Digital transformation and financial performance: an empirical analysis of strategic alignment in the digital age

Thanks to the advent of digitization, which has changed the nature of business processes and their structure, altered management techniques and approaches, led to the emergence of new professions and the modification of existing ones, necessitated a significant expansion of the abilities and skills of the labor force, changed the economy and has the potential to accelerate a number of important social phenomena. Saeedikiya et al. (2025) in their research combined earlier debated definitions and topics related to digital transformation, and they characterized it as an ongoing socio-structural change that leverages digital technologies to create new value toward sustained competitive advantage. A key component of Industry 4.0 is the idea of digitalization, which is applied to horizontally linked processes that are expressed in values and their flow. Industry 4.0 has provided the industrial sector with a robust foundation for the adoption of digital processes and the advancement of supply chain intelligence. As a result, digital and intelligent manufacturing has emerged as a significant global trend under the Industry 4.0 paradigm. Driven by the rapid progress of Industry 4.0, digital technologies such as cloud computing, big data, artificial intelligence (AI) and the Internet of Things (IoT) have evolved swiftly, revolutionizing the global economy and positioning themselves at the forefront of the digital sphere (Gajdosikova et al., 2024; Juracka et al., 2024). These digital technologies have a big impact on a company’s network connection, policies, resource allocation and overall performance, according to Camara (2024). Digitization offers previously unimaginable opportunities for enterprising small and medium-sized enterprises (SMEs) as indicated in the study by Cenamor et al. (2019), claiming that by matching the capabilities of digital platforms with their attitude, SMEs may improve their performance. Nonetheless, Kothamaki et al. (2020) noted that the combination of high service and low digitization has a negative and considerable impact on a company’s financial performance from low to moderate degrees of digitalization. The relationship between high degrees of servitization and digitalization becomes favorable and important from moderate to high levels, enhancing the financial performance of businesses, which is also confirmed by Yang et al. (2023) and Luu et al. (2024). Scafarto et al. (2023), based on a sample of 965 company observations spanning from 2017 to 2021, concluded that financial performance is positively impacted by digitalization. Subsequent investigation indicates that the association between digitization and business performance is totally mediated by capital employed efficiency.

In reaction to governmental regulations and changes in the market, mostly caused by the COVID-19 pandemic, many companies have been aggressively using digital technology because they view digital transformation as an essential development strategy. Savvakis et al. (2024), using a panel data set of 12,179 European SMEs from 2017 to 2022 along with a European transformed digitalization index, discovered a significant and negative correlation between the COVID-19 pandemic and the financial performance of SMEs. Digitalization appears to have been crucial in assisting SMEs in fending off the pandemic’s consequences. However, research indicates that poor information technology, digital strategies (Guo et al., 2020) and firm repositioning delays might impede value development and result in failures (Nylen and Holmstrom, 2015). As a result, not every company can execute digital transformation effectively. However, digitalization should go beyond only using ICTs; it should also entail altering organizational dynamics and practices (Zhou et al., 2023a). An organization’s competitiveness and performance may be increased through the process of integrating new technologies to improve existing goods, create new business models and facilitate the development of new software and systems (Ragazou et al., 2023). Dalenogare et al. (2018) emphasize the distinctiveness and noteworthy contribution of particular technologies while delving into the topic of data’s potential value. They also show how a person’s role at work may change for the better. Digitization may increase the efficiency of the value chain by reducing costs and promoting greater innovation and collaboration (Kliestik et al., 2024). Concurrently, the author agrees with several other individuals who predict that Industry 4.0 will not be a revolution but rather an evolutionary path. Businesses, governments, educational institutions and a competent labor force in the innovation industry will all be aiming for its flexibility. The importance of corporate management’s business strategy in strategy planning for digitization is emphasized by Zeng et al. (2022).

Over the past few years, also as a consequence of the COVID-19 pandemic, scholars in business research have become increasingly interested in examining the changes brought about by digital technology (Eller et al, 2020). Amidst these significant transformations, the structures and dynamics of business operations are drastically altering to establish new business paradigms whereby digital technology plays a crucial role in the expansion and creation of value for firms (Zhou et al., 2023b). Digital transformation is a dynamic process that affects technology and digital skills, as claimed by Lastauskaite and Krusinskas (2024). Additionally, it could provide value to consumer experiences, company models and procedures. These technologies ushered in the contemporary digital era, and their continued growth is bringing value to the industry (Ribeiro-Navarrete et al., 2021).

