Institutional, Resource-Based, Stakeholder and Legitimacy Drivers of Green Manufacturing Adoption in Industrial Enterprises

Abstract

The present paper investigates the adoption of green manufacturing approaches among industrial enterprises in Slovakia, emphasizing the interplay between institutional pressures and enterprise-level resources. Based on a survey of 88 enterprises from energy- and material-intensive sectors, the study evaluates how regional context and enterprise size influence the adoption of green practices. Using logistic regression and the chi-squared test, the findings reveal minimal regional variation, suggesting strong isomorphic effects of harmonised European Union environmental regulations. In contrast, enterprise size significantly correlates with the adoption of complex green practices, confirming the relevance of the resource-based view. These results highlight the dominance of internal capabilities over regional factors in green transition pathways within small post-transition economies. The study contributes to cross-national theorising by showing how resource asymmetries, rather than institutional diversity, shape environmental behaviour in uniform regulatory environments. Specifically, the paper examines how institutional pressures, enterprise-level resources, stakeholders, and legitimacy influence the adoption of green manufacturing practices in Slovak industrial enterprises. The study draws on institutional theory, the resource-based view, stakeholder theory, and legitimacy theory to explore the relationship between enterprise size, regional location, and the adoption levels of green manufacturing.

1. Introduction

Green manufacturing is a transformative practice in the manufacturing sector that focuses on sustainability, environmental responsibility, and the economic viability of businesses (Yuchi et al., 2022). It comprises multidisciplinary practices that reduce material and energy intensity, minimise emissions and waste while promoting efficient resource use (Haleem et al., 2023). Common practices include Green Product Design, Waste Reduction, Adoption of Clean Energy, Circular Economy, Life Cycle Assessment (LCA), Collaboration of Organisations in the field of Green Manufacturing and Certification, and Green Logistics (Ullah et al., 2022; Abualfaraa et al., 2020; W. Zhang et al., 2023; Yuan & Cao, 2022). These strategies form the operational core of sustainability transitions in industrial enterprises (Vrchota et al., 2020; Rashid et al., 2024).

The adoption of green manufacturing practices varies across enterprises depending on their size and regional location. Larger companies often possess the financial and technological resources necessary to adopt capital-intensive practices such as clean energy systems or LCA. In contrast, small and micro enterprises face greater constraints, typically focusing on less costly strategies like green product design or internal environmental management (Sangwan & Choudhary, 2018; Karuppiah et al., 2020; Shahzad et al., 2022; Gomes et al., 2023; Bıçakcıoğlu-Peynirci & Tanyeri, 2022). Regional context also plays a key role: enterprises in more developed regions, where institutional pressures are stronger, are more likely to implement green strategies than those in less-developed areas with limited infrastructure and regulatory enforcement (Ullah et al., 2022; Siyal et al., 2023; Baah et al., 2021; Jabbour et al., 2020).

Motivations to adopt green manufacturing are shaped by both external and internal drivers. External drivers include regulatory pressure, stakeholder expectations, and institutional norms, while internal drivers reflect the enterprise’s available resources and capabilities (Dornfeld, 2013; Baah et al., 2021; Bhandari et al., 2022). In the Slovak context—characterised by uniform European Union (EU)-aligned regulation but significant regional and enterprise-level heterogeneity—understanding the interplay between these forces is crucial. Although legislation establishes a common minimum, disparities in innovation capacity, human capital, and industrial infrastructure create an uneven landscape for adoption of green manufacturing practices across the country.

The purpose of the paper is to interpret the results of research focused on the adoption of green manufacturing in industrial enterprises, identify differences in the adoption of green manufacturing according to the size of enterprises and the regions in which enterprises are located, identify factors that influence whether companies adopt green manufacturing practices, and compare the findings with previous research. Previous research has been conducted primarily in large countries, and the paper will complement existing studies by focusing on the current state of green manufacturing adoption in industrial enterprises in a small country such as Slovakia. The study of industrial enterprises in Slovakia integrates the aforementioned theoretical perspectives into a unified analytical framework. It is assumed that regional homogeneity in the adoption of green manufacturing practices primarily results from a unified legislative framework stemming from EU and national regulations, which exert a common coercive pressure on enterprises regardless of their geographical location (in line with institutional theory). However, differences in the adoption of specific green practices may still persist between regions due to varying intensity of normative and mimetic institutional dynamics such as professional norms, benchmarking, and local societal expectations (in accordance with institutional isomorphism and legitimacy theory).

In contrast, differences across enterprise size categories are likely to be most pronounced in capital-intensive green practices, as large enterprises are better equipped to mobilise and combine strategic resources (resource-based view) and are more frequently exposed to stakeholder pressure from customers, investors, and other key actors (stakeholder theory). In doing so, this contribution addresses a gap in the literature on small, open economies by offering a synthesis of multi-layered theoretical perspectives that broadens the discourse on the determinants of green innovation in post-transformation countries. Furthermore, the study contributes to administrative science theory by analysing the interplay between institutional homogeneity and enterprise-level resource heterogeneity in the adoption of green manufacturing within a unified regulatory environment.

In the context of the above knowledge, it is, therefore, assumed that companies based in regions with the same legislation and support schemes will exhibit relatively similar green manufacturing practices, while enterprise size remains the main differentiator, especially in capital-intensive processes such as LCA or intelligent energy systems. These expectations will form the theoretical framework for the following empirical section, in which the extent to which the regional environment and enterprise size modulate the adoption of green manufacturing in 88 industrial companies in Slovakia is tested.

The starting point is the assumption that external conditions such as legislation, economic maturity of the region, and access to resources can influence the level of implementation of environmental strategies. The study contributes to the literature by combining an organisational and territorial perspective on the adoption of green practices in manufacturing enterprises, providing empirical evidence from a small transforming economy and identifying key challenges, especially for small- and medium-sized enterprises, in implementing green manufacturing, which lags in the application of green manufacturing. The results can serve as a basis for designing targeted support for businesses, especially small- and medium-sized ones, in their transition to a more sustainable manufacturing model. From this background emerge two research questions:

RQ1. 

How does the regional environment, shaped by institutional pressures, influence the adoption of green manufacturing practices in industrial enterprises?

RQ2. 

How does the size of an enterprise and its resources influence the adoption of green manufacturing practices in industrial enterprises?

2. Theoretical Framework

The behaviour and actions of enterprises when adopting green production can be explained by the following theories.

2.1. Institutional Theory

Institutional theory provides a fundamental lens for understanding how and why industrial enterprises adopt environmentally sustainable practices, including green manufacturing. This theory posits that the behaviour and decision-making of enterprises are shaped by pressures arising from their institutional environment, which includes governments, regulators, customers, competitors, professional organisations, and broader societal expectations (DiMaggio & Powell, 1983; J. C. Chen & Roberts, 2010). In response to these external influences, enterprises seek legitimacy, reduce uncertainty, and align themselves with prevailing norms, often leading to institutional isomorphism.

The core premise of institutional theory is that enterprises conform to external pressures that can be coercive, normative, or mimetic. Coercive pressures emerge from formal regulatory systems such as environmental legislation, policy mandates, or financial incentives supporting the adoption of sustainable technologies (Saeed et al., 2018; Zhu et al., 2013). Normative pressures are rooted in professional standards and shared societal values; for example, growing expectations from customers and business partners for environmentally responsible products and production methods (Saeed et al., 2018; Jiang et al., 2024). Mimetic pressures arise when enterprises emulate the practices of successful or innovative competitors, especially under conditions of market uncertainty, such as in the case of adopting circular economy principles or implementing green logistics strategies (Jiang et al., 2024; R.-J. Lin & Sheu, 2012; Y. Chen et al., 2024).

Enterprises adopt green manufacturing practices in response to these pressures as a means of maintaining legitimacy, achieving compliance, and improving competitive positioning (DiMaggio & Powell, 1983). Empirical studies from diverse regions, including China and Europe, demonstrate that the adoption of green manufacturing is more frequent and extensive in areas where institutional pressures are stronger and more consistent (W. Zhang et al., 2023; J. Yang et al., 2024). These findings suggest that the strength and configuration of institutional environments—defined by the rigor of regulations, the activism of enforcement bodies, and the environmental awareness of stakeholders—are decisive factors in shaping corporate sustainability strategies (C. Chen & Panichakarn, 2024; Fu et al., 2020).

Applying this theoretical lens to the Slovak context, it is important to note that while environmental legislation is uniformly defined at the national level, the actual experience of institutional pressure may vary significantly across regions. More economically advanced regions, such as western Slovakia, are typically characterised by more effective regulatory enforcement, heightened public scrutiny, and stronger competitive dynamics, all of which contribute to higher levels of institutional pressure on enterprises operating in these areas. This contrasts with less-developed regions, where such pressures may be weaker or less consistently applied. As a result, enterprises located in more developed parts of the country are more likely to perceive and respond to demands for sustainability through the adoption of green manufacturing practices. This interpretation is consistent with empirical findings indicating that regions with stronger institutional infrastructures exhibit higher levels of green adoption (J. Yang et al., 2024; W. Zhang et al., 2023).

Furthermore, larger enterprises, which are more frequently located in developed regions, often experience intensified institutional scrutiny and expectations from various stakeholders (Zhu et al., 2013; Y. Chen et al., 2024). Their greater visibility and resource endowment enhance their ability and motivation to implement green innovations. Although institutional theory emphasises the role of external pressures, it also acknowledges the mediating effect of internal organisational characteristics. Managerial commitment, environmental values, and corporate culture can either amplify or moderate the influence of institutional forces on green practice adoption (Li et al., 2019).

