This paper explores the relationship between digital readiness and sustainable development in European Union (EU) countries through a two-level cluster analysis based on the 2024 Network Readiness Index (NRI) and selected Sustainable Development Goals (SDGs). The first analysis groups countries by digital maturity across the NRI pillars: Technology, People, Governance, and Impact. The second focuses on SDG performance in areas such as health, education, gender equality, clean energy, and sustainable cities. Results reveal significant disparities between digital readiness and SDG achievements: some digitally advanced countries underperform in sustainability, while others with lower digital scores excel in SDG outcomes. The study identifies regional patterns, confirms a moderate positive correlation between NRI and SDG clusters (r_s = 0.526), and underscores the need for integrated digital and sustainability policies. Recommendations are provided for targeted policymaking and international cooperation.

Natural healing resources in the form of mineral and thermal waters and climatic conditions, together with a rich history and modern medical procedures, rank Slovakia among the important European countries in the field of spas. At the same time, spa tourism has a significant economic benefit for the country. This study examined the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on spa tourism in Slovakia. The Box-Jenkins methodology was used to model and forecast the time series for selected indicators. The analysis used monthly data on the capacity and performance of spa facilities for 2006–2019 and compared the forecast development for 2020–2021 with the reality as affected by the pandemic. Despite the high quality of the models, the methodology used did not take into account an unexpected event such as a pandemic. Therefore, the models were quite inaccurate and had little predictive value. At the same time, it is clear that the pandemic significantly affected spa tourism.

The digital economy, driven by innovative technologies and artificial intelligence (AI), is transforming economic systems and increasing the demand for accurate assessments of digital competitiveness. This study addresses the inconsistencies in country rankings derived from global digital indices and aims to determine whether these rankings differ due to methodological variations. It also examines whether the rankings correlate significantly across different evaluation frameworks. The research focuses on 29 European countries and analyzes rankings from four widely recognized indices: the World Digital Competitiveness Ranking (WDCR), Network Readiness Index (NRI), AI Readiness Index (AIRI), and Digital Quality of Life Index (DQLI). To assess the consistency and variability in rankings from 2019 to 2024, the study applies Friedman’s ANOVA and Kendall’s coefficient of concordance. The results demonstrate strong correlations at the level of country rankings, indicating a high degree of consistency, but also confirm statistically significant differences in rankings among the indices, which reflect the diversity of their conceptual foundations. Countries such as Finland, the Netherlands, and Denmark consistently achieve top rankings, indicating convergence, while more variability is observed in indices like the DQLI. These findings highlight the importance of rank-based, multidimensional assessments in evaluating digital competitiveness. They support the use of such assessments as policy tools for monitoring progress, identifying gaps, and promoting inclusive digital development.

Forecasting using historical time series data has become increasingly important in today’s world. This paper aims to assess the potential for stable positive development within the wholesale and retail trade sector (SK NACE Section G) and the operations of HORTI, Ltd.( Košice, Slovakia), a company within this industry (SK NACE 46.31—wholesale of fruit and vegetables) by predicting three financial indicators: costs, revenues, and earnings before taxes (EBT) (or earnings after taxes (EAT)). We analyze quarterly data from Q1 2009 to Q4 2023 taken from the sector and monthly data from January 2013 to December 2022 for HORTI, Ltd. Through time series analysis, we aim to identify the most suitable model for forecasting the trends in these financial indicators. The study demonstrates that simple legacy forecasting methods, such as exponential smoothing and Box–Jenkins methodology, are sufficient for accurately predicting financial indicators. These models were selected for their simplicity, interpretability, and efficiency in capturing stable trends, and seasonality, especially in sectors with relatively stable financial behavior. The results confirm that traditional Holt–Winters’ and Autoregressive Integrated Moving Average (ARIMA) models can provide reliable forecasts without the need for more complex approaches. While advanced methods, such as GARCH or machine learning, could improve predictions in volatile conditions, the traditional models offer robust, interpretable results that support managerial decision-making. The findings can help managers estimate the financial health of the company and assess risks such as bankruptcy or insolvency, while also acknowledging the limitations of these models in predicting large shifts due to external factors or market disruptions.

Purpose: The aim of the contribution is to present the results of research that dealt with customers’ perception of the quality and range of services provided by spa companies in Slovakia.Design/methodology/approach: The primary data were obtained using a questionnaire which evaluated customer satisfaction with the services provided by spa companies in Slovakia at the beginning of 2020, before the outbreak of the COVID-19 pandemic. The research sample consisted of 863 customers of spa companies. Importance-performance analysis and other methods of inductive statistics were used to process the results.Findings: Customer satisfaction with the quality of services provided is generally at a high level. Spa companies should pay more attention to the quality of catering services for patient groups A (the patient is reimbursed by the health insurance company for medical care, accommodation and meals at the spa) and B (the patient is only reimbursed by the health insurance company for healthcare) and to better planning of procedure dates for self-payers. When deciding on the choice of a spa company, the focus of the spa on the treatment of a specific diagnosis, and after this, the accessibility of transport, turned out to be the most common reasons.Research limitations/implications: Limits of IPA analysis related to the measurement of performance and also the importance of individual attributes based on respondent preferences. The length of the survey increases. Low discriminatory power of respondents who may consider all attributes important.Originality/value: The authors designed a novel questionnaire to identify the impact of individual factors on the level of services provided. Through IPA analysis, companies can precisely determine the services on which they should focus their attention in the future.

This paper presents a cluster analysis of 20 spa enterprises operating in Slovakia, based on key financial indicators for the years 2018 and 2023. A comparative time-based approach was adopted to capture changes in financial performance over time. The primary objective is to group the spas into homogeneous clusters to better understand their financial performance and strategic positioning. Ten financial indicators were selected across five dimensions: profitability (return on assets, return on sales), efficiency (assets turnover), cost efficiency (personnel cost ratio, cost-to-sales ratio, return on costs), liquidity (net working capital, current ratio), and indebtedness (equity to total liabilities ratio, debt ratio). Hierarchical cluster analysis—a widely used statistical method in unsupervised machine learning and a foundational technique in artificial intelligence—was employed, serving as a robust tool for data-driven decision making. The analysis identified distinct clusters of spas with similar financial characteristics. The results reveal meaningful segmentation that can inform resource allocation, performance benchmarking, and strategic planning. The findings provide spa managers, investors, and policy makers with a clearer understanding of financial patterns in the Slovak spa sector and offer practical implications for enhancing competitiveness and operational effectiveness.