In the 20^th century, transportation heavily relied on hydrocarbons. Currently, the initial transition to electric propulsion is being witnessed.There's an ongoing debate among technical experts regarding its effectiveness alongside the start of battery production. Our research focuses on the labour market impact of companies operating in battery manufacturing, component production, and disposal in Hungary, as well as the opinions of local residents regarding the establishment of these organizations. Our research aims to track the differences and similarities between the governmental standpoint and the population's views. Hungary stands out for its rapid industrialization, focusing on enhancing GDP and job creation.The population is concerned about employing immigrant workers and potential battery production and disposal accidents. Our qualitative study adheres to the European Green Deal principles, including insights on battery manufacturing from transportation experts, manufacturers' associations, and environmentalists. Our quantitative research shows a preference for living at an unrealistic distance from such facilities. Many citizens advocate for the cessation of battery factory operations or investments, a stance mainly due to limited public awareness. Middle-aged individuals exhibit the most fear, and we correlate our findings with societal facts and negative incidents. To mitigate these tensions, mass education, tighter regulation, and increased sanctions are recommended.

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.

Purpose – This study aims to explore how the Human Development Index (HDI) is associated with students’ perceived academic, personal and skill-development outcomes related to the integration of generative artificial intelligence, particularly ChatGPT, into higher education. From a knowledge management perspective, the research examines adaptive use of AI tools, structuring of information and support of autonomous learning in countries with varying development. Design/methodology/approach – The study draws on 11, 910 valid responses from the 2024 Global ChatGPT student survey, covering 58 countries. Based on 33 Likert-scale items, three reflective constructs were identified. To explore the relationships between HDI, usage intensity and perceived impacts, the analysis combined descriptive statistics, K-means clustering and a partial least squares structural equation modeling (PLS-SEM) mediation model. Findings – The regression analysis showed a weak but statistically significant negative correlation between HDI and perceived impacts: students from lower-HDI countries tended to view ChatGPT’s impacts more positively. The PLS-SEM results indicated that higher national development is associated with lower perceived academic, developmental and skill-related benefits. This relationship appears both direct and indirect, as students in more developed countries report using ChatGPT less frequently and less creatively for academic purposes. Practical implications – The findings highlight the need for context-sensitive, pedagogically grounded artificial intelligence strategies, particularly in highly developed countries and in the support of students from disadvantaged backgrounds. Originality/value – This study is among the first to examine how national development levels shape perceived ChatGPT impacts in higher education. By combining HDI, cluster analysis and mediation modeling, it offers a novel perspective on digital inequality.

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.