Purpose: The central research question of the study is how objective financial knowledge and subjective financial confidence interact and relate to digital financial behavior and the use of FinTech tools. By examining both objective knowledge refers to measured, test-based financial competence and subjective confidence denote self-assessed financial understanding, the research offers insight into the psychological and demographic drivers of FinTech use and perceived financial well-being. Design/methodology/approach: Based on the OECD’s 2023 international financial literacy survey, the study uses a nationally representative Hungarian sample. It employs non-parametric statistical methods, linear regression, and two-step cluster analysis. Three composite indicators, general digital activity, digital financial engagement frequency, perceived financial security were developed to measure general digital activity, frequency of digital financial engagement, and perceived financial security. Findings: Results reveal a moderate but significant correlation between actual and self-assessed financial knowledge. Men score higher on both measures, though self-assessment bias does not significantly differ by gender. Higher education and income levels are associated with stronger financial literacy and more frequent use of FinTech tools, while age correlates negatively. However, the accuracy of self-perception is not explained by these demographic factors. Cluster analysis identifies four distinct financial knowledge profiles and five consumer digital behavior types, revealing disparities in digital financial inclusion and confidence. Originality: This research contributes a multidimensional perspective on how consumer capabilities, attitudes, and digital behavior influence FinTech adoption. By integrating behavioral, demographic, and psychological factors, the study offers practical implications for targeted financial education and the design of inclusive, human-centered digital financial services—especially relevant for emerging European markets.

Objective: This study investigates resource productivity by exploring the factors that influence it and the strength of their impact using sustainability statistics from Eurostat. Theoretical Framework: The research analyzes the interdependence between national income and material consumption, with a focus on the paradoxical effects of circular material use on these variables. Method: Data from bibliographic sources are analyzed using statistical methods, including regression and path models, to assess the relationships between material consumption, national income, and the use of circular materials. Results and Discussion: The study concludes that national income and material consumption are interdependent, with material consumption directly affecting national income. Notably, the use of circular materials, contrary to expectations, increases material use and decreases national income. The cases of Hungary and the Netherlands are specifically examined to illustrate these findings. Research Implications: The findings suggest that strategies to increase resource productivity and sustainability performance need to consider the complex and sometimes counterintuitive impacts of circular material use on national income and material consumption. Originality/Value: This study provides new insights into the relationship between resource productivity, national income, and material consumption, highlighting the unexpected effects of circular material use. It contributes to the broader understanding of sustainability performance, particularly in the context of Hungary and the Netherlands.

Objective: This study investigates resource productivity by exploring the factors that influence it and the strength of their impact using sustainability statistics from Eurostat. Theoretical Framework: The research analyzes the interdependence between national income and material consumption, with a focus on the paradoxical effects of circular material use on these variables. Method: Data from bibliographic sources are analyzed using statistical methods, including regression and path models, to assess the relationships between material consumption, national income, and the use of circular materials. Results and Discussion: The study concludes that national income and material consumption are interdependent, with material consumption directly affecting national income. Notably, the use of circular materials, contrary to expectations, increases material use and decreases national income. The cases of Hungary and the Netherlands are specifically examined to illustrate these findings. Research Implications: The findings suggest that strategies to increase resource productivity and sustainability performance need to consider the complex and sometimes counterintuitive impacts of circular material use on national income and material consumption. Originality/Value: This study provides new insights into the relationship between resource productivity, national income, and material consumption, highlighting the unexpected effects of circular material use. It contributes to the broader understanding of sustainability performance, particularly in the context of Hungary and the Netherlands.

The employment and labor market landscape has undergone significant transformations globally, including the three Central European countries examined in this study. Over the past decades, organizations in this region have transitioned from a state of full employment to labor shortages, raising the question: What factors have driven these changes? Our study aims to present a theoretical framework highlighting key macro-level factors, such as demographic trends, economic development, labor market dynamics, the impact of the COVID-19 pandemic, and the role of robotization and artificial intelligence. Based on two empirical studies conducted in 2019 and 2022 among Czech, Hungarian, and Slovak organizations, we analyzed the extent and causes of labor shortages, as well as the labor market effects of robotization. Using descriptive and non-parametric statistical methods, including frequency analysis and Mann–Whitney U tests, the study examined key trends and compared the two periods to identify significant shifts. The analytical approach of this study primarily aims to compare perceptions across occupational groups and between the two survey waves (2019 and 2022). Because most variables were measured on ordinal Likert-type scales and the datasets represent independent cross-sectional samples rather than a panel dataset, non-parametric methods were considered the most appropriate. More advanced causal modeling techniques, such as regression or factor analysis, were not applied because the objective of the research was exploratory and comparative rather than to establish causal relationships between variables. The findings reveal significant shifts in the perceived causes of labor shortages across occupational groups in the surveyed Central European organizations. In particular, increasing labor shortages were observed in specific job categories, alongside changes in the relative importance of the underlying drivers of labor shortages. While adopting robotization and artificial intelligence has been positively received, demographic decline and emigration remain critical challenges. The study provides practical insights for policymakers and corporate leaders regarding labor market challenges, workforce planning, and the potential role of robotization and artificial intelligence in addressing labor shortages. Although the research is based on a non-representative sample, it offers valuable insights into the Central European region’s employment and labor market trends. Future research could examine whether, in hard-to-fill positions, robotization and AI primarily provide indirect support by augmenting and reallocating human work, or whether they may serve as direct substitutes.

The study examines the relationship between fossil energy consumption and economic growth in carbon-based economies, with a particular focus on the role of energy intensity and energy efficiency. We aim to quantify the impact of fossil energy consumption on economic growth through model-based analysis and identify the development and sustainability challenges of energy-intensive economies. The study used a Random Forest model to analyze the relationship between fossil energy consumption and GDP growth. The data are from the World Bank’s World Development Indicators database for the period 2013–2023, considering the economic structure of different countries. Energy intensity showed a strong positive correlation with GDP growth in China, Russia and Mongolia, while in more developed economies such as Canada and the United States, a lower or negative relationship is observed. Fossil energy-intensive economies benefit in the short term, but diversification is needed in the long term for sustainability. Diversifying the economy and increasing energy efficiency are key to sustainable development. The results can help policymakers plan the energy transition, especially focusing on reducing fossil fuel dependence and integrating renewable energy sources for economic and environmental stability. JEL codes: O13, Q43, Q56, C53, O44

Digital technologies are increasingly used to supervise, direct, and evaluate work, yet European evidence on their psychosocial implications remains limited. This study examined how work-related stress, depression, or anxiety (WRSDA) varies with the number of digital monitoring practices, how much of this association is statistically attributable to lack of autonomy, and whether it differs across countries. Using 2022 OSH Pulse data (22,856 employed respondents, EU-27), we estimated weighted logistic regression models with country-clustered standard errors, conducted a KHB decomposition, and pooled country-specific estimates using random-effects meta-analysis. The predicted probability of WRSDA was 22.7% with no monitoring practices and 35.9% with all three. The association remained after adjustment for personal and work-related characteristics and was the strongest for digital supervision and third-party evaluation; interaction analyses provided no evidence that the practices reinforced one another. In the statistical decomposition, 29.0% of the association was attributable to lack of autonomy. The association was positive in 24 countries, varied moderately, and was weaker where more employees reported increasing workplace surveillance. By examining distinct monitoring practices across all EU Member States, this study adds to existing evidence; however, the findings are cross-sectional, exploratory, and based on single-item measures.