Based on this theoretical reasoning and the observed empirical trends, the following hypothesis is proposed:

Research Hypothesis RH1:

Industrial enterprises located in more developed regions, where institutional pressures—particularly normative and mimetic—tend to be stronger, are more likely to adopt individual green manufacturing practices than enterprises in less-developed regions.

2.2. Resource-Based View

The resource-based view (RBV) represents one of the most influential theories in strategic management, offering a robust explanation of why certain enterprises achieve sustainable competitive advantage while others lag behind. At its core, RBV contends that an enterprise’s long-term success stems not only from external market conditions, but primarily from its internal resources and capabilities—specifically, those that meet the VRIN criteria: resources must be Valuable, Rare, Inimitable, and Non-substitutable to yield sustained competitive advantage (Wernerfelt, 1984; Baeshen et al., 2021; Okorie et al., 2023; Andersén, 2021). This internal focus aligns particularly well with the domain of green innovation, where organisations must mobilise and recombine diverse internal assets to effectively adopt environmental practices. According to RBV, enterprises possessing unique combinations of physical, technological, human, and organisational resources are more capable of overcoming barriers to innovation and responding flexibly to sustainability challenges (Gavronski et al., 2011; Andersén, 2021; Malik et al., 2020).

RBV explains the adoption of green manufacturing practices through the lens of internal capacity and strategic readiness. Larger industrial enterprises typically have more access to financial capital, advanced technological infrastructure, R&D capabilities, and innovation-oriented organisational cultures, which collectively support the implementation of capital- and technology-intensive green solutions such as clean energy, circular economy models, or life-cycle assessments (McGahan, 2021; Mazraani et al., 2025; Al-Hakimi et al., 2022).

These VRIN-based resources often include advanced technical know-how, experienced managerial leadership, and strong inter-organisational networks that enable not only implementation but also continuous innovation and upgrading of green practices (Okorie et al., 2023; Andersén, 2021; Malik et al., 2020). In this context, internal drivers such as top management commitment, a culture of continuous improvement, and the ability to absorb and apply new knowledge are considered crucial (Khan et al., 2023; Al-Hakimi et al., 2022). For instance, green product design requires deep knowledge and innovation capabilities, while the adoption of clean energy or life-cycle assessment (LCA) depends heavily on access to technological and financial resources (Okorie et al., 2023; Al-Hakimi et al., 2022; Andersén, 2021).

In contrast, micro and small enterprises often lack such slack resources, making it significantly more difficult for them to invest in costly or complex green technologies. These enterprises may instead rely on low-cost, incremental practices such as basic green design or internal environmental management systems (Karuppiah et al., 2020; Ullah et al., 2022; Andersén, 2021). RBV, therefore, not only explains the variance in green manufacturing implementation across enterprises but also provides a theoretical justification for size-related differences in green behaviour (Okorie et al., 2023; Al-Hakimi et al., 2022). In the Slovak industrial context—characterised by diverse enterprise sizes and uneven regional resource availability—these differences are particularly visible. While large enterprises are able to adopt and institutionalise complex green practices, smaller enterprises often remain dependent on external incentives or regulatory pressure and lack the ability to translate temporary advantages into sustained environmental performance.

RBV also provides the foundation for complementary theoretical developments such as the dynamic capabilities perspective, which stresses an enterprise’s ability to reconfigure its internal resources in response to environmental changes and stakeholder demands (Shahzad et al., 2022; Singh et al., 2022). From a green manufacturing perspective, this means that only those enterprises capable of continuously adapting and renewing their VRIN resources can maintain long-term environmental competitiveness. Enterprises that integrate digitalisation, innovation networks, and knowledge-sharing systems into their green strategy are more likely to sustain such capabilities over time (Okorie et al., 2023; Andersén, 2021; Malik et al., 2020).

The explanatory power of RBV thus lies in its ability to connect resource possession with resource orchestration. While having VRIN resources is a prerequisite for green innovation, their effective deployment depends on internal mechanisms that support learning, innovation, and collaboration. Therefore, RBV offers a compelling rationale for hypothesising that enterprise size significantly influences the type and extent of green manufacturing adoption.

Research Hypothesis RH2:

Large industrial enterprises are more likely than small enterprises to adopt green manufacturing practices, due to greater resource availability and technical capabilities.

2.3. Stakeholder Theory

Stakeholder theory is a foundational perspective in management and organisational research that extends the understanding of enterprise behaviour by emphasising the role of various stakeholder groups in shaping strategic decisions (Freeman, 1984; Freeman et al., 2021). Unlike theories centred purely on shareholder interests or internal resource advantages, stakeholder theory posits that companies should consider the needs, expectations, and influence of all relevant groups that affect or are affected by their operations. These include not only shareholders, but also employees, customers, suppliers, communities, governments, and civil society actors (Baah et al., 2021; Jiang et al., 2024; Qi et al., 2013). This broader lens enables an explanation of why and how enterprises adopt practices that may not yield immediate financial returns but enhance long-term legitimacy and responsiveness—such as green manufacturing.

In the context of environmental sustainability, stakeholder theory provides a robust conceptual framework for interpreting the adoption of green manufacturing practices. Enterprises increasingly face pressure from regulators, consumers, investors, local communities, and non-governmental organisations to implement environmentally responsible solutions, including green product design, resource conservation, clean energy, circular economy practices, life-cycle assessment (LCA), green logistics, and inter-organisational collaboration. These pressures are not uniform and often differ in strength and nature depending on enterprise size and location (Jiang et al., 2024; Singh et al., 2022; Bello-Pintado et al., 2023). Larger enterprises, particularly those operating in economically developed or urbanised regions, are subject to more intense scrutiny due to their visibility and potential reputational exposure (Mazraani et al., 2025; Ozdemir et al., 2023). Consequently, they are more likely to adopt visible green practices in order to meet stakeholder expectations and enhance their legitimacy in the eyes of the public and regulatory bodies.

Stakeholder theory highlights that companies respond to both direct and indirect forms of stakeholder pressure. Direct mechanisms include specific requirements such as customer demand for green products or regulatory obligations, while indirect mechanisms include the pursuit of reputational capital or pre-emptive compliance to avoid sanctions and stay ahead of the competition (Baah et al., 2021; Jiang et al., 2024). For instance, customer expectations have been found to strongly influence green product design and logistics practices, while government policies and investor concerns often motivate clean energy use and LCA implementation (Baah et al., 2021; F. Zhang & Zhu, 2019). Stakeholders thus act as both catalysts and gatekeepers for green transformation.

Importantly, the reaction to stakeholder pressure is selective. Not all enterprises respond equally or to the same stakeholders. Stakeholder theory suggests that companies strategically prioritise responses based on their own structural and contextual attributes. Enterprises with greater capabilities, visibility, or exposure—typically larger enterprises—are more likely to implement comprehensive and externally visible environmental practices, such as environmental certifications or partnerships within green value chains (Baah et al., 2021; Jiang et al., 2024; Singh et al., 2022). In contrast, smaller enterprises may respond more to immediate regulatory pressure or local community expectations and adopt less complex, internally focused green initiatives. Moreover, the level of stakeholder activism and environmental awareness varies across regions, influencing how companies perceive and react to external pressure. Enterprises located in developed regions are more likely to face demands for environmental accountability and transparency, and thus adapt their manufacturing systems accordingly.

Therefore, stakeholder theory explains not only whether green practices are adopted, but also what types of practices are more likely to be implemented depending on the stakeholder landscape. For example, circular economy initiatives may be more prevalent in enterprises responding to community and customer pressure, whereas clean energy adoption may reflect alignment with investor and government expectations (Jiang et al., 2024; Bello-Pintado et al., 2023; Kannan et al., 2022). The stakeholder perspective also provides theoretical clarity for observed patterns in the Slovak industrial context, where regional and size-related disparities in green manufacturing adoption are evident.

In sum, stakeholder theory offers a strong theoretical basis for examining how external social and institutional expectations influence green manufacturing behaviour, particularly in terms of variation across enterprise size and regional development level. It supports the assumption that larger companies in economically advanced regions are more motivated or compelled to implement externally visible environmental initiatives.

Research Hypothesis RH3:

Larger enterprises are more likely to adopt green manufacturing practices that are visible to stakeholders, such as green product design, inter-organisational collaboration, and environmental certifications.

2.4. Legitimacy Theory

Legitimacy theory offers a crucial perspective for understanding the strategic behaviour of firms in adopting environmentally responsible practices such as green manufacturing. It builds on the assumption that organisations strive to align their operations with prevailing societal values, norms, and expectations to gain legitimacy, which is essential for maintaining their “license to operate” (Soewarno et al., 2019). Unlike stakeholder theory, which emphasises direct interactions with specific stakeholder groups, legitimacy theory addresses a broader societal context, focusing on the need for organisations to be perceived as acting appropriately within the norms of the system in which they operate (Acquah et al., 2023; Baah et al., 2022).

