Digital Maturity as a Driver of Sustainable Development Goal Achievement in Polish Enterprises: Evidence from Empirical Research

Abstract

The aim of this article is to assess the digital maturity of Polish enterprises and to identify the most and least developed dimensions of maturity within these organizations in the context of their potential to achieve sustainable development goals. The authors pose research questions regarding the overall level of digital maturity in Polish enterprises, its variation depending on the type of business activity, and the specific dimensions of digital maturity that were rated the highest and lowest. The main thesis of the article assumes that the level of digital maturity determines a company’s sustainable orientation. The article presents the results of empirical research conducted among 697 Polish enterprises operating in the manufacturing, trade, and service sectors. The study employed the seven-dimensional Digitalcheck Mittelstand model for assessing digital maturity. The average scores of digital maturity, both by industry and by specific dimensions, were mapped to six levels of digital maturity adapted for Polish enterprises. The findings confirm that Polish enterprises demonstrate a moderate level of digital maturity. Among the analyzed sectors, manufacturing enterprises exhibit the highest level of maturity. The study also confirmed that the highest maturity levels are observed in the areas of organization and processes. Conversely, the lowest level of digital advancement is found in the environmental dimension, indicating a gap in aligning corporate strategies with green funding programs and eco-initiatives. Future research should take into account causal mechanisms and disruptive factors affecting digital transformation in organizations.

1. Introduction

Maturity is understood as the ability of an individual or organization to achieve excellence through continuous development and improvement [1]. In the digital context, it takes a more complex form and refers to the systematic way in which enterprises adapt to dynamic and ongoing changes in their digital environment [2].

Digital maturity is not synonymous with digital transformation; rather, it serves as its foundation. While digital transformation involves a comprehensive reshaping of processes, business models, and customer relations, digital maturity reflects an organization’s readiness to effectively implement such changes [3].

The process of digital transformation in enterprises can take many forms, and its success depends on numerous factors, particularly the level of digital maturity. Digital maturity can thus be defined as an organization’s ability to effectively leverage digital technologies to achieve strategic priorities. To measure this capability, digital maturity models are used to identify key competencies characteristic of organizations at advanced stages of digital development. The maturity level functions as an indicator showing the stage of digitization at which a given enterprise currently operates.

Digital maturity models serve several important functions: they act as diagnostic tools to identify current competencies, define the desired target state, and provide guidance on how to achieve it. They are also used to assess enterprises’ preparedness for implementing the concepts of Industry 4.0 [4]. In practice, this means not only diagnosing the current level of digitalization but also identifying areas in need of improvement and initiating the appropriate transformational activities. As such, the assessment of digital maturity becomes an indispensable component of enterprise transformation strategies.

Research shows that the level of digital maturity has a direct impact on the achievement of sustainable development goals by enterprises. By using advanced technologies, enterprises can improve their performance in the areas of environment, society, and corporate governance (ESG), thereby promoting sustainable development [5]. The integration of digital tools enables more efficient resource management, a reduction in environmental impact, and increased transparency in business activities [6,7].

There are numerous digital maturity models on the market, varying in scope, structure, and methodology. However, they share a common goal: to assess the digital maturity of organizations and indicate directions for further development. One such model is the Digitalcheck Mittelstand model [8]. There is no evidence that the Digitalcheck Mittelstand model has previously been applied in research on Polish enterprises. Although there are publications addressing digital transformation in Poland [9,10,11], they do not reference the use of this specific model. Therefore, our study may be the first attempt to adapt and apply the Digitalcheck Mittelstand model in the Polish context. Such replication is important because it enables validation of the model in a new cultural and economic environment and supports the development of diagnostic tools tailored to the Polish market.

The aim of the article is to assess the digital maturity of Polish enterprises and identify the most and least developed maturity dimensions. The assessment is based on a dataset collected directly from 697 business units with diverse profiles (manufacturing, trade, and services). The following research questions were posed:

Q1: What is the overall level of digital maturity in Polish enterprises?

Q2: Does the level of digital maturity vary depending on the type of business activity (manufacturing, trade, services)?

Q3: In which dimensions of digital maturity—strategy, customer, products and services, processes, infrastructure, environment, organization—do Polish enterprises perform best and worst?

The authors propose the thesis that the level of digital maturity determines an enterprise’s sustainable orientation. The higher the level of digital maturity, the greater the enterprise’s potential to achieve ESG goals. Therefore, answering the research questions will provide insights into the capacity of Polish enterprises to fulfill the objectives of sustainable development strategies.

2. Theoretical Background

2.1. Definitions and Theories on the Digital Maturity of Enterprises

The digital maturity of enterprises is a compelling topic that has been addressed in the literature through numerous concepts regarding its definition and measurement. The starting point for defining digital maturity is the process of digital transformation. Digital transformation is a highly relevant and widely discussed issue in the literature. It involves the adoption of advanced technologies to optimize economic and social processes. Technologies support organizations and societies in reducing resource consumption, minimizing waste generation, and maximizing efficiency. The contemporary global challenges undoubtedly accelerate the search for solutions, which often take the form of technological transformations [12].

Babacoglu et al. [13] emphasize that many authors define digital transformation as the implementation of technology across all areas of an organization. They regard the process as evolutionary in nature, aimed at using digital capabilities and technologies to create value propositions within established business models.

Enterprises that invest resources in digital transformation tend to grow and achieve their intended outcomes [14,15]. This process can be viewed as a path along which organizations learn how to achieve maturity in digital transformation. Accordingly, their level of digital maturity evolves [16]. Digital maturity is thus a defined state that, according to Nikkhou et al. [17], enables organizations to achieve set goals and find ways to resolve or prevent problems.

Berghaus and Back [18] define digital maturity as a set of organizational skills and resources that enable effective execution of digital transformation processes.

Zaoui and Souissi [19] observed that the ongoing digitization of the environment, and the resulting development of digital maturity within organizations, is synonymous with increasing organizational competitiveness. One of the most frequently cited definitions of digital maturity is offered by Chanias and Hess [20], who describe it as “the status of a company’s digital transformation” and a measure of “what a company has already achieved concerning transformation efforts”. Digital maturity serves as an indicator to determine the organization’s position and to identify further steps in the digital transformation process. However, measuring digital maturity is challenging, primarily due to the multiple interpretations of digital transformation [19]. Ka et al. [21] point out that digital transformation is a continuous process, and therefore, digital maturity supports the assessment of an organization’s current level of digitization while providing real-time feedback on its progress.

Digital maturity is also presented in the literature as a trait attributed to individuals. In this context, Laaber et al. [22] conceptualize digital maturity as the skills and attitudes that allow individuals to use digital technologies in ways that support personal growth and social integration. This understanding is grounded in the assumption that growth and adaptation are the foundations of psychosocial maturity, with an added emphasis on the use of digital technologies, which represent a unique set of challenges individuals must face.

