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.

The current research examines the psychological and perceptual predictors of sustainable consumption behavior in a rural Romanian context, with specific reference to the traditional product Pită de Pecica. A sample of 485 consumers (n = 485) who were familiar with Pită de Pecica completed validated instruments measuring brand identification and brand attributes perceived. An exploratory factor analysis (EFA) was applied to find two dimensions on each scale—brand-based self-definition (α = 0.92) and emotional brand attachment (α = 0.86); and sensory-affective brand association (α = 0.87) and product functional-symbolic value (α = 0.84). Emotional brand attachment (EBA) refers to the emotional bond a consumer forms with a brand; sensory-affective brand association (SABA) captures affective and sensory connections; and decision tree regression is a machine learning technique that identifies non-linear predictors. In this study, sustainability is operationalized across cultural, economic, and environmental dimensions, reflecting both traditional product preservation and support for regional food systems. A decision tree regression model was then applied to predict the frequency of sustainable consumption behavior. Emotional brand attachment was the strongest predictor (relative importance = 26.13%), sensory-affective brand association was second most important (16.91%) and brand-based self-definition was third (13.99%). Demographic variables (e.g., income, age) were weak predictors. The model explained 43% of the behavior variance (R² = 0.43) despite considerable behavioral unpredictability (MAPE = 236.85%). Findings show that emotional and identity-driven brand connections are central to leveraging sustainable consumption in rural contexts, which has important implications for future branding initiatives, agri-food policies, and local economic revitalization initiatives that promote sustainable development. Findings support adopting cultural, psychological, and economic dimensions of sustainable development across development paradigms.

This study examines the relationship between apartment prices in Romania and various influencing factors, for which we have constructed a model using multiple linear regression. This type of regression equation's parameters hold practical significance as they aid in establishing the regression equation. The form of the connection, specifically the parameters that define it, greatly influences our understanding of the connection between various quantified variables in the proposed model. By determining the regression function in this manner, we created a model that allows us to make predictions or draw conclusions based on the variables within the model. Microeconomic variables are crucial in influencing real estate price variations at the individual property level. These variables are more specific and relate to factors within the local market of Arad or the property itself. Some key microeconomic variables can influence real estate prices, which are important to ascertain using statistical tools.

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.

Recent global events have slowed progress toward achieving the Sustainable Development Goals (SDGs), making robust sustainability reporting (SR) systems critical for monitoring and corrective actions. While research on the link between corporate sustainability performance (CSP) and corporate financial performance (CFP) is extensive, the specific role of sustainability reporting as a communication channel remains insufficiently explored. Therefore, the objective of this paper is to address this gap in the literature by assessing the relevance of sustainability reporting for modeling the relationship between CSP and CFP. In this study, a univariate meta-analysis based on a PRISMA screening framework was performed to assess the unidirectional relationship between SR and CFP, specifically investigating whether SR acts as a moderating or mediating factor in the CSP-CFP nexus. The analysis is limited to 19 high-quality articles published in top-tier accounting journals between 2014 and 2024 to minimize publication bias and ensure reliability. The meta-analysis reveals no statistically significant moderating effect of SR on CFP. Instead, the results confirm a significant mediating effect, particularly when considering the presence of sustainability reports rather than just their specific content. These findings suggest that SR serves as a vital catalyst for corporate communication, providing more positive effects in voluntary compared to mandatory disclosure settings. This paper has both theoretical and practical implications, which are mainly relevant to standard-setters for assessing the efforts of SR disclosure regulation, and is of fundamental importance to managers as it indicates that SR does not relate solely to the practice of conformity, but rather to essential channels of communication and value creation.

This study maps European countries’ resilience to cognitive warfare by developing a cross-national composite measure. The framework integrates three pillars: information ecology, institutional-digital capacity, and socioeconomic context—drawing on a systemic perspective linking social structures to societal functions. Publicly available secondary indicators are compiled from online sources for EU (European Union) and EEA (European Economics Area) states. The dataset is examined through descriptive analysis, association testing, multivariate modelling, dimensionality reduction to derive a composite resilience score, and unsupervised clustering to produce a country typology. Indicators capture governance effectiveness, e-government maturity, public-sector AI (Artificial Intelligence) readiness, digital connectivity and infrastructure, media freedom and broader media-ecosystem quality, academic freedom, and socioeconomic vulnerabilities such as youth labour market exclusion. Results show that resilience aligns most strongly with institutional capacity and governance performance; a healthy ecology acts as a reinforcing layer. Digital infrastructure appears necessary but insufficient without capable, credible institutions and coherent public policy. Socioeconomic vulnerabilities tend to erode resilience and heighten susceptibility to hostile cognitive influence. The study concludes that policy efforts should prioritise governance integrity and effectiveness, end-to-end digital government, responsible public-sector AI capability, and safeguards for media and academic autonomy, alongside measures that improve youth inclusion.