In this view, environmental practices are not only about economic efficiency or resource optimisation, but also about conforming to what society deems acceptable. This is particularly relevant in regions with high levels of environmental awareness and media scrutiny, where companies are expected to go beyond compliance and adopt visible, certifiable green manufacturing practices to build reputational capital. In such contexts, organisations are incentivised to communicate their environmental efforts proactively through public commitments, certifications, and sustainability reports, which help them secure legitimacy among key audiences including regulators, investors, and the public (Y. Yang & Liu, 2024; Zhou et al., 2021).

Legitimacy pressures are dynamic and vary significantly based on contextual factors such as enterprise size and regional setting. Larger firms, because of their visibility and exposure, tend to be under greater public and media scrutiny, and thus are more inclined to adopt externally visible green initiatives like ISO certifications, clean energy commitments, or participation in circular economy programmes (Soewarno et al., 2019). These activities allow firms to signal alignment with environmental expectations and thereby enhance their image as responsible corporate actors (Acquah et al., 2023; Baah et al., 2021). Conversely, companies operating in regions with low public sensitivity to environmental issues may feel less pressure to commit to such initiatives, resulting in uneven adoption of green manufacturing across territories (Mitra & Datta, 2013).

Legitimacy theory also helps explain the performative nature of some corporate sustainability actions. While many firms adopt genuine pro-environmental practices, others may engage in “greenwashing” to appear environmentally responsible without undertaking substantive changes (Testa et al., 2018). This strategy, although initially helpful in maintaining legitimacy, carries long-term reputational risks if stakeholders detect inconsistencies between declared and actual practices. Therefore, transparency and credibility in green communication become essential components of legitimacy management.

In the Slovak context, where the adoption of green manufacturing varies across regions, legitimacy theory suggests that enterprises located in more environmentally aware and media-active areas are more likely to feel compelled to adopt and communicate green practices. These pressures often result in the implementation of visible and certifiable environmental initiatives, especially among larger industrial enterprises that are more susceptible to public and institutional scrutiny. The regional heterogeneity in environmental expectations thus represents a critical factor in understanding the differentiated adoption of green manufacturing practices. Legitimacy theory explains that enterprises adopt green manufacturing practices that are publicly visible, formally declared or certified, and easily recognised by society and stakeholders, in order to maintain or enhance their legitimacy and social acceptance. Enterprises operating in regions with higher environmental awareness (more developed regions) face greater societal expectations and legitimacy pressures and, therefore, are more motivated to implement publicly declared and certified green practices.

In summary, legitimacy theory provides a theoretical foundation for interpreting how regional and enterprise-level factors shape the motivation to adopt green practices. By highlighting the need for organisational conformity with societal norms, it supports the expectation that companies operating in environments with higher public environmental sensitivity—particularly large enterprises—are more likely to engage in transparent, publicly declared green manufacturing efforts.

Research Hypothesis RH4:

Enterprises operating in regions with higher environmental awareness (more developed regions) are more likely to adopt green manufacturing practices that enhance public legitimacy, such as green product design, inter-organisational collaboration and environmental certifications.

2.5. Integration of Institutional, Resource-Based, Stakeholder, and Legitimacy Perspectives

Over the past decade, green manufacturing has emerged as a multidimensional phenomenon that cannot be fully understood without considering the interplay between external institutional pressures and the internal resource–capability configurations of companies. The implementation of green manufacturing approaches is influenced by a range of factors that can be explained through several theoretical perspectives. Institutional theory suggests that companies adopt green manufacturing in response to pressures from regulators, customers, suppliers, and society, with institutional isomorphism—comprising normative, coercive and mimetic mechanisms—pushing firms in similar environments toward comparable practices (DiMaggio & Powell, 1983; Baah et al., 2021; Jum’a et al., 2022; C. Chen & Panichakarn, 2024). External pressure also stems from stakeholders, whose influence varies by region and firm size (Freeman, 1984; H. Zhang et al., 2020). Large companies often face stronger stakeholder pressure and possess greater resources to implement green approaches (Baah et al., 2021; Mazraani et al., 2025).

The resource-based view explains that a company’s ability to adopt and effectively implement green approaches depends on its internal resources—knowledge, technology, human capital, and organisational support (Baeshen et al., 2021; Shahzad et al., 2022). Dynamic capabilities theory reminds us that firms must continuously reconfigure these VRIN resources to meet evolving environmental expectations (Shahzad et al., 2022). Legitimacy theory posits that firms adopt green practices to maintain or enhance their social legitimacy, an imperative often more pronounced for larger companies or those operating in regions with heightened environmental awareness (Baah et al., 2021; J. Yang et al., 2024).

By integrating these theoretical perspectives, it can be anticipated that large companies—and those located in regions with greater innovation capacity or stronger regulatory environments—will implement green manufacturing approaches more frequently and comprehensively (Mazraani et al., 2025; C. Chen & Panichakarn, 2024; Fu et al., 2020). In other words, the highest probability of adopting advanced green innovations arises when strong external pressures converge with abundant internal resources, whereas a mismatch tends to yield only baseline compliance. Combining all four perspectives, we, therefore, assume that in the Slovak environment, regional homogeneity will result from strong coercive pressures, while size heterogeneity will reflect differences in the ability to mobilise VRIN resources and respond to stakeholder and legitimacy pressures. A conceptual framework was developed, grounded in relevant theoretical principles, to examine internal and external factors influencing the adoption of green manufacturing practices, particularly with regard to enterprise size and regional development (Figure 1).

3. Materials and Methods

The research was conducted based on the stated theoretical background, previous research, the authors’ professional experience, and the proposed theoretical and conceptual framework for studying the adoption of green manufacturing practices in industrial enterprises.

The research was conducted via an online questionnaire, which is described in the current section. Data were collected through an author-designed online questionnaire consisting of eight questions. The first part of the questionnaire contained three closed background questions to identify the basic enterprise profile (sector according to SK NACE, enterprise size category, and region). The second part of the questionnaire contained five analytical questions. The analytical block comprised (1) one open question identifying the organisational unit responsible for environmental activities; (2) a multiple-choice question allowing respondents to select which of the following seven green manufacturing practices they adopt—sustainable product design, resource conservation, waste reduction, life-cycle assessment (LCA), adoption of clean energy, collaboration, and sustainability certification; (3) a multiple-choice question listing eight perceived barriers (e.g., lack of initial investment, shortage of qualified workers, insufficient state support), with multiple answers allowed; (4) a multiple-choice question determining the manufacturing phase(s) in which green measures are implemented (pre-manufacturing, manufacturing, post-manufacturing—respondents could mark one, two, or all phases); and (5) a single-choice question on the existence of an environmental strategy (yes/no). The questionnaire thus contained seven closed and one open question. Data from 88 respondents were processed in Microsoft Excel software and analysed using Spyder 5.5.1 software.

In this paper, the factors influencing the adoption of green manufacturing practices (Green Product Design, Resource Protection, Adoption of Clean Energy, Circular Economy, Life Cycle Assessment (LCA), Collaboration of Organisations in the field of Green Manufacturing and Certification, and Green Logistics), with two possible outcomes—adopted and not adopted. Given the binary nature of the response variable, a model based on logistic regression has been created to analyse the factors that may influence the decision to adopt a particular green manufacturing practice. Logistic regression is used to solve research tasks. Research hypotheses from the theoretical framework were tested using binary logistic regression to determine the impact of enterprise size and regional development on the adoption of selected green manufacturing practices. Based on a review of the literature, variables such as enterprise size and the region in which the enterprise is located were selected to test their impact on the application of individual green manufacturing practices. Focusing on only two variables is a very simplistic view of the problem of green manufacturing adoption. Reference categories were chosen based on the highest frequency in the sample: medium-sized enterprises and the Trenčín region. Logarithmic probabilities were calculated to better explain the impact of the variables. The model is as follows:

log (p_i/(1 − p_i)) = β₀ + β₁.Size + β₂.Region,

(1)

where:

• p_i denotes the probability that enterprise i adopts the given practice;
• Size is the size of the enterprise;
• Region is the region in which the enterprise is located;
• β₀, β₁, β₂ is a constant and coefficient of the logistic regression.

The Pearson’s chi-square test was also employed. Pearson’s chi-square test of independence was applied to examine the relationship between the region and the application of the green manufacturing practice. The same test was applied to examine the relationship between enterprise size and the application of the green manufacturing practice. Respondents (employees of the companies surveyed) could select multiple practices from a predefined list, which led to the creation of a dataset with multiple responses. The mechanism for selecting enterprises is shown in Figure 2.

The data were collected via an anonymous questionnaire distributed to selected industrial enterprises in the Slovak Republic. The sampling frame consisted of enterprises classified under NACE codes 23, 24, 25, 26, 27, 28, 29, 30, and 32, which are widely recognised by EU legislation, e.g., Emissions Trading System (ETS), Carbon Border Adjustment Mechanism (CBAM), and academic research as energy- and material-intensive sectors. These industries are characterised by high levels of emissions and waste and a significant consumption of raw materials, making them particularly relevant for studying green manufacturing.

As of 31 December 2023, there were 30,020 such enterprises in Slovakia. The list of enterprises was obtained from FinStat, a public Slovak online database that aggregates legal and economic data on companies. To ensure balanced representation, the full population of 30,020 enterprises was stratified by enterprise size (micro, small, medium, and large) and by the eight administrative regions of Slovakia. From each stratum, 2.5% of enterprises were selected using systematic sampling, resulting in a total of 750 enterprises invited to participate.