The models used to assess digital maturity levels extensively describe the current state of the enterprise and strategies for implementing the Industry 4.0 concept [4]. These are research-based methods that enable the evaluation of an enterprise and the actions it has taken to reach a particular level of maturity.

The architecture and areas of application of digital maturity models vary significantly [23]. Nevertheless, important similarities can be observed: these models outline analogous stages that guide organizations toward digital maturity [24]. The starting point for any digital organization is a clearly developed digital strategy aligned with the overall corporate strategy. In the early phases, models emphasize strategic prioritization, work flexibility, and executive support for the transformation process. These initial stages highlight the strategic importance of innovation and the need to consciously develop digital innovation capabilities, foster collaboration, and systematically explore the potential of new technologies. Organizations then begin to realize new business opportunities and models, including platformization, which serves as a central concept linking these ideas [25]. Current research confirms that platformization plays a crucial role in the digitization of traditional sectors [26]. Intermediate stages focus on organizational culture, structure, leadership style, and effective change management. The stages of achieving digital maturity are illustrated in Figure 1.

At the stage of shaping the organizational structure, leadership style, and transformation management, Berghaus and Back define this phase as “commit to transform” [18]. During this period, a profound shift in organizational culture occurs, along with a redefinition of roles and responsibilities. Key competencies at this stage include an increased willingness to take risks and a proactive approach to learning from failure.

As organizations approach the final stages, user orientation becomes central [27], which manifests in the personalization of customer experiences and the customization of products and services. This shift in perspective is made possible through data-driven decision-making. Coherent processes and real-time customer data analysis bring the organization closer to a state of digital maturity.

Due to the rapid development of technologies and growing customer expectations, digital maturity can be described as the continuous anticipation of, and adaptation to, a changing environment. The ability to critically monitor business performance, combined with a readiness for constant evolution, are hallmarks of a digitally mature organization.

2.2. Dimensions of Digital Maturity in Enterprises

Digital maturity is assessed across several areas, commonly referred to in the literature as dimensions of digital maturity. One such area is business strategy and management, which includes the alignment of the digital strategy with the organization’s overall strategy, leadership capacity in digital transformation, and governance mechanisms. Organizational culture and capabilities are also evaluated, particularly with regard to change readiness, pro-innovation attitudes, risk-taking propensity, leadership style, and the ability to learn from mistakes.

Another dimension concerns internal processes and business models. Here, the assessment focuses on the extent of digitization in key operational processes, work flexibility, and the implementation of new digital business models, including platform-based models.

Technology and IT infrastructure are also key areas of evaluation. These include the technological advancement of the enterprise (e.g., cloud computing, IoT, AI), system integration, and real-time data processing and analysis capabilities. Particular attention is paid to the ability to collect, process, and leverage data effectively (i.e., being a data-driven enterprise), including the maturity of BI tools, predictive analytics, and real-time analytics.

Digital maturity is also assessed in the area of customer experience, with a focus on the degree of personalization of products and services, user-centric design, and the use of digital communication and sales channels [28,29].

Petzolt et al. [30] identify seven dimensions for assessing digital transformation maturity in enterprises. Including all of them reflects the complexity of digital transformation, as each represents a different area of organizational competence and resources that together determine an enterprise’s ability to function effectively in the digital era. This study adopts the following dimensions:

• Strategy. The strategy dimension refers to the organization’s ability to formulate and implement a coherent vision for digital transformation and to plan long-term technology adoption. Key elements include clearly defined goals, implementation timelines, and the involvement of external partners in the innovation process. Developing a digital strategy requires management not only to understand the benefits of new technologies but also to continuously adapt the business model in response to changing market conditions [31,32].
• Customers. The customer dimension focuses on the use of digital technologies to collect, analyze, and segment customer data and to provide a consistent, omnichannel customer experience. Digitally mature organizations systematically use big data and analytics tools to create customer profiles and segments, enabling personalized offers and stronger customer relationships [33].
• Products and Services. This dimension assesses the organization’s ability to develop, deliver, and continuously improve its digital offerings based on data analysis. Agile methods and pilot deployments are particularly valuable here, as they enable rapid market feedback and risk mitigation. Digitally enhanced products support ongoing innovation and competitiveness [34].
• Processes. This dimension concerns the digital mapping and automation of key operational processes. Maturity in this area is measured by the completeness, consistency, and timeliness of process data, as well as the ability to use such data in real time. Automation of routine tasks and standardized cooperation with external partners contribute to increased efficiency and flexibility [35].
• Organization. The organization dimension includes company culture, management structure, and employees’ digital competencies. Key factors include leadership openness to feedback, promotion of a culture of experimentation and learning, and systematic training in new technologies. A high level of maturity also means involving employees at all levels in the digital transformation process [3].
• Infrastructure and Technology. This dimension focuses on the flexibility, scalability, and security of IT systems. Digitally mature organizations can quickly deploy new functionalities, integrate data from various sources, and manage user roles and permissions effectively. Regular technology updates and centralized data storage form the basis of a stable digital environment [36].
• Environment. The environment dimension includes external factors that influence the digitalization process, such as access to funding and cooperation with public institutions. Digitally mature enterprises actively leverage government support programs and digitalization grants, accelerating innovation implementation and strengthening market competitiveness [3].

2.3. Levels of Digital Maturity in Enterprises

The literature predominantly describes digital maturity levels in general terms. The most common labels are “low–high maturity levels” [37], or simplified classifications that divide organizations into “less digitally mature” and “more digitally mature” [38], without indicating intermediate stages. In Salume’s study [39], no distinct “levels” are defined in categorical terms; instead, digital maturity is measured using a continuous scale from 0 to 10 across eight competence areas (strategy, leadership, market, operations, people, culture, governance, and technology). The quantitative PLS-based model is designed to assess the intensity of these competencies rather than to define discrete maturity thresholds. Higher values on this scale correlate with greater digital maturity, but the authors do not specify additional “stages” or categories.

One of the few examples in which authors define and name maturity levels is the Digitalcheck Mittelstand model [30]. This model defines five levels, with each organization receiving an online report based on the average score from 70 questions rated on a 1–5 scale. Each level is labeled and briefly described:

• Dreamers—digital activities are scattered, ad hoc, and lack strategy.
• Beginners—isolated pilot initiatives are in place, but still fragmented.
• Up-and-Comers—early-stage integrated solutions emerge, with growing leadership and team engagement.
• Secret Favourites—digital processes are well-established, a culture of experimentation is present, and new business models are being developed.
• Trailblazers—optimization across all dimensions and co-creation of industry standards.

The level assigned to a company is determined by the average score across all seven dimensions of digital maturity; exceeding the set threshold qualifies the organization for the next stage.

