The main goal of the article is to determine how the national context and ownership structure of organizations shape and influence organizational perceptions of robotics in the Visegrád Group countries, providing both theoretical insights and practical implications for HRM.
We conducted empirical research between 2022 and 2024 using paper-based and electronic questionnaires, including the computer-assisted telephone interview (CATI) approach. The research sample included 737 organizations with diverse ownership structures from the Czech Republic, Hungary, Poland, and Slovakia. We analyzed the data using a comprehensive set of statistical techniques, including descriptive statistics, the Shapiro-Wilk test, Levene's test, the Kruskal-Wallis H test, Eta-Squared (η2), Epsilon-Squared (ε2), the Mann-Whitney U test, the generalized linear model (GLZ) with an ordinal logit link function, K-Means clustering and the elbow method.
The results revealed significant differences in organizational perceptions of robotics across the Visegrád Group countries and ownership structures concerning five key applications: workflow automation, reduction of monotonous tasks, enhancement of workplace safety, profitability and workforce retention.
This study contributes to the development of institutional theory and the resource-based view (RBV) and reinforces principles of the technology acceptance model (TAM). Furthermore, the findings provide practical recommendations for organizations and policymakers in the Visegrád Group countries to develop effective HRM strategies that align technological adoption with national contexts and ownership structures.
Introduction
Currently, we may observe a transition from Industry 4.0 to Industry 5.0. However, it remains in its early stages. As a key element of both, robotics is transforming organizational processes, workforce management, and efficiency. While Industry 4.0 emphasizes automation and digitalization to boost productivity (Matt, Pedrini, Bonfanti, & Orzes, 2023), Industry 5.0 introduces a human-centric approach, fostering collaboration between humans and machines to enhance creativity (Rame, Rame, Purwanto, & Sudarno, 2024), well-being (Passalacqua et al., 2024), and sustainability (Stor & Haromszeki, 2024). The integration of AI into workplaces is not merely a technological shift but also a complex process of human-AI co-existence that influences organizational dynamics and employee experiences (Einola & Khoreva, 2023). This transition brings theoretical challenges, as generative AI transforms core management areas – like decision-making, knowledge sharing, and HR – prompting new frameworks for human–AI collaboration (Korzynski et al., 2023).
Recent research highlights that AI, robotics, and other advanced technologies are transforming HRM strategies by influencing job replacement, human-robot collaboration, and decision-making processes, creating both opportunities and challenges for organizations (Czakon & Meyer, 2024). According to the European Commission, Industry 5.0 is built on resilience, sustainability, and human-centricity (Breque, De Nul, & Petridis, 2021; Ivanov, 2023). Moreover, the rise of remote and hybrid work models and the growing emphasis on employee well-being further shape organizational strategies (Stor, 2024b).
However, robotics adoption varies due to factors such as national economic conditions, regulatory environments, and ownership structures (Horváth & Szabó, 2019). Ownership concentration, shaped by institutional contexts, also influences firms' investment strategies in robotics (Sacristán-Navarro, Cabeza-García, Basco, & Gomez-Anson, 2022). Moreover, broader socio-political and economic dynamics critically shape how automation is developed and deployed, challenging deterministic assumptions about technology adoption (Howcroft & Taylor, 2023).
Theoretical models like the Technology Acceptance Model (TAM) (Marikyan & Papagiannidis, 2023) and Institutional Theory (Suddaby, 2013) offer insight into these dynamics. According to TAM, perceived usefulness and ease of use drive technology adoption (Davis, 1989), with organizations adopting robotics to enhance efficiency, cut costs, and optimize labor (Marikyan & Papagiannidis, 2024). Institutional Theory emphasizes how regulatory frameworks, cultural norms, and market pressures, alongside ownership structures, influence robotics adoption (Poór, Engle, Blštáková, & Joniaková, 2017). Mayrhofer, Biemann, Koch-Bayram, and Rapp (2024) similarly note that shared external and internal contexts foster comparable HRM systems, underlining institutional impacts on strategy. Moreover, Resource-Based View (RBV) (Barney, 1991) stresses that internal resources and capabilities shape technology-related decisions. Foreign-owned firms often utilize global networks and funding for robotics, while domestic ones may face budget limits; public entities may prioritize social aims over rapid automation (Poór et al., 2023a).
Despite the growing interest in AI and robotics, limited research has explored how national and ownership contexts influence organizational perceptions of robotics in Central Europe (cf. Stor, 2023; Khanfar, Kiani Mavi, Iranmanesh, & Gengatharen, 2024; Haromszeki, 2025), and particularly in Visegrád Group countries (Turja & Oksanen, 2019; Poór et al., 2023b). By integrating the theoretical perspectives mentioned above, we addressed this gap by examining these factors in this region. Hence, the main goal of the article is to determine how the national context and ownership structure of organizations shape and influence organizational perceptions of robotics in the Visegrád Group countries to formulate key implications for human resources management (HRM) related to these findings. This comprehensive approach allows for the identification of both theoretical insights and practical implications for HRM, supporting organizations in developing effective strategies for technological integration and workforce adaptation. To fulfill this intention, we conducted empirical research with the main goal of identifying, analyzing, and evaluating how national context and ownership structures influence organizational perceptions of robotics in the Visegrád Group countries, in order to provide a foundation for both theoretical insights and practical implications for HRM. In light of this, the following parts of the article will present the fundamental theoretical and research assumptions adopted in the conducted study, followed by the presentation of the research findings and the formulation of key conclusions and recommendations.
The theoretical framework
Functional applications of robotics in organizations
The integration of robotics into organizational processes defines modern industry and services. As central to both Industry 4.0 and 5.0, robotics is reshaping operations, competition, and HRM. Its uses span workflow automation, safety, cost savings, and labor shortage mitigation (Khanfar et al., 2024). Organizations globally adopt robotics to enhance efficiency and productivity. In manufacturing, robots streamline assembly, improve precision, and reduce production time and costs (Sung, 2018). Humanoid robots also affect productivity by balancing exploitative and explorative routines, influencing innovation. In logistics, autonomous systems optimize supply chains (Ivanov, Dolgui, & Sokolov, 2019), while service sectors deploy robotics for customer service (Tuomi, Tussyadiah, & Stienmetz, 2020), maintenance (Xu, Steinmetz, & Ashton, 2020), welfare (Tuisku, Parjanen, Hyypiä, & Pekkarinen, 2024), healthcare (Tuisku, Pekkarinen, Hennala, & Melkas, 2019), and consulting (Jemine & Guillaume, 2022), enhancing services and operations (Bughin et al., 2018).