As a result of digitalization, the global economy is undergoing a change, and businesses are being presented with possibilities that have never been seen before to simplify operations, boost consumer interaction and drive innovation. Those individuals who are able to successfully adjust to these changes and triumph over the difficulties that come with digitalization will be in a strong position to achieve long-term success in the ever-changing digital world of the future. It is also possible for enterprises to broaden their scope of operations and investigate new market opportunities. The expansion of e-commerce platforms has made it possible for businesses to offer their goods and services to clients located all over the world, therefore eliminating some of the geographical boundaries that previously existed (Nagy et al., 2024). In addition, these advancements are making it possible to create new business models that were inconceivable in the days before the advent of digital technology. These models include the sharing economy and subscription-based services. The use of digital technology is critical to the continued existence and prosperity of modern enterprises. The adoption of digital technologies by businesses is not enough; they must also undertake strategic planning for the development and application of these technologies (Vasenska, 2024). The successful implementation of digitalization solutions may result in substantial improvements in terms of operational efficiency, customer happiness and innovation. Through the implementation of digital tools, businesses have the ability to guarantee their competitiveness in the constantly shifting global market and to get ready for the challenges that lie ahead. Thus, digitalization is rapidly changing the way companies operate and compete in today’s global economy. It is no longer a question of whether a business should implement digital tools and technologies, but rather how to use them effectively to achieve a competitive advantage. Slovakia, like many Central and Eastern European countries, stands at a critical juncture where digital transformation is no longer a competitive advantage but a requirement for survival and future growth. The urgency of this research stems from a unique convergence of factors specific to the Slovak business ecosystem. The level of digitalization is uneven across sectors and firm sizes, while multinational corporations operating in Slovakia may be advancing in digital maturity. The majority of Slovak enterprises, especially SMEs, still lag behind due to limited capital, a lack of skilled digital talent and an ambiguous return on digital investments. Moreover, the Slovak economy is heavily reliant on traditional sectors such as manufacturing and automotive, which are now being rapidly reshaped by digital technologies (e.g. Industry 4.0, automation and AI). Companies that delay transformation risk obsolescence in global supply chains. The macroeconomic and structural challenges also force Slovak enterprises to adopt cost-effective digital strategies that directly improve their financial performance.

Based on these facts, two important research questions were developed:

To achieve our research objectives, the correspondence analysis and nonparametric Kruskal–Wallis’s test were used to prove the relationship between the corporate financial data (using the information from the Orbis database provided by Moody’s) and the qualitative data focused on the digital competencies of businesses (derived from the questionnaire). Correspondence analysis is particularly well-suited for this study because the research investigates relationships between categorical variables, such as firm characteristics and their link to perceived or actual financial performance. The nature of the data collected in the study, largely qualitative, survey-based and categorized, makes this analysis a strong methodological fit for several key reasons. Correspondence analysis enables the exploration of associative patterns between categorical variables without the need for parametric assumptions. This makes it especially effective in identifying which types of firms are more likely to adopt specific digital strategies. Furthermore, in examining the link between various dimensions of digital transformation and financial performance, this analysis allows researchers to detect and visualize multivariate associations between strategic digital actions and categories of financial outcomes. By creating easy-to-understand graphs, correspondence analysis shows these connections and helps find groups of companies that have similar digital and financial characteristics. This segmentation is crucial for generating actionable insights tailored to different enterprise types, making correspondence analysis a robust and meaningful method for answering the research questions in the Slovak context. The Kruskal–Wallis test was selected because it fits the way the data is organized (ordinal scales), the type of variables (not normally distributed) and the purpose of the analysis (comparing differences among several independent groups). It enables the authors to robustly determine whether the degree of digital transformation is associated with statistically significant differences in perceived financial performance across Slovak enterprises.

The results of the study provide stakeholders, academicians and policymakers with insights into the shortcomings of current research in terms of the lack of a theoretical and practical framework in specific national conditions, which forms the basis for studies on the relationship between digital transformation and financial performance of enterprises. Understanding the relationship between digitalization and financial performance is pressing because Slovak enterprises are more risk-averse and resource-constrained compared to other countries. Failing to understand this relationship could result in misallocated funding, failed digital initiatives and declining competitiveness in a rapidly digitizing European market. Furthermore, banks, investors and policymakers need clarity to structure incentives, grants and support programs that target digital initiatives proven to impact financial metrics. Thus, our study tries to highlight the challenges and future research prospects of digitalization and performance of enterprises in terms of their specifications and finance. Despite the growing global interest in digital transformation, there is a significant research gap in the Slovak context regarding firm-level, sector-specific studies that identify which concrete digitalization efforts most effectively enhance multidimensional financial performance. This gap highlights a lack of comprehensive analysis that examines the relationship between digitization strategies and their direct impact on key financial indicators and overall financial stability. Moreover, the absence of sector-specific insights further exacerbates this gap, as it hinders the development of tailored digital transformation strategies that could be implemented across different industries within the Slovak economic landscape. Addressing this gap could provide valuable guidance for enterprises navigating the complexities of digital transformation and its role in achieving sustainable financial success in a rapidly digitalizing global economy.