A second framework comes from the Patterns of School Improvement model [40]. Although it was originally developed for the education sector, its five levels can be applied to enterprises. The authors define:

• Exchange—organizations implementing isolated digital projects without strategy or integration.
• Enrich—organizations coordinating digital experiments and sharing experiences across teams.
• Enhance—organizations methodically changing data-based processes and evaluating outcomes.
• Extend—organizations in which digital culture is embedded in daily operations; employees create their own digital environments.
• Empower—organizations act as regional or industry leaders of innovation, co-creating standards.

A third proposal comes from a model developed for telecommunications operators [41], which distinguishes six maturity levels specific to that industry:

• Not started—no digital transformation activities.
• Initiating—ad hoc pilot projects are being launched.
• Enabling—the organization builds the foundations for transformation using technology, processes, and competencies.
• Integrating—end-to-end solutions are implemented, and a culture of collaboration is fostered.
• Optimizing—processes are optimized using data, advanced technologies, and personalization.
• Pioneering—the organization sets trends and develops new models and standards in digitalization.

A fourth model, developed by Deloitte, uses self-assessment across more than 90 parameters in four areas and identifies six digital archetypes:

• Laggards—organizations with neither strategic nor operational digital capabilities, and no use of digitalization to improve efficiency.
• Followers—organizations systematically building digital competencies in both strategy and operations.
• Operators—enterprises focusing on digitalizing core value chains using flexible and innovative solutions.
• Innovators—enterprises introducing major digital innovations and building innovative portfolios, though operational performance remains average.
• Potentials—organizations simultaneously developing digital strategies and enhancing operations to gain cost benefits.
• Champions—enterprises combining a coherent digital strategy with operational excellence, gaining competitive advantage through agility [42].

All four approaches to defining digital maturity levels emphasize a progression from ad hoc activities to becoming a leader or pioneer. They highlight changes across strategy, processes, culture, and technology, and they use descriptive labels to illustrate the nature of each stage. What differentiates them is the level of detail, scope of application, and measurement methodology. For example, Digitalcheck Mittelstand is based on average questionnaire scores, Valdez-de-Leon [41] uses threshold-based evaluation in each dimension, while Pata et al. [40] offer a more qualitative description of progress through phases.

As a result, each model provides a useful framework for classifying organizations by digital maturity level, and the choice of model should depend on the characteristics of the studied population and the objectives of the research.

2.4. The Impact of Digital Maturity of Enterprises on Sustainable Development

Our research on the digital maturity of enterprises provides an important contribution to the analysis of the relationship between digital transformation and the achievement of sustainable development goals. Hariyani et al. [1], in their article, integrated the issue of digital technologies into discussions on sustainable development, emphasizing their dual role in both environmental and socio-economic sustainability. Whereas traditional models of sustainable development focused exclusively on ecological, economic, and social factors, digital tools are now increasingly recognized as drivers of efficiency, transparency, and integration. The contemporary approach to sustainability must be expanded to include technological infrastructure and digital skills as fundamental elements necessary for achieving the sustainable development goals [5].

Polishchuk et al. [43] argue that digitalization is a factor leading to economic, environmental, social, and cultural changes, as well as in other areas, and constitutes a necessary condition for sustainable development, as evidenced by the main directions of SDG implementation. The findings of Celary and Piwowarczyk [44] confirm the existence of a significant, moderately strong correlation between digital transformation activities and ESG outcomes. This underscores the contribution of such practices to improvements in organizational sustainability. These publications highlight the need to examine the digital maturity of enterprises as a key determinant of how firms manage the implementation of sustainability objectives.

Several research projects have addressed this issue. For instance, Polyanska et al. [45] conducted studies in Ukrainian energy companies, finding a link between their digital maturity and sustainable development initiatives. Similarly, research by Irimias and Mitev [46] confirmed the positive impact of digital maturity on green development. Particularly relevant are the findings of Košíková and Vašaničová [47], which confirm the connection between digital readiness and sustainable development. In their research, the authors applied the NRI index, analyzing its relation to 26 European Union countries. The study examined links between NRI dimensions—technology, people, governance, impact—and selected SDGs: Good Health and Well-Being, Quality Education, Women’s Economic Opportunity, Affordable and Clean Energy, and Sustainable Cities and Communities. The key findings demonstrate that digital readiness contributes to better sustainability outcomes, though there is no automatic or direct correlation. Correlation and cluster analyses confirmed that digital readiness and SDG performance are, in most cases, positively related, but this relationship is not uniform across all countries. The decisive factor lies in how digital technologies and tools are integrated into public policies and sustainability strategies.

An additional perspective is offered by the resource-based view (RBV), which frames digital maturity as a key organizational resource supporting competitive advantage in sustainability [48]. RBV emphasizes that digital technologies alone do not generate value; only when combined with unique organizational competences and routines do they enhance ESG outcomes [49]. From this standpoint, digital maturity enables the effective use of resources such as data, predictive analytics, or artificial intelligence in pro-environmental and pro-social processes. The dynamic capabilities framework, in turn, highlights the role of continuous adaptation and reconfiguration of these resources, allowing organizations to respond to rapid changes in regulation, technology, and stakeholder expectations [50]. Particularly important are the capabilities to identify opportunities and threats, exploit them effectively through innovation, and transform business models, which form the basis for translating digital transformation into lasting ESG results [51]. Juxtaposing these two perspectives provides a deeper explanation of how digital maturity contributes to sustained advantage in sustainability, rooted in organizations’ unique resources and adaptive capabilities.

By leveraging advanced technologies, enterprises can improve their performance in environmental, social, and governance (ESG) areas, thereby promoting sustainable development. The integration of digital tools such as cloud computing, the Internet of Things (IoT), and intelligent robotics enables more efficient resource management, reduces environmental impact, and enhances transparency. These advancements not only benefit individual enterprises but also generate positive spillover effects across the entire supply chain.

The implementation of digital technologies increases operational efficiency and corporate innovativeness, which are key mediators in improving corporate sustainability. Enterprises that successfully implement digital transformation can achieve higher levels of operational effectiveness, thus supporting their sustainability goals [52].

The effectiveness of digital transformation in achieving sustainable development goals is influenced by factors such as leadership empowerment, employee education, and internal controls. Collecting customer data plays a vital role in developing effective marketing strategies targeted at environmentally conscious segments. By gathering behavioral, preference, and demographic data, firms can identify distinct segments that prioritize environmental concerns [53]. This data-driven approach enables targeted marketing efforts that resonate with environmentally aware consumers, enhancing both engagement and loyalty [54].

