The Impact of the COVID-19 Pandemic on the Economic Development of Selected Sectors: Case Study in Slovakia II (Secondary and Tertiary Industry)

Abstract

The study analyzes the heterogeneous impacts of the COVID-19 pandemic on financial performance across five strategic sectors of Slovakia’s economy. Using a longitudinal dataset of 500 companies (100 per sector) spanning 2015–2022, we examine changes in profitability (ROE) and liquidity (quick ratio). The examination is made by multivariate analysis and crisis matrix visualization. The research reveals four distinct sectoral response patterns: (1) the automotive industry maintained exceptional profitability (>65% ROE) but with critically low liquidity; (2) tourism and gastronomy experienced severe profitability decline but preserved stable liquidity; (3) healthcare demonstrated conservative liquidity strengthening with modest profitability impacts; (4) metallurgy and hazard sectors showed moderate volatility patterns. We introduce a crisis matrix framework combining profitability and liquidity indicators for sectoral resilience assessment. The results are validated through PERMANOVA analysis addressing non-normal data distributions that are common in crisis periods. The results demonstrate the need for differentiated crisis support policies, challenging uniform approaches to economic resilience. The study provides empirical evidence for sector-specific vulnerability patterns. It can inform strategies for future crisis preparedness. This research contributes to the crisis management literature by demonstrating how sectoral characteristics determine financial resilience pathways. The results offer insights that are applicable to similar transition economies in Central and Eastern Europe.

1. Introduction

Economic crises are an integral part of economic cycles that significantly affect the business environment and financial health of companies. The COVID-19 pandemic has brought not only health but also extensive economic challenges. The article focuses on examining the impact of the financial crisis caused by the pandemic on the financial indicators of selected business sectors in Slovakia. The topic stems from the unprecedented nature of the pandemic and its impacts, which surpass traditional economic crises.

Investigating the impact of financial crises on the business environment is crucial for understanding the dynamics of business cycles. It is also crucial for developing effective strategies for managing future crises. The analysis will focus on comparing the periods before, during, and after the crisis in order to identify changes and trends caused by the crisis. The aim of this work is therefore to analyze and interpret how the crisis affected the financial health of business in different sectors of the economy.

The research addresses these gaps by focusing on five contrasting sectors: automotive (export and supply-chain driven), metallurgy (core industry), hazard (directly restricted services), and healthcare (regulated, public facing), and tourism and gastronomy (most acutely crisis-hit). The sectors are selected for maximal heterogeneity in pandemic impact.

• The research objectives and questions are as follows:

• How did the COVID-19 pandemic affect profitability and liquidity in key Slovak sectors from 2015–2022?
• Are post-pandemic differences statistically significant or does resilience prevail across sectors?
• What sector-specific patterns or risks emerge when considering both indicators jointly?

While most regional studies are sector-specific or national-level, this work integrates a multi-dimensional approach with a dataset representing top economic performers. Such an approach incorporates international evidence and comparative discussion. The novelty of this study lies in the application of a two-dimensional crisis matrix to assess the combined effects of profitability and liquidity on company resilience in selected Slovak sectors during the COVID-19 pandemic. The key contribution is a cross-sectoral, statistically robust comparison that is rarely covered in Central and Eastern Europe. The findings confirm sectoral heterogeneity, and illustrate how structural factors, state interventions, and sectoral adaptability determine company survival and recovery.

2. Literature Review

Even though the pandemic’s length was not too long, a huge volume of research was published results relating to COVID-19 in different contexts (Abed, 2022). The main factor is the size of the business. According to Assefa (2023), over 44% of small businesses would fail in the first month of lockdown restriction. Moreover, only 6% have cash reserves to survive twelve months. Sector type is also important to regard during the evaluation of pandemic influences. In this area, Islam and Fatema (2023) found that manufacturing firms have a higher survival probability than service firms. However, their findings differ from other research. Islam and Fatema (2023) showed that SMEs have a higher survival probability than large firms. On the contrary, Zhan and Lin (2021) found that small businesses in China were unable to reopen when the lockdowns were relaxed. Also, Mikusova and Horvathova (2023) showed that small businesses in the Czech Republic often suffer from a lack of resources. This is, in many cases, a reason why they cannot be prepared for crises. It is therefore a question of whether such findings are applicable in any other country. The aim of the contribution is to investigate the situation in the Slovakian sectors.

The main reason for the decline or bankruptcy of business due to the pandemic situation is losses in sales. In this area, Fairlie and Fossen (2022) found in USA conditions that sales losses were largest in businesses. These were affected by mandatory lockdowns, for example, accommodations lost 91%, whereas online sales grew by 180%. Moreover, due to the pandemic, firms were engaging in wage cuts (Prescott & Sheng, 2022). These cuts stem from firms that have been disproportionally and negatively impacted by the pandemic. In addition, firms responded to the pandemic by lowering their one-year-ahead inflation expectations.

We must analyze the impacts of the pandemic in accordance with business finance. It is reflected in the financial statements of the business (Lukáč et al., 2021). In this area, Hertati et al. (2020) analyzed the influence of the pandemic on the financial statements of Indonesian medium and small micro-traders. These play an important role in the economic crisis, so business finance is very influential when there is uncertainty in the environment. The financial statements of small and micro-business enterprises sweeping across the globe were hit, demonstrating bad changes in the financial statements of micro and small business entrepreneurs in Indonesia.

In addition, macrostructures had to be included to reveal the weaknesses of the organizations and possible risks to decline in a pandemic crisis (Dobrowolski, 2020). Therefore, we must regard not only the size of the business, but also the quality of the management (especially crisis management). Companies that had strong environmental, social and governance records performed better during the initial stages of the crisis. The same was shown in family-owned firms and those that avoided high levels of leverage prior to the crisis (Johnstone-Louis et al., 2020). Managers still do not consider crisis management to be part of the preparedness to react to pandemic situations. Whole CSR practice in organizations should be adapted to crises and pandemic situations. For example, in Dubai, the practice of CSR in organizations underwent a fundamental change to combat the pandemic (Mellahi et al., 2023). Since individual sectors are vulnerable to crises and pandemics, one of the ways to cope with vulnerability is to develop a resilience capacity by planning strategies, changes in business culture, etc. (Dogantan & Kozak, 2019).

The sectors must be evaluated for mutual connections and relations. One cannot survive without the other (Sheth, 2020). For example, the construction industry, automotive industry, etc., are dependent on the mining industry (Stehlíková et al., 2024). There are a number of authors studying individual sectors that would be included in the research. In the area of tourism, pandemics pose major challenges to local tourism, especially in developing countries (such as Indonesia). Local tourism businesses respond to crises and the necessary resources, which these businesses employ to build resilience in an unpredictable business environment (Dahles & Susilowati, 2015). Dogantan and Kozak (2019) studied the resilience capacity of the individual types of tourism, such as accommodation, travel and airline business. They revealed that there were no significant differences between different types of tourism. The other aim of the contribution is therefore to find out if the situation in Slovakia in the accommodation sector and other sectors is the same.

The automotive industry is necessary to study not only due to its economic relevance, but also due to international ties (Izguierdo, 2021), mostly in times of crisis. Izguierdo (2021) found similarities with the 2008–2009 crisis and the COVID-19 crisis. This means that industries can learn from the previous situation and be more prepared for other crises. Long et al. (2023) found the main factors influencing the automotive industry during a crisis. Those are business effects, the supply chain and manufacturing, response and imperatives, leadership, government, and regulatory intervention. From the view of profitability and indebtedness, financial indicators in Czech automotive organizations showed that manufacturers had greater losses during the COVID-19 pandemic than in years before (Kucera & Ticha, 2022). This is connected with the development of sales. Moreover, Hoeft (2021) found that the negative development of sales in the automotive industry during the COVID-19 crisis presents a strategic tool to improve business. State and EU burdens on automotive organizations, such as restrictions and limitations on exhaust emissions, worsened the solving of the COVID-19 situation, which was the case in Poland (Stojczew, 2021).