The questionnaire—containing eight structured items—was distributed by email during a two-month period, with the final day of data collection on 29 February 2024. In total, 88 fully completed responses were received, corresponding to a response rate of 11.4%. The authors contacted employees working in the following departments: environmental department, health and safety, quality and sustainability, research and innovation, procurement and logistics, manufacturing and technical departments, and top management. The sample of responding enterprises was classified based on the number of employees according to the EU Recommendation 2003/361/EC (European Commission, 2003). The final distribution by enterprise size is shown in

Table 1. Distribution of enterprises by number of employees in absolute and relative frequency.

	Absolute Frequency	Relative Frequency
Micro Business/up to 10 employees	10	11.36%
Small Business/up to 50 employees	23	26.14%
Medium Business/up to 250 employees	37	42.05%
Large Business/more than 250 employees	18	20.45%

.

The largest representation in the given survey belongs to the group of medium-sized enterprises, followed by small enterprises as the second largest group. The enterprises were also categorised by geographical location across the regions of the Slovak Republic, as illustrated in Figure 3. The highest concentration of manufacturing enterprises was found in the Trenčín, Bratislava, and Trnava regions. For the paper, the Bratislava region is classified as a more developed region based on the European Commission’s cohesion policy classification for the programming period 2021–2027. According to this classification, only the Bratislava region exceeds 100% of the EU average GDP per capita, while all other Slovak regions are considered transition or less-developed regions.

To evaluate the collected data, the descriptive statistical analysis methods were used by the authors, and the results were presented in the form of tables (cross tables, tables of absolute and relative frequencies, and tables of statistical test results).

The Pearson’s chi-square test was used to verify the existence of a relationship between the adoption of individual green manufacturing practices and the region in which the company operates. The region represents a nominal category (there are eight administrative regions in Slovakia). The adoption of green manufacturing practices is a dichotomous variable (in simple terms, a specific green manufacturing practice is either applied or not applied). The requirements for performing the test were met, as most of the data obtained contain more than five responses and each respondent belongs to only one region. Since the questionnaire allowed multiple selections of practices, one respondent appears more than once in the table (for each selected practice). The test, therefore, evaluates the overall intensity of practices, not the number of “green” companies. This method is common in the literature for multiple responses, but at the enterprise level it may overestimate variability.

The Pearson’s chi-square test was used to verify the existence of a relationship between the adoption of individual green manufacturing practices and the size of the company. The aim was to verify whether the distribution of positive responses is the same in all categories of enterprises according to size. The first variable is enterprise size (four categories: large enterprise, medium-sized enterprise, small enterprise, micro-enterprise). The second variable is the application of a specific practice (dichotomous: adopted/not adopted). Both variables are nominal and represent frequency (numerical) data, and it is necessary to compare more than two groups. Therefore, Pearson’s chi-square test of independence is most appropriate.

After applying the selected methodology, quantitative results were obtained that provide an overview of the level of green manufacturing adoption across various business categories, reflecting differences based on enterprise size and geographical location.

4. Results

The following part of the paper is focused on the evaluation of research questions (RQs) and research hypotheses (RHs).

The frequency of responses is shown in

Table 2. Adoption of green manufacturing practices in industrial enterprises.

	Absolute Frequency	Relative Frequency
Resource Conservation	63	71.6%
Waste Reduction	54	61.4%
Adoption of Clean Energy	32	36.4%
Sustainable Product Design	31	35.2%
Collaboration	29	33.0%
LCA	14	15.9%
Green Logistics	25	28.4%

,

Table 3. Adoption of green manufacturing practices in individual regions.

Green Manufacturing Practices /Region	Sustainable Product Design	Resource Conservation	Adoption of Clean Energy	Waste Reduction	Life Cycle Assessment (LCA)	Collaboration	Green Logistics	Total
Bratislava Region	3 23.1%	8 61.5%	4 30.8%	9 69.2%	3 23.1	4 30.8	5 38.5	13 100%
Banská Bystrica Region	2 18.2%	9 81.8%	3 27.3%	7 63.6%	2 18.2%	5 45.5%	3 27%	11 100%
Košice Region	2 33.3%	6 100%	4 66.7%	5 83.3%	1 16.7%	2 33.3%	2 33.3%	6 100%
Nitra Region	3 42.9	5 71.4	0 0%	5 71.4%	1 14.3%	3 42.9%	2 28.6%	7 100%
Trenčín Region	8 36.4%	12 54.5%	7 31.8%	8 36.4%	3 13.6%	4 18.2%	6 27.3%	22 100%
Trnava Region	6 50%	9 75%	5 41.7%	8 66.7%	2 16.7%	5 41.7%	3 25%	12 100%
Prešov Region	2 22.2%	8 88.9%	4 44.4%	6 66.7%	1 11.1%	4 44.4%	2 22%	9 100%
Žilina Region	5 62.5%	6 75%	5 62.5%	6 75%	1 12.5%	2 25%	2 25%	8 100%
∑Sum	31 35.2%	63 71.6%	32 36.4%	54 61.4%	14 15.9%	29 33%	25 28.4%	88 100%

and

Table 4. Adoption of green manufacturing practices in individual categories of enterprises.

Categories of Enterprises/Green Manufacturing Practices	Large Enterprises	Medium Enterprises	Small Enterprises	Micro Enterprises	∑ Sum
Sustainable Product Design	9	10	7	5	31
Resource Conservation	15	27	16	5	63
Adoption of Clean Energy	4	20	8	0	32
Waste Reduction	17	26	10	1	54
Life Cycle Assessment (LCA)	5	5	3	1	14
Collaboration	12	11	5	1	29
Green Logistics	7	10	6	2	25
Number of respondents	18	37	23	10	88

. For the purposes of evaluating research questions and research hypotheses, responses from the research questionnaire were compared, and logistic regression was used.

Insight into the adoption of green manufacturing in industrial enterprises in Slovakia are provided by the research results, which analyse regional and size-based differences among enterprises adopting green manufacturing practices.

4.1. Level of Green Manufacturing Adoption

Resource conservation is the most widely adopted practice in Slovak enterprises, encompassing the minimisation of raw material and energy consumption as well as recycling.

On the other hand, LCA is the least implemented practice, indicating a lack of awareness or technical readiness among enterprises to adopt advanced methods for evaluating environmental impact, as illustrated in Figure 4.

Based on Figure 4, it can be concluded that resource protection is indicated to the greatest extent, confirming its fundamental position among green measures. Resource protection is supported legislatively in Slovakia through several pieces of legislation aimed at protecting natural resources such as water, soil, and air. For example, Decree No. 29/2005 Coll. lays down details on the designation of protection zones for water resources and measures to protect water. The circular economy and adoption of clean energy are practices that are also widespread, pointing to efforts to reduce waste and use renewable energy sources. LCA, with its low adoption, suggests the need for education and support to better understand this tool.

4.2. Regional Differences in the Adoption of Green Manufacturing Practices

Geographical differences in the adoption of green manufacturing practices among industrial enterprises may influence the extent to which green manufacturing practices are adopted; for example, enterprises in economically developed regions, such as the Bratislava region, may adopt advanced practices more widely, while enterprises in the Prešov and Banská Bystrica regions may focus more on basic environmental measures mandated by legislation.

To evaluate and test research hypotheses RH1 and RH4, responses of respondents were analysed according to region. Table 3 shows the respondents’ answers to the question, “What green manufacturing/issues practices do you adopt in your organization?”

4.3. Differences in the Adoption of Green Manufacturing Practices According to Enterprise Size

Based on a study of the available literature, attention was given to differences in the application of green manufacturing, specifically to individual practices within green manufacturing, in different size categories of enterprises. To evaluate and test research hypotheses RH2 and RH3, responses of respondents were analysed according to enterprises’ size categories. Table 4 shows the respondents’ answers to the question, “What green manufacturing/issue practices do you adopt in your organisation?”.

This section is devoted to the description of the results of logistic regression, which was used in this study to explain what factors increase the probability of adoption of individual green manufacturing practices by industrial enterprises in Slovakia. Results are shown in

Table 5. Logistic regression results—Sustainable Product Design.

Variable	Logit Model	Log Odds	p-Value	Probit Model
Intercept	−1.0070 (*)	0.3653	0.0965	−0.6274 (*)
Enterprise Size Micro	1.0499	2.8575	0.9631	0.6589
Enterprise Size Small	0.0306	1.0310	0.1878	0.0183
Enterprise Size Large	1.1762	3.2420	0.1077	0.7249 (*)
Region Banská Bystrica	−1.0151	0.3624	0.277	−0.6369
Region Bratislava	−0.3964	0.6727	0.6412	−0.2343
Region Košice	−0.3411	0.7110	0.7439	−0.1835
Region Nitra	0.0065	1.0066	0.9946	0.0151
Region Prešov	−0.4172	0.6589	0.6634	−0.2228
Region Trnava	0.4431	1.5575	0.5578	0.2778
Region Žilina	1.5064 (*)	4.5103	0.0924	0.9393 (*)
McFadden Pseudo R2	0.0994			0.1008

The significance code marked as (*) corresponds to the significance levels p < 0.1.

,

Table 6. Logistic regression results—Resource Conservation.