Moreover, continuous data analysis allows enterprises to refine their strategies, ensuring they remain relevant in dynamic market environments and effectively respond to changing preferences within eco-conscious segments. Enterprises are increasingly utilizing digital data analytics tools (including predictive analytics) to design and implement environmentally sustainable product and service innovation strategies [55]. These tools enable organizations to assess and improve sustainability performance by incorporating environmental and social criteria into their operations. Conducting such analyses fosters eco-innovation and helps build competitive advantage.

Digitalization significantly improves process optimization and decision-making, contributing to sustainable development through better resource efficiency. Research indicates that effective digital practices can support the achievement of sustainable development goals related to resource use, particularly SDG 9, SDG 11, SDG 12, and SDG 13, as well as SDG 17 [56].

Modern IT infrastructure plays a crucial role in integrating environmental, social, and governance reporting with enterprise energy management systems. By leveraging advanced technologies such as cloud computing, artificial intelligence (AI), and big data analytics, organizations can improve their ESG indicators and streamline reporting processes [57].

Securing external funding for digitalization through green grants and funds presents both significant opportunities and challenges. Integrating digital tools with green finance can increase efficiency and reduce costs while addressing funding gaps for sustainable development [58]. However, barriers such as perceived risk and lack of reliable data may hinder access to these funds.

Table 1. The impact of digital maturity on sustainability.

Dimensions of Digital Maturity	The Impact of Digital Maturity on Sustainability	References
Strategy	A strategic focus on digital transformation enables the integration of environmental goals into business models.	[52,59]
Client	Customer data collection and analysis support segmentation and offer personalization based on pro-environmental attitudes.	[53,54,60]
Product and services	Data-driven product development facilitates eco-innovation and faster validation of solutions aligned with circular economy principles.	[54,61,62,63,64]
Processes	Digital automation and process optimization reduce resource consumption and increase energy efficiency.	[56,65]
Organization	An organizational culture that promotes innovation and openness to change supports the implementation of green management practices.	[6,7]
Infrastructure	Modern and flexible IT infrastructure enables the integration of ESG reporting and energy management solutions.	[57,66,67,68]
Environment	Accessing external funding for digitalization can be linked to green grants and low-emission support schemes.	[58,69,70,71]

Source: own preparation.

presents a selection of literature sources in which the authors identify the impact of technological advancement levels on achieving sustainable development goals.

3. Materials and Methods

3.1. Measurement Model

The benchmark for this study was the framework developed by Petzolt et al. [30], which includes a measurement tool designed to assess the maturity of digital transformation in small and medium-sized enterprises. The authors emphasize that their model is universal and can also be applied to larger enterprises: it was “not designed exclusively for a specific type of organization” and “can reveal change potential in larger firms as well.” The construction of the model is based on dimensions that are universal for any organization, regardless of its size, profile, or other characteristics. The differences between SMEs and large firms concern the scale of resources or the degree of process formalization, but not the areas of investigation themselves. Therefore, the indicators and questions of the model are equally valid in the context of large enterprises—they may only reveal a different response profile. For this reason, the choice of the model is considered justified both on substantive and methodological grounds. The model, tested in Germany, demonstrates high reliability (Cronbach’s α = 0.97) and comprehensive coverage.

The objective of this study was to empirically verify the validity and applicability of the German model in the Polish context.

To measure the digital maturity of Polish enterprises, a seven-dimensional model was adopted. Each dimension is represented by 2 to 21 questionnaire items evaluated on a seven-point Likert scale (1—no advancement, 7—full advancement). For each dimension, an average score (1–7) was calculated and then averaged across all dimensions to produce a global digital maturity index.

The 1–7 score range was divided into six levels (1–6), which define the degree of maturity, and six transformation stages (I–V + maturity), reflecting phases in organizational development (

Table 2. Assumptions of the digital maturity measurement model for Polish enterprises.

Level	Average Score Range	Description	Transformation Stage
Level 1— No Digital Advancement	1.00–1.99	The enterprise does not conduct formal digital initiatives. Nearly all processes are manual and lack visualization. The enterprise is experimenting with its first digital tools.	Stage I
Level 2— Minimal Digital Advancement	2.00–2.99	Initial attempts with digital tools emerge within the enterprise, but implementations are sporadic and limited to selected pilot projects.	Stage II
Level 3— Basic Digital Advancement	3.00–3.99	The enterprise deploys core digital systems, and data collection becomes systematic; however, cross-departmental integration remains limited.	Stage III
Level 4— Moderate Digital Advancement	4.00–4.99	Digital processes cover multiple departments, repetitive tasks are automated, and key performance indicators are monitored in real time.	Stage IV
Level 5— High Digital Advancement	5.00–5.99	Advanced analytics and predictive models support decision-making, and systems dynamically optimize operations based on incoming data.	Stage V
Level 6— Full Digital Advancement	6.00–7.00	The enterprise operates as a fully data-driven ecosystem, using artificial intelligence and machine learning to develop new products, services, and business models.	Digital maturity

Source: own preparation.

). The model assumes six stages and levels of digital maturity, specifically adapted to Polish enterprises.

The aim of the study was to identify the level of digital maturity of Polish enterprises across seven dimensions. The research sought to determine both the transformation stage and the maturity level of enterprises operating in Poland, as well as to compare digital maturity between three groups of enterprises: manufacturing, trade, and service enterprises.

3.2. Questionnaire Development

There are numerous models for assessing digital maturity. Thordsen and Bick [72] conducted a systematic literature review of existing solutions developed between 2011 and 2022. One of the issues addressed in this review was the quality and content of digital maturity models, which proved to be incomparable. Most models do not align with common academic standards, as they are often developed by business consultants designing them outside the academic context. The choice of model for the purposes of this study was based on the availability of the scale (e.g., Deloitte or PwC models are not accessible without paying for their use), its reliability, and the universality of the model (preference was given to models not limited to a single industry, such as manufacturing enterprises). Another important criterion was the dimensions included in a given scale. The authors sought to cover the broadest possible spectrum of digital maturity dimensions.

The measurement tool in our study was based on the “Digitalcheck Mittelstand” model (https://digitalzentrum-berlin.de/digitalcheck-mittelstand, accessed on 20 August 2025). This model identifies seven core dimensions of digital maturity, which served as the framework for the assessment tool. The questionnaire was directly translated from German into Polish, preserving the structure of the maturity dimensions: strategy, customers, products and services, processes, organization, IT infrastructure, and external environment.

The final instrument comprised 70 items rated on a 7-point Likert scale (1—strongly disagree, 7—strongly agree with the given statements). The version tested in Poland demonstrated high internal consistency (Cronbach’s α = 0.99). All questions in the questionnaire were analyzed by the authors in terms of their applicability to the Polish context and their relevance at this stage of the project (exploratory research).

The maturity dimensions, along with their associated variables (in both English and Polish) and reliability indicators, are presented in Appendix A. The adaptation and verification procedure ensured that the applied scale was both consistent and valid, providing a solid foundation for subsequent empirical analyses.