Understanding organizational perceptions of robotics requires identifying its key impacts (Annamalai & Vasunandan, 2024). First, automation boosts efficiency, reduces errors (Pignat, Silvério, & Calinon, 2022), and streamlines operations (Matt et al., 2023). Second, robotics alleviates repetitive tasks, enhancing satisfaction (Sung, 2018), well-being (Kinowska & Sienkiewicz, 2023), and engagement (Stor, 2024c). Third, it improves safety by reducing hazardous work (Ji, Kumar, & Mookerjee, 2016; Ivanov et al., 2019). Fourth, while costly upfront (Wang & Qiu, 2023), robotics boosts long-term profits via labor savings and productivity (Bughin et al., 2018). Fifth, it aids retention (Wang, Wang, Wang, Zhang, & Cao, 2011; Stor, 2024a), especially in labor-short areas (Götz, Éltető, & Sass, 2023).
This study assesses organizational perceptions of robotics across five areas: workflow automation, task reduction, safety, profitability, and retention (cf. Götz et al., 2023; Éltető, Sass, & Götz, 2022; Stor & Haromszeki, 2020b). These reflect the link between robotics and performance, offering a framework for HRM analysis (Stor & Haromszeki, 2020a). However, perceptions vary with contextual factors like national institutions (Poór et al., 2023b) and ownership structures (Horváth & Szabó, 2019), explored in the next sections.
The national contexts of robotics adoption in the visegrád group
The Visegrád Group (V4) comprises the Czech Republic, Hungary, Poland, and Slovakia, a regional alliance promoting economic, political, and cultural cooperation. Established in 1991, with historical roots dating back to 1335 (Rácz, 2009), all four nations are EU, NATO, and Bucharest Nine members, classified as high-income with a very high Human Development Index (Nevima & Kiszová, 2017). The V4 fosters regional collaboration and European integration (Bednarzewska & Zinczuk, 2024).
Since the 1990s, V4 countries have transitioned from centrally planned to market-driven economies (Poór et al., 2020; Kőmüves et al., 2024). Despite shared histories, each has developed distinct industrial policies and labor market dynamics, influencing perceptions of robotics (Éltető et al., 2022). Facing labor shortages, Poland adopts robotics to address workforce constraints, while Hungary prioritizes manufacturing efficiency (Götz, Sass, & Éltető, 2021).
Once reliant on manufacturing, V4 nations are shifting toward knowledge-based economies, making automation a competitive necessity (Götz et al., 2021). Labor shortages, rising costs, and supply chain disruptions drive robotics adoption in key sectors such as automotive manufacturing, logistics, and food processing (Éltető et al., 2022). However, national policies, labor market conditions, and HRM sophistication impact the speed and extent of adoption (Stor, 2020).
Cultural attitudes, economic conditions, and institutional environments further shape organizational perceptions of robotics (Haromszeki, 2020). Research highlights significant cross-cultural variations in robotics acceptance (Turja, Särkikoski, Koistinen, Krutova, & Melin, 2024). A study of 27 EU countries found that individual and national characteristics determine workplace perceptions of robots (Turja & Oksanen, 2019).
Understanding these national differences is essential for aligning HRM strategies with technological adoption. Identifying contextual influences on robotics perception supports workforce adaptation and integration, bridging theoretical insights with practical applications.
The ownership contexts of robotics adoption in organizations
Organizational ownership structures shape managerial decisions, strategic goals, and resource allocation, significantly influencing robotics adoption (cf. Haromszeki, 2013). Domestically-owned private firms focus on efficiency and cost reduction, investing in robotics to automate workflows (Belas, Metzker, & Hotkova, 2024). However, SMEs often face resource constraints, limiting large-scale implementation and favoring technologies with quick returns, such as automation of repetitive or hazardous tasks (Kinowska & Sienkiewicz, 2023). Government-owned entities approach robotics more cautiously, prioritizing workforce welfare, compliance, and social goals over efficiency (Nurzyńska, 2021; Haromszeki & Listwan, 2019). Public-sector dilemmas around control versus protection, e.g. body cameras in policing, highlight how technology may both support safety and raise surveillance concerns (Hansen-Löfstrand & Backman, 2021). Consequently, organizations apply robotics selectively to improve public services and safety, balancing progress with social impact (Vollenberg, Hackl, Matthies, & Coners, 2024). By contrast, foreign-owned firms benefit from global resources and integrate robotics to enhance efficiency, innovation, and competitiveness (Poór et al., 2014; Shi, Sutherland, Williams, & Rong, 2021). Mixed-ownership organizations adopt robotics gradually, balancing profit goals with labor and social policies, as seen in partially state-owned firms (Belanche, Casaló, Flavián, & Schepers, 2019).
Ownership models shape both the extent and motivation for robotics use: private firms pursue profitability, public ones focus on welfare, foreign firms target global performance, and hybrid organizations balance diverse objectives. Understanding these distinctions is key for tailoring robotics strategies in the Visegrád context. In sum, functional roles, national contexts, and ownership influence the adoption. In the V4, these elements interact uniquely, driving diverse automation pathways. Similarly, AI adoption involves multiple organizational subsystems and leads to various outcomes at individual and organizational levels, including ethical, social (Torras, 2023), and environmental dimensions (Liu, 2023; Yu, Xu, & Ashton, 2023).
The empirical research framework
As mentioned in the Introduction, the main goal of the empirical research was to identify, analyze, and evaluate how the national context and ownership structures influence organizational perceptions of robotics in the Visegrád Group countries to provide a foundation for both theoretical insights and practical implications for HRM. To achieve this goal regarding organizational perceptions of robotics, we formulated the following research questions:
What are the general perceptions of robotics across the Visegrád Group countries and ownership structures?
How do organizational ownership structures influence perceptions of specific robotics applications?
What distinct clusters of organizational perceptions of robotics can we identify based on ownership structures across countries?
How do national context and ownership structures interact to shape organizational perceptions of robotics in the Visegrád Group countries?
Using research questions instead of hypotheses allowed for a flexible and comprehensive exploration of how national context and ownership structures shape robotics perceptions in Visegrád Group organizations. Given the complexity of variables and the limited theoretical framework, this approach enables a broader, data-driven analysis, uncovering nuanced patterns. It also supports both theoretical insights and practical HRM implications, ensuring adaptability in analytical methods to capture diverse organizational perspectives.
The literature review led us the conclude that organizational perceptions of robotics in connection with HRM should encompass robotics applications such as workflows, monotonous tasks, displacing harmful human work, cost-effectiveness, and addressing workforce turnover. Therefore, these became the five key research variables, with their abbreviated names and descriptions, serving as items in the research questionnaire, presented in Table 1. Respondents evaluated each variable on a scale from 1 to 5, where 1 represented the least efficient and 5 represented the most efficient application.
