Shifting Employment: Labor Challenges in Czechia, Hungary and Slovakia Beyond the Pandemic

Abstract

The employment and labor market landscape has undergone significant transformations globally, including the three Central European countries examined in this study. Over the past decades, organizations in this region have transitioned from a state of full employment to labor shortages, raising the question: What factors have driven these changes? Our study aims to present a theoretical framework highlighting key macro-level factors, such as demographic trends, economic development, labor market dynamics, the impact of the COVID-19 pandemic, and the role of robotization and artificial intelligence. Based on two empirical studies conducted in 2019 and 2022 among Czech, Hungarian, and Slovak organizations, we analyzed the extent and causes of labor shortages, as well as the labor market effects of robotization. Using descriptive and non-parametric statistical methods, including frequency analysis and Mann–Whitney U tests, the study examined key trends and compared the two periods to identify significant shifts. The analytical approach of this study primarily aims to compare perceptions across occupational groups and between the two survey waves (2019 and 2022). Because most variables were measured on ordinal Likert-type scales and the datasets represent independent cross-sectional samples rather than a panel dataset, non-parametric methods were considered the most appropriate. More advanced causal modeling techniques, such as regression or factor analysis, were not applied because the objective of the research was exploratory and comparative rather than to establish causal relationships between variables. The findings reveal significant shifts in the perceived causes of labor shortages across occupational groups in the surveyed Central European organizations. In particular, increasing labor shortages were observed in specific job categories, alongside changes in the relative importance of the underlying drivers of labor shortages. While adopting robotization and artificial intelligence has been positively received, demographic decline and emigration remain critical challenges. The study provides practical insights for policymakers and corporate leaders regarding labor market challenges, workforce planning, and the potential role of robotization and artificial intelligence in addressing labor shortages. Although the research is based on a non-representative sample, it offers valuable insights into the Central European region’s employment and labor market trends. Future research could examine whether, in hard-to-fill positions, robotization and AI primarily provide indirect support by augmenting and reallocating human work, or whether they may serve as direct substitutes.

1. Introduction

The International Labor Organization (ILO, 2019) reported the following global trends in employment. A total of 3.3 billion people were employed worldwide during the period in question, while the average unemployment rate stood at 5%. Significant regional differences were observed in the field of employment. This raises the question: What was the situation in the three countries examined in our article (Czech Republic, Hungary, and Slovakia) in 2019, the last year before COVID-19? Labor shortages became increasingly significant in the developed world, including the labor markets of the three countries we analyzed (Kézdi, 2002; Cappelli, 2015). This trend was evident across a range of categories, affecting both highly skilled and physical labor positions, leading to increasing tension in the labor markets of these countries.

The International Labor Organization (ILO, 2022) reported new global trends in employment, including in the three Central European countries examined. The previously mentioned unemployment figure rose to 202 million people worldwide due to the impact of COVID-19. A significant shift occurred with the widespread adoption of remote work during the pandemic, a practice that had been relatively limited in earlier years. The low prevalence of remote work was particularly notable in former Eastern Bloc countries and the newly joined EU member states. Labor shortages in Eastern Europe are not a new phenomenon (Brunello & Wruuck, 2021). They were a constant feature of the socialist planned economy (Kornai, 2000), particularly evident in the physical labor sector. This issue re-emerged during the second and third decades of the new capitalist systems following the transition stage (Berglund et al., 2004; Barr, 2005). Although the 2008–2009 crisis temporarily eased labor shortages in these countries, the phenomenon resurged with greater intensity after 2012 (Darvas & Raposo, 2018). One of the key challenges today in these countries is the drastically increased labor shortage, influenced by various factors, including post-transition emigration, unfavorable national and regional demographic trends, economic crises, and growing wage disparities within the European Union, which combine to disadvantage these three focal nations (Brixiova et al., 2009).

The 2004 accession of the region’s countries to the European Union, followed by the 2007 enlargement, had significant implications for the entire labor market in Central and Eastern Europe. It enabled citizens of new member states to immediately take up employment in some of the older EU member states. As labor migration within the European Union is not well documented, exact figures are difficult to establish. However, the phenomenon affected hundreds of thousands of workers in each Central and Eastern European country and millions in larger new member states like Poland and Romania. It is estimated that approximately seven million workers left the transitioning Central and Eastern European countries, including the Czech Republic, Hungary, and Slovakia, examined in our study (Astrov, 2022; Horbulák, 2022). As a result, alongside deteriorating demographics and other factors, regional labor shortages emerged in 2018 across various professions and positions in these countries.

Our empirical research aims to uncover the multifaceted nature of labor shortages across occupational segments and to examine evolving perceptions of robotization and artificial intelligence in the workplace in the Czech Republic, Hungary, and Slovakia, with a particular focus on changes between 2019 and 2022. To this end, the study addresses three key questions concerning the underlying drivers of labor shortages, national and temporal differences in labor market challenges, and the evolution of views on technological developments in the workplace.

RQ1a. How do the causes of labor shortages vary across occupational segments in the three countries, with a focus on highly educated professionals?

RQ1b. How do the causes of labor shortages vary across occupational segments in the three countries, with a focus on administrative staff?

RQ1c. How do the causes of labor shortages vary across occupational segments in the three countries, with a focus on blue-collar workers?

RQ2. How have organizational leaders and HR professionals perceptions of the positive impacts of robotization and artificial intelligence evolved in the three countries during the studied period?

Figure 1 illustrates the conceptual framework of the factors influencing labor shortages in the examined period. Not all elements of the framework are empirically tested in this study; some are included to illustrate the broader economic and technological context of labor shortages.

The model illustrates the transformation of labor shortages between 2019 and 2022 in Hungary, the Czech Republic, and Slovakia. In 2019, labor shortages were primarily driven by direct national or regional macroeconomic factors, such as low wages, poor working conditions, and a lack of work–life balance, which themselves evolved over time and triggered further changes. Additionally, indirect factors, such as the spread of artificial intelligence and robotization, the climate crisis, shifting employee attitudes, and the pandemic, shaped the labor market in the longer term. As a result, by 2022, the causes of labor shortages had significantly shifted, reflecting the labor market’s ongoing responses to globally shared economic, social, and technological transformations. The second part of our article presents the theoretical background of our research. The third section outlines our methodological approach and the key characteristics of our sample. The fourth part includes a statistical analysis of our empirical data. The fifth section focuses on the discussion, consolidating the key research findings and highlighting the new theoretical and practical insights derived from the results and also summarizes the practical implications of the research. The concluding section presents future opportunities and highlights the study’s limitations.