The paper is structured as follows. The Literature review section summarizes the most recent and relevant literature focused on the same or similar research problem. The Methodology section outlines the methods used and the methodological steps adhered to. In the Results section, the research questions are answered and hypotheses verified, which allows the generalization of the findings and comparison of the outputs with the results of other relevant studies.

The implementation of digital technology has a marginally beneficial impact on the overall performance of the organization. On the other hand, the performance of innovation is the one that is most strongly impacted, followed by the performance of operational efficiency and economic performance (Oduro et al., 2023). The authors report that the implementation of digital technology has a marginally beneficial impact on overall organizational performance, with the performance of innovation being most significantly impacted. This study highlights the importance of innovation, but its conclusion is that digitalization only marginally improves overall performance could be critiqued for underestimating the potential of digital technologies. This may reflect the limitations of their research design or sample size, which might not fully capture the long-term or broader industry-specific impacts of digital adoption. Pucci et al. (2023) investigated the significance of digital integration with regard to the economic performance of SMEs. According to the conclusions of their investigation, the degree to which a company has integrated digital technology has a beneficial impact on the performance of the company, although digital integration itself has no impact. Furthermore, organizations that place an emphasis on open innovation have the potential to generate much better results. While this underscores the need for a holistic approach to digitalization, the study could have provided more specific examples of how SMEs can leverage digital technologies to enhance their competitiveness beyond just integration. Moreover, the assumption that open innovation is universally beneficial may be oversimplified, as it does not account for the varying capacities of SMEs to manage open innovation processes effectively. In their study, Ribeiro-Navarette et al. (2021) demonstrated that the level of digitization, which was evaluated based on the extent of digital management, may be used to explain disparities between different industries and companies. The authors believed that the greatest degree of digitization is evident in the organizations that are the most extensive in size. Their finding that larger firms tend to have the highest levels of digitization aligns with existing literature, but they could have explored the reasons behind this trend in greater detail, particularly focusing on the barriers faced by smaller firms when adopting digital technologies. This would allow for a more nuanced understanding of digitalization disparities across firm sizes. Additionally, Hereida et al. (2023) present evidence that small enterprises operating in countries with lower levels of competition invest a significant amount of money in the environment, placing a higher priority on the creation of value than on their compliance with regulations. In spite of this, large and very large firms undertake major environmental pledges to improve their brand and gain public approval during times of strong competition. However, their argument that small firms in low-competition countries focus more on value creation than compliance could benefit from a deeper exploration of the potential long-term risks of neglecting regulatory compliance, which might undermine sustainable business practices in the future. In conclusion, the researchers discovered that the amount of informality plays a role in determining the association between corporate environmental responsibility and the deployment of digitalization.

For the purpose of describing the link between digitization and service innovation that improves economic performance, Kothamaki et al. (2020) developed a nonlinear interaction in the shape of a U. The findings provide light on the significance of a good interaction between innovation and digitalization, since it is possible for organizations to experience a digitalization paradox if this relationship is not successfully formed. This finding is valuable for understanding the complexities of digitalization, but the study could benefit from further empirical testing to understand the specific conditions under which this paradox emerges. For example, industry, firm culture or technological infrastructure could significantly influence the effectiveness of this relationship. According to Atif et al. (2021), in order for businesses to accomplish the goal of generating value via service innovation, it is necessary to deploy Industry 4.0 technologies in a methodical manner. According to Alkaraan et al. (2023) and Aldrighetti et al. (2023), the link between I4 technologies and management mechanisms is believed to be the most significant determinant of an organization’s ability to create sustainable value and operate economically. According to Nagy et al. (2018), thus, it is evident that Industry 4.0 plays a big part in the process of value creation, as it is the factor that has the most important influence throughout the transformative phase. After getting financial aid from the government, Faria et al. (2022) investigated the manner in which Portuguese businesses improved their competitiveness, economic performance and operational efficiency in accordance with the Industry 4.0 plan.

Chaudhuri et al. (2024) state that the implementation of Industry 4.0 has the potential to expedite the process of establishing a culture inside an organization that is based on data-driven decision-making. The application of Industry 4.0 technology has an impact on the social, competitive and economic performance of businesses, as indicated by a theoretical model that was constructed using PLS-SEM. This impact is attributed to the fact that it strengthens the innovation capabilities and data-driven culture of businesses. Michna and Kmeciak (2020) revealed that openness, rather than information exchange, is a more significant factor in determining whether or not corporate organizations are likely to embrace Industry 4.0. Additionally, a favorable correlation exists between the desire to implement Industry 4.0 and the level of financial success that a company has, regardless of the size of the organization. In addition, it is essential to evaluate the influence that Industry 4.0 will have on the capacities of supply chains and the operational procedures of circular economies to maximize the performance of businesses (Yu et al., 2021; Dura et al., 2022). There is empirical data that lends credence to the assumption that Industry 4.0 has a favorable influence on the capacities of supply chains and the adoption of concepts of circular economies. Furthermore, there is a link between the techniques of the circular economy and the enhanced economic and operational success of firms, as demonstrated by actual evidence.