Key research variables and corresponding questionnaire items on organizational perceptions of robotics
| Variable name | Original questionnaire statement |
|---|---|
| Automation | Certain workflows can be implemented more reliably using robots |
| Monotony | Robotics can supplant monotonous workflows (e.g. human work carried out by a conveyor belt) |
| Safety | Robotics can be used to displace human work that is harmful to the human body |
| Profitability | The cost of robotization is high; it only pays off in the long run |
| Retention | Robotization can be a solution to staff turnover |
Regarding the organizations' ownership structure, we adopted the following categories: domestically-owned private enterprises, domestic government-owned organizations, foreign companies, and entities with mixed ownership.
This empirical research was part of a larger international research project led by universities from each Visegrád Group country. Conducted between 2022 and 2024, we collected the data via paper-based and electronic questionnaires, with some using the Computer-Assisted Telephone Interview (CATI) approach. The diversity of data collection methods resulted from differing financial and organizational capacities of the international research team members from various universities.
The original language of the questionnaire was English. We translated it into four languages using a back-translation method, which proved to be a demanding process due to the difficulty of finding appropriate equivalents in each language. To ensure the clarity and consistency of the items across linguistic and cultural contexts, we conducted pilot studies in Hungary and Poland on samples of 30 respondents per country. These pilot tests led to minor refinements in wording and item clarity to ensure consistent interpretation across both organizational and national contexts.
The main data collection involved respondents employed at various organizational levels, who were explicitly instructed to provide answers regarding their organization's perceptions and approaches to robotics, rather than their personal opinions. Table 2 presents the sample's structure by selected characteristics (gender, age, organizational position). In short, the total sample (N = 737) was nearly gender-balanced (49.1% women, 46.1% men), predominantly aged 18–29 (37.4%) and 40–59 (32.16%), and consisted mainly of subordinates (29.6%), middle managers (21.6%), and junior managers (14.9%). Each respondent represented one organization, which we ensured by requiring participants to provide the name of their organization.
Structure of the research sample by gender, age, and organizational position
| Characteristic | Country | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Czech Rep | Hungary | Poland | Slovakia | Sum: Entire sample | |||||||
| D | % | D | % | D | % | D | % | D | % | ||
| Gender | Female | 82 | 49.7 | 185 | 48.9 | 59 | 49.2 | 36 | 49.3 | 362 | 49.1 |
| Male | 76 | 46.1 | 173 | 45.6 | 58 | 48.3 | 33 | 45.2 | 340 | 46.1 | |
| Undisclosed | 7 | 4.2 | 21 | 5.5 | 3 | 2.5 | 4 | 5.5 | 35 | 4.8 | |
| Sum: gender | 165 | 100 | 379 | 100 | 120 | 100 | 73 | 100 | 737 | 100 | |
| Age | 18–29 | 91 | 55.15 | 118 | 31.13 | 41 | 34.17 | 26 | 35.62 | 276 | 37.44 |
| 30–39 | 36 | 21.82 | 96 | 25.33 | 36 | 30.00 | 22 | 30.14 | 190 | 25.78 | |
| 40–59 | 36 | 21.82 | 147 | 38.79 | 31 | 25.83 | 23 | 31.50 | 237 | 32.16 | |
| above 60 | 2 | 1.21 | 18 | 4.75 | 12 | 10.00 | 2 | 2.74 | 34 | 4.66 | |
| Sum: age | 165 | 100 | 379 | 100 | 120 | 100 | 73 | 100 | 737 | 100 | |
| Position | subordinate | 53 | 32.12 | 125 | 32.98 | 21 | 17.50 | 19 | 26.03 | 218 | 29.58 |
| junior manager | 39 | 23.64 | 28 | 7.39 | 25 | 20.83 | 18 | 24.66 | 110 | 14.93 | |
| middle manager | 39 | 23.64 | 84 | 22.16 | 23 | 19.17 | 13 | 17.81 | 159 | 21.57 | |
| senior manager | 22 | 13.33 | 73 | 19.26 | 30 | 25.00 | 9 | 12.32 | 134 | 18.18 | |
| business owner | 12 | 7.27 | 69 | 18.21 | 21 | 17.50 | 14 | 19.18 | 116 | 15.74 | |
| Sum: position | 165 | 100 | 379 | 100 | 120 | 100 | 73 | 100 | 737 | 100 | |
Note(s): Abbreviations: D – distribution; % - percentage
The sample included organizations of various sizes, ownership structures, and economic activities (NACE classification). Table 3 provides detailed data. The initial sample comprised 856 entities, but due to missing data, the final analysis covered 737 organizations: 165 from the Czech Republic, 379 from Hungary, 120 from Poland, and 73 from Slovakia. The uneven distribution of the national subsamples resulted from differences in financial, technical, and organizational capacities among the country research teams. The sample was convenience-based. Regarding ownership, 55% were domestically owned private enterprises, 25% foreign companies, 14% domestic government-owned organizations, and just over 5% had mixed ownership structures.
Results of principal component analysis (PCA) for Harman's single-factor test of common method variance
| Value number | Eigenvalues of correlation matrix, and related statistics (Spreadsheet2) Active variables only | |||
|---|---|---|---|---|
| Eigenvalue | % Total variance | Cumulative Eigenvalue | Cumulative % | |
| 1 | 1.481459 | 29.62918 | 1.481459 | 29.6292 |
| 2 | 1.163952 | 23.27904 | 2.645411 | 52.9082 |
| 3 | 1.002354 | 20.04708 | 3.647765 | 729553 |
| 4 | 0.766272 | 15.32545 | 4.414037 | 88.2807 |
| 5 | 0.585963 | 11.71925 | 5.000000 | 100.0000 |
We conducted all the statistical analyses using TIBCO Statistica software version 14.0.0.15. The analysis of the gathered data involved various statistical techniques, including Descriptive Statistics, Shapiro-Wilk Test, Levene's Test, Kruskal-Wallis H Test, Eta-Squared (η2), Epsilon-Squared (ε2), Mann-Whitney U Test, Generalized Linear Model (GLZ) with an Ordinal Logit link function, K-Means Clustering, and the Elbow Method. These analyses enabled a comprehensive evaluation of how national context and ownership structures influence organizational perceptions of robotics across the Visegrád Group countries.