Although previous studies have examined the impact of automation and robotization on labor markets, several gaps remain in the literature. Most existing research focuses on global trends or large developed economies, while relatively little attention has been paid to the specific labor market dynamics of Central and Eastern European countries. Previous research (Szabó, 2020; Bachmann et al., 2024) has highlighted that the introduction of automation in Central and Eastern European countries is proceeding more slowly and at different speeds (e.g., the automotive sector is a leader in this area) than in more developed countries due to lower labor costs. Furthermore, comparative analyses based on recent post-pandemic data remain limited.

This study aims to address this gap by analyzing labor market changes related to robotization in the V4 region using comparative data from 2019 and 2022. By focusing on sectoral and occupational patterns, the research provides new insights into how labor markets in Central Europe adapt to technological change.

2. Theoretical Framing

Labor shortages are shaped by the interaction of several macro-level factors rather than by a single driver. In the Central European context, economic development, demographic trends, labor market dynamics, technological change and external shocks such as the COVID-19 pandemic jointly influence the supply and demand of labor. Economic growth increases labor demand, while demographic decline and emigration reduce labor supply. At the same time, technological developments—particularly robotization and artificial intelligence—may partially compensate for labor shortages by increasing productivity or replacing certain tasks. The conceptual framework presented in Figure 1 therefore integrates these economic, social and technological factors and illustrates how their interaction contributes to the emergence and transformation of labor shortages in the examined countries.

2.1. Economic Development

The population trends of the three examined countries show several similarities alongside certain differences. Over the past decades, low fertility rates and the emigration of the active population, primarily to more developed Western EU countries, have become prevalent, albeit with varying dynamics. The largest population decline was observed in Hungary (Bakó et al., 2019; KSH, 2020; World Bank, 2024) while recent Czech demographic data alternated between periods of growth and decline. In Slovakia, the previously rapid population growth has gradually slowed to very low levels and has recently begun to decrease (World Bank, 2024). Following the financial crisis of 2008–2009, the GDP of all three countries shifted in a clearly positive direction starting in 2014, maintaining this trend until 2019 (ranging from 2.31% to 4.58%) (World Bank, 2024). During the global COVID-19 crisis, the GDPs of all three countries turned negative, but with economic recovery, their GDPs showed growth again by 2022 (

Table 1. Population and GDP of the examined countries.

Countries	2019 (Million People)	2022 (Million People)	GDP 2019 (%)	GDP 2022 (%)
Czech Republic	10.89	10.83	3.57	2.85
Hungary	9.77	9.68	4.86	4.58
Slovakia	5.45	5.43	2.51	1.87

Source: (Eurostat, 2022).

).

2.2. Employment and Labor Market

Eastern European countries—including the three examined here—faced two opposing tendencies at the beginning of the transition. On the one hand, their labor markets could not meet the specific demands (e.g., market-oriented managerial skills, new business, marketing, financial, controlling, and entrepreneurial knowledge) of international companies entering the market, as well as the market-driven requirements of local businesses. On the other hand, the emergence of privatization and market competition (both domestic and international) rendered many manual labor jobs redundant. Following decades of full employment, these countries experienced an unprecedented rise in unemployment (Allison & Ringold, 1996; Lipton, 2018; Valuch, 2023). Today, the labor markets of the analyzed countries have undergone a significant transformation. Labor shortages in managerial, legal, economic, and administrative positions have considerably decreased. However, there remain severe shortages of manual laborers, technicians, IT engineers, doctors, and skilled workers (European Labour Authority, 2023; OECD, 2022b). New labor market phenomena and conditions, previously unseen, have emerged.

Privatization following regime change, the influx of foreign capital, and shifts in market orientations resulted in the end of full employment. It led to unusually high unemployment rates: 8.7% in the Czech Republic, 12% in Hungary, and 18% in Slovakia (World Bank, 2024). This situation gradually improved as economic growth gained momentum, reducing the unemployment figures. However, the global financial crisis of 2008–2009 negatively impacted these trends, increasing unemployment rates to 7% (CZ), 11% (HU), and 14% (SK) (World Bank, 2024). The positive trajectory resumed in the early 2010s as economic growth returned, reducing unemployment as labor market tightness (increased number of unfilled jobs) grew. This progress was disrupted by the economic downturn caused by COVID-19, which temporarily raised the 2019 unemployment figures by 1–2 percentage points. As shown in

Table 2. Unemployment rates in the researched countries during the years 2019 and 2022.

Countries	Unemployment (%)
	2019	2022
Czech Republic	2.02	2.22
Hungary	3.42	3.61
Slovakia	5.76	6.14

Source: Eurostat (2022).

below, unemployment growth had also declined by 2022 (World Bank, 2024).

Thus, among other things, significant differences in the minimum wage of 500–800 euros in Eastern and Central Europe and 1000–2500 euros in Western Europe, and the free flow of labor following EU accession (Eurostat, 2004), initiated and intensified the outflow of domestic labor to better-paying developed Western countries. It should be noted that this trend is not a new phenomenon in the history of the region (Hautzinger et al., 2014). The migration balance (immigration and emigration) is negative in the case of Hungary and Slovakia, while it shows a positive picture in the case of the Czech Republic (Eurostat, 2022). For a long time, serving one employer for one’s entire life or working for one organization for one’s entire life (Lifelong employment) was very typical in the labor markets of the countries examined. In organizations operating in these three nations, it has become increasingly difficult not only to attract talent but also to retain it (Boudreau, 2010). Nowadays, retaining employees is as difficult as acquiring them. Therefore, it is necessary for businesses to be able to retain the employees they have already acquired. How? First, they can do so with a workplace environment in which the employee feels good (Gelencsér & Szabó-Szentgróti, 2023). HR practices that build employee commitment, satisfaction, and loyalty have gained prominence. In this process, managers strive to achieve satisfaction while creating a sense of “ownership” among employees (Rosenau, 2003; OECD, 2020).

2.3. Effects of COVID-19

The global COVID-19 pandemic brought almost revolutionary changes to the labor markets of the countries studied. In cases of previously limitedly applied solutions, discontinuous effects were observed in these countries. The two primary mechanisms were flexible working hours and remote work. While these HR solutions were already present in the labor markets of these countries, their widespread application was observed during the COVID-19 pandemic (Łobos et al., 2020; Manyika et al., 2021). Beyond remote work and flexible work arrangements, the pandemic also impacted two additional trends that will have lasting effects on the workforce: the use of digital tools for transactions, consultations, and collaboration and the application of automation and artificial intelligence technologies in the workplace. Although the importance of these two areas had already been growing before the emergence of the virus, the COVID-19 pandemic permanently altered the pace of applications. The impact of COVID-19 highlighted the need for greater flexibility and communication in the workplace (Collings et al., 2021a). The pandemic accelerated the adoption of fully digital methods to recreate the best aspects of in-person learning through live video and collaborative sharing. Remote work also gained traction due to social distancing measures (Lennert, 2020; Lipták, 2021), and HR professionals were tasked with supporting employees in adapting to home-based work (Newman et al., 2023). Organizations had to care for their employees’ mental health like never before. Undoubtedly, COVID-19 brought significant changes to work patterns across different organizational levels (Brammer et al., 2020; Roy, 2024). The COVID-19 pandemic not only highlighted shifts in understanding of the work environment and tensions between shareholder and stakeholder perspectives but also underscored the need to balance HR’s operational and strategic roles (Collings et al., 2021b; Dajnoki et al., 2023).

