In Industry 4.0, predictive maintenance (PdM) is key to optimising production processes. While its popularity among companies grows, most studies highlight theoretical benefits, with few providing empirical evidence on its economic impact. This study aims to fill this gap by quantifying the economic performance of manufacturing companies in the Visegrad Group countries through PdM algorithms. The purpose of our research is to assess whether these companies generate higher operational profits and lower sales costs. Using descriptive statistics, non-parametric tests, the Hodges–Lehmann median difference estimate, and linear regression, the authors analysed data of 1094 enterprises. Results show that PdM significantly improves economic performance, with variations based on geographic scope. Regression analysis confirmed PdM as an essential predictor of performance, even after considering factors like company size, legal structure, and geographic scope. Enterprises with more effective cost management and lower net sales were more likely to adopt PdM, as revealed by decision tree analysis. Our findings provide empirical evidence of the economic benefits of PdM algorithms and highlight their potential to enhance competitiveness, offering a valuable foundation for business managers to make informed investment decisions and encouraging further research in other industries.

Research background: Digital transformation has become a central pillar of public sector modernisation in the European Union (EU), with initiatives focusing on digital infrastructure, e-government services, human capital development, and research and innovation. The EU’s long-term strategy for digital advancement is aimed at narrowing performance gaps and fostering inclusive progress among member states. However, regional disparities, stagnation in certain countries, and inconsistent investments in research and public services reveal the complex nature of digital convergence. The COVID-19 crisis further exposed structural weaknesses while simultaneously accelerating digital transitions across the EU. Purpose of the article: This article aims to assess the development and inequality of public sector digitalisation across EU–27 countries. It focuses on identifying trends, regional shifts, and performance dynamics based on multidimensional evaluation. The study seeks to determine whether digital convergence is taking place, which countries are leading or lagging, and what policy implications emerge from the observed trajectories. Methods: The research employs a multi-method approach combining the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE) method, Lorenz curve, Gini coefficient analysis, k-means clustering with PCA transformation, sensitivity analysis, and scenario modelling. A total of 24 criteria reflecting the digital maturity of the public sector, such as broadband access, digital skills, e-government services, and research and development (R&D) activity, were used. This comprehensive framework enables robust comparisons of performance levels, structural inequalities, and resilience to weight variations in composite indicators. Findings & value added: The analysis reveals a measurable decline in digital inequality within the EU, as shown by a steady reduction in the Gini coefficient over time. Countries like Austria, Cyprus, and Croatia demonstrated significant upward mobility, driven by strategic investment and consistent policy support. Conversely, countries such as Romania and Belgium experienced relative decline due to stagnation in innovation and public service quality. Sensitivity testing confirmed the robustness of the applied model, while scenario analysis highlighted the transformative potential of targeted interventions. The value added of this research lies in its integrated analytical framework, which not only maps digital progress but also identifies structural challenges and policy leverage points essential for achieving a more cohesive digital Europe.

Artificial intelligence (AI) is becoming a significant driver of transformation in the business environment, particularly for startups, which are characterised by high levels of adaptability, innovation orientation and strategic flexibility. Startups that incorporate artificial intelligence into their structures and processes are reshaping traditional approaches to value creation. This study aims to explore how artificial intelligence influences the formation and evolution of business models in startups across different industries. The analysis draws on academic literature, case study evidence and current market observations in order to identify key areas where artificial intelligence may affect fundamental business model components. The comparative part of the study focuses on differences between AI-driven startups and traditional companies that did not emerge as startups and do not rely on artificial intelligence as a core strategic technology. The comparison is carried out within three selected industries, namely banking, automotive manufacturing and retail. The study applies the Business Model Canvas (BCM) as a conceptual tool to evaluate the configuration of business models in both categories of companies. The results of the analysis indicate that startups using artificial intelligence are creating new types of business models in which AI plays a significant role in shaping the value proposition, sales channels, and revenue streams. Traditional companies, on the other hand, are not transforming their business models radically but are integrating artificial intelligence mainly in support areas. Building on these findings, this research contributes to the broader understanding of business model innovation under the influence of artificial intelligence and outlines the analytical foundations for identifying structural distinctions between AI-driven startups and traditional companies.