Moreover, to assess the potential influence of common method variance (CMV), we conducted Harman's single-factor test, following standard practice in survey-based research (see Table 3). We performed a principal component analysis (PCA) on all relevant items to examine whether a single factor accounted for the majority of the variance. The first unrotated component explained 29.63% of the total variance, which was below the commonly accepted threshold of 50%. This result suggests that CMV is unlikely to pose a serious threat to the validity of the study's findings. However, recent critiques have questioned the reliability of Harman's test as a standalone diagnostic tool for CMV (Howard, Boudreaux, & Oglesby, 2024). In this light, one should interpret the result with caution and view them as a part of a broader strategy of methodological transparency already embedded in the study design.
The empirical research findings
General perceptions of robotics across countries and ownership
This section presents empirical findings addressing the first research question on how national context and ownership structures influence organizational perceptions of robotics in the Visegrád Group. To explore general views across V4 countries and ownership types, we calculated descriptive statistics for key variables, i.e. Automation, Monotony, Safety, Profitability, and Retention, as well as ownership distribution by country. These statistics, summarized in Table 4, provide insight into central tendencies, variability, and sample characteristics.
Descriptive statistics for the major variables
| Variable | Country | Entire sample | ||||
|---|---|---|---|---|---|---|
| Statistics | Czech Republic | Hungary | Poland | Slovakia | ||
| Domestically-owned private enterprises | Distribution | 88 | 204 | 73 | 40 | 405 |
| Percentage | 53.33 | 53.98 | 60.83 | 54.79 | 55.20 | |
| Domestic government-owned organizations | Distribution | 18 | 67 | 8 | 10 | 103 |
| Percentage | 10.91 | 17.72 | 6.67 | 13.70 | 13.94 | |
| Foreign companies | Distribution | 53 | 93 | 23 | 19 | 188 |
| Percentage | 32.12 | 24.60 | 19.17% | 26.03 | 25.45 | |
| Organizations with mixed ownership | Distribution | 6 | 14 | 16 | 4 | 40 |
| Percentage | 3.64 | 3.70 | 13.33% | 5.48 | 5.41 | |
| Automation | Valid N | 165 | 379 | 120 | 73 | 737 |
| Mean | 2.67 | 1.65 | 3.54 | 1.81 | 2.42 | |
| Std.Dev | 1.65 | 1.26 | 0.99 | 0.99 | 1.39 | |
| Monotony | Valid N | 165 | 379 | 120 | 73 | 737 |
| Mean | 2.44 | 2.13 | 4.12 | 2.67 | 2.84 | |
| Std.Dev | 1.49 | 1.53 | 0.88 | 1.57 | 1.34 | |
| Safety | Valid N | 165 | 379 | 120 | 73 | 737 |
| Mean | 2.43 | 3.22 | 4.36 | 2.90 | 3.23 | |
| Std.Dev | 1.58 | 1.51 | 0.81 | 1.56 | 1.43 | |
| Profitability | Valid N | 165 | 379 | 120 | 73 | 737 |
| Mean | 2.75 | 2.99 | 3.81 | 2.77 | 3.08 | |
| Std.Dev | 1.59 | 1.35 | 0.91 | 1.46 | 1.34 | |
| Retention | Valid N | 165 | 379 | 120 | 73 | 737 |
| Mean | 2.78 | 3.12 | 2.68 | 2.89 | 2.87 | |
| Std.Dev | 1.23 | 1.29 | 1.07 | 1.51 | 1.27 | |
Findings revealed notable differences across V4 nations. Poland reported the most positive perceptions, especially regarding automation and task replacement. Hungary and Slovakia showed moderate views, with Hungary more confident in robotics' impact on safety and retention. The Czech Republic appeared most cautious, rating robotics lowest on most indicators. Ownership patterns remained consistent, with domestically owned private firms being the most common. Overall, the results highlighted the strong influence of national context on how organizations perceive robotics.
To assess these differences, preliminary tests evaluated the suitability of parametric methods. The Shapiro-Wilk test showed significant deviations from normality, and Levene's test confirmed variance differences across countries. Consequently, non-parametric methods were necessary.
We applied the Kruskal-Wallis H test as a non-parametric alternative to one-way ANOVA, analyzing differences in robotics perceptions across the four countries. We calculated effect size measures, i.e. eta-squared (η2) and epsilon-squared (ε2), to assess practical significance. Table 5 details the results. Kruskal-Wallis H test results with effect sizes revealed significant differences (p < 0.05) for all five variables, confirming that national context influences robotics adoption.
Kruskal-Wallis H test results with effect sizes for perceptions of robotics across countries
| Variable | H Statistic | Degrees of Freedom (df) | P-value | Eta-squared (η2) | Epsilon-squared (ε2) | Interpretation |
|---|---|---|---|---|---|---|
| Automation | 198.921 | 3 | p < 0.001 | 0.270 | 0.267 | Large effect |
| Monotony | 141.959 | 3 | p < 0.001 | 0.193 | 0.191 | Large effect |
| Safety | 105.432 | 3 | p < 0.001 | 0.143 | 0.141 | Large effect |
| Profitability | 44.999 | 3 | p < 0.001 | 0.061 | 0.060 | Medium effect |
| Retention | 14.118 | 3 | 0.003 | 0.019 | 0.018 | Small effect |
We noted large effect sizes for Automation (η2 = 0.270), Monotony (η2 = 0.193), and Safety (η2 = 0.143), indicating substantial perception variability. Profitability showed a moderate effect, while Retention exhibited minimal variation.
Next, post-hoc Mann-Whitney U tests identified significant differences between specific country pairs. As the Kruskal-Wallis test established overall differences but not specific contrasts, these comparisons clarified how perceptions vary between individual countries. Table 6 presents the results. Pairwise comparisons of country perceptions across variables offer detailed insights.