In the area of hazards, Lal et al. (2022) found that higher risk was observed in larger cities and regions, in comparison with smaller cities and regions. Megacities have been affected the most due to the complex urban and social systems. The majority of research on pandemic influences on hazards is performed through questionnaires (Ichsan, 2022; Nugroho et al., 2022), considering multi-hazard environments. The studies found that the economic and industrial sectors are considered as the most important factors in comparison to health.

In response to the problems arising from the current crisis, several countries have outlined post-COVID-19 strategies for the supply of the critical metals from the metal sector to reduce their dependence on other states for these commodities (Giese, 2022). Areas of metallurgy are studied, for example, by Golubev et al. (2021) in the conditions of Russia. The study considers the long-term and short-term influences of a pandemic. The study found that Russia is less sensitive to the crisis in comparison to the EU. The ambition of this contribution is to find out if the situation in Slovakia is the same. Dudin et al. (2019) studied the Russian metallurgical sector as well. They suggested that innovation is an important part of how to respond to pandemic influences, mostly by supporting direct and indirect innovation within metallurgy. Ongel et al. (2022) studied the metal sector from the view of working conditions influenced by the pandemic in Turkey. They found that metal sectors were influenced by the pandemic less in comparison with the automotive sector.

Data to study the effectiveness of healthcare due to pandemic influences are limited (Thompson et al., 2023). This means that profitability is mostly dependent on the healthcare system, which is different in different countries. The profitability and effectiveness of healthcare were studied before and after the pandemic. This brings an amount of interesting information. For example, Egbujie et al. (2024) found that during the first wave of the pandemic, the influences were the worst. However, long-term influences increased due to the higher level of reduced contact with family, fewer visits, less medical care, etc. (Bekkers & Koopman, 2020). This was found in Canada by Betini et al. (2021), as well as by Zamora et al. (2022) in Spain.

COVID-19 was a trigger for unpredictable indicators and unusual situations, when methods of prediction have use limitations (Kempa, 2021). Experiences from prediction models have proved their relatively high prediction ability. But only in perfect conditions, which have use limitations in conditions of post-communist countries (Csikosova et al., 2019). Therefore, the research is orientated on crisis management indicators.

The literature addressing COVID-19’s economic impact often focuses on (a) survival and sectorial resilience (Abed, 2022; Dogantan & Kozak, 2019), (b) firm-level liquidity and profitability response (Kucera & Ticha, 2022; Egbujie et al., 2024), and the (c) robustness of modeled crisis outcomes (Altman Z-score, Sun et al., 2022). The importance to assess liquidity as a single indicator of financial health results from the need to inject liquidity into the economy to overcome crisis (Estrada et al., 2021; Alexakis et al., 2021). Moreover, liquidity improvement of the financial market, resulting in overall economic situation improvement, demands the development of profitability indexes (Gherghina et al., 2020).

International comparative research suggests that the link between pandemic restrictions and financial deterioration is strongest in consumer-facing and service sectors (Fairlie & Fossen, 2022; Crespí-Cladera et al., 2021). Meta-analyses across the EU indicate the importance of the adaptive capacity and government support in crisis resilience (Albulescu, 2021), with a growing emphasis on dynamic panel models and risk-adjusted performance. Hossain (2021) in India, Cyprus and Yemen showed the downgraded development of sectors’ economy, provided through cross-sectional analysis. The study of Delgado-Martínez (2024) shows the importance of fostering the regional economy to overcome a worldwide economic crisis, stabilizing the global situation through the regional economy. Rimidis and Butkus (2025) recently confirmed the same. Szalai et al. (2025) highlighted significant regional differences: areas with diversified economies and adaptable sectors. Regions that are dependent on tourism experienced significant declines, while agriculture and industry remained relatively stable. In addition, Capati (2025) shows that governmental policy remains the key determinant of attitudes towards crisis solution. This underlines the study of not only strong economies, but also small ones, such as those of Slovakia and other post-communist countries.

The goal of this contribution is connected to our previous research in the mining and construction industry (Stehlíková et al., 2024). The previous study showed that the mining industry and construction sector managed to avoid the heavy decline and bankruptcy of certain organizations in other industries. The study in Slovakia results from previous studies with co-authors, finding that Slovakia recorded the largest declines in the tourism, gastronomy, and hotel sectors, but also in administration, auxiliary work, and customer support. However, the rate of decline in Slovakia was moderated (Tomkova et al., 2024). The goal of the presented contribution is to evaluate the situation in sectors, such as automotive and tourism, as pointed out above. We focused on the selected sectors, since they are most likely to be affected by the pandemic restrictions (see also Snellman et al., 2024).

3. Methodology

3.1. Scientific Goal and Research Hypotheses

The scientific goal of the research is to contribute to the understanding of the impact of a crisis, specifically the COVID-19 pandemic, on the financial performance of selected sectors of the Slovak Republic’s economy. The goal is achieved through the analysis of changes in financial position in the time horizon before, during, and after the pandemic.

The analysis of Slovakia is supported by the fact that Slovakia is a small, highly open and export-oriented post-transformation economy with a strong concentration on automotive and industrial exports (long-term highest per capita car production in the EU and one of the highest shares of exports in GDP in the EU). This configuration creates a sensitive yet analytically informative “stress test” laboratory situation for monitoring sectoral resilience during shocks. At the same time, Slovakia is representative of a group of CEE economies with common institutional features (post-socialist transformation, high interconnectedness to German/European value chains, and a mix of regulated services and export production).

• Research hypothesis:

SH1. 

Within each examined industry, there are statistically significant changes in the distribution of companies within the crisis matrix (defined by second-level liquidity and return on total capital) between the period before the COVID-19 pandemic, the period during it, and the period after it.

We selected five business sectors based on their expected heterogeneous responses to the pandemic, considering four key criteria:

• Operational dependency on physical presence: Tourism and gastronomy sectors require high customer contact, making them vulnerable to lockdown measures.
• Supply chain complexity: Automotive and metallurgy sectors have complex international supply chains that are susceptible to global disruptions.
• Essential service classification: Healthcare was classified as essential, while the hazard sector faced regulatory restrictions.
• Economic significance: All selected sectors represent significant portions of Slovak GDP and employment.

These five sectors were selected due to their inherent differences in the expected COVID-19 impact: automotive (export focus, global supply chain), metallurgy (core industry, stable demand), hazard (regulatory and closure risks), tourism and gastronomy (restrictions hit hardest), and healthcare (frontline, regulated). Overall, these five sectors account for approximately X% of Slovak GDP and span a population of ~Y companies (automotive: N1; metallurgy: N2; hazard: N3; tourism and gastronomy: N4; healthcare: N5) (Finstat, 2024).

This selection enables the comparative analysis of different vulnerability patterns and provides insights that are applicable to similar economies in Central and Eastern Europe. The sectors chosen represent approximately 35% of Slovak GDP. They demonstrate varying degrees of government regulation, international exposure, and customer contact requirements.

The selection of the top 100 companies per sector was based on the following statistical and practical considerations:

• Statistical power: With n = 100 per group, we achieve 80% power to detect medium effect sizes (Cohen’s d = 0.5) at a α = 0.05 significance level.
• Representativeness: The top 100 companies typically represent 60–80% of sectoral revenue in the Slovak economy, ensuring meaningful coverage.
• Data quality: Larger companies provide more reliable and complete financial reporting.
• Computational feasibility: The total sample (N = 4000 observations) allows for robust multivariate analysis.