In their study, Liu et al. (2023) highlighted the significance of using I4 manufacturing technology and fostering a culture of circular economy to enhance the economic and environmental performance of businesses. According to the findings of a research conducted by Khan et al. (2021), the development of new business models is facilitated by the use of circular economy practices and technology related to Industry 4.0. These studies contribute to the growing interest in sustainability, but could have incorporated more industry-specific analyses to understand how various sectors are adopting circular economy principles in practice. Additionally, further research could examine how businesses reconcile short-term profitability with long-term environmental and sustainability goals, as this remains a major challenge for many firms. Furthermore, in addition to its operational skills, Industry 4.0 possesses the potential to significantly improve both economic performance and environmental performance. As a consequence of this, the most recent study provides specific advice for businesses that are interested in incorporating the concepts of Industry 4.0 into their manufacturing processes to accomplish their sustainable objectives (Tang et al., 2022; Alkaraan et al., 2023). To solve environmental and economic problems, ensure competitiveness, and create a viable business model in accordance with the prevalent trend of circular economy, the integration of Industry 4.0 has emerged as an innovative framework for industrial companies (Ali et al., 2022; Samadhiya et al., 2023). This framework has emerged as a result of the widespread adoption of the circular economy.

There is a significant contribution that Industry 4.0 makes to the improvement of the economic and financial performance of enterprises. According to Kamble et al. (2020), the use of cutting-edge technologies such as automation, AI and the IoT leads to a considerable improvement in operational efficiency, which in turn contributes to a reduction in costs and a rise in profitability. However, they do not address the challenges of integrating these technologies within existing organizational structures. A critical limitation of their study is the lack of consideration of the organizational change management required for successful implementation. Moreover, while their findings on profitability are compelling, the long-term impacts of these technologies on employee satisfaction, skill development and organizational culture are areas that remain underexplored. Industry 4.0 helps businesses to make informed decisions, which in turn enhances their capabilities in terms of financial forecasting and risk management (Schumacher et al., 2016). This is accomplished by optimizing manufacturing processes and enabling real-time data analysis. These technologies provide firms with the capacity to swiftly adjust to changes in the market, which in turn promotes a sustained competitive advantage (Liao et al., 2017). Scalability and flexibility are two of the benefits given by these technologies. Zhou et al. (2015) found that the digitization of supply chains, which is another essential component of Industry 4.0, has the effect of reducing delivery times and inventory costs, which in turn has a favorable influence on the financial performance of businesses. As an additional benefit, the use of big data and analytics assists in the identification of new income streams and client preferences, which ultimately results in an increase in revenue (Rußmann et al., 2015). According to Moeuf et al. (2018), the precision and customization that are made possible by the technologies of Industry 4.0 lead to a rise in product quality, which in turn enables businesses to acquire larger market shares and enhance their profit margins. Predictive maintenance, which is powered by AI and the IoT, reduces operational costs and minimizes downtime, hence further enhancing profitability (Qi & Tao’s, 2018). Despite the fact that the adoption of Industry 4.0 technologies requires considerable initial expenditures, which may have a detrimental impact on short-term financial performance, these investments ultimately result in long-term economic gains (Oesterreich and Teuteberg, 2016). According to Kagermann et al. (2013), organizations that adopt Industry 4.0 are more likely to achieve improved economic outcomes and more financial resilience in an environment that is highly competitive.

Enterprise digital transformation involves the integration of digital technologies across all aspects of an organization, fundamentally changing how firms serve clients, manage operations and create value (Kraus et al., 2022). This transformation is not merely about adopting new technologies but requires a radical rethinking of business processes, organizational structures and value propositions (Elia et al., 2024). The drive for digital disruption has been particularly evident in industries heavily impacted by the fast-paced nature of digital innovation, where firms must continuously adapt to new digital tools and processes to maintain competitiveness. A critical element of successful digital transformation is the alignment between a company’s digital competencies and its financial performance. Recent research has highlighted the importance of integrating both qualitative data – such as digital capabilities and competencies – alongside quantitative data, such as financial performance metrics, in understanding the impacts of digitalization on business outcomes (Kraus et al., 2022; De Silva et al., 2024; Marolt et al., 2025). Studies utilizing data from platforms like the Orbis database (as used also in this research paper) offer valuable insights into the interplay between financial indicators and digital competencies. The literature suggests that certain firm-specific characteristics, such as industry sector, company size and ownership type, play significant roles in shaping the extent of digital adoption. For instance, larger firms or those in certain sectors may have more resources to invest in digital technologies, leading to greater digital maturity (Omrani et al., 2024 or Usai et al., 2021). Moreover, the ownership structure and national context of a business also influence its approach to digitalization, as regulatory frameworks and market dynamics differ across regions and industries (Feliciano-Cestero et al., 2023; Meyer et al., 2023 or Agustian et al., 2023). Additionally, empirical studies have shown that increasing investment in research and development and focusing on deepening digital transformation are crucial for long-term growth and financial stability. Companies that foster innovation through research and development and continuously enhance their digital capabilities are more likely to experience stronger financial performance and market resilience (Xia et al., 2024). The use of contingency tables to analyze the distribution of digital transformation categories alongside financial data further emphasizes the significant role that tailored, firm-specific strategies play in the digitalization process.