Variable	Logit Model	Log Odds	p-Value	Probit Model
Intercept	0.2797	1.3228	0.6313	0.1790
Enterprise Size Micro	−0.7536	0.4707	0.8155	−0.4634
Enterprise Size Small	−0.1557	0.8559	0.3429	−0.0972
Enterprise Size Large	0.5226	1.6865	0.5468	0.2970
Region Banská Bystrica	1.2189	3.3835	0.1790	0.7000
Region Bratislava	0.3238	1.3824	0.6739	0.1987
Region Košice	23.3707	14,117,642,617.6639	0.9997	5.6183
Region Nitra	0.4284	1.5348	0.6711	0.2674
Region Prešov	1.7890	5.9834	0.1254	1.0367
Region Trnava	0.9662	2.6281	0.2358	0.5977
Region Žilina	0.8787	2.4077	0.3550	0.5340
McFadden Pseudo R2	0.1114			0.1108

,

Table 7. Logistic regression results—Adoption of Clean Energy.

Variable	Logit Model	Log Odds	p-Value	Probit Model
Intercept	0.2827	1.3267	0.6411	0.1925
Enterprise Size Micro	−23.4696	0.0000	0.1130	−8.3738
Enterprise Size Small	−1.0161	0.3620	0.9994	−0.6361
Enterprise Size Large	−1.7697 (**)	0.1704	0.0357	−1.0982 (**)
Region Banská Bystrica	−0.5223	0.5932	0.5610	−0.3595
Region Bratislava	−0.7649	0.4654	0.3753	−0.4960
Region Košice	1.7104	5.5314	0.1168	1.0634
Region Nitra	−106.8328	0.0000	1.0000	−6.0617
Region Prešov	−0.1239	0.8835	0.8882	−0.1030
Region Trnava	0.9036	2.4685	0.3099	0.5407
Region Žilina	0.6355	1.8880	0.4884	0.3841
McFadden Pseudo R2	0.2382			0.2397

The significance code marked as (**) corresponds to the significance levels p < 0.05.

,

Table 8. Logistic regression results—Waste Reduction.

Variable	Logit Model	Log Odds	p-Value	Probit Model
Intercept	−0.3151	0.7297	0.6142	−0.1807
Enterprise Size Micro	−3.2446 (***)	0.0390	0.1052	−1.9387 (***)
Enterprise Size Small	−1.0503	0.3498	0.0083	−0.6234
Enterprise Size Large	2.2905 (**)	9.8803	0.0497	1.2917 (**)
Region Banská Bystrica	0.9616	2.6158	0.2979	0.5860
Region Bratislava	1.7732 (*)	5.8898	0.0541	1.0481 (*)
Region Košice	1.8554	6.3942	0.1687	1.1254
Region Nitra	0.9619	2.6166	0.4325	0.4127
Region Prešov	1.1306	3.0976	0.2258	0.6699
Region Trnava	2.0276 (**)	7.5959	0.0476	1.1981 (**)
Region Žilina	1.8709 (*)	6.4943	0.0644	1.1568 (*)
McFadden Pseudo R2	0.2949			0.2961

The significance codes marked as (*), (**), (***) correspond to the significance levels p < 0.1, p < 0.05, p < 0.01.

,

Table 9. Logistic regression results—Life Cycle Assessment (LCA).

Variable	Logit Model	Log Odds	p-Value	Probit Model
Intercept	−2.2787 (***)	0.1024	0.0070	−1.3115 (***)
Enterprise Size Micro	−0.2795	0.7562	0.6992	−0.1357
Enterprise Size Small	0.3325	1.3944	0.8166	0.1633
Enterprise Size Large	1.4163	4.1218	0.1108	0.8049
Region Banská Bystrica	0.2265	1.2542	0.8276	0.1806
Region Bratislava	1.0867	2.9646	0.2820	0.5742
Region Košice	−0.2704	0.7631	0.8376	−0.2046
Region Nitra	−0.5641	0.5689	0.6667	−0.3638
Region Prešov	−0.1164	0.8901	0.9272	−0.0661
Region Trnava	0.1624	1.1763	0.8746	0.0144
Region Žilina	0.1983	1.2193	0.8776	0.0818
McFadden Pseudo R2	0.0532			0.0540

The significance code marked as (***) corresponds to the significance p < 0.01.

,

Table 10. Logistic regression results—Collaboration.

Variable	Logit Model	Log Odds	p-Value	Probit Model
Intercept	−1.9244 (**)	0.1460	0.0109	−1.1327 (***)
Enterprise Size Micro	−1.2255	0.2936	0.8752	−0.7325
Enterprise Size Small	−0.1075	0.8980	0.2948	−0.0685
Enterprise Size Large	2.0574 (**)	7.8254	0.0101	1.2391 (***)
Region Banská Bystrica	1.3225	3.7527	0.1549	0.7499
Region Bratislava	1.2689	3.5569	0.1777	0.7202
Region Košice	0.0387	1.0394	0.9736	−0.0294
Region Nitra	0.3915	1.4792	0.7197	0.2426
Region Prešov	1.5247	4.5939	0.1173	0.8901
Region Trnava	1.3689	3.9309	0.1404	0.8260
Region Žilina	0.8654	2.3760	0.4164	0.4801
McFadden Pseudo R2	0.1601			0.1612

The significance codes marked as (**), (***) correspond to the significance levels p < 0.05, p < 0.01.

and

Table 11. Logistic regression results—Green Logistics.

Variable	Logit Model	Log Odds	p-Value	Probit Model
Intercept	−1.1495 (*)	0.3168	0.0653	−0.6944 (*)
Enterprise Size Micro	−0.3562	0.7003	0.8005	−0.2556
Enterprise Size Small	0.1661	1.1806	0.6968	0.0863
Enterprise Size Large	0.8311	2.2960	0.2524	0.4933
Region Banská Bystrica	−0.0893	0.9146	0.9164	−0.0418
Region Bratislava	0.7190	2.0524	0.3699	0.4389
Region Košice	−0.0370	0.9637	0.9714	0.0011
Region Nitra	−0.3365	0.7143	0.7414	−0.1922
Region Prešov	−0.2501	0.7788	0.7949	−0.1680
Region Trnava	−0.1543	0.8570	0.8541	−0.1253
Region Žilina	−0.0130	0.9871	0.9894	−0.0085
McFadden Pseudo R2	0.0277			0.0287

The significance code marked as (*) corresponds to the significance levels p < 0.1.

. To make the effect of the individual variables more visible, log odd values were calculated. At the same time, the model is presented as a robustness check.

Table 5 presents the results of binary logistic regression, focusing on the identification of determinants of sustainable product design adoption, such as eco-friendly materials, recyclability, and reduced environmental impact during the product life cycle.

The intercept is statistically significant at the 10% level (logit: p = 0.0965; probit: p < 0.1), and its negative value (−1.0070) indicated that the reference group (medium-sized enterprises in the Trenčín region) has a relatively low probability of implementing sustainable design—approximately 36.5%. However, this estimate is to be interpreted cautiously due to the limited sample size and model sensitivity.

None of the size categories reached statistical significance, at the conventional 5% level, though the probit model yielded a borderline significant positive effect for large enterprises (0.7249, p < 0.1). This suggests that larger enterprises are somewhat more likely to implement sustainable design compared to medium-sized enterprises, but the evidence remains indicative rather than conclusive. No statistically significant differences were identified for micro and small enterprises.

Among regions, the Žilina region showed a borderline significant effect in the probit model (0.9393, p < 0.1), which could reflect a higher concentration of technologically advanced or export-oriented enterprises. Still, given the overall sample limitations, such regional patterns should be treated as exploratory rather than definitive.

The McFadden pseudo R² values (0.0994 for logit, 0.1008 for probit) indicate a relatively low explanatory power, which is not uncommon in models of environmental behaviour. Nevertheless, the model captures tentative indications, particularly among larger enterprises and in the Žilina region.

Given the small, non-random sample and borderline significance levels, the results should not be overgeneralised. Findings should be considered exploratory and interpreted with appropriate caution.

Table 6 presents the results of binary logistic regression aimed at identifying determinants of resource conservation adoption.

The results of the logistic regression for the resource conservation practice indicate that none of the analysed variables are statistically significant at conventional significance levels (p < 0.1). All p-values exceed this threshold, suggesting that neither enterprise size nor regional location has a statistically demonstrable effect on the likelihood of adopting resource conservation measures within this model.

While micro and small enterprises exhibit negative coefficients, suggesting a lower likelihood of engaging in resource conservation relative to medium-sized enterprises, the lack of significance prevents enterprise conclusions. Similarly, large enterprises and those located in regions such as Prešov, Trnava, or Žilina show positive coefficients, but these effects should be interpreted as indicative rather than conclusive.

The extremely high coefficient for the Košice region likely results from a small subsample, in which all participating enterprises reported adopting resource conservation, possibly inflating the effect size.

The McFadden pseudo R² = 0.1114 reflects a low to moderate explanatory power of the model. However, in light of the small, non-random sample and the lack of statistically significant findings, these results should be treated as exploratory. No generalisations should be made beyond the studied sample.

Table 7 presents the results of binary logistic regression aimed at identifying determinants of the adoption of clean energy.

The logistic regression results for the adoption of clean energy (Table 7) indicate that only the size category of large enterprises shows statistical significance at the 5% level (B = −1.7697, p = 0.0357). Interestingly, the negative coefficient suggests that large enterprises are less likely to adopt clean energy compared to medium-sized enterprises, which serve as the reference group. While this finding is statistically significant, it is counterintuitive given the expectations derived from RBV and should be interpreted cautiously, especially in light of the sample structure and potential unobserved confounders.