Given the scope, the sample of 100 companies per sector was drawn from all active firms as of 2022 and ranked by revenue.

We acknowledge potential survivorship bias in selecting companies that were active throughout the period. This approach was intentionally chosen to analyze resilience patterns among surviving firms rather than overall sectoral mortality. This limitation means that our results reflect the financial behavior of companies that weathered the crisis, not the broader population, including failed businesses. Future research should complement this approach with comprehensive survival analysis including failed companies to provide a complete picture of sectoral impacts.

3.2. Theoretical Foundation of Financial Indicators

When selecting financial indicators, we based our selection on classical estimates of the development of the financial situation, resulting from crisis management tools. A company’s crisis is characterized by a downward trend in profitability (optimal value of 0.01) and quick ratio liquidity (optimal value of 1.0) (Kula et al., 2012).

The relationship between liquidity and profitability, which predetermines a possible crisis in an organization, is in Figure 1 below, including optimal indicator values (Stehlíková et al., 2024).

• Theoretical Justification of Threshold Values:

The selection of optimal threshold values (ROE ≥ 0.01, Quick Ratio ≥ 1.0) is based on established financial theory and empirical research:

• ROE indicator (1%):

• Represents the minimum return above the riskless rate required for economic value creation;
• Aligns with European corporate finance standards for minimum profitability (Kula et al., 2012);
• Supported by meta-analysis of crisis period performance indicators across CEE economies.

• Quick Ratio indicator (1.0):

• Classic liquidity criterion ensuring the ability to meet short-term obligations without inventory liquidation;
• Validated across multiple economic cycles as a stability indicator;
• Corresponds to industry-standard risk management practices.

These thresholds create four distinct quadrants in the crisis matrix, enabling the classification of companies based on their financial health status during different economic periods.

A financial crisis of a company can also be defined as a state caused by the failure of two levels of financial indicators:

• Failure caused by low liquidity is manifested by the inability of the company to repay its obligations on time. A liquidity crisis involves insufficient cash generation, tying up large amounts of funds in less liquid forms of current assets, implementing risky investment projects, and excessive indebtedness.
• Failure caused by low profitability is caused by insufficient sales from the company’s business activities, resulting from a loss of production sales or a decrease in prices and an increase in costs.

3.3. Company Sample Selection and Data Processing

When collecting data, we used the financial statements published in the Finstat database as a source. For five selected industries, we collected data for the years from 2015 to 2022.

The criterion for filtering companies into groups by industry was their field of business. For the categorization of companies into groups, we also determined the following criteria as conditions:

• Companies had to be established no later than 1 January 2014.
• Companies had to be active until the present.
• Companies were not allowed to be in restructuring during the monitored period.

To determine the pandemic’s impact, we selected five business sectors in Slovakia: the automotive industry, tourism and gastronomy, hazard, metal production and metallurgy, and healthcare. We selected the sectors based on the possibility of a diverse impact of the pandemic on the given sectors.

From the companies that met all three criteria, we subsequently sorted the companies in each industry in descending order. The sorting was based on their sales revenue in 2022 and we selected the top 100 from this list.

3.4. Statistical Framework

We processed the data using Microsoft Excel 2019 (version 16.0), R version 4.3.2 with the vegan packages (version 2.6–4) for PERMANOVA analysis, and JMP Pro 18.0 for multivariate visualizations.

A statistical framework for multivariate data analysis was applied. Primarily, the application of the MANOVA (Multivariate Analysis of Variance) test was considered to compare the mean vectors of financial indicators. Prior to the implementation of MANOVA, the validity of its fundamental assumptions was explicitly verified using Mardia’s test for normality and Box’s M test for the homogeneity of covariance matrices.

In cases where the strict assumptions of MANOVA were not met, the non-parametric method, PERMANOVA (Permutation Multivariate Analysis of Variance), was chosen as a more robust alternative. PERMANOVA requires less restrictive assumptions, with the key being the verification of the homogeneity of multivariate dispersion between groups, assessed using the Beta dispersion test (Betadisper).

4. Results

The following is elucidated for verifying Scientific Hypothesis SH1.

SH2. 

The Impact of the COVID-19 Pandemic on the Distribution of Companies within the Crisis Matrix across Individual Industries.

In this part, we will progressively analyze the potential effects of the pandemic on the economic standing of five business sectors in Slovakia through highly defined financial indicators.

4.1. Automotive Industry

This industry plays a significant role in the economy of Slovakia. The analysis of ROE development in 2015–2022 points to a gradual improvement in the performance of companies in this sector. The first analyzed year, 2015, registered the lowest values in the entire monitored period. The median value of ROE reached 0.12, with a significant proportion of companies showing a negative value for this indicator. Since 2016, a significant move towards higher profitability has been observed with a median of 0.63. In the following years, the indicator stabilized at relatively high levels in the interval from 0.650 to 0.729. This indicates an improvement in the management of most companies and the stabilization of the financial health of the sector. This development is further supported by the movement of the bottom quartile (25%). It has moved significantly from zero since 2016, meaning that even companies with lower performance have shown positive ROE values (Figure 2).

From the view of maximal ROE values, it is clear that companies with the best performance in this sector achieve an exceptionally high return on equity, often above 1.8. This confirms the presence of strong players with efficient use of capital. The boxplot visualization supports these conclusions. It shows gradual data aggregating around higher ROE values and moving from low or negative values compared to 2015. The green reference line represents the optimal ROE value when most observations exceeded the limit since 2017. Overall, the automotive industry has shown a positive trend in the ROE indicator in the analyzed period, with increasing stability and performance in the sector’s companies.

The analysis of quick ratio liquidity development in 2015–2022 shows a significant decline in the ability of companies to cover short-term liabilities. While the median of the indicator reached 0.769 in 2015, it fell to 0.103 in the following year and stabilized at a low level in subsequent years. Immediately before the pandemic (2019), the median was only 0.069. In 2020, it fell further to a minimum of 0.059. This decline results from production outages, the disruption of supply chains, and weakened payment discipline in the sector. In 2021 and 2022, there was a slight recovery (0.092 and 0.080). But liquidity remains low and does not even reach the pre-pandemic level (Figure 3). The results point to the ongoing vulnerability of the sector and the need for more consistent management of short-term financial stability.

Most companies are in the upper left quadrant (transitional crisis). This indicates that a significant part of the industry achieves good profitability, but with relatively weak quick ratio liquidity (Figure 4). This is a potentially risky situation, in which companies are profitable but dependent on inventory and less able to immediately pay their liabilities.

During the crisis period (red points), we do not observe a dramatic move in the quadrants. The dispersion of points around zero on the ROE and LII axes indicates a weakening of both indicators for some entities. The upper right quadrant is only minimally occupied even in the pre-crisis period. This confirms that the simultaneous maintenance of high liquidity and profitability is exceptional in this industry.

2D Assessment:

For the multivariate comparison of financial indicators across the monitored periods, the MANOVA test was used.

• Verification of MANOVA Assumptions:

  ○

  Normality of Residuals: Mardia’s test indicated that the residuals were not multivariate normal (Mardia Skewness = 161.869, p < 0.001; Mardia Kurtosis = 23.55, p < 0.001).

  ○

  Homogeneity of Covariance Matrices: Box’s M-test revealed a violation of the assumption of equal covariance matrices (Box’s M-test = 329.63, df = 21, p < 0.001).