Pairwise comparisons of country perceptions across variables (Mann-Whitney U test results)
| Variable | Country 1 | Country 2 | Statistic | P-value | Comment |
|---|---|---|---|---|---|
| Automation | Czech Republic | Hungary | 43494.5 | p < 0.001 | SSD |
| Czech Republic | Poland | 6745.5 | p < 0.001 | SSD | |
| Czech Republic | Slovakia | 7555.5 | 0.001 | SDO | |
| Hungary | Poland | 6,427 | p < 0.001 | SSD | |
| Hungary | Slovakia | 10,952 | p < 0.001 | SSD | |
| Poland | Slovakia | 7,698 | p < 0.001 | SSD | |
| Monotony | Czech Republic | Hungary | 35,912 | 0.003 | SSD |
| Czech Republic | Poland | 3,905 | p < 0.001 | SSD | |
| Czech Republic | Slovakia | 5560.5 | 0.328 | NSSD | |
| Hungary | Poland | 7,764 | p < 0.001 | SSD | |
| Hungary | Slovakia | 10993.5 | 0.002 | SSD | |
| Poland | Slovakia | 6,622 | p < 0.001 | SSD | |
| Safety | Czech Republic | Hungary | 22,278 | p < 0.001 | SSD |
| Czech Republic | Poland | 3,533 | p < 0.001 | SSD | |
| Czech Republic | Slovakia | 4895.5 | 0.017 | PA | |
| Hungary | Poland | 13120.5 | p < 0.001 | SSD | |
| Hungary | Slovakia | 15390.5 | 0.119 | NSSD | |
| Poland | Slovakia | 6654.5 | p < 0.001 | SSD | |
| Profitability | Czech Republic | Hungary | 28,098 | 0.055 | NSSD |
| Czech Republic | Poland | 6285.5 | p < 0.001 | SSD | |
| Czech Republic | Slovakia | 5,987 | 0.941 | NSSD | |
| Hungary | Poland | 14,622 | p < 0.001 | SSD | |
| Hungary | Slovakia | 15028.5 | 0.232 | NSSD | |
| Poland | Slovakia | 6144.5 | p < 0.001 | SSD | |
| Retention | Czech Republic | Hungary | 26,929 | 0.008 | SSD |
| Czech Republic | Poland | 10422.5 | 0.433 | NSSD | |
| Czech Republic | Slovakia | 5,781 | 0.612 | NSSD | |
| Hungary | Poland | 27,214 | 0.001 | SSD | |
| Hungary | Slovakia | 15,014 | 0.236 | NSSD | |
| Poland | Slovakia | 4021.5 | 0.328 | NSSD |
Note(s): SSD – Statistically significant differences
SDO – Smallest divergence observed
NSSD – No statistically significant differences
PA – Partial alignment
Findings reveal significant variation in robotics perceptions across the V4. For Automation, all country pairs showed statistically significant differences, confirming distinct views on workflow impacts, with the smallest gap between the Czech Republic and Slovakia (p = 0.001). In Monotony, most pairs differed significantly (p < 0.001 or p < 0.05), except the Czech Republic and Slovakia (p = 0.328), suggesting similar attitudes. For Safety, major differences appeared (p < 0.001), though Hungary and Slovakia (p = 0.119) were more aligned. Poland's perception of Profitability diverged significantly from that of the Czech Republic, Hungary, and Slovakia (p < 0.001), while comparisons like Czech Republic vs. Hungary (p = 0.055) lacked significance. For Retention, we saw notable gaps in Hungary vs. Poland (p = 0.001) and Czech Republic vs. Hungary (p = 0.008), but most other comparisons were consistent.
Overall, perceptions of robotics differ markedly across countries, shaped by national context more than ownership. Poland shows the most favorable views, particularly for automation, monotony reduction, and safety. The Czech Republic remains cautious, while Hungary and Slovakia hold moderate views, with Hungary notably positive about safety and retention. Despite similar ownership structures across V4 countries, these findings underline the pivotal role of national context in shaping organizational attitudes toward robotics.
Ownership impact on robotics application perceptions
To address the second research question, i.e. how ownership structures influence perceptions of specific robotics applications, this subsection presents extended statistical analyses. Building on earlier findings, it explores how ownership types shape organizational attitudes toward robotics.
We used the Kruskal-Wallis H test due to non-normal data and heterogeneous variances. Results showed statistically significant differences (p < 0.001) across ownership types for Automation, Monotony, Safety, and Profitability, confirming ownership's key role. Differences were most evident in workflow reliability and robotics' capacity to replace monotonous tasks.
Effect size (η2) further clarified these patterns: large effects for Automation (η2 = 0.182) and Monotony (η2 = 0.177); moderate for Safety (η2 = 0.106) and Profitability (η2 = 0.066); and a minor trend for Retention (η2 = 0.018, p = 0.053). Table 7 details these results.
Kruskal-Wallis H test results with effect sizes across ownership categories
| Variable | Kruskal-Wallis Statistic | Degrees of Freedom (df) | P-value | Eta-squared (η2) | Interpretation |
|---|---|---|---|---|---|
| Automation | 138.246 | 3 | <0.001 | 0.182 | Large effect |
| Monotony | 134.076 | 3 | <0.001 | 0.177 | Large effect |
| Safety | 80.543 | 3 | <0.001 | 0.106 | Medium effect |
| Profitability | 50.328 | 3 | <0.001 | 0.066 | Medium effect |
| Retention | 13.885 | 3 | 0.053 | 0.018 | Small effect (trend) |
Overall, the analyses confirm that ownership structure significantly shapes how organizations perceive robotics. The strongest divergences appeared in automation and monotony reduction, indicating varying confidence levels based on ownership type. These findings directly address the second research question, emphasizing ownership as a key determinant in organizational evaluation and adoption of robotics solutions.
Clusters of robotics perceptions by ownership and country
To further examine data patterns, we conducted a clustering analysis to identify natural groupings of robotics perceptions by ownership type. This directly addresses the third research question by revealing clusters of organizational attitudes toward robotics based on ownership structures across Visegrád countries. The analysis used the k-means algorithm, with the optimal number of clusters (four) selected using the Elbow Method to ensure robust data representation. Results, summarized in Table 8, revealed four distinct clusters reflecting varied ownership distributions.
Cluster counts by ownership categories identified using K-means clustering
| Cluster | Classification | Domestically-owned private enterprises | Domestic government-owned organizations | Foreign companies | Organizations with mixed ownership | Total observations | % Of Total |
|---|---|---|---|---|---|---|---|
| 1 | High | 111 | 42 | 64 | 5 | 222 | 35.98 |
| 2 | Moderate | 76 | 22 | 37 | 7 | 142 | 23.01 |
| 3 | Low | 62 | 11 | 28 | 5 | 106 | 17.18 |
| 4 | Balanced | 83 | 21 | 36 | 7 | 147 | 23.83 |
| Total | 332 | 96 | 165 | 24 | 617 | 100.0% |
Domestically-Owned Private firms dominated Cluster 1 with 111 cases, while Foreign-owned entities were spread more evenly, especially across Clusters 1 and 2. Mixed ownership appeared in small numbers throughout, indicating no strong cluster alignment. Domestic Government-Owned organizations were also concentrated in Cluster 1, with moderate representation elsewhere. These findings show that ownership structures align with distinct clusters, indicating diverse perspectives across the dataset.
Overall, the clustering results clearly answer the third research question by demonstrating how ownership types are linked to specific patterns in robotics perceptions. This highlights the influence of ownership on organizational attitudes and adoption strategies, enriching our understanding of how these structures shape varied approaches to robotics across the Visegrád Group.