2.4. Effects of Robotization

The radical advancement of robotization demands a paradigm shift in the labor market, as its stakeholders are unprepared for the increasingly rapid impacts. The effects of robotization are widespread but progress at varying paces, not only across countries but also among companies and industries (Goštautaitė et al., 2024). Robots first appeared in the 1960s, predominantly in the automotive industry. The mid-2000s marked an important turning point in the diffusion of industrial robotization, as the use of robots gradually expanded beyond the automotive sector to other industries. This development allowed a growing number of sectors to benefit from the precision and efficiency of robotic technologies. Since then, robots have spread across a wide range of activities, from manufacturing and logistics to medical and surgical applications. However, this increasingly close relationship between technological, natural, and social systems has resulted in growing tensions due to differences in their developmental levels and adaptability (Duan et al., 2019).

Estimates regarding the potential impact of automation on employment vary considerably. Some studies suggest that a significant share of jobs could be affected by automation, while other analyses argue that many occupations cannot yet be easily or cost-effectively automated. A comprehensive survey by PWC (2018) indicates that automation will soon extend beyond simple computational tasks to more structured data analysis. According to World Robotization statistics (IFR, 2019), by 2020, four million industrial robots were operating in factories worldwide. A detailed data analysis shows that while the automotive industry remained a leader in industrial robots between 2017 and 2018, its growth rate slowed to just 2%. In contrast, the food industry experienced a 32% increase in robot adoption within a single year. In the three countries studied, the level of robotization was assessed by the number of robots per 10,000 workers, revealing moderate levels of development (200 in the Czech Republic, 180 in Slovakia, and 175 in Hungary) (IFR, 2022). This situation is largely attributed to the presence of automotive plants in these countries. Our world is undergoing another paradigm shift, driven by the recent twin transitions: artificial intelligence and sustainability. These transitions pose significant challenges for various areas of corporate and human resource management. Some forecasts suggest that a substantial share of jobs could be affected by automation by 2030 as a result of these developments (World Economic Forum, 2025). Transformations of this magnitude are unlikely to leave the field of HR untouched.

3. Methodology

3.1. Aim of Research

This study presents findings from international questionnaire surveys conducted in the Czech Republic, Hungary, and Slovakia. We examine the drivers of labor shortages across occupational segments and the perceived labor-market effects of robotization and artificial intelligence. The partial overlap between the 2019 and 2022 survey waves informed our research design and allows cautious comparisons over time.

The analysis primarily focuses on the aggregated regional sample of the three countries (Hungary, Czech Republic and Slovakia). Country-level differences are presented mainly for illustrative purposes.

The main statistical analyses were conducted on an aggregated regional sample from Hungary, the Czech Republic, and Slovakia, while country-level values are presented primarily for descriptive and illustrative purposes.

3.2. Questionnaire

The following can be said about the questionnaire used in our research:

Many items in the questionnaire were measured using a consistent 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). This odd-numbered scale allows the inclusion of a neutral midpoint (3), which is a standard practice in both the CRANET and CEEIRT research methodologies. In addition to this scale, some variables were measured using 10-point scales, which provide a more detailed picture of respondents’ evaluations, and 4-point scales, which encourage respondents to make clearer decisions without a neutral option.

The present study builds on long-term international HR research collaborations. The research team has been a member of the CRANET international HR research network since 2004 (Morley et al., 2020). In 2007, the authors also established the CEEIRT (Central and Eastern European International Human Resource Management) research network (Poór et al., 2020). Since 2012, the research group has been conducting empirical studies focusing specifically on workforce-related issues in the Central and Eastern European region (Poór et al., 2017). The questionnaires used in the current study were developed based on these earlier research instruments and served as the starting point for the present survey.

The survey instrument was translated from the original English version into Czech, Hungarian, and Slovak by native-speaking researchers. To ensure linguistic equivalence across the different language versions, a back-translation procedure was applied, and the translated questionnaires were checked for semantic consistency.

The target respondents were senior HR managers, HR professionals, business owners, or chief executive officers (CEOs) who possess a strategic overview of the organization’s workforce, including recruitment, employment practices, and workforce management. Their positions ensured that the responses reflected informed organizational perspectives.

The questionnaire was distributed through several professional channels, including chambers of commerce, LinkedIn, professional HR associations, and the authors’ networks of employed evening and correspondence students. Data collection relied on snowball and convenience sampling techniques to reach a broad range of organizations across the three countries.

Before the full deployment of the survey, a pilot test was conducted within the HR ResearchLab teaching environment with 10 HR professionals, who were also evening and correspondence students. The purpose of the pre-test was to ensure the clarity and comprehensibility of the questionnaire items and to identify potential ambiguities.

Regarding data quality procedures, cases with more than 20% missing values were excluded from the dataset. Minor missing values were handled during the data cleaning process to maintain the consistency of the analysis.

The causes of labor shortages were operationalized using a set of predefined factors included in the questionnaire, such as competition among employers, low wages, shortage of skilled labor, foreign migration, problems in the education system, poor working conditions, infrastructure deficiencies, and work–life balance challenges. Respondents were asked to evaluate the extent to which each factor contributed to labor shortages in their organization using a five-point Likert scale ranging from 1 (not at all typical) to 5 (very typical). Minor variations in item-level valid response counts (N) occurred due to missing data; however, response counts remained broadly consistent with the total sample sizes (2019: N = 436; 2022: N = 729).

3.3. Hypotheses

During the research process, the hypotheses were developed based on a thorough review of the relevant literature, ensuring that they are grounded in existing theoretical frameworks and empirical studies and are closely aligned with the research questions. The formulated hypotheses are as follows:

H1a. 

Among highly educated professionals, significant changes can be observed in the perceived causes of labor shortages between 2019 and 2022 across the three countries.

H1b. 

Among administrative staff, significant changes can be observed in the perceived causes of labor shortages between 2019 and 2022 across the three countries.

H1c. 