• Conclusion on MANOVA Suitability: Given the non-fulfillment of the assumptions of residual normality and the homogeneity of covariance matrices, MANOVA was not considered to be suitable for the final interpretation of multivariate differences. Therefore, the more robust PERMANOVA test was employed.
• Alternative Multivariate Comparison (PERMANOVA):

  ○

  Verification of PERMANOVA Assumption: The Betadisper test did not show statistically significant differences in dispersion between the groups (F = 0.3801, df = 7, 343, p = 0.903), suggesting that the assumption of homogeneous dispersion was met.

  ○

  PERMANOVA Results: The PERMANOVA test demonstrated a statistically significant difference in the multivariate response variables among the monitored years (F = 21.419, R² = 0.30416, p = 0.001). This indicates that there are significant differences in the 2D distribution of companies within the crisis matrix across the different years.

• Pairwise Comparisons (PERMANOVA):

Subsequently, a pairwise PERMANOVA analysis was performed. The results are presented in

Table 1. Pairwise Permanova results.

	2015	2016	2017	2018	2019	2020	2021	2022
2015	-	0.001 (0.004)	0.001 (0.004)	0.001 (0.004)	0.001 (0.004)	0.001 (0.004)	0.001 (0.004)	0.001 (0.004)
2016	0.001 (0.004)	-	0.793 (0.889)	0.794 (0.889)	0.469 (0.889)	0.789 (0.889)	0.964 (0.964)	0.726 (0.889)
2017	0.001 (0.004)	0.793 (0.889)	-	0.536 (0.889)	0.245 (0.889)	0.628 (0.889)	0.637 (0.889)	0.638 (0.889)
2018	0.001 (0.004)	0.794 (0.889)	0.536 (0.889)	-	0.794 (0.889)	0.531 (0.889)	0.754 (0.889)	0.445 (0.889)
2019	0.001 (0.004)	0.469 (0.889)	0.245 (0.762)	0.794 (0.889)	-	0.299 (0.837)	0.378 (0,889)	0.181 (0.634)
2020	0.001 (0.004)	0.789 (0.889)	0.628 (0.889)	0.531 (0.889)	0.299 (0.837)	-	0.848 (0.913)	0.917 (0.951)
2021	0.001 (0.004)	0.964 (0.964)	0.637 (0.889)	0.794 (0.889)	0.378 (0.889)	0.848 (0.913)	-	0.699 (0.889)
2022	0.001 (0.004)	0.726 (0.889)	0.638 (0.889)	0.445 (0.889)	0.181 (0.634)	0.917 (0.951)	0.699 (0.889)	-

.

The results of the pairwise comparisons indicate that the year 2015 was statistically significantly different from all other years, even after Bonferroni correction (p < 0.004).

Brief Summary of the 2D Assessment for the Automotive Industry:

The multivariate analysis revealed statistically significant changes in the 2D distribution of companies in the automotive industry across the monitored years. Pairwise comparisons suggest that the year 2015 differed significantly from the other years in the analyzed period.

The distinctiveness of the year 2015 from the other monitored periods could be partly explained by the specific characteristics of the automotive industry. This sector has established stringent standards for management systems, quality assessment, and supply chain management. This may have exhibited a different setup or level of implementation in 2015 compared to later years. Furthermore, the introduction of lean manufacturing principles in 2016 and their subsequent consolidation in the following years may have contributed to the standardization of processes and thus to less variability in the financial position of companies in the subsequent periods.

4.2. Tourism and Gastronomy

For the tourism and gastronomy sector, given its objective vulnerability to pandemic measures, a statistically significant impact of the COVID-19 pandemic was anticipated on the examined financial indicators, as well as on their 2D distribution within the crisis matrix.

To begin with, we can note that in 2015, more than a quarter of the companies were below the optimal limit. This means that the profitability of the companies did not even reach the value of 0.01. An improvement was recorded in 2016 and 2017. However, a further decline occurred in 2018, i.e., before the pandemic itself, and continued in 2019. However, the worst year for tourism and gastronomy was 2020, when the ROE fell sharply from the median value of 0.070 (2019) to 0.024, with 25% of companies still achieving negative profitability (25th percentile = −0.073). However, in the following years, there was a gradual return to performance; in 2021, the median was already 0.074, and in 2022 it increased significantly to 0.188 (see Figure 5).

The development of quick ratio liquidity (LII) shows that companies in this sector have maintained—and even increased—their ability to cover short-term liabilities without inventories (Figure 6). A slight decrease occurred in 2019 with a median value of 0.755. However, in the following years, it starts to increase again, in 2020 (0.878) and further up to 1.042 in 2022. This represents a move in the liquidity position to optimal values. This development results from the operations limitations and the decrease in inventories, as well as the active management of short-term liabilities and state support.

Based on Figure 6, we can see that in 2015 and 2016, exactly half of the companies were at the optimal liquidity threshold equal to one. In the years from 2017 to 2019, more than 50% of companies subsequently exceeded the threshold. In 2020, there was a decrease in companies, but more than half still achieved optimal liquidity values. In 2021 and 2022, we can again observe an increase in the number of companies with optimal liquidity values.

The development of return on equity and quick ratio liquidity for enterprises in the tourism and gastronomy sector from 2015 to 2022 is given in Figure 7. There has been no significant increase in the number of enterprises moving in chronic or transitional crisis. However, compared to the previous sector, we can observe a slightly larger percentage of enterprises in a state of chronic crisis. Figure 7 shows that the pandemic had a particularly significant impact on the monitored enterprises in this business sector.

2D Assessment:

According to the established procedure for the tourism and gastronomy sector, the 2D assessment is as follows:

• Verification of MANOVA Assumptions:

  ○

  Normality of Residuals: Mardia’s test indicated that the residuals were not multivariate normal (Mardia Skewness = 26.837, p < 0.001; Mardia Kurtosis = −0.099, p = 0.920; MVN: Non-normal).

  ○

  Homogeneity of Covariance Matrices: Box’s M-test revealed a violation of the assumption of equal covariance matrices (Box’s M-test = 39.44, df = 21, p = 0.00869).

• Conclusion on MANOVA Suitability: Given the non-fulfillment of the assumptions of residual normality and the homogeneity of covariance matrices, MANOVA was not considered to be suitable for the final interpretation of multivariate differences. Therefore, the more robust PERMANOVA test was employed.
• Alternative Multivariate Comparison (PERMANOVA):

  ○

  Verification of PERMANOVA Assumption: The Betadisper test did not show statistically significant differences in dispersion between the groups (F = 0.8621, df = 7, 344, p = 0.517), suggesting that the assumption of homogeneous dispersion was met.

  ○

  PERMANOVA Results: The PERMANOVA test did not demonstrate a statistically significant difference in the multivariate response variables among the monitored years (F = 1.1898, R² = 0.02364, p = 0.314). This implies that no statistically significant differences were found in the 2D distribution of companies within the crisis matrix across the different years.

• Pairwise Comparisons (PERMANOVA):

Since the overall PERMANOVA test did not reveal a statistically significant difference between the groups, pairwise PERMANOVA tests were not performed.

• Brief Summary of the 2D Assessment for Tourism and Gastronomy:

The multivariate analysis did not indicate statistically significant changes in the 2D distribution of companies within the tourism and gastronomy sector across the monitored years. This means that a statistically significant impact of the COVID-19 period on the distribution of companies within the crisis matrix was not confirmed.

4.3. Hazard

The hazard business sector, specifically due to its regulation and character, may have been influenced by changes in consumer behavior, which could result in economic activity development during the COVID-19 pandemic.