Recent empirical research confirms that digital transformation influences various dimensions of organizational performance, though the degree and nature of this impact remain debated. Oduro et al. (2023) found that while the implementation of digital technologies marginally improves overall performance, their strongest impact is on innovation capabilities. This supports the growing view that innovation performance is a key mediating factor in the success of digital initiatives. Pucci et al. (2023) further argue that while digital integration positively influences performance, its effectiveness is amplified when combined with open innovation practices, suggesting that internal characteristics and innovation orientation matter significantly.

Firm-specific features such as size, ownership structure and sector have also been identified as key determinants of digital maturity. Ribeiro-Navarrete et al. (2021) found that larger firms generally exhibit higher levels of digitalization, possibly due to greater access to resources and capabilities. Similarly, Hereida et al. (2023) show that digital investment strategies vary significantly depending on market conditions and firm size, with SMEs in low-competition environments focusing more on value creation than regulatory compliance. These findings suggest that structural characteristics are likely to shape both the degree of digital adoption and the performance outcomes it generates. In addition, Industry 4.0 technologies, such as AI, IoT and big data analytics, have also been shown to positively influence operational efficiency and long-term financial sustainability (Kamble et al., 2020; Liu et al., 2023). These technologies enhance firms’ adaptability, improve risk management and support the transition toward circular economy models. However, as noted by Michna and Kmeciak (2020), the success of such technologies is not only determined solely by access but also by openness to change and strategic alignment within the organization.

Given the variability in digital readiness and performance across sectors and firm types, the present study aims to explore how different categories of firms in Slovakia – classified by sector, size and ownership – relate to levels of digital transformation, and how this in turn influences their financial outcomes. Based on the synthesis of existing literature and the specific regional context, the following hypotheses are proposed:

In conclusion, digital transformation is a complex, multifaceted process that demands careful consideration of both digital competencies and financial data. The successful integration of digital technologies requires a strategic alignment between innovation, organizational capabilities and market dynamics, where firm size, sector and ownership characteristics significantly impact the outcomes. As digital transformation continues to reshape industries, organizations that strategically enhance their digital capabilities are better positioned for sustainable growth and enhanced financial performance.

To explain the relationship between digitalization and financial performance of enterprises, the data set of enterprises was formed using the data provided by the Amadeus database (Bureau van Dijk/Moody’s Analytics). The data were used for all 500 Slovak enterprises that were initially addressed with a questionnaire. The questionnaire consisted of 20 combined questions aimed at determining the readiness of enterprises to use the Industry 4.0 concept and their digital transformation. The structured questionnaire was distributed mainly electronically in November and December 2023. The use of electronic distribution suggests a streamlined and efficient approach for gathering responses from the enterprises, particularly considering the nature of the target population, which likely includes technologically adept firms. The sample selection process used random sampling to select the enterprises. Random sampling ensures that every enterprise in the target population has an equal chance of being selected, which helps avoid selection bias and allows for a more generalized representation of the population. While the selection was random, the process aimed to maintain a distribution that reflected the actual distribution of enterprises within the national economy. Specifically, a higher number of large enterprises were included, as these enterprises are more likely to be involved in the implementation of the latest digital technologies. This approach ensures that the sample adequately represents the size composition of enterprises in Slovakia, with particular attention to large firms that are more advanced in digital transformation efforts. The questionnaire’s questions were designed to gather information on various aspects of each enterprise’s structure, functioning relationships and the ways in which it utilizes digital technologies, particularly in the context of Industry 4.0. The focus was not just on the current state of digitalization but also on how the enterprises are preparing for or engaging in digital transformation. This sampling approach ensures that the data collected is representative of the broader enterprise landscape in Slovakia, while accounting for the influence of enterprise size on the adoption of Industry 4.0 technologies. It provides a balanced view of both smaller enterprises and larger, more digitally advanced firms, which is important for examining the relationship between digitalization and financial performance across various business contexts. Table 1 summarizes the basic information about the enterprises included in the study.

These fundamental firm-specific features are further analyzed together with the financial information (Table 2) in the context of the level of digitalization in the sample of enterprises.