3.3. Data Collection

The survey was conducted by the Research and Development Center of the University of Economics in Katowice using the SurveyMonkey research platform. The research unit maintains an extensive enterprise database, which enabled the construction of a sample comprising enterprises from various economic sectors (manufacturing, trade, and services). This allowed for the collection of cross-sectional data reflecting the diversity of business activity in Poland. Access to the company database also made it possible to include firms of different legal forms (limited liability enterprises, civil law partnerships, sole proprietorships, and joint-stock enterprises).

This sampling approach ensured that the findings account for the specific characteristics of both corporate-structured entities and individual entrepreneurs, thereby increasing the generalizability of conclusions concerning business operations in the Polish market. Based on the available data, the sample was balanced across the three primary types of business activity: manufacturing, trade, and services. These proportions were selected to reflect the actual sectoral distribution nationwide, allowing for comparisons of firm characteristics and behaviors across different areas of the economy.

To increase response rates and reach a broad group of respondents, a mixed-mode survey was employed, utilizing three channels: CAWI (Computer-Assisted Web Interviewing) [73], telephone-supported CAWI, and CATI (Computer-Assisted Telephone Interviewing) [74]. The choice of methods was justified by their wide reach and the ability to access respondents based on predefined criteria. Moreover, the CAWI method is commonly used in business research [75,76,77].

Fieldwork was conducted in January 2025. The average completion time for the questionnaire was 9.5 min. In total, 697 fully completed questionnaires were collected, of which 69 were obtained through CAWI, 501 through telephone-supported CAWI, and 127 through CATI.

3.4. Sample

The research sample consists of enterprises with manufacturing profiles (N = 230), trade (N = 216), and service (N = 251). Among the 697 surveyed enterprises, the largest share comprised limited liability enterprises (37.9%), civil law partnerships (21.4%), and sole proprietorships engaged in trade and services (18.8%). Micro and small enterprises accounted for 41.9% of the sample, medium-sized enterprises for 28.7%, and large enterprises for 29.4%. The oldest company in the sample has been operating in the Polish market since 1932, while the youngest was established in 2022 (

Table 3. Characteristics of the surveyed enterprises [%].

Characteristics	Items	Overall Sample (N = 697)	Manufacturing Enterprises (n = 230)	Trading Enterprises (n = 216)	Service Enterprises (n = 251)
Legal form *	Limited Liability Company	37.9	58.7	31.5	24.3
	Civil Law Partnership	21.4	16.5	19.0	27.9
	Sole Proprietorship	18.8	-	28.2	27.9
	Registered Partnership	9.2	9.1	10.2	8.4
	Joint Stock Company	8.5	12.6	6.9	6.0
	Other forms of partnership *	4.4	3.1	4.2	5.6
Size (employees)	Up to 9	29.7	-	41.2	47.0
	From 10 to 49	12.2	-	1.9	32.3
	From 50 to 249	28.7	50.9	35.2	2.8
	From 250 to 499	23.4	36.1	16.2	17.9
	500 and more	6.0	13.0	5.6	-
Year of establishment	Min.	1932	1932	1946	1949
	Max.	2022	2022	2022	2022
	Median	2005	2000	2005	2007

* Other forms of partnership include limited partnerships, professional partnerships and similar legal entities. Source: own research.

).

A total of 697 representatives of Polish enterprises participated in the study. Among them, slightly more were men (53.8%) than women (46.2%). The average age of respondents was 43 years, with the youngest being 27 and the oldest 65. Two out of three respondents held a university degree, and half were employed in managerial positions. The average length of professional experience was 17 years (

Table 4. Characteristics of the respondents.

Characteristics	Items	Overall Sample (N = 697)	Manufacturing Enterprises (n = 230)	Trading Enterprises (n = 216)	Service Enterprises (n = 251)
Gender [%]	Female	46.2	37.0	42.6	57.8
	Male	53.8	63.0	57.4	42.2
Age [years]	Min.	27	27	27	27
	Max.	65	65	63	63
	Mean	43	43	44	43
	Median	43	42	43	44
Education [%]	Vocational	6.3	5.2	9.7	4.4
	Secondary	29.3	27.8	38.4	22.7
	Higher education	64.4	67.0	51.9	72.9
Work experience [years]	Min.	2	2	2	2
	Max.	44	44	42	44
	Mean	17	16	17	8
	Median	16	15	17	17
Position [%]	Manager	51.1	48.3	47.2	57.0
	Specialist	48.9	51.7	52.8	43.0

Source: own research.

).

3.5. Methods of Analysis

The research conducted by the research team is of an exploratory nature due to the new and relatively poorly understood research problem. The objective of the analysis was to examine the level of digital maturity among enterprises operating in three sectors: manufacturing, trade, and services. In the first stage, the seven dimensions of digital maturity were assessed—strategy, customer, products and services, processes, infrastructure, environment, and organization—using a 7-point Likert scale. Based on the respondents’ answers, descriptive statistics were calculated, including means, medians, standard deviations, as well as minimum and maximum values.

Due to the non-normal distribution of data (confirmed by normality tests) and the heterogeneity of variances between groups (verified using Levene’s test), non-parametric statistical methods were applied in the subsequent stages of the analysis. The key analytical tool used to compare differences between enterprise groups was the Kruskal–Wallis test, which allowed for evaluating the statistical significance of differences in medians across the three groups (manufacturing, trade, and service enterprises).

For those dimensions in which the Kruskal–Wallis test indicated statistically significant differences (p < 0.05), an additional post hoc analysis was conducted in the form of pairwise comparisons between the enterprise groups. This was performed using rank-based tests with p-value adjustment (adjusted significance), allowing for identification of the specific group comparisons where the differences were significant.

The analysis was performed both for individual dimensions of digital maturity and for the aggregated index of overall digital maturity (calculated as the mean across all dimensions). In all cases, the significance level was set at α = 0.05. The calculations were carried out using statistical tools (PS IMAGO PRO), and the detailed results are presented in the tables included later in this report. The analytical procedure is illustrated in Figure 2.

4. Results

4.1. Overall Digital Maturity Rating

The mean overall digital maturity rating, represented by a synthetic variable calculated as the average of the ratings of its dimensions, was 4.65 on the 7-point Likert scale (median 5.08). The highest-rated dimensions of digital maturity were organization (mean 4.94) and processes (mean 4.85). The lowest-rated dimension was Environment (mean 4.04). The other ratings for the dimensions examined were at a relatively similar level (

Table 5. Descriptive statistics of dimensions of digital maturity.