The analyzed quantile values of the ROE indicator show clear changes in the period before, during, and after the pandemic crisis caused by COVID-19. Between 2015 and 2017, the median ROE was stable, ranging between approximately 12 and 17%. It indicates a relatively high performance of companies in the sector. On the other hand, it is also accompanied by high values in the upper quantiles (Figure 8). However, since 2018, the median ROE has been declining, falling below 2% for the first time in 2019. This may indicate a deterioration in financial performance even before the outbreak of the pandemic. On the other hand, negative values in the lower quantiles also signal increasing losses of several entities in this sector. The most significant decline in performance occurred in 2020, which may reflect the impact of the pandemic crisis—although the median remains just above zero. The quantile range narrows, and the extreme values shift downwards. In the following years (2021 and 2022), there is a slight recovery, especially in the upper quantiles, but the median remains lower than before the crisis.

The liquidity indicator in the hazard sector showed high variability. In 2015–2016, the median LII was relatively high (around 1.9–2.3). It indicates a good short-term financial position of the companies (Figure 9). At the same time, increasing values in the upper quantiles showed that some entities had more than average reserves.

The years of 2017–2018 brought a decrease in the minimum and the occurrence of extreme negative values, although the median remained relatively stable. In 2019, there was a significant improvement (median above 3). It was disrupted by the pandemic in 2020; although some companies significantly strengthened their liquidity, the overall dispersion increased. In 2021 and 2022, there was stabilization: extreme values receded; the median remained between 2.2 and 3.0. The sector as a whole shows signs of adaptation to crisis conditions.

The visualization of the crisis matrix (Figure 10) shows clear movement in the financial performance of the hazard sector before and after the pandemic. In 2015–2019 (blue points), companies were concentrated mainly in the quadrant with high liquidity and a high ROE. This indicates a healthy financial condition. During 2020–2022 (red points), there was a slight move to lower performance. Several entities show a lower ROE and liquidity, moving closer to the central and lower areas of the graph. At the same time, we see that some companies have high liquidity. Nevertheless, their ROE remains low or negative. This may indicate reduced efficiency in the use of capital. Overall, the visualization suggests that the pandemic led to increased financial prudence. At the same time, it reduced the ability of many companies to generate profit.

2D Assessment:

According to the established procedure for the hazard sector, the 2D assessment is as follows:

• Verification of MANOVA Assumptions:

  ○

  Normality of Residuals: Mardia’s test indicated that the residuals were not multivariate normal (Mardia Skewness = 56.4839, p < 0.001; Mardia Kurtosis = 1.4829, p = 0.138; MVN: Non-normal).

  ○

  Homogeneity of Covariance Matrices: Box’s M-test revealed a violation of the assumption of equal covariance matrices (Box’s M-test = 56.287, df = 21, p < 0.001).

• Conclusion on MANOVA Suitability: Given the non-fulfillment of the assumptions of residual normality and the homogeneity of covariance matrices, MANOVA was not considered to be suitable for the final interpretation of multivariate differences. Therefore, the more robust PERMANOVA test was employed.
• Alternative Multivariate Comparison (PERMANOVA):

  ○

  Verification of the PERMANOVA Assumption: The Betadisper test did not show statistically significant differences in dispersion between the groups (F = 1.834, df = 7, 314, p = 0.099), suggesting that the assumption of homogeneous dispersion was met.

  ○

  PERMANOVA Results: The PERMANOVA test did not demonstrate a statistically significant difference in the multivariate response variables among the monitored years (F = 0.8421, R² = 0.0184, p = 0.545). This implies that no statistically significant differences were found in the 2D distribution of companies within the crisis matrix across the different years.

• Pairwise Comparisons (PERMANOVA):

Since the overall PERMANOVA test did not reveal a statistically significant difference between the groups, pairwise PERMANOVA tests were not performed.

• Brief Summary of the 2D Assessment of Hazard:

The multivariate analysis did not indicate statistically significant changes in the 2D distribution of companies within the hazard sector across the monitored years. This means that, based on our findings, the COVID-19 pandemic did not have a statistically significant impact on the complex distribution of the financial positions of companies in this sector, measured by the combination of liquidity and profitability. Their placement in the crisis matrix remained relatively stable over the monitored period.

4.4. Metal Production and Metallurgy

Metal production and metallurgy, may have faced specific challenges during the COVID-19 pandemic, resulting from disruptions in global economic activity. The following section focuses on the analysis of financial indicators and the 2D distribution of companies in this sector during the monitored period.

The ROE indicator in this sector has been relatively stable in the analyzed period, but with clear declining efficiency since 2017 (Figure 11). In 2015–2016, the median was around 12.5%, indicating a relatively favorable ROE. However, there has been a gradual decline since 2017. The lowest median was recorded in 2019 (6%), which may be related to rising costs or decreasing demand.

The development during the pandemic is interesting. In 2020 the median increases slightly (9.5%). This could be the result of production process adaptation or metal price fluctuations. In 2021, the ROE approached the pre-crisis level (12.5%). However, in 2022, it dropped again to 7.8%, confirming some instability after the pandemic.

The quantile values show that the sector was relatively cohesive—without significant extremes or sharp differences. This means that firms faced similar market challenges. Nevertheless, the ROE in this sector remains lower than in the hazard sector. This may reflect the sector’s capital intensity and sensitivity to cyclical developments in the economy.

Liquidity in this sector was relatively stable in the analyzed period, with a slight increasing trend (Figure 12). The median LII ranged from 0.66 to 0.74 between 2015 and 2019. This indicates a consistent ability of companies to meet short-term obligations. During the pandemic (2020–2022), the median increased slightly, reaching 0.88 in 2022. This may indicate a strengthening of short-term financial stability.

The range between the quartiles (25–75%) remained narrow, indicating a similar development across companies in the sector. At the same time, there were no extreme fluctuations like in the hazard sector. LII values remained within a relatively narrow range, confirming the more conservative financial profile of the metallurgical sector. Overall, the sector is characterized by balanced liquidity without significant shocks. There was even a slight improvement during the pandemic, probably due to health cash flow management.

Visualizing the industry through the crisis matrix, we see that most companies are located in the left quadrants and in the upper parts of the matrix. Before the pandemic, companies were more often represented in the upper right quadrant. This means that they combined relatively higher liquidity with a satisfactory ROE (Figure 13). On the contrary, after 2020, several companies move closer to the horizontal axis. This indicates a decrease in ROE, even while maintaining or slightly increasing liquidity. The high concentration of red points around the value of LII > 1 and ROE < 0.1 indicates increased attention—companies maintained reserves. However, at the same time, they did not achieve significant profitability. Overall, the graph (Figure 13) reveals that the sector responded to the crisis by stabilizing liquidity.

2D Assessment:

According to the established procedure for the metal production and metallurgy sector, the 2D assessment is as follows:

• Verification of MANOVA Assumptions:

  ▪

  Normality of Residuals: Mardia’s test indicated that the residuals were not multivariate normal (Mardia Skewness = 67.39, p < 0.001; Mardia Kurtosis = 0.8043, p = 0.4212; MVN: Non-normal).

  ▪

  Homogeneity of Covariance Matrices: Box’s M-test suggested a borderline violation of the assumption of equal covariance matrices (Box’s M-test = 32.639, df = 21, p = 0.05038).

• Conclusion on MANOVA Suitability: Given the non-fulfillment of the assumption of residual normality and the borderline violation of the homogeneity of covariance matrices, MANOVA was not considered fully suitable for the final interpretation of multivariate differences. Therefore, the more robust PERMANOVA test was employed.
• Alternative Multivariate Comparison (PERMANOVA):

  ▪

  Verification of PERMANOVA Assumption: The Betadisper test did not show statistically significant differences in dispersion between the groups (F = 1.1813, df = 7, 535, p = 0.293), suggesting that the assumption of homogeneous dispersion was met.

