University of Economics in Bratislava

The problems of society, summarised in the Sustainable Development Goals, cannot be addressed by national governments alone. Many of them are closely linked to social responsibility at all levels. Given the importance of social responsibility for development, it is important to understand the mindset, attitude and personal behaviour of young. As future job seekers, students can play an important role in promoting social responsibility and sustainable practices in society, the community or the work. Given the constantly changing demands of the labour market, our research question arose: how do students and human resources professionals perceive individual social responsibility as part of the competencies important for employment in the labour market? The research study focuses exclusively on conditions in Slovakia with the aim of identifying the current state of knowledge, skills, and attitudes of young people towards social responsibility and proposing recommendations for decision-makers, practical application, and further research in the field of social responsibility. In relation to the research problem and question, we opted for a mixed research strategy. Based on the interdisciplinarity of the phenomenon, we applied a triangulation of methods, theory and approaches. Our research is based on an emic perspective, so we decided for questionnaire survey, focus group and expert interview. Based on the results, the students have basic knowledge about social responsibility, they apply their skills as well as have the right attitude towards solving challenges. However, according to human resources experts, young people entering the job market do not have the necessary knowledge in the area of social responsibility and do not classify it as a standard. In their opinion, the competence to act in a socially responsible manner can be classified as a competence, but only in combination with other important competences. Given the specifics of the Slovak labour market and the shortage of skilled labour, the social responsibility of job seekers will not be decisive for their recruitment. It is expected that its importance will increase in the future and that for some positions it will be an important part of the competency model. The topic of social responsibility should be a natural part of everyone and the state education system and should be seen in a comprehensive way in relation to agile approach and civic engagement.

Artificial Intelligence (AI) has emerged as a key innovation in the global economy, with AI-related equities becoming major drivers of the recent stock market upswing. Yet, the internal dynamics and heterogeneity within the AI sector remain largely unexplored. We address this gap by analyzing the connectedness and risk transmission among four AI industries: hardware manufacturers, cloud providers, application developers, and AI-intensive BigTech firms, using self-constructed stock market indices for 2023-2025. Adopting an industry-level perspective, we explicitly account for the heterogeneous structure of the AI sector. By capturing both static dependence and dynamic patterns, we assess how intrasectoral relationships evolve over time and under varying market conditions. Results indicate strong positive relationships among the four AI industries, which intensify during periods of market stress. Cross-industry spillovers account for more than half of return variation in the AI sector, though they remain modest under normal conditions. During sharp market swings, particularly downturns, shocks propagate widely across all industries. While AI hardware manufacturers and AI BigTech typically act as net shock transmitters and AI application developers and cloud providers as net receivers, their roles shift over time. Our results emphasize the need for dynamic hedging, as static diversification offers limited protection in stressed markets, and closer monitoring of cross-industry exposures. Given the growing dependence of many sectors on AI infrastructure, disruptions in key AI segments may have wider systemic effects and should be incorporated into macroprudential oversight.

This article provides a critical analysis of risk management and the security architecture for large-scale events, focusing on the interdependencies among established security measures, system risk factors, and psychological determinants of crowd behavior. The central research problem addresses the structural deficits in the interplay of architectural, organizational, and socio-psychological components that lead to critical system failures in contemporary event planning. The study aims to answer the question of how integrated risk assessment frameworks can systematically mitigate these systemic hazards. Methodologically, the paper employs a multi-stage systematic literature review across seven academic databases utilizing a multi-layered keyword cluster, combined with a comparative, qualitative case study analysis of two contrasting structural paradigms: the Love Parade 2010 (representing a centralized bottleneck infrastructure failure) and the UEFA Euro 2024 (representing a decentralized, intelligence-led security architecture). The results demonstrate that classic mass-psychological models fail to accurately predict crowd dynamics. Instead, applying the Social Identity Approach (SIA) reveals that asymmetric or perceived as illegitimate external interventions by security forces catalyze behavioral escalation. Furthermore, the analysis establishes that conventional, isolated risk metrics ignore the compounding effects of hybrid threats and localized structural bottlenecks. Finally, this paper delivers a newly synthesized, holistic security framework that bridges architectural design, cross-agency digital communication, and behavioral psychology into an actionable, integrative operational strategy for modern hazard prevention.

Type of the article: Research Article Although financial reporting and auditing standards are substantially harmonized across the European Union, important differences remain in national accounting regulations, audit thresholds, and the practical application of the going concern principle. These differences are particularly relevant for small and medium-sized enterprises (SMEs), which constitute the dominant segment of the Visegrad Four (V4) economies. This paper examines differences in financial reporting and auditing frameworks among the Czech Republic, Hungary, Poland, and Slovakia and develops sector-specific distress prediction models to support going-concern assessments in SME auditing. The empirical analysis is based on financial statement data for 66,988 active firms obtained from the Orbis database. After data cleaning and consistency checks, a modelling sample of approximately 41,000 SMEs was constructed. Financial distress is defined as a persistent inability to cover interest obligations, represented by two consecutive years in which earnings before interest and taxes (EBIT) are lower than interest expenses. Distress status is modelled using financial ratios, firm-size indicators, industry characteristics, and selected variables inspired by ISA 570. Separate binomial logistic regression models are estimated for country–industry groups derived from NACE classifications and evaluated using hold-out samples. The results confirm that country- and sector-specific models achieve satisfactory predictive performance and provide useful support for assessing going-concern risks. The results also show that the most informative predictors differ across countries and industries, reflecting differences in regulatory environments and economic structures. The study highlights the importance of local calibration when developing distress prediction models and demonstrates that a universal approach may lead to reduced predictive accuracy. The proposed models provide a practical screening tool for auditors, lenders, and SME managers, complementing professional judgement and broader audit procedures. Acknowledgments This study is co-financed by the governments of Czechia, Hungary, Poland, and Slovakia through Visegrad Grants from the International Visegrad Fund. Visegrad Grant No. 22420285, Title of the project: “Distress prediction models in V4 countries and their audit applicability”. The mission of the Fund is to advance ideas for sustainable regional cooperation in Central Europe. The authors gratefully acknowledge the support of their home institutions and thank the anonymous reviewers for their insightful comments and suggestions.

This study examines the determinants of the digital divide and identifies differences in key factors driving digital development between countries in Central Europe and Southeast Asia over the period 2002–2024. The digital gap is understood as disparities in digital infrastructure, digital public service delivery, and human capital that influence broader economic and social development. The analysis integrates institutional quality (government effectiveness, rule of law, and political stability), economic development (log GDP per capita and income inequality), and skills formation inputs (government spending on education and tertiary enrollment) to explain cross-country and temporal variation in digital performance. Given the limited number of countries, the study employs an econometric strategy suitable for small panel data. Dynamic relationships are estimated using bias-corrected least squares dummy variables (LSDVC), while robustness is assessed through static fixed-effects estimation with Driscoll–Kraay standard errors. To address model uncertainty and multicollinearity, Bayesian Model Averaging (BMA) is applied with family constraints to identify the most robust predictors. The results show that income inequality constitutes the main constraint on inclusive digital development in Central Europe, whereas tertiary education expansion is the strongest driver in Southeast Asia. Across both regions, institutional credibility emerges as a universal foundation for sustainable digital progress. The novelty of this study lies in its cross-regional comparative approach using a harmonized digital development index, the combined use of LSDVC and BMA in a small-panel context, and the identification of both universal and region-specific determinants relevant for digital policy design.