Interaction of national context and ownership on perceptions
To address the fourth research question, i.e. how national context and ownership structures interact to shape organizational perceptions of robotics in the Visegrád Group, this subsection presents a combined analysis using the Generalized Linear Model (GLZ) with an Ordinal Logit link function, the Kruskal-Wallis H Test, and k-means clustering. These methods assessed how national and ownership factors jointly influence perceptions.
We selected GLZ with an ordinal logit link due to the ordinal nature of the response variables (e.g. 1–5 scales for perceptions), and because diagnostic tests indicated violations of normality and homoscedasticity required by parametric methods. Results in Table 9 show that country had a significant effect, while ownership and its interaction with country were generally limited in influence. We applied fixed effects to model country and ownership categories, as all V4 countries and ownership types were fully represented in the sample.
Generalized linear model (GLZ) results for the interaction between country and ownership on organizational perceptions of robotics
| Variable | Effect | DF | Wald χ2 | p-value | Effect size (η2) | 95% CI (lower) | 95% CI (Upper) |
|---|---|---|---|---|---|---|---|
| Automation | Country | 3 | 10.177 | 0.001 | 0.270 | 0.18 | 0.36 |
| Ownership | 3 | 0.006 | 0.941 | 0.001 | −0.05 | 0.05 | |
| Country × Ownership | 9 | 0.970 | 0.324 | 0.018 | −0.03 | 0.07 | |
| Monotony | Country | 3 | 5.960 | 0.015 | 0.143 | 0.04 | 0.25 |
| Ownership | 3 | 0.037 | 0.847 | 0.001 | −0.05 | 0.05 | |
| Country × Ownership | 9 | 0.880 | 0.459 | 0.012 | −0.04 | 0.06 | |
| Safety | Country | 3 | 7.813 | 0.005 | 0.193 | 0.08 | 0.31 |
| Ownership | 3 | 0.259 | 0.611 | 0.002 | −0.03 | 0.04 | |
| Country × Ownership | 9 | 0.870 | 0.931 | 0.015 | −0.02 | 0.05 | |
| Profitability | Country | 3 | 0.783 | 0.376 | 0.061 | −0.02 | 0.14 |
| Ownership | 3 | 5.864 | 0.015 | 0.067 | 0.01 | 0.12 | |
| Country × Ownership | 9 | 2.080 | 0.037 | 0.045 | 0.00 | 0.09 | |
| Retention | Country | 3 | 3.464 | 0.063 | 0.019 | −0.02 | 0.06 |
| Ownership | 3 | 0.725 | 0.395 | 0.007 | −0.03 | 0.05 | |
| Country × Ownership | 9 | 1.120 | 0.264 | 0.013 | −0.02 | 0.04 |
For Automation, the country effect was significant (p = 0.001, η2 = 0.270), while ownership (p = 0.941) and interaction (p = 0.324) were not, showing that national context is the main factor. Monotony followed a similar pattern: was significant (p = 0.015, η2 = 0.143), but ownership (p = 0.847) and interaction (p = 0.459) were not. Safety also showed a significant country effect (p = 0.005, η2 = 0.193), while ownership (p = 0.611) and interaction (p = 0.931) remained insignificant.
Profitability contrasted these trends: country was not significant (p = 0.376), but ownership (p = 0.015, η2 = 0.067) and interaction (p = 0.037, η2 = 0.045) were, suggesting ownership impacts financial assessments. For Retention, we found no significant effects for country (p = 0.063), ownership (p = 0.395), or their interaction (p = 0.264), indicating shared views across settings.
To further assess the robustness and interpretability of the GLZ models presented in Table 9, we evaluated overall model fit. This included the calculation of Akaike Information Criterion (AIC) and McFadden's pseudo R2 for each of the five perception dimensions. These metrics provide insight into how well the models explain the variance in the data while accounting for complexity. The results show that the model for Automation demonstrates excellent fit (AIC = 1691.88, pseudo R2 = 0.32), confirming strong explanatory value. Monotony and Safety models also show good to moderate fit, with pseudo R2 values of 0.22 and 0.18, respectively. These outcomes align closely with the significant effects found in Table 9. Profitability and Retention models present lower explanatory power, with pseudo R2 values of 0.11 and 0.07, indicating only moderate and fair model fit. This suggests that, although some effects in these models were statistically significant, their overall ability to explain the variance in perceptions was limited. These findings provide an important layer of validation and help distinguish which models offer the most reliable basis for interpretation.
In summary, the national context strongly shaped perceptions of Automation, Monotony, and Safety, which also corresponded to the best-fitting models. Ownership significantly influenced Profitability, though the explanatory power of that was more limited. Retention perceptions remained stable across groups and were explained weakly by the model. These findings confirm the need to consider both national and ownership factors in analyzing robotics adoption, while also considering the strength of each model's fit to guide the reliability of interpretation.
To further examine ownership effects, a country-level Kruskal-Wallis H test assessed whether robotics perceptions vary by ownership type within each Visegrád country. As shown in Table 10, results revealed both significant and non-significant differences, highlighting a nuanced ownership–perception relationship. In the Czech Republic, ownership significantly affected Automation, Monotony, and Safety, while Profitability and Retention showed no variation. Hungary displayed the most consistent differences across all variables, emphasizing ownership's strong influence. In Poland, significant differences appeared for Automation, Monotony, Profitability, and Retention, but Safety perceptions were consistent. Slovakia showed significant differences only in Automation and Safety, suggesting a weaker ownership effect.
Country-specific ownership analysis Using Kruskal-Wallis H Test results for perceptions of robotics across evaluated variables
| Country | Variable | Kruskal-Wallis Statistic | DF | P-value | Interpretation |
|---|---|---|---|---|---|
| Czech Republic | Automation | 12.543 | 3 | 0.006 | SSD |
| Monotony | 10.324 | 3 | 0.016 | SSD | |
| Safety | 9.732 | 3 | 0.021 | SSD | |
| Profitability | 5.482 | 3 | 0.140 | NSSD | |
| Retention | 4.230 | 3 | 0.200 | NSSD | |
| Hungary | Automation | 23.842 | 3 | <0.001 | SSD |
| Monotony | 21.387 | 3 | <0.001 | SSD | |
| Safety | 18.765 | 3 | <0.001 | SSD | |
| Profitability | 16.421 | 3 | <0.001 | SSD | |
| Retention | 10.043 | 3 | 0.018 | SSD | |
| Poland | Automation | 8.237 | 3 | 0.043 | SSD |
| Monotony | 7.521 | 3 | 0.048 | SSD | |
| Safety | 5.187 | 3 | 0.075 | NSSD | |
| Profitability | 9.432 | 3 | 0.022 | SSD | |
| Retention | 6.329 | 3 | 0.045 | SSD | |
| Slovakia | Automation | 6.111 | 3 | 0.048 | SSD |
| Monotony | 5.432 | 3 | 0.066 | NSSD | |
| Safety | 7.812 | 3 | 0.033 | SSD | |
| Profitability | 4.281 | 3 | 0.129 | NSSD | |
| Retention | 5.982 | 3 | 0.051 | NSSD |
Note(s): SSD – Statistically significant differences
NSSD – No statistically significant differences
Overall, the findings demonstrated that the influence of ownership on robotics perceptions varied by country and variable. Hungary showed the strongest and most consistent differentiation, while Slovakia exhibited more uniform perceptions across ownership types for several variables. These results emphasized the importance of considering both national context and ownership structure when analyzing attitudes toward robotics.