  ▪

  PERMANOVA Results: The PERMANOVA test did not demonstrate a statistically significant difference in the multivariate response variables among the monitored years (F = 1.4836, R² = 0.01904, p = 0.185). This implies that no statistically significant differences were found in the 2D distribution of companies within the crisis matrix across the different years.

• Pairwise Comparisons (PERMANOVA):

Since the overall PERMANOVA test did not reveal a statistically significant difference between the groups, pairwise PERMANOVA tests were not performed.

• Brief Summary of the 2D Assessment for Metal Production and Metallurgy:

The multivariate analysis did not indicate statistically significant changes in the 2D distribution of companies within the metal production and metallurgy sector across the monitored years. This means that a statistically significant impact of the COVID-19 period on the distribution of companies within the crisis matrix was not confirmed.

4.5. Healthcare

The healthcare sector encompasses a wide range of providers from acute care to rehabilitation and wellness services. The sector may have been influenced during the monitored period not only by the COVID-19 pandemic but also by ongoing legislative changes and economic factors.

During the analyzed period, the ROE in the healthcare sector was above 1% in only ¾ of companies, with the median ROE oscillating between 7% and 12%. The highest median was recorded in 2015 (12.8%). However, in subsequent years, there was a slight decline, culminating mainly in 2017–2019, where the median fell below 8% (Figure 14). The development during the pandemic is particularly interesting. In 2020, there was a temporary improvement (median 10.5%). This may be related to the higher demand for healthcare services. However, this effect did not last long; in 2021 and 2022, the ROE decreased again and stabilized below 10%.

The quartile ranges indicate that the sector showed high variability. While some businesses achieved very high returns (e.g., upper quartiles above 40–60%), others had negative results. This diversity reflects the structural diversity of the sector, where non-profit facilities, private clinics, and start-ups coexist.

The healthcare sector has shown a gradual and consistent increase in the liquidity ratio during the analyzed period. The baseline in 2015 (median LII = 0.89) signaled a prudent ability to cover short-term liabilities, but it was still below the level of total security of liquidity. In the following years, there has been a moderate increase. The median gradually increased to 1.13 in 2022, with the most significant increase occurring during the pandemic period (Figure 15). This trend indicates a change in the behavior of healthcare entities. It began, probably in response to crisis conditions, to accumulate more liquid funds in order to increase their financial resilience. From 2020 to 2022, we can observe a stabilized growth of the median, as well as consistent values in the upper quantiles (75th–90th percentile), ranging within 1.5–2.3. This indicates the ability of most entities to maintain an adequate reserve.

The differences between the quantiles are relatively narrow and without extreme fluctuations. This indicates homogeneous behavior across the sector. In contrast to the hazard or metallurgy sectors, there are no negative values or sharp deviations. This indicates stability and a conservative approach to managing short-term assets. Overall, the healthcare sector has demonstrated an adaptive capacity in difficult times, and has strengthened liquidity in order to ensure continuous operation even in conditions of increased volatility and pressure caused by the pandemic.

Based on the distribution in the crisis matrix, the pre-pandemic period was characterized by a dispersion of points across the upper right and middle part of the graph. This indicates a combination of medium to high liquidity and a relatively higher ROE. However, at the same time, companies with low profitability despite maintaining liquidity were also visible. This indicates a diversity of business models (Figure 16). During the pandemic, there was a remarkable move. The red points are concentrated mainly in the medium to higher liquidity zone (LII > 1). At the same time, they had a slightly lower ROE (often below 10%). This move signals increased financial prudence and the accumulation of short-term reserves, but without significant capital appreciation.

The graph (Figure 16) shows that the healthcare sector focused on stabilizing liquidity during the crisis period, but ROE remained limited. The visualization confirms the trend we also identified in the quantile analysis: a strengthened short-term financial position, but with slightly subdued profitability.

2D Assessment:

According to the established procedure for the healthcare sector, the 2D assessment is as follows:

• Verification of MANOVA Assumptions:

  ○

  Normality of Residuals: Mardia’s test indicated that the residuals were not multivariate normal (Mardia Skewness = 67.39, p < 0.001; Mardia Kurtosis = 0.8043, p = 0.4212; MVN: Non-normal).

  ○

  Homogeneity of Covariance Matrices: Box’s M-test revealed a violation of the assumption of equal covariance matrices (Box’s M-test = 44.142, df = 21, p = 0.002241).

• Conclusion on MANOVA Suitability: Given the non-fulfillment of the assumptions of residual normality and the homogeneity of covariance matrices, MANOVA was not considered to be suitable for the final interpretation of multivariate differences. Therefore, the more robust PERMANOVA test was employed.
• Alternative Multivariate Comparison (PERMANOVA):

  ▪

  Verification of PERMANOVA Assumption: The Betadisper test did not show statistically significant differences in dispersion between the groups (F = 0.8991, df = 7, 437, p = 0.485), suggesting that the assumption of homogeneous dispersion was met.

  ▪

  PERMANOVA Results: The PERMANOVA test demonstrated a statistically significant difference in the multivariate response variables among the monitored years (F = 2.468, R² = 0.03803, p = 0.014). This implies that statistically significant differences were found in the 2D distribution of companies within the crisis matrix across the different years.

• Pairwise Comparisons (PERMANOVA):

Based on the results of the homogeneity of dispersion test, a pairwise PERMANOVA between groups was performed. The results are presented in

Table 2. Pairwise PERMANOVA test between groups.

Comparison	2015	2016	2017	2018	2019	2020	2021	2022
2015	-	0.590 (0.933)	0.018 (0.077)	0.022 (0.077)	0.003 (0.021)	0.003 (0.021)	0.001 (0.021)	0.002 (0.021)
2016	0.590 (0.933)	-	0.105 (0.267)	0.132 (0.308)	0.025 (0.078)	0.029 (0.081)	0.010 (0.056)	0.021 (0.077)
2017	0.018 (0.077)	0.105 (0.267)	-	0.827 (0.965)	0.705 (0.940)	0.795 (0.965)	0.398 (0.796)	0.649 (0.940)
2018	0.022 (0.077)	0.132 (0.308)	0.827 (0.965)	-	0.552 (0.933)	0.600 (0.933)	0.339 (0.730)	0.500 (0.933)
2019	0.003 (0.021)	0.025 (0.078)	0.705 (0.940)	0.552 (0.933)	-	0.947 (0.976)	0.789 (0.965)	0.976 (0.976)
2020	0.003 (0.021)	0.029 (0.081)	0.795 (0.965)	0.600 (0.933)	0.947 (0.976)	-	0.675 (0.940)	0.914 (0.976)
2021	0.001 (0.021)	0.010 (0.056)	0.398 (0.796)	0.339 (0.730)	0.789 (0.965)	0.675 (0.940)	-	0.869 (0.973)
2022	0.002 (0.021)	0.021 (0.077)	0.649 (0.940)	0.500 (0.933)	0.976 (0.976)	0.914 (0.976)	0.869 (0.973)	-

Note: The values in the table are p-values, with adjusted p-values (FDR correction) in parentheses. Significant differences (p < 0.05) are found mainly between 2015 and other years (2017–2022) and 2016 vs. 2019–2022. Non-significant comparisons (p > 0.05) indicate no significant difference between those years.

.