Research background: Digital transformation has become a strategic imperative for companies worldwide, yet the heterogeneity in adoption patterns remains poorly understood, particularly in transitional economies. Existing research predominantly focuses on large economies, whilst small European countries face distinct institutional environments. The interplay between digital maturity, strategic orientation, and economic performance represents a critical knowledge gap. Purpose of the article: The study aims to identify distinct clusters of Slovak companies based on their attitudes towards artificial intelligence, sustainability orientation, and strategic resilience. The research determines how these clusters differ in technology adoption, perceived barriers, and economic performance outcomes. Methods: The study employs quantitative research based on a structured survey of 402 Slovak companies conducted between February and July 2025. The methodological framework integrates k-means cluster analysis using twelve indicators. The optimal two-cluster solution was validated through the elbow method and silhouette analysis, with principal component analysis providing visualization. Chi-square tests examined differences between clusters across technology adoption, barriers, and performance indicators. Findings & Value added: The analysis reveals a fundamental structural divide within Slovak companies, identifying digitally advanced and traditional companies with significantly different profiles. Advanced companies demonstrate substantially higher adoption of Big Data analytics, ERP systems, and e-commerce platforms. Paradoxically, these companies perceive stronger barriers to transformation, including legal and regulatory uncertainty, implementation costs, system integration issues, and financial and network constraints, revealing a paradox of digital advancement. Most significantly, advanced companies report superior economic performance in turnover and EBIT changes. The research contributes novel evidence from a transitional economy, addressing gaps in comparative digital transformation research. Findings provide actionable insights for policymakers and managers navigating transformation complexities.

Research background: The increasing role of intangible assets in the digital economy is significantly transforming business structures in Europe. While the relevance of assets such as software, patents, or know-how is growing, their impact on company performance remains underexplored, particularly in Central and Eastern European countries. Gaps persist in understanding how intangible assets contribute to revenue generation and profit creation across company sizes and national contexts. Purpose of the article: The aim of this article is to analyse the evolution and financial implications of intangible assets in manufacturing companies operating in Slovakia and the Czech Republic. The research investigates the relative importance of intangible versus tangible assets and compares their influence on key performance indicators such as operating revenue and operating profit, with a specific focus on size-based differences. Methods: The analysis is based on data from the years 2018 to 2023, covering a sample of 1,393 manufacturing companies from the ORBIS database. The analysis combines descriptive statistics, year-on-year growth comparisons through paired samples t-tests, Spearman correlation analysis and separate linear regression models for each country. Findings & Value added: The results show that intangible assets are growing faster than total assets, especially in Slovak SMEs and very large companies. While correlations with financial performance are statistically significant, they remain moderate in strength. Regression models explain over 98 percent of revenue variability and confirm the economic relevance of intangible assets, which outperform tangible ones in predictive value. The value added of this study lies in confirming the role of intangible assets as a relevant but not standalone factor in financial performance.

Research background: The petroleum products manufacturing sector is a key industry in the international trade of crude oil. The activities of this sector exert a significant influence on the global economy, particularly through the production and processing of oil, which represents a vital commodity for the daily functioning of societies worldwide. Purpose of the article: The main goal of this research is to calculate the correlations between the WACC and the volume of speculative trading positions in the crude oil market. Speculative trading in the context of this research is considered the trading volume of futures or options contracts without any direct involvement in the underlying physical commodity. The analysis primarily examines the relationship between the WACC and Brent crude oil futures prices, emphasizing the significant role of the futures price volatility in the companies’ costs of capital. Methods: The article calculates the weighted average cost of capital (WACC) for the petroleum products manufacturing sector in the member states of the OECD. Overall, 96 calculations of WACC are made for companies from 16 OECD member countries. Findings & Value added: Country-specific disparities in WACC are identified, with Greece, Poland and Germany showing the highest values, which reflects the variable economic and market conditions. While speculative trading is proven to have no direct effect on the WACC, its indirect impact through crude oil price volatility is significant. A potential indirect effect through crude oil futures price volatility emphasizes its role as a significant factor influencing the financial environment for companies in the petroleum manufacturing sector. If we assume a continually rising trend in the traded oil futures positions, we consider further studies on the relationship between the WACC and speculative trading of great importance. The findings of this research contribute to understanding the financial implications of crude oil market dynamics and provide actionable insights for corporate risk managers.