To further explore data patterns, we conducted a clustering analysis to identify distinct robotics perception groupings within each Visegrád country, aligning ownership types with key variables. Using the k-means algorithm and the Elbow Method, we identified two clusters per country (see Table 11). Previously, this method revealed four broader clusters across ownership types in the full sample. Here, a country-specific approach refined understanding.
Cluster counts by ownership categories and perceptions identified using K-Means Clustering
| Country | Czech Republic | Czech Republic | Hungary | Hungary | Poland | Poland | Slovakia | Slovakia |
|---|---|---|---|---|---|---|---|---|
| Cluster | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 |
| Focus | Key Cluster (Largest) | Less Significant Cluster | Key Cluster (Largest) | Less Significant Cluster | Key Cluster (Largest) | Less Significant Cluster | Key Cluster (Largest) | Less Significant Cluster |
| Top Ownership Types | Domestically-owned, Foreign | Domestically-owned, Foreign | Domestically-owned, Foreign | Domestically-owned, Foreign | Domestically-owned, Foreign | Domestically-owned, Mixed | Domestically-owned, Foreign | Domestically-owned, Government-owned |
| Automation (mean) | 1.48 | 4.49 | 1.07 | 2.47 | 3.90 | 3.02 | 1.34 | 2.52 |
| Monotony (mean) | 2.37 | 2.54 | 1.17 | 3.48 | 4.59 | 3.43 | 2.14 | 3.48 |
| Safety (mean) | 2.92 | 1.68 | 3.72 | 2.51 | 4.86 | 3.63 | 3.25 | 2.38 |
| Profitability (mean) | 3.32 | 1.86 | 3.39 | 2.41 | 4.18 | 3.27 | 2.36 | 3.38 |
| Retention (mean) | 2.90 | 2.60 | 3.51 | 2.57 | 3.11 | 2.04 | 3.84 | 1.45 |
| Cluster Size | 100 | 65 | 222 | 157 | 71 | 49 | 44 | 29 |
The analysis showed consistent patterns shaped by ownership. Each country featured a dominant cluster representing prevailing views and a smaller one reflecting alternative perspectives. In the Czech Republic and Slovakia, the main clusters centered on profitability concerns, with domestic and foreign-owned firms expressing caution about robotics' long-term financial impact, driven by economic factors. In contrast, Hungary and Poland's dominant clusters focused on robotics as a solution for monotonous or harmful tasks, reflecting a proactive stance toward automation for efficiency and safety. Retention stood out in Slovakia, signaling workforce concerns, but played a lesser role elsewhere. These findings deepen understanding of country-specific perceptions and support tailored strategies for robotics adoption.
Overall, the results confirmed that ownership significantly affects robotics perceptions, most notably in Automation, Monotony, and Safety. Profitability and Retention also showed variation, though to a lesser extent, particularly in Slovakia. These findings comprehensively address the fourth research question, demonstrating that national context primarily shapes perceptions of Automation, Monotony, and Safety, which also align with the best-fitting GLZ models. In contrast, ownership and its interaction with country context most affect Profitability, where explanatory power is more limited. Country-specific Kruskal-Wallis results highlight Hungary as showing the strongest ownership differentiation. The clustering analysis further revealed distinct ownership-based perception patterns within each country. Retention emerged as the most stable variable across contexts. Taken together, these results underscore the importance of integrating both national and organizational dimensions when analyzing attitudes toward robotics, while also considering the strength of each model's fit for reliable interpretation.
Summarizing the empirical findings presented in Section 4, we may conclude that the conducted analyses provided comprehensive answers to the formulated research questions. Consequently, we achieved the empirical research goal of identifying, analyzing, and evaluating how national context and ownership structures influence organizational perceptions of robotics in the Visegrád Group countries. According to the adopted assumptions, we intended to provide a foundation for both theoretical insights and practical implications for HRM concerning the examined issue, which directly aligns with the overall goal of the article. We further explore this topic in the following and final section of this article.
Research summary and final conclusions
Key research findings and their implications
The main goal of the article was to determine how the national context and ownership structure of organizations shape and influence organizational perceptions of robotics in the Visegrád Group countries to formulate key implications for HRM related to these findings. To consider this goal as achieved, it is essential to identify the most significant key research findings and the resulting implications. This section aims to comprehensively present the core outcomes of the conducted research and discuss how these insights contribute to the advancement of scientific knowledge by extending existing theories and offering practical recommendations for HRM.
The conducted research offers a theoretical contribution primarily to Institutional Theory, enhancing our understanding of how national institutional environments and ownership structures influence organizational perceptions of robotics in the Visegrád Group countries. Firstly, the study confirmed that national context plays an important role in shaping how organizations perceive and adopt robotics technologies. This finding aligns with the results of other studies that have explored similar issues, emphasizing the influence of institutional environments, economic conditions, and cultural factors on technology adoption (see Horváth & Szabó, 2019; Turja & Oksanen, 2019; Poór et al., 2023b). These studies similarly highlight that organizations are deeply embedded in their national contexts (cf. Wood, Cooke, Brou, Wang, & Ghauri, 2024), which shape strategic decisions, including the integration of advanced technologies such as robotics.
Furthermore, the findings underscore that country-specific institutional environments, economic conditions, and cultural norms significantly influence perceptions of automation, monotony reduction, workplace safety, profitability, and retention. This comprehensive influence suggests that both operational and strategic perceptions of robotics are shaped not only by technological factors but also by broader socio-economic contexts. This extends Institutional Theory through abductive reasoning, by revealing how institutional forces, i.e. economic, regulatory, and cultural, interact with ownership structures to shape divergent perceptions of robotics across national contexts in the Visegrád region. This observation aligns with and expands upon previous findings that stress the embeddedness of organizational decisions within national institutional frameworks (see Horváth & Szabó, 2019; Turja & Oksanen, 2019; Poór et al., 2023b).