Brief Summary of the 2D Assessment for Healthcare:

The multivariate analysis revealed statistically significant changes in the 2D distribution of companies within the Healthcare sector across the monitored years. Pairwise comparisons indicate that the year 2015 differed statistically significantly from the years from 2017 to 2022. The year 2016 differed statistically significantly from the years from 2019 to 2022. These differences may be partially explained by legislative changes in the Slovak healthcare system between 2015 and 2022, aimed at more efficient financing and the regulation of drug prices. The changes began to manifest more significantly after 2017. The introduction of mandatory price reductions for generics and biosimilars from 2018 likely contributed to changes in liquidity and profitability after 2017. The period before the implementation of these changes (e.g., the comparison of 2015 with 2017–2022) may have led to preventive adjustments in companies’ liquidity management. The introduction of further regulatory measures and pressure on the efficiency of contracting by insurance companies after 2016 (comparison with 2019–2022—see

Table 3. Summarization of reaction to the COVID-19 pandemic.

Sector	ROE in Pandemic	LII in Pandemic	Total Reaction
Hazard	Decrease, light recovery	Volatile, uneven	Weakened and risky sector
Metal production and metallurgy	Smooth, temporary improvement	Stable and conservative	Even, low flexible reaction
Healthcare	Stable with decrease	Strengthened liquidity	Correspondent, but low profitability
Automotive	High and stable	Very low LII	Efficient, but financially strained sector
Tourism and gastronomy	Rapid decrease, rapid revival	Stable and high	Most flexible reaction

) may also have contributed to the observed differences in financial indicators.

Although the impact of the COVID-19 pandemic on the overall distribution of companies in the crisis matrix was not confirmed as statistically significant in our research, it is important to consider the specifics of the lockdown and measures adopted in Slovakia. This may have mitigated changes that are more pronounced:

• Exemptions for Strategic Industrial Sectors: From the declaration of the state of emergency and the lockdown on 12 March 2020, production in key industrial sectors (automotive, metal production, metallurgy) could continue under strict hygiene conditions. The introduction of mandatory hygiene measures, temperature checks, disinfection, and distancing allowed for relatively safe operation without significant downtime.
• Flexible Management of Measures: The COVID-19 traffic light system (“COVID automat”) allowed for the adaptation of measures to the regional epidemiological situation. This means that businesses in less-affected regions could operate with fewer restrictions.
• Support for Healthcare and Medical Equipment Production: In addition to strengthening the healthcare sector, the production of medical equipment (e.g., ventilators at companies like Chirana Medical) was maintained. It helped sustain production capacities in the sector.
• Restrictions for Selected Sectors and the Exclusion of Small Businesses: The hazard, tourism, and gastronomy sectors were significantly restricted. However, it is important to note that small businesses, which were often the most affected by the pandemic, were not included in our sample. This may have influenced the overall results.
• Adaptation of the Gastronomy and Tourism Sectors: Despite the initial hard lockdowns, the gastronomy sector adapted through delivery services. In tourism, domestic tourism saw a revival in the summer of 2021. The flexible regional adaptation of measures through the COVID-19 traffic light system also allowed for operation with restrictions in certain periods and regions.

Explanation of statistically significant differences between periods:

Within our research, we identified statistically significant differences between some periods, which result from the following specific factors:

• Automotive Industry (difference in 2015): A logical explanation for the different 2D distribution of companies in the automotive sector in 2015 may be the fact that this period was still in the process of implementing and consolidating strict quality management standards, supply chain management, and production processes. Subsequent years saw a wider and uniform application of these standards and the intensive expansion of lean manufacturing principles from 2016. This led to the standardization of processes, improved efficiency, and the stabilization of financial results, thus resulting in a more homogeneous financial profile of companies in the years after 2015.
• Healthcare (difference in 2015 and 2016): Pairwise comparisons confirm a statistically significant difference between 2015 and the years from 2017 to 2022 and between 2016 and the years from 2019 to 2022. These differences result from the legislative changes in the Slovak healthcare system between 2015 and 2022, aimed at more efficient financing and organization. The introduction of mandatory price reductions for generics and biosimilars from 2018 likely contributed to changes in liquidity and profitability after 2017. The period before the implementation of these changes (e.g., the comparison of 2015 with 2017–2022) may have led to preventive adjustments in companies’ liquidity management. The introduction of further regulatory measures and pressure on the efficiency of contracting by insurance companies after 2016 (comparison with 2019–2022) also may have contributed to the observed differences in financial indicators (European Commission, 2025).

5. Discussion

The COVID-19 pandemic represented an exogenous crisis with a significant impact on the profitability and liquidity positions of companies across economic sectors. An analysis of five sectors (hazard, metallurgy, healthcare, automotive, and tourism and gastronomy) reveals heterogeneous responses with respect to both the return on equity (ROE) and the quick liquidity ratio (LII).

In terms of profitability, the hazard sector was already hit by a decline in performance before the pandemic. Its median ROE fell to around 2% in 2020. Although the upper quantiles indicated a partial recovery after 2021, the overall recovery of the sector was gradual. In contrast, in metallurgy, an increase in the median ROE was recorded in 2020 (from 6% to 9.5%). But this trend was not maintained until 2022. Healthcare showed only a marginal change in profitability during the pandemic; after a short-term increase in 2020, ROE decreased again. A markedly contrasting development has been observed in the automotive industry. Its median ROE in 2020–2022 was above 65%. This indicates high capital efficiency and resilience to the crisis. Finally, the tourism and gastronomy sector suffered the most significant decline (median ROE 2020 < 2%). However, its recovery and return to pre-crisis performance levels are already being observed in the next two years.

Liquidity analysis pointed to different approaches to the management of short-term assets and liabilities. In the hazard sector, significant differences were observed between companies: with a relatively stable median (0.87–0.88), extreme values appeared, reflecting the uneven behavior of entities. The metallurgical industry showed stable development without significant fluctuations. The median LII ranged from 0.75 to 0.88 throughout the period. In the healthcare sector, there was a systematic increase in liquidity during the pandemic (from 0.89 to 1.13). This indicates a preventive build-up of reserves in response to increased operational uncertainty (Tesar et al., 2019). In contrast, the automotive sector did not improve its short-term financial position during the pandemic. The median LII remained very low (0.05–0.11) and negative extremes persisted. The tourism and gastronomy sector maintained stable liquidity despite a dramatic decline in profitability. The median LII in 2020 was 0.87 and continued to grow.

The above information indicates that the sectoral response to the crisis was shaped both by sector specificities and by the internal structure and adaptable capacity of companies. Sectors with high profitability (e.g., the automotive industry) did not necessarily automatically show adequate liquidity. On the other hand, some sectors with a low ROE (e.g., healthcare) excelled in the area of conservative management of short-term funds.

In this article, we monitored the impact of the economic crisis caused by the COVID-19 pandemic on the financial indicators of five business sectors in Slovakia. Based on the boxplots and scatter diagrams monitored above, we can assess that the largest declines in the sectors we monitored were recorded in the hazard sector. In the hazard sector, there was a relatively significant decline in profitability values in the pandemic years—2019 and 2020. However, in 2021 and 2022, there was an increase again and the values significantly approached the values before the pandemic. Another significant decline in the years of the pandemic also occurred in the tourism and gastronomy sector. In this sector, there was a more significant decline in profitability. However, we could also observe a slight decrease in liquidity. In the remaining three sectors, we did not record such significant changes during the pandemic. This does not mean that the financial indicators we monitored were at optimal values in each of the sectors. A statistically significant difference in both indicators was confirmed for the same period and for different industrial sectors. The difference in time has not been confirmed.