Other enterprise size categories (micro and small enterprises) did not demonstrate statistically significant effects (p > 0.05), and none of the regions reached significance either. Notably, the Nitra region produced an extreme coefficient (−106.8328, p = 1.000), resulting from the fact that none of the enterprises from this region in the sample reported adopting clean energy. This likely reflects a data sparsity issue rather than a true absence of clean energy adoption.

Several regions—such as Košice (1.7104, p = 0.1168), Trnava (0.9036, p = 0.3099), and Žilina (0.6355, p = 0.4884)—displayed positive coefficients, but none reached statistical significance, even at the 10% level. These estimates may point to regional variation, but the evidence is suggestive at best and should not be over-interpreted.

The McFadden pseudo-R² value of 0.2382 indicates moderate model fit, which is generally acceptable in logistic models dealing with behavioural or environmental adoption. Nevertheless, given the small, non-random sample and the limited number of statistically robust predictors, these results should be considered exploratory and interpreted with caution. Generalisations beyond the studied population are not warranted.

The logistic regression results for the waste reduction practice (Table 8) reveal several statistically significant relationships across enterprise size and regional categories.

Among enterprise sizes, large enterprises exhibit a positive and statistically significant association with the likelihood of implementing waste reduction measures (B = 2.2905, p = 0.0497). This finding aligns with theoretical expectations, as larger enterprises often possess the resources and capacities to adopt structured waste management strategies.

By contrast, small enterprises show a statistically significant negative effect (B = −1.0503, p = 0.0083), suggesting a lower likelihood of adopting waste reduction initiatives compared to medium-sized enterprises (the reference group). Micro-enterprises also returned a negative coefficient (B = −3.2446), indicating a very low estimated probability; however, this effect did not reach statistical significance at the conventional 5% level (p = 0.1052), likely due to the small sample size and corresponding uncertainty.

Regarding regional variation, enterprises located in the Trnava region (B = 2.0276, p = 0.0476) demonstrated a statistically significant positive association, while the Žilina region (B = 1.8709, p = 0.0644) showed a borderline significant effect at the 10% level. The Bratislava region also presented statistical significance (p = 0.0541), though the coefficient was not reported. These results may reflect regional differences in infrastructure, regulatory enforcement, or stakeholder expectations, though further investigation would be necessary to consider such explanations.

The model’s McFadden pseudo-R² value of 0.2949 suggests a moderate to strong model fit, which is relatively high for logistic regression applied to behavioural or environmental adoption contexts.

Nonetheless, these findings must be interpreted with caution, given the study’s small, non-random sample and limited generalisability. While the results point to plausible trends in the role of enterprise size and regional context, they remain exploratory and should not be overextended beyond the observed sample.

Table 9 presents the results of binary logistic regression analysing factors associated with the likelihood of implementing life cycle assessment (LCA) in industrial enterprises.

None of the enterprise size categories reached statistical significance at conventional levels. The highest positive coefficient was observed for large enterprises (1.4163; p = 0.1108), which might suggest a higher probability of adopting LCA compared to the reference category (medium-sized enterprises). However, the result does not meet the 5% or even 10% significance threshold and should be interpreted with caution. Micro and small enterprises also did not show significant differences from the reference category.

Similarly, none of the regional variables demonstrated a statistically significant effect on LCA adoption. The highest positive values were found for companies located in the Bratislava (1.0867), Banská Bystrica (0.2265), and Žilina (0.1983) regions, which may reflect a slightly greater willingness or potential to adopt environmental innovations in these areas. Nonetheless, all p-values remain well above the conventional significance thresholds.

The McFadden pseudo R² values (0.0532 for logit, 0.0540 for probit) indicate weak explanatory power. This limited performance likely reflects the very small number of enterprises adopting LCA in the sample—only 14 out of 88 respondents. Given the low prevalence of LCA adoption and the small, non-random sample, the findings should be considered exploratory and not generalised beyond the studied context.

Table 10 summarizes the results of binary logistic regression, which examines the factors influencing the probability that an enterprise will cooperate with other actors (e.g., research institutions, other companies, or public organisations) in environmental initiatives.

The intercept is statistically significant (p = 0.0109), with a negative value (−1.9244) indicating a relatively low probability of cooperation between small businesses in the Trenčín Region, though caution is warranted, given the small and non-random sample.

Among the examined categories of enterprise size, large enterprises show a positive and statistically significant effect (p = 0.0101), suggesting a higher likelihood of cooperation compared to medium-sized enterprises. However, given the borderline significance levels and moderate explanatory power (McFadden pseudo R²), this finding should be interpreted with caution.

Micro and small enterprises did not show significant differences compared to medium-sized enterprises.

Although regions such as Prešov, Trnava, Bratislava, and Žilina showed higher values, none of the regional variables were statistically significant (all p-values > 0.10). These results may indicate potential regional variation, but the lack of statistical significance and limited sample size prevent definitive conclusions.

The McFadden pseudo R² value indicates only a moderate level of explanation of the variability of the dependent variable, which is acceptable considering the complexity of environmental cooperation, but suggests that other unmeasured factors may also play important roles.

Overall, these findings provide initial insights into factors influencing environmental cooperation; however, due to the study’s methodological limitations, including the small sample size and low pseudo R² values, the results should be interpreted cautiously and not overgeneralised. Further research with larger and more representative samples is recommended.

Table 11 presents the results of binary logistic regression, which analyses the determinants of the probability that an enterprise will implement green logistics—for example, measures to reduce emissions in transport, optimize distribution, or use environmentally friendly packaging solutions.

The intercept is marginally statistically significant at the 10% level (p = 0.0653), with a negative value, indicating a relatively low baseline probability of green logistics adoption among medium-sized enterprises. Given the borderline significance and the study’s limited sample size, this result should be interpreted cautiously.

None of the enterprise size categories (micro, small, or large) showed statistically significant effects. Although the estimate for large enterprises was positive, it was not statistically significant (p = 0.2524), suggesting no conclusive evidence of a higher likelihood of green logistics adoption in larger enterprises within this sample.

All regional variables yielded very low coefficients with high p-values (all above 0.36), indicating no statistically significant regional differences in the probability of implementing green logistics in Slovakia. The Bratislava region showed the highest positive (but still insignificant) effect, which might reflect a greater concentration of modern logistics solutions, although this cannot be statistically observed based on the current data.

The McFadden pseudo R² indicates a very low explanatory power of the model, suggesting that structural factors, such as enterprise size and region, alone do not sufficiently explain the decision to implement green logistics. This points to the potential importance of additional variables—behavioural, technological, or sector-specific—in future research.

Overall, these findings provide initial insights but must be interpreted with caution due to the small sample and limited model fit, highlighting the need for further research with more comprehensive data.

The Pearson’s chi-square test was used to verify the existence of a relationship between the adoption of individual green manufacturing practices and the region in which the enterprise operates (

Table 12. Results of the chi-square test for the relationship: region/green manufacturing practices.

Pearson’s Chi-Square Tests
Chi-square	14.3229
df	42
p	0.099998
Cramér’s V	0.098

).

To determine whether there are regional differences in the adoption of green manufacturing, or in the adoption of individual green manufacturing practices in companies in Slovakia, Pearson’s χ² test of independence was performed with the results (χ² = 14.3229; df = 42; p = 0.098). Several of the data obtained (responses) did not reach an absolute frequency value of five. Therefore, it is appropriate to determine Cramér’s V (effect size), which is 0.098. The analysis was based on a contingency table compiled from the responses of 88 respondents from 8 Slovak regions. Respondents identified which of the seven defined green manufacturing practices they adopt or do not adopt in their companies, and authors compare the number of occurrences of selected and unselected practices between regions.

The test results did not show statistically significant differences in the adoption of green manufacturing practices in enterprises between regions at a significant level of α = 0.05. p = 0.098, which indicates that the adoption of green manufacturing practices in enterprises is relatively uniform across regions. However, given the use of multiple responses, the results should be interpreted with caution, as this practice may affect the accuracy of the estimate of regional differences. Cramér’s V = 0.098 indicates a very weak effect—even if the test was significant, the practical impact would be minimal.

The Pearson’s chi-square test was used to verify the existence of a relationship between the adoption of individual green manufacturing practices and the size of the enterprise (

Table 13. Results of the chi-square test for the relationship: enterprise size/green manufacturing practices.

Pearson’s Chi-Square Tests
	Chi-Square	df	p
Sustainable Product Design	4.000	3	0.2615
Resource Conservation	3.594	3	0.3088
Adoption of Clean Energy	12.299	3	0.0064
Waste Reduction	23.777	3	0.00003
Life Cycle Assessment (LCA)	2.456	3	0.4833
Collaboration	13.127	3	0.0044
Green Logistics	1.415	3	0.7019

).

The result of the Chi-square test showed statistically significant associations between enterprise size and three green manufacturing practices: adoption of clean energy (p = 0.0064), waste reduction (p < 0.0001), and collaboration (p = 0.0044). These findings indicate that the likelihood of engaging in these practices differs across size categories of enterprises.

For the remaining practices—sustainable product design, resource conservation, life cycle assessment (LCA), and green logistics—no statistically significant differences were observed (all p-values > 0.05), suggesting a more uniform adoption across enterprise sizes in these areas.

Overall, enterprise size appears to be an influential factor in the adoption of selected green manufacturing practices, particularly those that may require greater investment, organisational capacity, or external engagement. These insights align with the assumptions of the resource-based view, which emphasises internal capabilities as determinants of strategic action.

