about how they developed together, especially concerning workforce skills and employment stability at the industry level. In existing research, artificial intelligence and sustainable finance have mostly been examined as separate factors that affect financial performance, or human capital. The joint structural alignment of artificial intelligence adoption, sustainable finance engagement, and human capital outcomes has received limited empirical investigation. Many existing studies use complex econometric models or organizations level surveys, which highlights the need for open, data-driven analysis using publicly available statistical sources. This study investigates whether artificial intelligence adoption and sustainable finance engagement evolve together with employment dynamics and workforce skills across industries in the European Union (EU-27). The research employs a quantitative approach incorporating descriptive statistics, change-based (Δ) analysis, correlation analysis, and the development of a composite Sustainable Digital Human Capital (SDHC) Index, using secondary industry level data from European statistical databases. Workforce development is measured by advances digital skills indicators, while employment stability is assessed through aggregate employment metrics. The findings provide consistent descriptive and analytical evidence of a positive directional relationship among artificial intelligence adoption, sustainable finance engagement, and human capital outcomes from 2021 to 2023. Change-based analysis indicates that all core variables improved simultaneously, and correlation analysis confirms their tendency to move together. These results suggest the presence of coevolving structural dynamics. This study advances the literature by integrating digital transformation and sustainable finance perspectives in workforce analysis based on accessible and replicable data.