This article investigates the validity of the Phillips curve in the current economic conditions of the Slovak Republic. The Phillips curve, which suggests an inverse relationship between inflation and unemployment, has been one of the most important concepts in macroeconomics since its creation. However, its role and relevance in modern economies have been questioned, primarily due to various factors such as monetary policies, globalization, and structural changes in labor markets. The analysis focuses on the Slovak economy. Examined was the relationship between inflation and unemployment after the country joined the European Union and adopted the euro. By utilizing multiple correlation analysis, the study assesses the strength, direction, and statistical significance of the relationship in recent years. The results suggest that, while the Phillips curve remains valid in the Slovak economy, the strength of the correlation has diminished compared to the period when the theory was first introduced. This decline in the relationship can be attributed to various factors, including the centralization of monetary policy under the European Central Bank, changes in labor market dynamics, and external economic shocks. These findings imply that policymakers should reconsider relying solely on traditional inflation-unemployment trade-offs when formulating monetary and labor market policies. Overall, the findings of this study provide valuable insights into the evolving nature of the Phillips curve and its relevance in today's economy. This research contributes to the limited empirical literature on small open economies within the euro area, providing updated evidence on the dynamics of the Phillips curve in the Slovak context.

This study investigates how corporate culture, work atmosphere, leadership, and pathological workplace relationships are linked to employees’ psychological well-being. Drawing on the JD-R framework and COR theory, we conceptualise supportive team dynamics and toxic interpersonal processes as parallel conditions shaping well-being at work. Survey data from 441 Slovak employees were analysed using a dual-moderated mediation model. The results show that corporate culture is positively associated with well-being mainly through its relationship with the work atmosphere, confirming the mediating role of workplace climate. Leadership moderates this pathway by attenuating the indirect association between culture and well-being. Contrary to expectations, pathological relationships did not exhibit a significant moderating effect, indicating that their role may be more complex and context-dependent. These findings extend existing models of employee well-being by incorporating negative social phenomena to an integrative framework. The study underscores the importance of examining both supportive and toxic aspects of organisational life simultaneously. Practically, the results suggest that fostering a positive culture, a healthy work atmosphere, and effective leadership practices can jointly strengthen employees’ psychological well-being.

Today, the key challenge is to ensure environmentally acceptable and sustainable transport. This paper analyzes the current state of emissions and greenhouse gases and their impact on the environment. In more detail, it is focused on relevant transport emissions data and their development in the Slovak Republic. It describes and analyzes proposals to improve the current state of rail transport and reduce greenhouse gas (GHG) emissions. Furthermore, it examines current transport trends and their impact on the environment and then analyzes and evaluates these relevant data concerning specific types of emissions. The differences between road, rail, and pipeline transport modes are analyzed and quantified through graphical analysis and analysis of variance (ANOVA) at 5% significance using IBM SPSS statistics software. A subsequent Scheffé post-hoc test is utilized. The usage of ANOVA enables the determination of whether the differences between the transport modes are statistically significant and generalizable or if they arise due to random variation. Based on the knowledge gained, specific measures and strategies are proposed that could contribute to a more efficient use of rail transport to reduce greenhouse gas emissions. The originality and novelty of the contribution lie in the connection between environmental science and applied statistics as well as in finding new progressive synergistic effects between them. The results provide important information for decision-making in the field of investment and planning of rail infrastructure and transport to promote environmental sustainability and reduce the negative environmental impact of rail transport. The research results contribute to the development of a sustainable transport system and will be used in other scientific research activities of the authors in more detailed research on individual proposed measures.

Unemployment is a factor that heavily influences the output of each economy. It is, therefore, one of the main concerns of any government worldwide. This study identifies key determinants of unemployment. By constructing an econometric model for the registered unemployment rate in Slovakia, the period from 2013 to 2022 was under scrutiny, while the impact of the COVID crisis was considered in the model through a dummy variable. Potential determinants of unemployment were selected based on theoretical knowledge and other scientific works, that is, average interest rates, gross minimum wage, GDP, inflation, exports, imports, government spending, corruption index, COVID-19 crisis, and month of the year. The final relevant factors for unemployment were tested and validated: interest rates, GDP, inflation, government spending, and exports. These study results may be valuable for the government when designing targeted interventions to optimise the unemployment rate in Slovakia or similar economies by influencing other macroeconomic indicators.

AI/IoT initiatives are increasingly adopted in business, yet reported success varies substantially across firms. This study develops and evaluates a firm-level predictive framework for the reported AI/IoT success rate, measured on a bounded 0–100 scale. Using enterprise survey data from Slovakia and the Czech Republic (n = 1250), we compare a regularized linear baseline (Elastic Net) with nonlinear approaches (Decision Tree and Random Forest) under a consistent out-of-sample evaluation framework, and we examine the best-performing model using permutation importance and PDP/ICE tools. Random Forest achieves the strongest out-of-sample predictive performance and reduces absolute errors relative to Elastic Net for most test observations, although diagnostics also reveal a small tail of extreme errors. Across model families, ai_iot_advantage_share emerges as the most stable predictor of reported AI/IoT success. Nonlinear diagnostics indicate a threshold-like transition in predicted success around the mid-range of advantage attribution and a saturation pattern at higher values. Readiness and performance-related variables are associated with higher predicted success, whereas higher barrier levels are associated with lower predicted success. The results position value realization as the most informative predictive signal in the dataset and provide an interpretable basis for enterprise-level screening and managerial reflection rather than causal inference.