Variables	Mean	Median	Std. Deviation	Min.	Max.	Range
Strategy	4.67	4.90	1.23	1	7	6
Client	4.74	5.00	1.38	1	7	6
Product and services	4.63	4.87	1.34	1	7	6
Processes	4.85	5.00	1.28	1	7	6
Infrastructure	4.69	5.00	1.38	1	7	6
Environment	4.04	4.00	1.87	1	7	6
Organization	4.94	5.08	1.13	1	7	6
Digital maturity (overall)	4.65	5.08	1.22	1	7	6

Source: developed on our own.

). The lowest-rated dimension was Environment (mean: 4.04). The other ratings for the dimensions examined were at a relatively similar level (Figure 3).

4.2. Digital Maturity Assessment in Manufacturing, Commercial, and Service Enterprises

The average assessment of the level of the different dimensions of digital maturity in the groups of enterprises surveyed, i.e., manufacturing, trade, and services, was then examined. The analysis showed that for all dimensions and for the assessment of the overall level of maturity, the highest assessment was made by the manufacturing enterprises. As for the trade and services enterprises, their assessment was at a similar level (

Table 6. Mean ratings of dimensions of digital maturity for the manufacturing, trade, and service enterprises examined.

Dimensions	Manufacturing Enterprises	Trade Enterprises	Service Enterprises
Strategy	5.02	4.54	4.46
Client	5.08	4.60	4.55
Product and services	5.05	4.48	4.37
Processes	5.11	4.69	4.74
Infrastructure	5.07	4.41	4.57
Environment	4.37	3.91	3.84
Organization	5.20	4.82	4.80
Digital maturity (overall)	4.99	4.49	4.48

Source: developed on our own.

).

4.3. Testing Differences in Digital Maturity Levels Among Polish Enterprises

In the next step, the aim was to identify differences in the average level of dimensions between manufacturing, trade, and service enterprises. Since the distribution of the study variables was not normal, and Levene’s test showed that the variance was not homogeneous, the non-parametric Kruskal–Wallis test was used to determine whether the differences in the average scores for the dimensions of digital maturity between the groups of enterprises studied were statistically significant.

4.3.1. Dimension of Strategy

Regarding the dimension of strategy, analysis showed that, at the level of significance 0.05, a profile of economic activity (manufacturing, trade, and service) was a significant factor that differentiated the scores of the dimension of strategy in the enterprises examined (

Table 7. Significance of the differences between the mean scores of the strategy dimension in manufacturing, trade, and service enterprises: results of the Kruskal–Wallis test.

Null hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the strategy dimension is the same across enterprises	Independent samples Kruskal–Wallis test	26.995	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

Then a pairwise comparison of the different groups of organizations was carried out to check the significance of the differences in the dimension of the strategy between manufacturing, trade, and service enterprises. The results showed that there were statistically significant differences in the ratings for this dimension between service enterprises, and manufacturing enterprises as well as between trade enterprises and manufacturing enterprises (

Table 8. The results of pairwise comparison between the enterprises examined: a dimension of the strategy.

Sample 1–Sample 2	Test Statistic	Std. Error	Std. Test statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	39.340	18.678	2.106	0.035	0.106
Service enterprises–Manufacturing enterprises	95.193	18.370	5.182	<0.001	0.000
Trade enterprises–Manufacturing enterprises	55.853	19.068	2.929	0.003	0.010

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

).

4.3.2. Dimension of Client

Subsequently, the results of the Kruskal–Wallis tests indicated that there were also significant differences in the ratings of the dimension of client, thus a pairwise comparison was also performed between the enterprises examined (

Table 9. Significance of the differences between the mean scores of the client dimension in manufacturing, trade and service enterprises: results of the Kruskal–Wallis test.

Null hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the client dimension is the same across enterprises	Independent samples Kruskal–Wallis test	22.399	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

As the Kruskal–Wallis test indicated the significance of the differences in the average rating of the strategy dimension level between the studied groups of manufacturing, trade, and service enterprises, a pairwise analysis was also performed (

Table 10. The results of pairwise comparison between the enterprises examined: a dimension of the client.

Sample 1–Sample 2	Test Statistic	Std. Error	Std. Test Statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	36.897	18.670	1.976	0.048	0.144
Service enterprises–Manufacturing enterprises	86.753	18.362	4.725	<0.001	0.000
Trade enterprises–Manufacturing enterprises	49.856	19.060	2.616	0.009	0.027

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

).

4.3.3. Dimension of Product and Service

The results of the pairwise comparison showed that there were significant differences in the product and service dimension ratings between service and manufacturing enterprises and between trade and manufacturing enterprises (at p = 0.027).

The dimensions of products and services were then analyzed using the Kruskal–Wallis test which proved that the differences between examined groups of organizations were statistically significant (

Table 11. Significance of the differences between the mean scores of the product and services dimension in manufacturing, trade and service enterprises: results of the Kruskal–Wallis test.

Null Hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the client dimension is the same across enterprises	Independent samples Kruskal–Wallis test	33.512	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

Therefore, a pairwise comparison analysis was also carried out. Significant differences were found between all groups of enterprises (

Table 12. The results of pairwise comparison between the enterprises examined: a dimension of the product and services.

Sample 1–Sample 2	Test Statistic	Std. Error	Std. Test Statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	49.028	18.675	2.625	0.009	0.026
Service enterprises–Manufacturing enterprises	106.306	18.367	5.788	<0.001	0.000
Trade enterprises–Manufacturing enterprises	57.278	19.065	3.004	0.003	0.008

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

).

4.3.4. Dimension of Processes

Another dimension explored was the dimension of processes. Again, the Kruskal–Wallis test pointed out the significance of the differences between the manufacturing, trade, and services enterprises (

Table 13. Significance of the differences between the mean scores of the processes dimension in manufacturing, trade and service enterprises: results of the Kruskal–Wallis test.

Null Hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the client dimension is the same across enterprises	Independent samples Kruskal–Wallis test	13.930	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

The pairwise comparison revealed significant differences in the average ratings of the processes dimension between enterprises in the services sector and enterprises in the manufacturing sector (

Table 14. The results of pairwise comparison between the enterprises examined: a dimension of the processes.

Sample 1–Sample 2	Test Statistic	Std. Error	Std. Test Statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	19.451	18.681	1.041	0.298	0.893
Service enterprises–Manufacturing enterprises	67.110	18.373	3.653	<0.001	0.001
Trade enterprises–Manufacturing enterprises	47.659	19.071	2.499	0.012	0.037

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

). Furthermore, there was a significant difference between trade enterprises and manufacturing enterprises (at p = 0.037).

4.3.5. Dimension of Infrastructure

The next dimension examined was infrastructure. The Kruskal–Wallis test revealed significant differences in the average assessment of infrastructure dimension scores between manufacturing, trade, and service enterprises (

Table 15. Significance of the differences between the mean scores of the infrastructure dimension in manufacturing, trade and service enterprises: results of the Kruskal–Wallis test.