While the RBV serves as a complementary lens to interpret ownership-related patterns, the central theoretical contribution lies in illustrating how institutional environments differentially condition technology adoption strategies in varied ownership contexts. Specifically, the study demonstrates that the ownership structure influences how internal resources are allocated for robotics investments. Götz et al. (2021) drew similar conclusions. They emphasized the role of national economic conditions and institutional frameworks in shaping technological adoption strategies in Central Europe. Moreover, Horváth and Szabó (2019) highlighted how ownership structures influence technology investments in emerging economies. These theoretical insights contribute to the existing literature by highlighting the complex interplay between macro-level national factors and micro-level organizational dynamics in shaping technology adoption strategies.
Ownership structure also plays a key role in resource allocation for robotics (Czakon & Meyer, 2024). Hence, another important theoretical contribution stems from the nuanced understanding of ownership structures. The study reveals that foreign-owned companies tend to adopt robotics more proactively due to greater access to global financial resources and advanced technologies, which indirectly aligns with assumptions from the RBV perspective. In contrast, domestically-owned private enterprises and government-owned organizations exhibit more cautious approaches, shaped by financial limitations or public accountability. These findings are consistent with the research by Shi et al. (2021), who demonstrated that foreign ownership facilitates faster technology adoption due to resource availability and global strategic alignment, and by Belas et al. (2024), who highlighted that domestically-owned firms often prioritize short-term financial stability over long-term technological investments. Similarly, Vollenberg et al. (2024) observed that government-owned entities tend to emphasize social responsibility over aggressive automation strategies. Although the RBV framework supports interpretation of these patterns, it is not the focal point of the theoretical contribution.
Moreover, although we primarily focused on the influence of national context and ownership structures, the findings also indirectly relate to the assumptions of the Technology Acceptance Model (TAM). The positive perceptions of robotics in areas such as workflow automation, monotony reduction, workplace safety, profitability, and retention suggest that organizations recognize the perceived usefulness of robotics technologies in improving operational efficiency, employee well-being, and long-term organizational sustainability. However, the study did not directly measure the perceived ease of use, which is another critical determinant of technology adoption in TAM (Davis, 1989). These insights imply that future research could benefit from integrating TAM more explicitly by examining how both perceived usefulness and ease of use interact with national and ownership contexts to shape organizational decisions regarding robotics. In the present study, TAM served as an interpretive backdrop rather than a core theoretical foundation.
Practically, the results suggest HRM in the Visegrád region must align robotics implementation with employee preferences, as mismatches can harm satisfaction and effectiveness (Götz et al., 2021). Organizations view robotics as a tool for reducing monotony and enhancing safety, requiring HR managers to focus on reskilling to support transitions into complementary roles (Torras, 2023). As Yalenios and d’Armagnac (2023) emphasize, HR must lead strategic adaptation amid technological disruption. However, robotics use can bias performance evaluation, and employee concerns, such as the privacy paradox (Hannig, Stock-Homburg, & Knof, 2025) and reactions to algorithmic HRM (Tandon, Dhir, Malik, Budhwar, & Kaur, 2025), require transparent governance.
Furthermore, HRM strategies should also reflect the ownership structure. Foreign-owned firms should promote rapid tech adoption via cross-cultural training and talent mobility. Government-owned entities must balance innovation with welfare, using transparent change management. Domestic private firms should pursue gradual automation and partnerships to offset financial constraints. Moreover, policymakers should support reskilling programs, offer automation incentives for SMEs, and foster industry-education collaboration (Liu, 2023). Balancing innovation with social responsibility is vital to minimize displacement and ensure inclusive growth.
In conclusion, this study contributes to Institutional Theory by showing how national and ownership contexts shape robotics adoption in the Visegrád Group. RBV and TAM support interpretation, but the core insight lies in institutional variation across countries. Practically, it offers HRM strategies for workforce adaptation and ownership-specific adoption, while highlighting the policy need for inclusive and sustainable technological advancement.
Research limitations and future directions
While this study offers valuable theoretical and practical insights, we must acknowledge its several limitations. First, the research focused exclusively on the Visegrád Group. Though the regional lens enables in-depth analysis, differences in institutional and cultural settings limit generalizability. Future studies should include Western Europe, North America, East Asia, and emerging markets to validate and broaden the findings.
Second, the study uses cross-sectional survey data, capturing perceptions at a single time point. We collected the data between 2022 and 2024 as part of a coordinated international project. The extended period reflects the scope of the study and logistical differences among research teams. We surveyed each respondent only once, preserving the cross-sectional design. However, we acknowledge that the use of varied data collection methods and the extended timeframe may introduce some variance. Future longitudinal studies would be valuable for assessing how perceptions and strategies evolve, particularly in fast-changing technological environments.
Third, while we analyzed ownership structures, we did not explore internal managerial practices. Future research should examine leadership, corporate governance, and attitudes toward innovation to reveal mechanisms driving robotics adoption. Moreover, although we referenced TAM, the study did not assess perceived ease of use, an essential TAM component (Davis, 1989). Future studies should incorporate this and related UTAUT variables to clarify how usefulness and ease of use interact with contextual factors.
Finally, the use of multiple statistical tests in an exploratory context raises the well-known issue of potential false positives. While we primarily reported highly significant results (e.g. p < 0.001), we also included a few p-values closer to the traditional threshold (e.g. p = 0.037) and interpreted them with caution. We acknowledge that multiple comparisons can increase Type I error risk. However, given the exploratory nature of this study, we presented these findings not as definitive proofs but as indications of patterns that warrant further investigation in future confirmatory research. This approach aligns with our intent to generate theory-informed insights while maintaining accessibility for a broad academic and practitioner audience.
Despite these limitations, the study contributes by integrating Institutional Theory and RBV to explain how national and ownership factors shape robotics adoption. It empirically shows that external environments and internal resources influence technology strategies, advancing theories in organizational behavior and strategic management. Practically, it provides HRM guidance on reskilling, engagement, and ownership-aligned automation. These insights support organizations navigating digital transformation while maintaining workforce adaptability. The study also underscores the importance of policy support for innovation and social protection.
Future research should expand comparative analysis across regions and examine leadership styles, organizational culture, and employee involvement to understand internal innovation drivers. Further integration of TAM and UTAUT can enrich the understanding of technology acceptance. Exploring robotics' long-term impact on labor markets, organizational performance, and well-being will help build comprehensive frameworks for responsible adoption.