Among blue-collar (manual) workers, significant changes can be observed in the perceived causes of labor shortages between 2019 and 2022 across the three countries.

The issue of labor shortages presents distinct challenges for workers across different occupational segments. For highly skilled professionals, the primary causes include low wages, the poaching effect of competitors, and shortcomings in the education system, which fail to supply a sufficient number and quality of professionals for certain sectors (Green et al., 2015; Felbo-Kolding et al., 2017). In contrast, for administrative workers, digitalization and automation have drastically reduced the demand for traditional administrative roles. Meanwhile, for manual laborers, low wages, harsh working conditions, and the low prestige of their roles pose significant challenges (Gheorghiev, 2023; Oltean & Găvruș, 2018). The disparity between the labor market and educational systems further exacerbates the problem. Education systems are unable to keep pace with rapidly changing labor market demands, particularly in terms of technological and digital competencies. This issue is particularly pronounced in STEM (Science, Technology, Engineering, Mathematics) fields, where the lack of adequately trained labor poses significant barriers to industrial progress (Green et al., 2015). The missing expertise is often compensated for by migrant workers, who frequently face labor market segregation and low wages, undermining their opportunities for integration (Felbo-Kolding et al., 2017). The situation of manual laborers and migrant workers is particularly concerning, as they often find themselves in low-wage, low-prestige roles from which it is difficult to escape. Segregation exacerbates this problem, as these workers typically face worse working conditions and more precarious employment than their local counterparts (Oltean & Găvruș, 2018; Gheorghiev, 2023). While migration can serve as a temporary solution to address labor shortages, it also creates structural problems, as the concentration of workers in low-prestige sectors is unsustainable in the long term.

It should be noted, however, that the present analysis is based on aggregated data across the three countries, and therefore does not allow a full separation of occupational effects from country-specific influences. While the observed patterns are broadly consistent with prior studies and appear across the combined sample, potential cross-national differences in the drivers and experiences of labor market segmentation cannot be fully disentangled within the current research design. Accordingly, the findings should be interpreted primarily as occupational-level tendencies rather than country-specific causal relationships. Future studies using country-level or multilevel analyses could provide a more precise understanding of how occupational and national factors interact.

H2. 

In all three countries, the perception of the positive impacts of robotization and artificial intelligence among the surveyed organizations’ leaders and HR professionals improved.

Numerous studies have examined the labor market impacts of robotization and artificial intelligence over the past decade. These studies support the assertion that technological innovations, such as artificial intelligence and robotization, significantly impact organizational operations and employees’ and leaders’ attitudes toward technological changes (Vrontis et al., 2023; Bhargava et al., 2021). Vrontis et al. (2023) emphasize that artificial intelligence and robotization profoundly influence HR management, particularly in terms of collaboration between leaders and employees, highlighting diverse patterns in the acceptance of technology. Bhargava et al. (2021) examined the effects of AI and robotization on job satisfaction, employability, and job security in their research. Their findings indicate that these impacts are closely linked to employees’ and leaders’ perceptions. Arslan et al. (2022) pointed out that implementing artificial intelligence poses unique challenges for HR leaders, especially in managing interactions between employees and AI-based systems at the team level. This underscores the importance of understanding the effects of technological innovations on HR strategies. Przytuła (2018), in her analysis, highlighted how global labor market trends shape the future competencies of employees, emphasizing the role of robotization and AI. Based on these studies, examining the impacts of technological changes is warranted, as they have significant implications for labor market processes and organizational strategies.

3.4. Characteristics of the Sample

Data collection was conducted online in 2019 and 2022. The questionnaire covered three major thematic groups of questions. First, organizational characteristics were determined (sector, size, ownership type, and revenue). The second set of questions focused on the extent and causes of labor shortages, while the third group addressed the impacts of robotization. The survey consisted of closed-ended questions based on nominal and metric (scale) variables. The use of closed-ended questions enabled the collection of comparable quantitative data across countries and survey waves; however, this approach may limit the richness of the analysis, as respondents had fewer opportunities to provide detailed qualitative explanations regarding the causes of labor shortages. The data collected in 2019 and 2022 were analyzed using descriptive and non-parametric statistical methods. Descriptive statistics, such as frequency analysis and mean rank calculations, were employed to identify key trends and patterns. Among non-parametric statistical tests, the Mann–Whitney U test was used to compare the rankings of variables across the two survey periods, focusing on factors related to labor shortages and employee retention. These analytical methods enabled a comprehensive examination of labor shortages, employee retention strategies, and the perceived impacts of robotization and artificial intelligence on organizations in Hungary, the Czech Republic, and Slovakia. Because several Mann–Whitney tests were performed across different occupational groups and labor shortage factors, the potential inflation of Type I error due to multiple comparisons was considered. Accordingly, the results were interpreted with caution when evaluating statistical significance.

For practical reasons, data collection relied on non-probability sampling techniques, including snowball and convenience sampling (Noy, 2008; Ghaljaie et al., 2017). Consequently, the sample cannot be considered statistically representative of organizations in the examined countries, and the findings should be interpreted with caution.

Rather than providing generalizable estimates, the dataset enables the identification of indicative patterns and comparative tendencies across occupational groups and survey waves. The primary aim of the analysis is therefore exploratory and descriptive, not to produce population-level inferences or meta-analytic generalizations.

The questionnaires were completed voluntarily and anonymously. A total of 436 and 729 organizations from Hungary, the Czech Republic, and Slovakia participated in the surveys conducted in 2019 and 2022, respectively. The Czech Republic demonstrated relatively high respondent activity in both years, with over 200 organizations participating. Hungary consistently contributed the largest number of responses (between 200 and 400), while Slovakia showed lower participation levels (60 organizations in 2019 and 125 in 2022) (

Table 3. Number and percentage of responding organizations.

Year	Countries	Frequency	Valid Percent
2019	Czech Republic	165	37.8%
	Hungary	211	48.4%
	Slovakia	60	13.8%
	Total	436	100.0%
2022	Czech Republic	218	29.9%
	Hungary	386	52.9%
	Slovakia	125	17.1%
	Total	729	100.0%

Source: own editing.

).

Based on the Hungarian, Czech, and Slovak samples studied, significant shifts among industries were observed between 2019 and 2022. The number of respondents from the industrial sector decreased in all countries, particularly in Hungary and the Czech Republic. In contrast, the number of respondents from the service sector increased, especially in Hungary, while it declined in Slovakia. Based on the analyzed sample, the distribution of ownership types showed significant shifts between 2019 and 2022. The proportion of domestically owned private companies increased in the samples across all three countries, particularly in Slovakia, where it rose from 63.3% to 81.7%. In Hungary and the Czech Republic, the increase was more moderate but still reflected a strengthening role of domestically owned organizations in the samples. Simultaneously, the proportion of foreign-owned companies declined in all countries. The most significant drop was observed in Slovakia, where the proportion fell dramatically from 20.0% to 8.3%. There was also a notable but less drastic decrease in Hungary and the Czech Republic.