Null Hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the client dimension is the same across enterprises	Independent samples Kruskal–Wallis test	22.688	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

Because the hypothesis of equality of the mean of the infrastructure dimension was rejected, thus, to determine which groups of enterprises differ significantly from each other regarding this dimension, in the next step a pairwise comparison analysis was conducted. It was found that the average ratings of the infrastructure dimension in manufacturing enterprises differed significantly from those service and trade (

Table 16. The results of pairwise comparison between the enterprises examined: a dimension of the infrastructure.

Sample 1–Sample 2	Test Statistic	Std. Error	Std. Test Statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	7.361	18.680	0.394	0.694	1.000
Service enterprises–Manufacturing enterprises	80.366	18.372	4.374	<0.001	0.000
Trade enterprises–Manufacturing enterprises	73.005	19.070	3.828	<0.001	0.000

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

).

4.3.6. Dimension of Environment

With regard to the environment dimension, the Kruskal–Wallis test showed that there were significant differences in the ratings of this dimension in the groups of enterprises examined (

Table 17. Significance of the differences between the mean scores of the environmental dimension in manufacturing, trade and service enterprises: results of the Kruskal–Wallis test.

Null Hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the client dimension is the same across enterprises	Independent samples Kruskal–Wallis test	11.401	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

Non-parametric pairwise comparison analysis revealed significant differences in ratings of dimension of the environment between service and manufacturing enterprises (

Table 18. The results of pairwise comparison between the enterprises examined: a dimension of the environment.

Sample 1–Sample 2	Test statistic	Std. Error	Std. Test Statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	15.023	18.586	0.808	0.419	1.000
Service enterprises–Manufacturing enterprises	59.835	18.280	3.273	0.001	0.003
Trade enterprises–Manufacturing enterprises	44.812	18.975	2.362	0.018	0.055

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

).

4.3.7. Dimension of Organization

Regarding the last dimension, which is organization, it was found that there were significant differences in how the groups of in manufacturing, trade, and services enterprises surveyed rated this dimension (

Table 19. Significance of the differences between the mean scores of the organization’s dimension in manufacturing, trade and service enterprises: results of the Kruskal–Wallis test.

Null Hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the client dimension is the same across enterprises	Independent samples Kruskal–Wallis test	20.811	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

The pairwise comparison showed significant differences in the average ratings of the dimension of the organization between service and manufacturing enterprises and between manufacturing and trade enterprises (

Table 20. The results of pairwise comparison between the enterprises examined: a dimension of the organization.

Sample 1–Sample 2	Test Statistic	Std. Error	Std. Test Statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	23.677	18.686	1.267	0.205	0.615
Service enterprises–Manufacturing enterprises	82.030	18.378	4.463	<0.001	0.000
Trade enterprises–Manufacturing enterprises	58.353	19.077	3.059	0.002	0.007

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

).

4.3.8. Overall Digital Maturity

In the final stage, the Kruskal–Wallis test for heterogeneity of variance was used to check the significance of the differences in the overall assessment of digital maturity in manufacturing, trade, and services enterprises using an index that is the average of all its dimensions. The results show that, in this case also, the differences in the average assessment of digital maturity in the groups of enterprises studied are statistically significant (

Table 21. Significance of the differences between the mean scores of the digital maturity (overall) dimension in manufacturing, trade and service enterprises: results of the Kruskal–Wallis test.

Null Hypothesis	Test	Test Statistic	Sig.	Decision
The distribution of the client dimension is the same across enterprises	Independent samples Kruskal–Wallis test	22.278	<0.001	Reject the null hypothesis

Note. The significance level is 0.05. Asymptotic significances are displayed. Source: developed on our own.

).

Analysis of differences in the average assessment of the level of digital maturity between pairs of organizations showed that there were significant differences between manufacturing and services enterprises, as well as between manufacturing and trade enterprises (

Table 22. The results of pairwise comparison between the enterprises examined: a digital maturity (overall) dimension.

Sample 1–Sample 2	Test Statistic	Std. Error	Std. Test Statistic	Sig.	Adj. Sig.
Service enterprises–Trade enterprises	37.060	18.687	1.983	0.047	0.142
Service enterprises–Manufacturing enterprises	95.511	18.379	5.197	<0.001	0.000
Trade enterprises–Manufacturing enterprises	58.451	19.078	3.064	0.002	0.007

Note. In the each row the null is that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances are displayed. The assumed significance level is 0.05. Source: developed on our own.

).

5. Discussion and Conclusions

The results confirm that Polish enterprises demonstrate a moderate level of digital maturity—the average global score was 4.65 on a 7-point Likert scale, classifying them at the “Moderate Digital Advancement” stage (Level 4). This outcome is shaped by both internal factors and external conditions. From an organizational perspective, many enterprises focus primarily on operational improvements, introducing digital solutions in selected departments (e.g., logistics or accounting), yet without full cross-departmental integration. Digital transformation is often perceived more as a cost-optimization tool rather than as an element of a long-term development strategy, which limits progress to higher levels of advancement. Another barrier is the shortage of specialized digital skills combined with investment caution, particularly evident among SMEs, which, due to limited financial resources, are reluctant to take risks associated with the implementation of innovative technologies.

External conditions further reinforce these limitations. The Polish economy is characterized by a relatively low level of business investment compared to the EU average, and although the availability of European funds is increasing, their absorption is hampered by procedural barriers. Moreover, competitive pressure from more digitally advanced economies of Western Europe compels Polish enterprises to concentrate on catching up rather than creating breakthrough innovations. In recent years, crisis-related factors have also significantly influenced the dynamics of digital transformation. The COVID-19 pandemic accelerated the adoption of remote work, e-commerce, and digital customer service solutions, yet these investments were often reactive and ad hoc. Meanwhile, the war in Ukraine has introduced substantial macroeconomic uncertainty, disrupted supply chains, and increased energy costs, leading many enterprises to scale back development expenditures. As a result, although the digital transformation of Polish enterprises has clearly accelerated, it remains at a moderate level, with the key challenge being the transition from fragmented initiatives to a fully integrated and strategically planned digital transformation process.

This average level is also consistent with findings from Siemens research [78]. In 2021, Siemens reported that the average Digi Index score for 150 Polish manufacturing enterprises was only 1.8 on a 0–4 scale (very low). In 2024, this score increased to 2.3 (moderate), indicating positive progress in how Polish manufacturing firms approach digital transformation.

The most significant growth in Digi Index components in 2024 was observed in the areas of strategic planning and organization and administration. This suggests that enterprises have begun to approach digitalization in a more coordinated and deliberate manner, focusing on building comprehensive digital strategies to support transformation. The development of long-term plans reflects a more mature strategic mindset among enterprises, indicating a shift in priorities toward systematic planning within broader business development strategies. The Digi Index findings further show that Polish manufacturing firms are moving beyond isolated pilot projects and entering more holistic stages of digitalization that involve integrated planning and investment roadmaps.