Nonetheless, given the study’s limitations, including a non-random and relatively small sample, the results should be interpreted with caution and seen as exploratory. Future research should further examine how enterprise-specific and contextual factors shape the adoption of different green practices.

• Sustainable Product Design

This practice is used by 50% of large enterprises surveyed, 27% of medium-sized enterprises, 30% of small enterprises, and 50% of micro-enterprises. There is no significant difference between any of the categories of enterprises and the p-value = 0.2615; therefore, the difference is not statistically significant. The high proportion of micro enterprises using the practice (five out of ten) may be related to the specific focus of some startups on eco-design, but due to the small sample size it is not possible to generalise this statement.

• Resource Conservation

The use of the practice is high across all categories of enterprises, with 83% of large enterprises involved in the survey using the practice, 73% of medium enterprises, 70% of small enterprises, and 50% of micro enterprises. The differences are quantitative rather than qualitative; resource conservation is a “basic equipment” regardless of size. The slightly weaker performance of micro-enterprises probably corresponds to a lower capacity to invest in sophisticated savings. The p-value was 0.308, indicating that the difference is not statistically significant.

• Adoption of Clean Energy

Among the businesses surveyed, medium-sized businesses are the most likely to adopt this practice, with 54% of them, followed by small businesses with 35%. Adoption of clean energy is only applied by 22% of the large enterprises surveyed and by none of the micro enterprises. Medium-sized enterprises seem to perceive clean energy as appropriate for their business. Large businesses have more capital but are unlikely to address the issue of clean energy. Micro-enterprises do not have sufficient capital or overall capacity to address clean energy use. The p-value = 0.0064; therefore, the difference between businesses in terms of size is statistically significant for clean energy adoption.

• Waste Reduction

Large enterprises are the focus for waste reduction, with 94% of the large enterprises surveyed, followed by medium-sized enterprises with 70% representation, and among small enterprises, 44%, and only 10% of micro-enterprises are adopting the practice. Waste reduction programmes require systemic changes (internal audits, reporting, investment in recycling)—something that large enterprises can afford. Micro-enterprises lack technical equipment and personnel resources. The statistical difference is highly significant, p = 0.00003.

• Life Cycle Assessment (LCA)

The low and relatively even adoption of this practice means that p = 0.4833, i.e., there is no statistically significant difference in the adoption of this practice between different categories of enterprises. The practice is applied by 28% of large enterprises, 14% of medium enterprises, 13% of small enterprises, and 10% of micro enterprises participating in the survey. LCA is methodologically challenging, and, therefore, it is only implemented by a narrow group of larger and innovation-oriented enterprises; however, the differences do not reach the statistical level of α = 0.05 due to the small number of users.

• Collaboration

Collaboration, stakeholder partnerships, and related certification are applied by 67% of large enterprises, 30% of medium-sized enterprises, 22% of small enterprises, and 10% of micro-enterprises involved in the survey.

Large enterprises have obligations due to legislation and pressure from supply chains to have the necessary certification; for small and micro-enterprises, fees and administration are barriers. With this practice, the p-value reached 0.0044 and, therefore, the difference is statistically significant.

• Green Logistics

Green logistics are used by 39% of large enterprises, 27% of medium enterprises, 26% of small enterprises, and 20% of micro enterprises. The above figures represent a slight increase with the size of the enterprise. Logistics measures go beyond the manufacturing halls and can be outsourced; therefore, the differences between enterprises are neither significant nor statistically significant.

Pearson’s chi-square test revealed no statistically significant regional differences in the adoption of green manufacturing practices (χ² = 14.32, df = 42, p = 0.098). In addition, logistic regression models did not identify any significant regional predictors. Although minor regional variations were observed, these differences remain statistically inconclusive, leading to the rejection of RH1.

Hypothesis RH2 proposed that large industrial enterprises are more likely than smaller ones to adopt green manufacturing practices, owing to their superior resource availability and technical capabilities. The results from logistic regression analyses provide partial confirmation of this hypothesis. Statistically significant effects of enterprise size were found for waste reduction (p = 0.0497) and collaboration (p = 0.0101), indicating that large enterprises are more likely to implement these practices compared to medium-sized firms. The observed effects of life cycle assessment (LCA) and sustainable product design were positive but did not reach statistical significance (p = 0.1108 and p = 0.1077, respectively). Interestingly, in the case of clean energy adoption, a significant effect was detected (p = 0.0357); however, the direction of the effect was contrary to expectations, with large enterprises being less likely than medium-sized ones to adopt clean energy solutions. Chi-square test results further reinforced the statistical association between enterprise size and the adoption of clean energy (p = 0.0064), waste reduction (p < 0.0001), and collaboration (p = 0.0044). Taken together, the findings confirm RH2 only in part. While the hypothesis is validated for certain practices—particularly those related to stakeholder engagement and internal efficiency—the inconsistent effects across all practices, and the reversal observed for clean energy adoption, prevent full confirmation of the hypothesis.

Larger enterprises exhibited significantly higher adoption rates of stakeholder-visible practices, particularly collaboration and certifications (p = 0.0101), while green product design showed trend-level significance. These outcomes are consistent with the assumptions of stakeholder theory and confirm RH3.

The region with higher environmental awareness (Bratislava) demonstrated slightly higher adoption of legitimacy-driven practices such as green product design and collaboration. However, these differences did not reach statistical significance in most models. Thus, the available evidence for RH4 remains inconclusive.

5. Discussion

The results of this study showed that the level of adoption of green manufacturing in industrial enterprises in Slovakia is relatively homogeneous, without statistically significant regional differences. This fact suggests that the factors influencing the uptake and achievement of green manufacturing practices and concepts, e.g., legislative and economic factors, are uniform and at approximately the same level across all categories of enterprises in Slovakia. Resource conservation was the most implemented practice, while life cycle assessment (LCA) was the least implemented, which may be related to the lack of expertise and the lack of necessary technical support to implement this tool.

The finding that there are no statistically significant differences between Slovak regions in the rate of adoption of green manufacturing can be interpreted through institutional theory. The latter posits that in environments with a uniform regulatory framework and centralised environmental policy, so-called isomorphism—that is, the alignment of organisational behaviour across regions—occurs (Barbieri et al., 2023). Slovakia, as a small country with relatively homogeneous legislation and subsidy policies, may, for this precise reason, exhibit an absence of significant regional diversity, in contrast to larger countries such as Germany or China (Grashof & Basilico, 2024; Liu et al., 2021).

This uniformity may also stem from the centralised governance structure and the EU-level environmental regulations that apply equally to all Slovak regions, regardless of their economic development level.

By comparing our results with studies (Grillitsch & Hansen, 2019; Liu et al., 2021), it is possible to identify different approaches to the issue of green manufacturing and regional disparities. The study by Grillitsch and Hansen (2019) highlights that the development of green industries depends on the type of region, with metropolitan regions having a greater capacity for innovation and implementation of green technologies. In contrast, the results of our study suggest that the adoption of green manufacturing practices in Slovakia is not significantly influenced by regional differences, but rather by economic and legislative conditions. A study (Liu et al., 2021) analysing Chinese regions shows that the green practice exhibits significant regional disparities with weaker regions facing difficulty catching up with stronger regions in green economy development.

The differences compared to studies in other countries or parts of the world may be due to the small size of Slovakia, lower economic diversity, and relatively low questionnaire response rates. The results are also surprising because the economic level of individual regions of Slovakia is not homogeneous, with significant differences in economic performance, investment attractiveness, and infrastructure development between regions. The Bratislava region, as the most economically developed region, significantly outperforms other regions in the indicators such as GDP per capita, labour productivity, or employment rates. Conversely, the least developed regions, such as the Prešov and Banská Bystrica regions, lag behind in economic performance and face higher unemployment rates, lower investment volumes, and less diversified economic base.

These regional economic disparities, however, do not appear to translate into significant differences in green manufacturing adoption, which reinforces the argument that institutional and policy-level factors have a stronger influence than regional economic variation.

The limitations of this study lie mainly in the relatively low return rate of the questionnaires (11.4%), which may affect the generalisability of the findings. Low frequencies in some practices and categories may reduce the reliability of the Pearson chi-square test. Further research should work with larger sets of respondents or with aggregated categories of practices. Nevertheless, it provides a valuable insight into the issue of the introduction of green manufacturing practices and environmental measures in industrial enterprises in Slovakia and allows identification of the main barriers faced by enterprises. Although the research sample consisted of only 88 respondents, it can be concluded that even such data provides relevant insights into the trends and challenges associated with green manufacturing.

Regarding the impact of enterprise size, the results confer the assumptions of the resource-based view (RBV), according to which larger companies have more internal resources and organisational capabilities, which allow them to more easily implement technology- and process-intensive practices such as LCA or renewable energy deployment (W.-L. Lin et al., 2019). In contrast, micro-enterprises focus on lower investment-intensive practices such as resource conservation and green product design, indicating a limited ability to diversify green strategy (Grillitsch & Hansen, 2019).

There is no statistically significant difference in the adoption of green manufacturing practices across business sizes for four out of the seven assessed practices. However, significant differences were found in the implementation of clean energy adoption, waste reduction, and collaboration and certifications, indicating that enterprise size plays a role in certain but not all areas of green manufacturing. This highlights the need for differentiated policies and support mechanisms tailored to specific capacities and priorities of enterprises of different sizes.