Additional information about the characteristics of the responding organizations is presented in Appendix A, including company size, ownership structure and turnover categories.

4. Data Analysis and Findings

4.1. Causes of Labor Shortages Among Highly Educated Professionals

This analysis examined the causes of labor shortages across three major employee segments: highly educated professionals, administrative staff, and manual workers.

The causes of labor shortages among highly educated professionals underwent significant changes between 2019 and 2022 (

Table 4. Changes in the causes of labor shortages among highly educated professionals in the three countries (2019 and 2022).

Cause	Year	1	2	3	4	5	Total
Competition among employers	2019	19.60%	13.10%	22.00%	22.60%	22.80%	100.00%
	2022	19.70%	13.00%	20.50%	19.70%	27.10%	100.00%
Low wages	2019	19.90%	21.90%	25.10%	19.60%	13.50%	100.00%
	2022	16.60%	11.90%	25.50%	21.60%	24.40%	100.00%
Shortage of skilled labor	2019	18.90%	15.70%	29.30%	20.40%	15.70%	100.00%
	2022	19.70%	17.90%	22.10%	20.40%	19.90%	100.00%
Foreign migration	2019	38.70%	17.70%	19.50%	15.90%	8.10%	100.00%
	2022	36.30%	14.40%	19.10%	16.40%	13.90%	100.00%
Problems in the education system	2019	31.40%	22.70%	24.50%	13.90%	7.60%	100.00%
	2022	34.10%	18.20%	19.00%	16.70%	12.10%	100.00%
Poor working conditions	2019	58.40%	20.80%	13.90%	3.90%	3.00%	100.00%
	2022	51.60%	20.30%	14.70%	8.80%	4.50%	100.00%
Infrastructure deficiencies	2019	55.90%	19.30%	15.40%	6.90%	2.40%	100.00%
	2022	53.60%	21.70%	13.90%	7.20%	3.50%	100.00%
Work–life balance challenges	2019	34.90%	22.80%	22.50%	13.00%	6.80%	100.00%
	2022	32.70%	17.80%	22.90%	15.90%	10.70%	100.00%

Source: own editing; (1 to 5, where 1 = not at all typical and 5 = very typical).

). In 2022, 27.1% of respondents considered increasing competition among employers “very typical,” compared to 22.8% in 2019. The importance of wages also grew, with the proportion of “very typical” responses rising from 13.5% to 24.4%. The shortage of skilled labor intensified, with 19.9% of respondents in 2022 considering it a severe problem, up from 15.7% in 2019. The impact of foreign migration also increased, with the proportion of respondents selecting “very typical” rising from 8.1% to 13.9%. Deficiencies in the education system continue to pose a significant barrier, with the “very typical” responses increasing from 7.6% to 12.1%. In the case of poor working conditions, the proportion of “very typical” responses rose from 3% to 4.5%, while infrastructure deficiencies increased from 2.4% to 3.5%. Work–life balance challenges also intensified, with 10.7% of respondents in 2022 considering it “very typical,” compared to 6.8% in 2019.

4.2. Causes of Labor Shortages Among Administrative Workers

Among administrative workers, the impact of competition among employers slightly decreased, with 12.4% of respondents considering this factor “very typical” in 2022, compared to 13% in 2019 (

Table 5. Changes in the causes of labor shortages among administrative workers in the three countries (2019 and 2022).

Cause of Labor Shortage	Year	1	2	3	4	5	Total
Competition among employers	2019	19.9%	17.5%	29.6%	19.9%	13.0%	100.0%
	2022	22.8%	19.2%	31.6%	13.9%	12.4%	100.0%
Low wages	2019	20.8%	17.9%	29.2%	21.4%	10.7%	100.0%
	2022	14.3%	17.1%	29.6%	25.0%	14.0%	100.0%
Shortage of skilled labor	2019	21.0%	25.5%	32.1%	12.6%	8.7%	100.0%
	2022	24.7%	19.5%	30.4%	17.6%	7.8%	100.0%
Foreign migration	2019	48.3%	23.7%	14.0%	10.3%	3.6%	100.0%
	2022	48.4%	18.8%	22.0%	7.1%	3.7%	100.0%
Problems in the education system	2019	26.4%	32.8%	23.6%	11.3%	5.8%	100.0%
	2022	41.5%	17.5%	26.1%	9.4%	5.4%	100.0%
Poor working conditions	2019	54.7%	22.3%	16.2%	5.2%	1.5%	100.0%
	2022	49.6%	19.5%	19.2%	7.5%	4.2%	100.0%
Infrastructure deficiencies	2019	49.8%	22.9%	17.4%	5.5%	4.3%	100.0%
	2022	53.2%	17.3%	19.7%	7.7%	2.2%	100.0%
Work–life balance challenges	2019	38.8%	23.0%	26.4%	7.9%	3.9%	100.0%
	2022	33.4%	19.1%	26.4%	14.8%	6.3%	100.0%

Source: own editing; (1 to 5, where 1 = not at all typical and 5 = very typical).

). In contrast, the role of wages significantly increased, with the proportion of “very typical” responses rising from 10.7% to 14%, indicating that wage levels play a key role in addressing labor shortages. The shortage of skilled labor remained an important factor, although it showed a slight decrease, with 7.8% of respondents considering it “very typical” in 2022, compared to 8.7% in 2019. Deficiencies in the education system continue to pose a significant barrier, although the proportion of “very typical” responses slightly decreased from 5.8% to 5.4%. For poor working conditions, the proportion of “very typical” responses rose from 1.5% to 4.2%, while infrastructure deficiencies decreased from 4.3% to 2.2%. Work–life balance challenges slightly increased but continued to be an important consideration.

4.3. Causes of Labor Shortages Among Blue-Collar Workers

The term “blue-collar worker” refers to an employee who performs manual, physical labor, often in industrial, construction, or other skilled trades roles. The term denotes the working class, individuals who do not engage in intellectual work. It originated during the Industrial Revolution and now refers to individuals in lower-level positions in the labor market (Hodson, 2001). Among blue-collar workers, the impact of competition among employers increased significantly, with 33.7% of respondents considering it “very typical” in 2022, compared to 24.6% in 2019. The role of wages also became more prominent, with the proportion of “very typical” responses rising from 24.9% to 29.8%, indicating that wage levels continue to be a key factor in addressing labor shortages. The shortage of skilled labor also grew, with 28.6% of respondents considering it “very typical” in 2022, up from 23.7% in 2019. The impact of foreign migration slightly increased, with the proportion of “very typical” responses rising from 12.9% to 18.8%. Deficiencies in the education system also became an increasing problem, with the proportion of “very typical” responses rising from 9.5% to 15.6%. The impact of poor working conditions slightly increased from 7.6% to 10.8%, while infrastructure deficiencies remained largely unchanged. Work–life balance challenges also slightly increased, with the proportion of “very typical” responses rising from 9.6% to 11.6% (

Table 6. Changes in the causes of labor shortages among blue-collar workers in the three countries (2019 and 2022).