The analysis of differences between sectors confirmed that manufacturing enterprises achieved a higher overall maturity score (4.99) than those in trade (4.49) and services (4.48). These differences were statistically significant (p < 0.001). These findings align with previous research [79], which suggests that manufacturing firms, due to greater opportunities for process standardization and investment in infrastructure, progress through digitalization stages more rapidly. Manufacturing enterprises also display higher levels of digitalization of core IT systems compared to service-oriented firms. These observations extend to the use of advanced IT systems by both manufacturing and trade enterprises, indicating a digital advantage over service organizations [80].

In answering the third research question regarding differences in digital advancement across various dimensions of enterprise operation, it can be indicated that Polish enterprises achieved the highest digital maturity scores in the organization dimension (almost Level 5—“High Digital Advancement”, with an average of 4.94) and processes (Level 4, average of 4.85). This suggests that firms are relatively effective in organizing internal digital resources and optimizing operational processes. The lowest level of digital advancement was observed in the environment dimension (4.04) This finding may be surprising in the context of Poland’s “The 2030 National Environmental Policy” (PEP2030) [81], which emphasizes the need to build an innovative economy in accordance with the principles of sustainable development. The primary goal is to maximize the environmental potential for the benefit of citizens and businesses. PEP2030 also highlights the importance of digitization, which enables the creation of electronic databases providing direct access to the latest environmental information via the Internet as well as allowing for its exchange. These activities are supported by various funding programs, both from national resources and EU funds, e.g., the LIFE Programme, National Fund for Environmental Protection and Water Management, and Voivodeship Funds for Environmental Protection and Water Management. There are also subsidies available for entrepreneurs, such as ‘green loans’, as well as special funds, such as Norwegian Funds. However, despite the relatively many opportunities for pro-environmental action at the enterprise level, many enterprises still encounter resistance and negative attitudes toward such actions. The problem remains that social awareness of environmental protection is still relatively low, and certain sectors of the economy are unwilling to embrace pro-environmental changes.

These findings diverge from the original assumption that “products and services” would be the strongest dimension, but are consistent with results reported by Kowal, Radzik, and Domaracká [11], who found that digitalization levels vary across organizational areas. Their assessment across six dimensions—strategic planning, organization and administration, system integration, production and operational activities, data management, and use of digital processes—revealed the highest scores (above 3.6 on a 1–5 scale) in strategic planning and organization and administration, which closely align with the organization dimension in our model (scoring 4.94).

The central thesis of this study assumed that a higher level of digital maturity correlates with greater potential to achieve ESG goals. The relatively low score in the environment dimension suggests a gap in integrating business strategies with green funding programs and eco-initiatives. This area of enterprise activity requires particular attention when planning further steps in digital transformation. Enterprises committed to both profitability and environmental/social impact tend to exhibit higher levels of digital maturity [82], adopt new technologies faster, and invest more in developing digital competencies than those for whom ESG goals are less central [14], recognizing numerous benefits of digitalization [83]. The implication of the results obtained in our study motivates to broaden the perception and link the digital maturity with environmental awareness in society. Digitally mature organizations as employers are engaged in developing digital skills of their employees who as consumers present behaviors having strong and significant impact on their environmental awareness [84].

Our observations align closely with findings by Kane et al. [2], who emphasized the critical role of organizational structures and processes in implementing digital technologies. Conversely, the lower scores in the environmental dimension are consistent with studies [85,86,87,88] indicating insufficient institutional support for green technologies. Differences between results may stem from varying research contexts—while most studies focus on large corporations, our data includes a wide spectrum of enterprises with different legal forms.

From a theoretical perspective, the findings strengthen the conceptual value of the “Digitalcheck Mittelstand” model in the Polish context, while also suggesting the addition of a moderating variable, such as the level of institutional support and availability of green funding. When evaluating the validity of the seven-dimensional model proposed in the theoretical section, we found that the assumed dimensions accurately reflect the empirical reality. The collected data align with the model’s structure and other authors’ findings, confirming its conceptual consistency.

From a methodological standpoint, the use of a translated and validated German tool with high internal reliability (Cronbach’s α = 0.99) ensured coherence and validity of measurement. The study was conducted on a sample of 697 enterprises from various sectors, increasing the representativeness of the results.

In terms of practical implications, our results suggest that managers should prioritize investments in organizational structures and process optimization, but also actively seek environmental funding (e.g., ESG grants) to eliminate bottlenecks in the environment dimension. This recommendation applies not only to manufacturing enterprises but also to those in trade and services. These recommendations are consistent with the current macroeconomic challenges of the Polish economy. Although Poland maintains stable economic growth, the level of business investment remains below the EU average, and spending on pro-environmental activities is still concentrated within a narrow group of large firms. At the same time, the importance of funds related to the European Green Deal and the National Recovery Plan is increasing. By 2026, Poland is set to receive significant financial support for the green transition [89], a substantial portion of which is directed toward supporting digitalization, energy efficiency, and a low-carbon economy in the SME sector. From a macroeconomic perspective, the active acquisition of external funding by enterprises can generate a multiplier effect. Given the limited innovation absorption capacity and the low share of R&D expenditure in Polish enterprises [90], resources for digitalization and ESG may serve as a key modernization stimulus. In practice, this means that even trade and service enterprises, which have so far made less frequent use of support programs, should engage in the green-digital transformation, as its pace will largely determine the competitiveness of the entire Polish economy in the coming decade.

In summary, Polish enterprises exhibit a moderate level of digital maturity, with strengths in organization and processes, and weaknesses in external support. These findings are consistent with research by Tubis [91], who emphasized that digital transformation generates the need for tools to assess digital maturity levels. Moreover, the results confirm existing theories while highlighting the need to incorporate new environmental and institutional variables into digital maturity models.

6. Limitations and Future Research

This study has several limitations. First and foremost, it is cross-sectional and conducted at a single point in time, which limits the ability to draw causal inferences about digital maturity in enterprises. Although the sample is large and diverse (N = 697), it includes only Polish enterprises, which restricts the generalizability of the findings to other regions. In addition, potential confounding factors—such as organizational culture or the level of industry-specific digitalization—were not considered.

Therefore, for future research, we recommend replicating the study in other Central and Eastern European countries to compare the impact of regulatory contexts on enterprise digital maturity. Longitudinal and qualitative studies are also warranted to track changes in digital maturity over time. Furthermore, the model could be extended to include moderating variables such as the degree of institutional support and the availability of green funding. For future research, it also seems cognitively interesting to distinguish the profile of the most and the least digitalized Polish enterprises. The qualitative studies conducted will provide rich material for describing enterprise profiles at different levels of digital maturity.