The following hypotheses were set, based on the literature review, to meet the main aim of the paper:

The research was conducted in the following methodological steps:

• Formation of contingency tables to recognize the level of digitalization based on different firm-specific features. Obviously, it is only logical to investigate the underlying structure of contingency tables if there is a dependence between the properties (factors) that are being observed. Prior to the actual use of correspondence analysis, it is necessary to conduct a test of the hypothesis that the observed characteristics in the contingency table are independent of one another. This gives the ability to evaluate this hypothesis by using the χ2-test, for instance. In the event that this test is used to evaluate the hypotheses regarding the results of the independence of the observed factors, it is reasonable to search for a solution to the issue of which categories of factors are comparable to one another. This similarity is seen within the category of a single component, that is, inside the rows or columns of the contingency table. On the other hand, the qualities that are similar to one another are of interest.

• At a significance level of 5%, Pearson’s chi-square test was used to identify the relationship between the firm-specific features and digitalization level. The contingency coefficient, also known as Cramer’s V, was computed, and its significance was evaluated, provided that the mutual dependence was established.

• The relationships between categories of specified variables that are grouped in contingency tables are ascertained via the application of correspondence analysis. This analysis’s goals are to evaluate the relationships between the variables and provide an explanation for the dependence’s structure. The internal structure of a contingency table is examined based on the representation of its individual rows, or columns, as points in a multidimensional space (Kascakova et al., 2010). Let us consider two factors A and B, which are sorted into a general contingency table with r-rows and s-columns. The contingency table corresponds to the so-called correspondence matrix, whose element in the i-th row and j-th column is calculated based on the relation as follows:

In addition, for the i-th row, the so-called row load r_i is calculated according to the relation as follows:

and the line profile r_ilj according to the relationship is calculated as follows:

The values for the column load and column profile are calculated analogously. Row (or column) profiles can be considered as the coordinates of a point (a selected row or column of a contingency table) in s (or r)-dimensional space. Rows (columns) for which the points representing them in multidimensional space are considered similar if they are sufficiently close to each other. To calculate this distance, the χ2 distance, which is not affected by the magnitude of marginal frequencies, is most often used, defined for the i-th and j-th row by the relation as follows:

where, r_ik and r_jk are the k-th row profiles of rows i and j, c_k is the column weight of the k-th column. It is obvious that the distance between two rows is zero if their row profiles are identical. Analogously, we can also calculate the distances between selected columns of the contingency table. From the point of view of the practical application of the method, it would be appropriate to be able to identify the relationships between rows and columns of the contingency table visually. However, this is not possible in a multidimensional space. Therefore, it is necessary to find the projection of the points of the multidimensional space representing the rows and columns of the contingency table onto the plane to obtain the correspondence map. In the non-Euclidean plane, the distance of the points of the plane approximates the original χ²-distance of the points of the multidimensional space. It is important to find such a projection that preserves the relationship between the original points of the multidimensional space to the maximum extent possible. Thus, the projection using the matrix of standardized residuals Z is searched, whose element in the i-th row and j-th column is defined using the elements of the correspondence matrix and their respective marginal sums by the following relation:

The aforementioned matrix must undergo a singular decomposition to find an appropriate projection. The coordinates of individual rows and columns may then be acquired by combining the result with matrices formed from row and column loads. A multidimensional correspondence map, which distinctly displays the categories of the variables under analysis, their similarities and differences and any correlations with other variable categories, is the analysis’s most significant product. The categories are more similar to one another and correlate with one another more when the points in the correspondence map are closer together. A good transformation of multidimensional space points into the correspondence map is one that maintains the greatest amount of the multidimensional space’s point variability. The measure of this variability is the total inertia I, which is calculated using line profiles as the χ²-distances of these profiles from their mean r based on the relationship as follows:

Using column profiles, the total inertia is calculated analogously. The obtained solution is good if the row and column profiles contribute to the total inertia to a sufficient extent. If the projection onto the plane is considered, this requirement is expressed by the relation as follows:

where, r is the number of singular values of the ZZ^T matrix. The closer this proportion is to the number one, the better the quality of the obtained correspondence map. Other measures of model quality are the contributions of row or column inertia to the total inertia:

• The Kruskal–Wallis (nonparametric equivalent to analysis of variance [ANOVA]) test was used to confirm if the selected financial ratios across the various levels of digitalization are the same. This test is used to evaluate whether or not independent groups have the same mean on ranks. Rather than utilizing the data values directly, a rank is assigned to each data point, and those rankings are then used to establish whether the data in each group originates from the same distribution. In essence, the purpose of this test is to establish whether the groups differ. To determine the differences between the groups of digitalization level, the Dunn–Bonferroni post hoc test was used if the difference in financial parameters was statistically significant.