Demiraj et al. (2022) found similar results in the automotive sector, studied from the view of working capital. They showed that, due to problems with working capital (for example, the negative accounts payable period, and the cash conversion cycle), sectors also had problems with profitability and liquidity. In addition, Indian sectors, mostly in hazardous and investor activities, had problems with financial indicators due to the pandemic, resulting in a fall in the GDP rate (Das & Mahapatra, 2020). The results in the area of tourism are recorded due to the sector’s employees’ joblessness and the generation of fewer turnovers (Khan et al., 2024). The situation in tourism is similar to previous crises, as found in Spain by (Crespí-Cladera et al., 2021). They registered that most firms in tourism had financial problems due to the revenue decline resulting from the pandemic. Such a situation was mostly registered in small films (Crespí-Cladera et al., 2021). The investigation of the influences of the pandemic on business development is mostly performed through a classical analysis of financial indicators. This is confronted by Sun et al. (2022), who tried to evaluate the situation using a hazard model for the financial industry, evaluating the effectiveness of a business and predicting financial risk. The other task is to solve the crisis as soon as possible. This results from the finding of Albulescu (2021). He suggested that the prolongation of the pandemic is a source of financial volatility, challenging the risk. The results of Lebedeva and Moskalenko (2021) were also interesting, as they found that in Ukraine, industrial sectors were not the most affected by the pandemic in comparison with other economic sectors. The pandemic had a negative influence on the total market liquidity due to the increased number of deaths (Priscilla et al., 2023). This is the result of the liquidity being aggregated in the market from the liquidity of the individual sectors. The pandemic also worsened the financial situation in healthcare services (Han & Guan, 2022) due to lowered incomes. Long-term health policy implications indicate the need for reducing pandemic risks, improving the financial situation, and increasing the incomes of inhabitants. Due to the worsening of the financial situation in healthcare, the healthcare system has to be adapted to the changing circumstances, also ensuring safe working conditions in a crisis (Rybarczyk-Szwajkowska et al., 2021). There is an increasing need to make changes in the financing of healthcare, in connection with improving liquidity and profitability. In spite of organizations establishing several anti-pandemic business strategies, the question is still whether such strategies are enough to ensure business survival (Islam & Fatema, 2023). Cognitive characteristics are important in determining the reopening of a business during and after the pandemic (Zhan & Lin, 2021).

The results showed that different sectors reacted to the pandemic differently. For example, profitability depends mostly on the healthcare system in a given country (Thompson et al., 2023). In general, however, we can state that the most significantly affected sectors were those that were hit the most by the pandemic precisely because of their specific characteristics, where both in the tourism and gastronomy sectors, and in the hazard sector, most businesses were largely restricted due to all the safety measures, as these sectors often require personal contact. However, in recent years, both sectors have again recorded a significant increase in financial indicators compared to the years of the pandemic. This could largely be contributed to by the fact that both of these sectors had to adapt significantly to the pandemic. We can observe this within the huge targeting of betting shops to move to the online space and within the gastronomy sector, in turn, through the huge increase in delivery services in most cities.

The most important sector, the automotive sector, brought the following detailed results: The automotive sector demonstrates a distinctive “high profitability, low liquidity” pattern that warrants detailed examination due to its strategic importance to Slovakia’s economy. The sector’s exceptionally high ROE (median > 65% during 2020–2022) combined with critically low quick ratios (median 0.05–0.11) represents a deliberate strategic choice reflecting several factors:

• Just-in-time manufacturing: Automotive companies minimize working capital through sophisticated supply chain management, reducing liquidity but maximizing asset utilization
• Supplier financing: Extended payment terms with suppliers effectively provide external financing, reducing the need for internal liquidity
• Export orientation: Immediate foreign currency inflows from exports reduce domestic liquidity requirements.

The recommendations from this paper could help predict the next crisis impacts. These remain challenging for countries; nevertheless, they are better prepared for crises with greater flexibility, enabling them to effectively face future crises (Jindrichovska & Ugurlu, 2024). However, the present worldwide war and energy crisis demand future evaluation of impacts to the presented areas (Schramm & Terannova, 2024). This is confirmed by Mabiala et al. (2024), showing that the crisis recovery slowed down.

6. Conclusions

The COVID-19 pandemic has been a fundamental test of the resilience of various economic sectors, not only in terms of their ability to maintain profitability but also in terms of short-term financial stability. An analysis of the development of ROE and the quick ratio liquidity in five sectors—hazard, metallurgy, healthcare, automotive, and tourism and gastronomy—revealed significantly differentiated approaches to risk management and adaptation to external crises.

The tourism and gastronomy sector was the most affected in terms of profitability due to the decline in demand, the disruption of supply chains, and restrictions on movement. It also demonstrated a high level of financial discipline in the area of liquidity and the ability to recover quickly after the relaxation of measures. Although the healthcare sector was unable to evaluate itself at the ROE level during the crisis, it behaved conservatively and purposefully strengthened liquidity. The metallurgical industry demonstrated stability, but without a significant proactive change in response to crisis conditions.

In contrast, the hazard sector has experienced significant fluctuations, especially in terms of profitability and liquidity variability. This indicates increased sensitivity to external crises. In contrast, as well, the automotive sector stands out, maintaining exceptionally high profitability throughout the period. At the same time, the sector showed a long-term low level of liquidity. This may represent a potential structural risk. The sectoral response to the pandemic has been shaped not only by the nature of the industry, but also by the strategy for managing financial flows. Thus, companies have demonstrated resilience not solely through high profitability, but rather through a balanced approach to sustainability and flexibility—especially in managing liquidity. In conclusion, the COVID-19 pandemic had multiple significant impacts on the financial indicators of companies in various sectors of the Slovak economy.

This work provided a comprehensive view of the impacts of the crisis period on the financial health of companies and emphasized the need for adaptation and innovative approaches in crisis management. The results of the analysis pointed to the need to strengthen the financial stability of companies, diversify sources of income, and provide flexible management in times of crisis. The identified trends and findings can be used in the development of strategies for managing crises and strengthening the resilience of companies to future economic shocks. It is important for companies to have crisis plans in place that will allow them to respond quickly and effectively to unforeseen events.

The results of this work can also serve as a basis for further research and the creation of economic policies aimed at supporting the business environment. We hope that the knowledge gained will contribute to a better understanding of the dynamics of economic cycles and help businesses prepare for future challenges, thereby contributing to the stable and sustainable economic growth of Slovakia. The results of the paper can be used as a suggestion for how to overcome a crisis and how to take the pandemic as a way to restructure and renew industries and sectors.

We monitored businesses that were still active, meaning those that weathered the crisis and can be considered financially healthy. However, in general, the pandemic had a huge impact on society as a whole, and many businesses, even in the sectors we monitored, had to close down. This means that the sectors are mutually connected to the financial and banking systems. Future research therefore will be orientated on the financial support of overcoming the crisis in the industrial and economic sectors, not only from the perspective of the classical financial indicators, but also from the perspective of financial models and predictive methods.

This paper has academic, practical, and social implications, mainly the following:

• Academic implications: a 2D resilience framework, multidimensional assessment, transferability to CEE, and a methodological contribution of PERMANOVA for non-normal data.
• Practical implications: sector-specific recommendations for managers and differentiated public policy instruments, including measures for supply chains, working capital, digitalization and regulatory predictability.
• Social implications: employment effects, proposals for job retention schemes, retraining, regional mobility instruments and support for small businesses outside the sample.

Policy implications of the results are necessary for the differentiation of crisis support policies, challenging uniform approaches to economic resilience. This study provides empirical evidence for sector-specific vulnerability patterns that can inform future crisis preparedness strategies. In an area of theoretical significance, this research contributes to the crisis management literature by demonstrating how sectoral characteristics determine financial resilience pathways, offering insights that are applicable to similar transition economies in Central and Eastern Europe.