Cause of Labor Shortage	Year	1	2	3	4	5	Total
Competition among employers	2019	20.6%	12.5%	20.2%	22.1%	24.6%	100.0%
	2022	19.4%	9.8%	17.8%	19.4%	33.7%	100.0%
Low wages	2019	16.8%	16.5%	21.3%	20.7%	24.9%	100.0%
	2022	13.5%	13.9%	19.1%	23.8%	29.8%	100.0%
Shortage of skilled labor	2019	22.5%	16.5%	22.2%	15.3%	23.7%	100.0%
	2022	20.2%	13.3%	18.6%	19.3%	28.6%	100.0%
Foreign migration	2019	37.7%	15.3%	16.6%	17.5%	12.9%	100.0%
	2022	37.4%	15.1%	16.7%	11.9%	18.8%	100.0%
Problems in the education system	2019	37.7%	19.0%	19.9%	13.8%	9.5%	100.0%
	2022	37.9%	17.8%	18.7%	10.0%	15.6%	100.0%
Poor working conditions	2019	39.1%	22.6%	22.3%	8.3%	7.6%	100.0%
	2022	36.2%	14.4%	23.8%	14.9%	10.8%	100.0%
Infrastructure deficiencies	2019	46.5%	19.4%	20.0%	7.4%	6.8%	100.0%
	2022	43.4%	19.7%	19.4%	10.7%	6.8%	100.0%
Work–life balance challenges	2019	35.7%	18.9%	22.4%	13.4%	9.6%	100.0%
	2022	30.1%	18.3%	26.5%	13.6%	11.6%	100.0%

Source: own editing; (1 to 5, where 1 = not at all typical and 5 = very typical).

).

The analysis results indicate that the causes of labor shortages vary significantly across occupational segments. For instance, among highly skilled professionals, wage competition and shortcomings in the education system were key factors, whereas working conditions played a significant role for blue-collar workers.

4.4. Changing Reasons for Labor Shortages

For ordinal variables, it is not advisable to draw conclusions based solely on percentage distributions. In such cases, the Mann–Whitney test is recommended, as it helps determine whether there has been a significant change in conditions between the two survey time points. An increase in mean rank indicates that the factor was perceived as more important in contributing to labor shortages in the later survey period. Among employees with higher education the issues of low wages and infrastructure deficiencies showed a significant increase in the mean rank between 2019 and 2022, indicating that these problems have become increasingly important in the development of labor shortages (

Table 7. Causes of labor shortages among highly educated professionals.

	Year	N	Mean Rank	Sum of Ranks	Sig. (2-Tailed)
Competition among employers	2019	337	397.09	133,819.50	0.531
	2022	468	407.26	190,595.50
Too low wages	2019	342	363.55	124,333.50	<0.01
	2022	463	432.14	200,081.50
Shortage of skilled workforce	2019	338	358.66	121,227.50	0.06
	2022	410	387.56	158,898.50
Foreign migration	2019	338	388.52	131,318.50	0.622
	2022	447	396.39	177,186.50
Problems in the education system	2019	333	351.33	116,991.50	0.085
	2022	397	377.39	149,823.50
Poor working conditions	2019	331	354.88	117,466.00	0.454
	2022	390	366.19	142,815.00
Infrastructure deficiencies	2019	332	336.32	111,658.50	0.02
	2022	374	368.75	137,912.50
Difficulties in work–life balance integration	2019	331	348.63	115,396.50	0.6
	2022	373	355.93	132,763.50

Source: own editing.

). The growing significance of low wages can be attributed to rising inflation and the increasing cost of living, which have led to greater employee dissatisfaction, while infrastructure deficiencies, particularly the limited transportation options, hinder worker mobility, further exacerbating labor market tensions.

Among administrative workers, the causes of labor shortages changed significantly between 2019 and 2022 (

Table 8. Causes of labor shortages among administrative workers.

	Year	N	Mean Rank	Sum of Ranks	Sig. (2-Tailed)
Competition among employers	2019	331	407.12	134,758.00	0.1
	2022	452	380.92	172,178.00
Too low wages	2019	336	372.45	125,144.00	0.009
	2022	456	414.22	188,884.00
Shortage of skilled workforce	2019	333	376.85	125,491.50	0.761
	2022	425	381.58	162,169.50
Foreign migration	2019	329	352.09	115,838.50	0.804
	2022	378	355.66	134,439.50
Problems in the education system	2019	326	367.1	119,674.50	0.021
	2022	371	333.1	123,578.50
Poor working conditions	2019	327	328.61	107,456.50	0.041
	2022	359	357.06	128,184.50
Infrastructure deficiencies	2019	327	349.48	114,280.50	0.687
	2022	365	343.83	125,497.50
Difficulties in work–life balance integration	2019	330	341.87	112,816.00	0.006
	2022	398	383.27	152,540.00

Source: own editing.

). The issue of low wages increased significantly, with the mean rank rising from 372.45 to 414.22 (p = 0.009), indicating that low wages became increasingly important in labor shortages, primarily due to inflation and rising living costs. Problems in the education system and poor working conditions also showed an increase. The mean rank for problems in the education system decreased (from 367.1 to 333.1, p = 0.021), while the impact of poor working conditions increased (from 328.61 to 357.06, p = 0.041), signaling that the workplace environment and education are failing to meet labor market demands. Work–life balance issues also increased, with the mean rank rising from 341.87 to 383.27 (p = 0.006), reflecting the growing demand for flexible working arrangements, particularly in the wake of the COVID-19 pandemic.

The impact of competitors’ workforce draining significantly increased among blue-collar workers, with the mean rank rising from 361.62 to 394.31 (p = 0.037), indicating that competition among employers for employee retention has intensified (

Table 9. Causes of labor shortages among blue-collar workers.