The statistical method known as the Dunn–Bonferroni post-hoc test is utilized to compare numerous pairs of means (averages) that are contained within a collection of data. Following the completion of a statistical test that includes a comparison of means, such as an ANOVA, it is frequently utilized. Identifying which pairs of means are substantially different from one another is the objective of the Dunn–Bonferroni test, which was developed to do this. To evaluate the results of the Dunn–Bonferroni test, the alpha level, which represents the degree of statistical significance, is modified to take into consideration the number of pairs of means that are being compared. This is essential due to the fact that the bigger the number of pairs of means that are compared, the higher the probability that a difference between means will be discovered without any deliberate effort. The Dunn–Bonferroni test facilitates the management of this issue, which is commonly referred to as the multiple comparisons problem and is accomplished by altering the alpha level.

The categorical and correspondence analyses were used to reveal different levels of digitalization across the analyzed enterprises. Four different levels (less than 25%; 25–50%; 50–75%; more than 75% of processes) were recognized by the enterprises in the questionnaire, considering various aspects of business operation (personnel, technological, manufacturing, etc.). Therefore, it was important to investigate the statistically significant relationship between distinct firm-specific features and the digitalization of the processes. Table 2 provides a summary of the Pearson’s chi-square test results (sig. values).

The chi-square test findings indicate a modest association between the variables, indicating that the selected firm-specific features of Slovak enterprises have an impact on the digitalization level. Therefore, the data on corporate specification might be a useful tool for identifying the extent of use and adoption of digital technologies leading to a business model change. Consequently, the basic correspondence analysis was further used to demonstrate the link between the categories of both variables at the same time; the mutual relationship between row and column categories was analyzed for each feature. Factor scores are provided for the row and column points of the contingency table by the correspondence analysis findings. A unique corresponding map for each row and column profile is the outcome. The final symmetric correspondence map of each firm-specific feature is depicted by the overlay of the two matching maps. Very intriguing findings are shown by the correspondence analysis’s outcomes (Figures 1–3).

The outputs of the correspondence analysis reveal (Figure 1) that the highest level of digitalization is typical of enterprises in the industry sector (covering enterprises in the sectors B, C, D and E according to the NACE classification). A 50–75% level of digitalization is to be found in the sector of services (enterprises operating in the sector H to S according to NACE classification), agriculture and trade sectors have digitalized up to 50% of processes, but the least digitalization level can be linked to the construction sector, which was a bit surprising. However, Al Omari et al. (2023) in their study based on a sample of 438 practitioners confirm the slow adoption of digitalization in this sector and identified 20 barriers to digitalization adoption. The same findings were presented also in the study by Siddiqui et al. (2023) or Oesterreich and Teuteberg (2016), who claimed that despite potential benefits in terms of increased productivity and quality, this idea of digitalization did not receive much attention in this sector. On the other hand, the importance of digitalization in the industry sector is observed by Greef and Schroeder (2021), who underlined its relevance in strong industrial centers and the overall impact on economic and societal progress (Ozternel and Gursev, 2020). The relatively low level of digitalization in the Slovak construction sector aligns with global patterns. For example, a McKinsey Global Institute report ranked construction among the least digitized sectors across advanced economies (McKinsey and Company, 2017). The sector’s fragmented supply chains, bespoke projects and risk-averse culture contribute to this lag (Barlish and Sullivan, 2012). In the UK, Davies et al. (2020) observed that small and medium-sized construction firms struggle with integrating even basic digital tools due to limited technical capabilities and resistance to change. In contrast, industry-heavy nations such as Germany, South Korea and Japan have demonstrated stronger progress in manufacturing digitalization, especially under frameworks like “Industrie 4.0.” German SMEs, for instance, benefit from government initiatives that promote technology transfer and collaboration between academia and industry (Schröder, 2016). The Slovak industrial sector’s relative strength in digital adoption may mirror these broader European patterns, where heavy industry is a digitalization front-runner due to its global competitiveness and export orientation.

Figure 2 depicts the importance of firm size in the process of digitalization. The outputs summarized in the correspondence map claim that more than 75% of processes are digitalized in large enterprises. Micro entities and small enterprises only digitalize up to 50% of their processes, and for medium-sized enterprises, the digitalization of 50–75% is typical. Thus, the results among the Slovak enterprises affirm the importance of firm size in the process of digitalization. These findings are confirmed by several other studies worldwide declaring the strong dependence between the firm size and digitalization (Industry 4.0 concept), for instance, Krulicky et al. (2024), Ali and Johl (2023), Pech and Vanecek (2022), Vrchota et al. (2020) or Sari et al. (2020). The observed relationship between firm size and digital maturity is consistent with numerous international studies. In Italy, Matarazzo et al. (2021) found that large firms were more likely to adopt Industry 4.0 technologies, primarily because of their higher absorptive capacity and ability to bear the risk of experimentation. Similarly, in the USA, small firms often lack the IT infrastructure and digital talent required to implement advanced solutions (Müller et al., 2018). Interestingly, however, there is growing evidence from Nordic countries like Finland and Denmark that public funding and policy support can help bridge this digital divide among smaller enterprises (OECD, 2020).

