This article explores the relationship between digitalization and business performance in European small- and medium-sized enterprises (SMEs) amidst the COVID-19 pandemic, which has intensified the need for SMEs to adopt digital technologies for enhanced competitiveness. Conducted through a comprehensive literature review, this study investigates the impact of digitalization on SME performance. Employing the statistical software EViews 12, the research utilizes an econometric analysis of panel data to examine the influence of digitalization on SME performance and validate the formulated hypotheses. The dataset comprises 135 observations collected over a five-year period from 27 European countries. A linear regression model is used to test the hypotheses, revealing the relationship between the independent and dependent variables. Model specifications are determined through various tests. This study’s findings demonstrate that the integration of digital technologies and digital intensity significantly drives digitalization in European SMEs, leading to positive impacts on performance. This article emphasizes the importance of digitalization in SMEs for process streamlining, productivity improvement, and enhanced customer experience, ultimately fostering business growth and development. These empirical findings provide valuable evidence for policymakers to prioritize the identification of appropriate measures for stimulating the integration of new digital tools into SME infrastructure and serve as a foundation for future research in this domain.

In the rapidly evolving world of business intelligence (BI) and data analytics, MS Power BI Desktop has become a key tool for data visualization and improving decision-making across various sectors. This article examines the development of MS Power BI Desktop since its introduction in 2017 and...

This study analyses the adoption of artificial intelligence (AI) in enterprises in Slovakia in comparison with the EU27 and examines its relationship with labour productivity from the perspective of long-term economic sustainability. Using harmonised Eurostat data for the period 2021–2024, the analysis applies descriptive statistics, gap analysis, dynamics of change, correlation analysis, and an illustrative regression model. The results show that although AI adoption in Slovakia increased across all enterprise size classes, it consistently remained below the EU27 average. Labour productivity developments in Slovakia were characterised by substantial short-term volatility and did not show a stable association with AI diffusion. Both correlation and illustrative regression results confirm the absence of an immediate statistical relationship between AI adoption and productivity at the aggregate level. These findings suggest that potential productivity improvements associated with AI adoption are likely to depend on complementary investments in organisational transformation, digital skills, and institutional capacity. The study provides empirical evidence for a small open economy within the EU and offers policy-relevant insights into how AI adoption is more likely to support long-term economic sustainability than short-term performance gain.

This article presents a comprehensive analysis of the development and impact of Business Intelligence (BI) between 2018 and 2022. It focuses on examining research articles published in five reputable journals specified in the field of Business...

Understanding the determinants of electricity consumption is essential for designing effective economic and energy policies in advanced economies. This study examines the macroeconomic, institutional, and environmental factors influencing electricity consumption in 19 Euro Area countries during 2006–2024. Using a balanced panel dataset and a fixed-effects regression model, the analysis examines the associations between electricity consumption and GDP per capita, manufacturing value added, regulatory quality, electricity prices, renewable energy share, and heating degree days. The results indicate that GDP per capita, manufacturing value added, and regulatory quality are positively associated with electricity consumption, suggesting that higher levels of economic development, industrial activity, and institutional quality are associated with higher electricity demand. In contrast, electricity prices and renewable energy are negatively associated with electricity consumption, highlighting the importance of market incentives and structural changes related to the energy transition. Heating degree days exhibited the strongest estimated association with electricity consumption, highlighting the importance of climatic conditions across the Euro Area. The study contributes to the energy economics literature by providing an integrated assessment of economic, institutional, and environmental drivers of electricity consumption within a highly integrated economic region. The findings offer relevant implications for policymakers seeking to balance economic growth, energy transition objectives, and long-term electricity demand management.