	Year	N	Mean Rank	Sum of Ranks	Sig. (2-Tailed)
Competition among employers	2019	321	361.62	116,079.50	0.037
	2022	439	394.31	173,100.50
Too low wages	2019	334	370.5	123,748.00	0.028
	2022	446	405.48	180,842.00
Shortage of skilled workforce	2019	334	364.75	121,828.00	0.045
	2022	430	396.28	170,402.00
Foreign migration	2019	326	347.68	113,343.00	0.587
	2022	377	355.74	134,113.00
Problems in the education system	2019	326	338.48	110,344.50	0.554
	2022	359	347.1	124,610.50
Poor working conditions	2019	327	326.25	106,682.50	0.015
	2022	362	361.94	131,022.50
Infrastructure deficiencies	2019	325	338.71	110,080.00	0.338
	2022	366	352.48	129,006.00
Difficulties in work–life balance integration	2019	322	342.52	110,291.00	0.101
	2022	389	367.16	142,825.00

Source: own editing.

). The issue of low wages also increased, with the mean rank rising from 370.5 to 405.48 (p = 0.028), reflecting the growing importance of wages due to rising inflation and living costs. The shortage of skilled labor also significantly increased, with the mean rank rising from 364.75 to 396.28 (p = 0.045), indicating the tension between labor market demand and supply. The impact of poor working conditions also increased significantly, with the mean rank rising from 326.25 to 361.94 (p = 0.015), showing the growing dissatisfaction among workers due to the workplace environment and conditions.

The results indicate statistically significant differences between the two survey periods across occupational groups. Therefore, hypotheses H1a–H1c are supported, although the findings should be interpreted cautiously given the cross-sectional nature of the data and the non-representative sampling method.

Table 10. Interpretation of the magnitude of observed changes based on Mann–Whitney test results.

Occupational Group	Factor	Mean Rank 2019	Mean Rank 2022	p-Value	Interpretation of Change
Highly educated professionals	Low wages	363.55	432.14	<0.01	Strong increase in importance
Highly educated professionals	Infrastructure deficiencies	336.32	368.75	0.02	Moderate increase
Administrative staff	Low wages	372.45	414.22	0.009	Moderate to strong increase
Administrative staff	Poor working conditions	328.61	357.06	0.041	Moderate increase
Administrative staff	Work–life balance challenges	341.87	383.27	0.006	Strong increase
Blue-collar workers	Competition among employers	361.62	394.31	0.037	Moderate increase
Blue-collar workers	Low wages	370.50	405.48	0.028	Moderate increase
Blue-collar workers	Shortage of skilled labor	364.75	396.28	0.045	Moderate increase
Blue-collar workers	Poor working conditions	326.25	361.94	0.015	Moderate increase

Source: own calculations based on Mann–Whitney test results. Note: The interpretation reflects the direction and magnitude of changes in mean rank values between the two survey periods (2019–2022).

summarizes the magnitude and interpretation of the observed differences based on the changes in mean ranks between 2019 and 2022.

4.5. Robotization and Artificial Intelligence

Technological advancements in recent years and the labor market transformation have significantly influenced how companies and the workforce in Central Europe perceive artificial intelligence (AI) and robotization. Data from 2019 and 2022 indicate that the surveyed organizations in the three Central European countries showed varying perceptions regarding the application of these technologies, particularly in relation to workflow reliability, the replacement of monotonous tasks, and the substitution of hazardous jobs. These differing perspectives reflect not only the degree of technology adoption but also the associated societal and economic attitudes.

Table 11. Impacts of artificial intelligence and robotization on the labor market in the three countries (2019 and 2022).

Statement	Country	Average (2019)	Average (2022)
Certain workflows can be executed more reliably using artificial intelligence.	Hungary	2.32	2.68
	Czech Republic	3.02	3.72
	Slovakia	2.66	2.94
Robotization can replace monotonous workflows (e.g., human labor along assembly lines).	Hungary	2.1	2.69
	Czech Republic	2.66	3.91
	Slovakia	2.5	2.7
The use of robots replaces work that is harmful to the human body.	Hungary	2.39	4.22
	Slovakia	1.8	3.95
	Czech Republic	3.79	4.4

Source: own editing.

includes three statements about the impact of artificial intelligence and robotization, comparing Hungary, the Czech Republic, and Slovakia between 2019 and 2022. According to the first statement, trust in the application of artificial intelligence increased in all countries based on the examined samples, particularly in the Czech Republic. The acceptance of robotization also grew, with the largest increases observed in Hungary and the Czech Republic. The replacement of hazardous jobs with robots showed significant growth in all countries, especially in Hungary and the Czech Republic.

Table 11 includes three statements about the impact of artificial intelligence and robotization, comparing Hungary, the Czech Republic, and Slovakia between 2019 and 2022.

The analysis shows that organizational leaders and HR professionals’ perceptions of the positive impacts of robotization and artificial intelligence improved significantly in all three countries between 2019 and 2022. For instance, the acceptance of robotization replacing monotonous tasks and hazardous jobs increased notably, particularly in Hungary and the Czech Republic. These findings confirm our second hypothesis, as the examined samples consistently demonstrated a growing trust in and appreciation for these technologies.

The labor market impacts of artificial intelligence (AI) and robotization have received considerable attention in recent years, as these innovations fundamentally reshape work processes, enhance reliability, and create new opportunities to replace monotonous or hazardous tasks (Brynjolfsson & McAfee, 2014). Between 2019 and 2022, the acceptance of technology was analyzed along three key dimensions in Hungary, the Czech Republic, and Slovakia, each showing distinct development trajectories shaped by economic, technological, and cultural differences (OECD, 2022a). Based on the examined samples, the acceptance of AI applications increased in all three countries. In Hungary, the value rose from 2.32 to 2.68, indicating slow but steady growth in trust. However, implementing AI developments in Hungary often encounters barriers due to underdeveloped infrastructure and traditional corporate culture (Autor et al., 2020a). In contrast, in the Czech Republic, the average value was already higher in 2019 (3.02) and rose to 3.72 in 2022, demonstrating greater acceptance driven by technological maturity and practical applications of AI (European Commission, 2023). The Czech economy, which is particularly reliant on manufacturing and automation, has fostered a favorable environment for broader AI adoption (McKinsey & Company, 2023). In Slovakia, AI-related values were lower (rising from 2.66 to 2.94), reflecting limited industrial demand for AI and slower technological innovation (World Bank, 2023).