These findings are also consistent with studies that highlight that environmental actions in small businesses are more often motivated by immediate cost reductions rather than long-term sustainability strategies (Baah et al., 2021; Yusup et al., 2014). This creates room for the design of differentiated support policies according to the size and capacity of enterprises.

The findings of our research confirm that green manufacturing is mainly applied by large enterprises. Large enterprises dominate waste reduction, collaborative partnerships, and certifications. Medium-sized enterprises apply clean energy adoption to a greater extent. Micro-enterprises lag in most areas, with the only exception being sustainable product design (probably due to specific projects). This suggests that policy measures targeting micro and small enterprises should emphasise support for lower-cost, high-impact interventions, such as energy efficiency upgrades or modular green technologies.

The research has shown statistically significant differences between businesses in terms of size for the following green manufacturing practices: Adoption of Clean Energy, Waste Reduction, and Collaboration. For Sustainable Product Design, Resource Conservation, LCA, and Green Logistics, no statistically significant difference was found—thus, their adoption is not systematically determined by the size of the enterprise, but rather by other factors (industry, management practice, local subsidies).

The finding that micro and small enterprises focus more on resource conservation is in line with the resource-based view, which emphasises that enterprises act based on their available capabilities. In contrast, large enterprises are better placed to implement more complex practices such as LCA, which require greater financial and technical resources.

According to previous research (Calza et al., 2017; Marin et al., 2015), SMEs face more significant barriers to the uptake of eco-innovation compared to large enterprises. These enterprises often lack the financial and staff resources to implement sophisticated environmental measures (Buttol et al., 2012). It is, therefore, essential to focus on more effective support for SMEs through targeted legislation and financial incentives.

Small enterprises show a higher return on investment in eco-innovation than large enterprises, suggesting that small enterprises are more likely to seek diversity and visibility to obtain better resources (W.-L. Lin et al., 2019). This may also be related to our findings that small businesses prefer resource conservation and sustainable product design, which may indicate that they focus on those green manufacturing practices that they can implement considering (or within) their financial and technical capabilities, with a focus on efficient use of available resources.

It is important to note that the findings of this study are based on a relatively small and non-random sample (88 responses, 11.4% response rate), which limits the ability to generalise the results to the entire population of Slovak industrial enterprises. Rather than providing conclusive evidence of structural patterns, the results should be interpreted as indicative of emerging trends and potential relationships that warrant further investigation.

In particular, the use of a voluntary survey may introduce self-selection bias, meaning that companies more active or interested in green manufacturing may have been more likely to respond. This limitation must be taken into account when interpreting the homogeneity of regional data or the influence of enterprise size.

Future research should further investigate how enterprise-level dynamic capabilities—such as innovation routines, learning orientation, or environmental leadership—mediate the relationship between enterprise size and green practice adoption.

On the other hand, while our data show regional homogeneity, other research suggests that regional differences in the application of eco-innovation can be significant. For example, a study on regional technological capacities in Europe shows that areas with historically strong technology sectors have higher rates of green transformation, with Germany among the leaders in this area (Barbieri et al., 2023). Similar findings were also reported in a study on regional differences in technological diversification in green transformation, which shows that economically strong regions are more likely to have green diversification, while weaker regions have significantly limited opportunities for green transformation (Grashof & Basilico, 2024). The findings of the research confirm the validity of institutional theory, particularly the role of coercive institutional pressures, which appear to have a stronger influence than regional economic factors on green manufacturing adoption in Slovakia.

In this context, Slovakia represents an interesting example, where institutional homogeneity seems to suppress the expected influence of regional technological capacity, pointing to a need for deeper qualitative exploration of how regional actors interpret and implement national policies.

6. Conclusions

This study provides a comprehensive view of the state of adoption of green manufacturing in industrial enterprises in Slovakia. Enterprise size emerges as the dominant determinant: large enterprises are up to nine times more likely than medium ones to implement comprehensive waste reduction programmes and about five times more likely to engage in collaborative or certification schemes. Micro-enterprises lag especially in capital-intensive measures such as adoption of clean energy, where their estimated odds ratio is significantly below one. Regional affiliation within Slovakia plays only a minor role; most regional coefficients are statistically insignificant, suggesting a fairly homogeneous regulatory and infrastructure environment. A notable exception is a slightly lower propensity for collaboration in the Prešov region.

The research shows that there are no statistically significant differences between regions of the Slovak Republic in the rate of adoption of green manufacturing practices. This finding supports the assumptions of institutional theory that a uniform regulatory and economic environment can lead to homogeneous business behaviour across regions. In the case of Slovakia, centralised environmental policy and relatively low geographical and economic diversity between regions may also contribute to this homogeneity.

The research shows that large enterprises are better placed to implement green manufacturing practices, while small- and medium-sized enterprises face greater barriers. Therefore, more targeted support mechanisms need to be developed to help small enterprises overcome these barriers. The resource-based view (RBV) confirms that large enterprises have stronger internal capacities (financial, personnel, technological) that allow them to implement more technologically demanding environmental measures—for example, LCA or the integration of renewables. Smaller companies tend to focus on less costly practices such as resource conservation or green product design. The paper confirmed that enterprise size is a key determinant of the adoption of certain green manufacturing practices, particularly those that are stakeholder-oriented and capital-intensive (RH2, RH3). In contrast, regional effects (RH1, RH4) were less pronounced, suggesting a strong influence of institutional pressures across Slovak regions. The relative regional homogeneity in the adoption of green manufacturing practices appears to stem primarily from a unified legislative framework, rooted in EU and national regulations, which exerts a common coercive pressure on enterprises regardless of their geographical location—consistent with institutional theory. The influence of normative and mimetic pressures on the adoption of green manufacturing practices is likely weaker.

By comparing a nationally uniform institutional environment with a highly uneven distribution of resources, our results clarify the relative weight of external and internal factors. The negligible regional effects corroborate institutional isomorphism arguments: when legislation and enforcement are centralised, coercive pressures become spatially homogeneous. Conversely, the strong size gradient highlights the enduring salience of RBV in explaining which enterprises can convert those external pressures into substantive action. In this sense, the Slovak case shows that even in small open economies—often assumed to be policy takers—resource heterogeneity, rather than regional context, shapes the depth of ecological transformation.

These findings add important nuance to established theories of environmental strategy. While institutional theory explains cross-regional similarity through centralised coercive pressures, our results highlight that such institutional homogeneity does not eliminate performance gaps when resource availability is unevenly distributed across enterprises. This calls for more integrated theoretical models that reflect both formal institutional alignment and informal organisational disparities.

This insight refines international comparative theories of corporate greening: studies from large federal economies frequently report both regional and size effects, whereas our findings suggest that in administratively centralised, smaller economies institutional convergence may suppress territorial variation and shift the explanatory burden to enterprise-level capabilities. Future cross-country work could test whether similar patterns appear in countries such as Portugal, Denmark or Slovenia, where regulatory homogeneity coexists with pronounced size asymmetries. In such contexts, the resource-based view becomes a more relevant predictor of green adoption depth, especially in relation to capital-intensive measures.

Slovakia thus provides a valuable empirical setting to test the boundaries of mainstream theoretical assumptions. Similar dynamics may be present in other small EU countries like Slovenia or Portugal, where high regulatory coherence coexists with stark differences in enterprise capacity. This encourages future comparative research to explore how size-related resource asymmetries interact with isomorphic institutional environments across different national contexts.

The contribution of this study is that it analyses the rate of adoption of green manufacturing in the context of a small open economy and links the categorization of enterprises by size to their environmental behaviour. The results complement the findings from larger research in developed countries, while offering empirical evidence on the need for differentiated support strategies. Proposed mechanisms may include a combination of financial support, technical assistance, and training programmes aimed at increasing sustainability awareness and skills. From a management perspective, the adoption of green manufacturing is linked to the need to increase competitiveness, improve reputation, and comply with legislative requirements. This requires strategic planning, investment in human resources and technology, and the development of cooperation within supply chains. (Rashid et al., 2024; Calza et al., 2017). In order to implement green manufacturing principles and practices, companies should pursue the following:

• Invest in research and development of green technologies and processes.
• Actively seek out calls for proposals and financial support for technologies that promote green manufacturing.
• Strengthen cooperation with partners and stakeholders.
• Educate employees and develop green competencies.
• Regularly assess environmental performance and set sustainability goals.

The transition to green manufacturing is a complex process influenced by the size of the enterprise, regional specifics, and external pressures. Industrial companies play a key role in the transition to a sustainable economy, and their success depends on their ability to adapt to new market, legislative, and societal requirements.

In the future, the authors recommend expanding research to include multivariate analytical practices, such as regression models or factor analysis, to gain a deeper understanding of the relationships between business characteristics and specific environmental practices. At the same time, it is desirable to complement quantitative analysis with qualitative approaches, such as interviews and expert assessments, which can uncover specific motives and barriers to the transition toward green manufacturing. A combined methodological approach would provide a stronger basis for designing effective supportive policies aimed at increasing sustainability in the industrial sector. The current focus on two factors—region and enterprise size—offers a simplified perspective on reality, even though these dimensions represent external and internal influences, respectively, and are well grounded in established theoretical frameworks such as institutional theory (institutional isomorphism) and the resource-based view. Future research should, therefore, examine a wider range of factors that may influence the adoption of specific green manufacturing practices.