Replacing monotonous, labor-intensive processes with robotization showed varying trends across the three countries based on the examined samples. In Hungary, acceptance rose significantly (from 2.10 to 2.69), partly attributed to widespread automation experiences in the automotive industry (Frey & Osborne, 2017). However, concerns about job losses remain, particularly regarding workforce reductions (ILO, 2022). In the Czech Republic, acceptance increased from 2.66 to 3.91, indicating an open attitude toward technological innovations and efficiency gains associated with robotization (OECD, 2022a). Slovakia saw slower growth (from 2.50 to 2.70), reflecting lower industrial automation rates and limited adoption of robotization (European Commission, 2023). Replacing hazardous jobs with robots showed significant increases in all countries, highlighting the growing recognition of workplace health protection. In Hungary, the value rose from 2.39 to 4.22, emphasizing the increasing importance of health considerations, particularly in the aftermath of the pandemic (Brynjolfsson & McAfee, 2014). A similar increase was observed in Slovakia (from 1.80 to 3.95), reflecting growing support for replacing dangerous environments with robots (McKinsey & Company, 2023). In the Czech Republic, values increased from 3.79 to 4.40, attributed to more advanced local technological levels and workplace safety regulations (OECD, 2022a; ILO, 2022).

5. Discussion

5.1. Interpretation of the Empirical Findings

The present study examined how organizations in three Central European countries perceive the causes of labor shortages and the potential role of technological solutions in addressing them. The results presented in Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and Table 10 indicate that labor shortages remain strongly linked to structural labor market factors. Low wages, shortages of skilled labor and increasing competition among employers appear consistently among the most frequently mentioned drivers across occupational groups. These findings are consistent with earlier labor market research highlighting the importance of wage dynamics, skill mismatches and labor market institutions in shaping labor shortages (Cappelli, 2015; Brunello & Wruuck, 2021).

The results also suggest that the perceived importance of certain factors has changed between 2019 and 2022. The increasing relevance of work–life balance and working conditions indicates that employee expectations and preferences have become more influential in labor market dynamics. Similar trends have been observed in several studies examining the changing nature of work and employee attitudes in advanced economies (Autor, 2015). In this context, labor shortages are not solely the result of demographic developments or economic growth but also reflect shifts in organizational practices and employee expectations.

Another important dimension highlighted by the analysis concerns technological change. The descriptive results presented in Table 10 show that perceptions regarding artificial intelligence and robotization became more favorable between 2019 and 2022 across the surveyed organizations. This trend may reflect the growing acceptance of automation as a potential response to labor shortages and productivity challenges. Previous studies have demonstrated that automation technologies can influence labor demand and productivity, although their impact varies significantly across sectors and occupations (Acemoglu & Restrepo, 2020; Graetz & Michaels, 2018).

Overall, the empirical findings suggest that labor shortages in the examined Central European context emerge from a complex interaction of economic, technological and organizational factors. The results underline that technological solutions alone are unlikely to resolve labor shortages but may play a complementary role alongside labor market reforms, skill development and organizational adaptation.

5.2. Broader Implications and Contextual Considerations

Beyond the empirical findings, the results may have broader implications for labor market policy and organizational strategies. However, these implications should be interpreted cautiously, as the study is based on two cross-sectional surveys rather than a longitudinal panel and relies on a non-representative sample.

In many European economies, labor shortages have increasingly been addressed through a combination of workforce development policies, technological innovation and labor market participation measures. In this context, automation and robotization are often discussed as potential tools for mitigating labor shortages, particularly in sectors characterized by repetitive or physically demanding tasks (Acemoglu & Restrepo, 2020). Nevertheless, the empirical evidence suggests that technological adoption should be viewed primarily as a complement to human labor rather than a direct substitute.

The findings therefore contribute to the broader discussion on how organizations adapt to structural labor market constraints. In particular, the increasing importance of employee expectations, working conditions and technological acceptance suggests that successful responses to labor shortages require integrated organizational strategies that combine human resource management, technological innovation and institutional support.

At the same time, the positive perception of robotization and artificial intelligence does not necessarily exclude concerns about potential job displacement. While automation may improve efficiency and replace hazardous or monotonous tasks, it may also create uncertainty among employees regarding long-term job security. This dual perception highlights the importance of balancing technological adoption with workforce development and reskilling strategies.

In practical terms, organizations may address labor shortages through targeted reskilling programs, cooperation with vocational and higher education institutions, and the introduction of more flexible work arrangements that support work–life balance.

6. Conclusions

This study demonstrates that both the perceived causes of labor shortages and their relative importance changed significantly between 2019 and 2022. These shifts were evident across all occupational groups examined (highly skilled, administrative, and blue-collar), indicating that the dynamics of labor shortages extend beyond a single segment of the labor market and reflect a broader systemic transformation.

Beyond the empirical findings, the study contributes to the literature by providing a comparative analysis of labor shortages and technological adaptation in three Central European countries before and after the COVID-19 pandemic. By combining survey data from two time periods and examining differences across occupational groups, the research offers new insights into how labor market perceptions evolve in response to economic and technological change.

Future research could examine whether, in hard-to-fill positions, artificial intelligence and robotization primarily complement human labor by augmenting and reallocating tasks or can directly substitute certain types of work. It may also explore the effectiveness of different retention strategies used to mitigate labor shortages and support organizational adaptation to technological change.

7. Limitations and Potential Future

This research is subject to several limitations that should be acknowledged. First, the sampling method applied in the study relied on snowball and convenience sampling techniques; therefore, the sample cannot be considered statistically representative of all organizations operating in the examined countries. In addition, the voluntary nature of participation and the snowball sampling procedure may have introduced self-selection bias, as organizations that are more engaged with labor market challenges or HR practices may have been more likely to respond to the survey. Although efforts were made to involve a wide range of organizations from different sectors and institutional backgrounds, the possibility of selection bias cannot be entirely excluded.

Second, the data are based on self-reported responses provided by organizational leaders, HR professionals, or senior managers. While these respondents possess a strategic overview of workforce-related issues within their organizations, their answers may still reflect subjective perceptions rather than purely objective organizational conditions.

Third, the two surveys conducted in 2019 and 2022 represent cross-sectional data collections rather than a longitudinal panel study. The participating organizations were not necessarily identical in the two waves of the survey, which means that the observed differences between the two periods may partly reflect changes in the composition of the sample rather than purely temporal developments.

Fourth, although the dataset contains information about sectoral distribution and organizational characteristics, the comparative analyses presented in this study did not explicitly control for industry differences or company size effects. Consequently, some of the observed variations may also be influenced by structural differences in the composition of the sample between the two survey waves.

Despite these limitations, the study provides valuable insights into the labor market challenges faced by organizations in the Central European region. By comparing two survey waves conducted before and after the COVID-19 pandemic, the research offers a meaningful overview of how perceptions of labor shortages and technological solutions have evolved during a period of significant economic and social transformation.

Future research could build on these findings by applying more representative sampling methods, incorporating panel data that follow the same organizations over time, and examining sectoral and firm-size differences in greater detail. Such approaches would allow a deeper understanding of how structural labor market dynamics and technological change jointly shape workforce challenges in Central Europe.