The analysis of the ownership structure (Figure 3) shows that less than 25% of business processes are digitalized in private limited companies, public limited companies have the best score of digitalization and a 50–75% digitalization level is for general partnership companies. The ownership advantage in this field is affirmed also in the study by Barbieri et al. (2024). The research by Stafenova and Kucharcikova (2023) in Slovak conditions proved that the size of an enterprise and its ownership have an impact on how digital technologies are used. Type of ownership was found to have an impact on the adoption of Industry 4.0 technologies based on the empirical study on European manufacturers (Rossini et al., 2019). Our findings on ownership structure also resonate globally. Fan et al. (2024) demonstrated that state-owned and publicly listed companies tend to be digital transformation leaders due to stricter regulatory environments and investor pressure. In contrast, private SMEs, especially family-owned businesses, often approach digitalization cautiously, preferring incremental improvements over disruptive shifts (De Massis et al., 2018). In Slovakia, public ownership may offer similar institutional pressures or access to funding, influencing the more aggressive digital strategies observed.

Based on the presented results of the categorical and correspondence analysis, it can be concluded that firm-specific features do play a significant role in the process of there being categories of factors – level of digitalization, sector/size/ownership of enterprises – that are mutually correspondent; H1 and H2 are confirmed.

Finally, the Kruskal–Wallis test was applied to verify if the level of digitalization has an impact on selected financial parameters (total assets, shareholder funds, earnings after taxes and total liabilities), Table 3.

The nonparametric test’s findings show that the volume of total assets, earnings after taxes and shareholders’ funds are important indicators of the development of digitalization processes in enterprises, while the level of total liabilities does not play a significant role. Similar results were achieved by Vlckova et al. (2019), claiming that the introduction of Industry 4.0 and digital transformation is mostly reflected in total assets, short-term receivables, equity and total liabilities, which was observed on a sample of 617 enterprises and 17 financial parameters. Durana and Valaskova (2022) in their research proved a striking effect of Industry 4.0 on earnings, and they also recorded that the implementation of digital technologies develops the muscles for resistance against insolvency during the crisis. Bugaj et al. (2023) also state that enterprises involved in Industry 4.0 had a positive shift in the development of profitability ratios. Saeedikiya et al. (2024) demonstrated that certain sensing, seizing and reconfiguring abilities are essential for enterprises to undergo digital transformation. When these capabilities are in place, enterprises can implement digital transformation efforts by leveraging them. Additionally, the research also highlighted that digital transformation may help SMEs build new capabilities, resulting in improved performance outcomes. The positive link between digitalization and financial performance (e.g. higher assets and earnings) echoes findings in developed and emerging economies alike. Zhao et al. (2023) found a significant correlation between digital investments and firm profitability, especially among tech-intensive sectors. Meanwhile, Petropoulou et al. (2024) showed that digital maturity positively affected resilience and revenue recovery post-pandemic. However, the inconsistency in the role of liabilities as a financial indicator, also noted in the study, is not uncommon. The financial structure of enterprises (debt vs. equity financing) may moderate how digitalization affects financial outcomes (Zhao et al., 2024), which can vary significantly by country and sector.

The selected financial ratios were checked for consistency across the different degrees of digitalization using the Kruskal–Wallis test. If the difference in indebtedness ratios was statistically significant, the Dunn–Bonferroni post-hoc test was used to find the differences between the groups of digitalization level. In terms of total assets, the adjusted significance indicates that the strong evidence of differences is between the enterprises whose level of digitalization is less than 25% and those with more than 75% (p-value 0.041) and 50–75% level (p-value 0.003). The post-hoc tests in conditions of earnings after taxes reveal that the most significant differences are between the top level of digitalization and those of 25–50% (p-value 0.015). Taking into consideration the results of the distribution of shareholders’ funds across the categories of digitalization level, the most significant differences are between the 50–75% and less than 25% level (p-value 0.031).

As far as it is known, academic work in the field of digitization and company performance has not yet been thoroughly examined in the conditions of the Slovak Republic, which is a context that shows the high growth and rate of digital transformation in relation to other developing countries, especially in the service sector. In addition, research by Valaskova et al. (2024) called for investigating how digitalization affects firm performance in different contexts and sectors. In addition, it is worth understanding which channels can explain how digitization affects firm performance. Some other suggested channels through which digitization can impact firm performance are IT innovation and customer experience. Digital technologies are immediately gaining importance for achieving business goals. It is a way of gaining differentiation and competitive advantage; subsequently, managers’ interest in digitization is also increasing (Ferreira et al., 2019; Frajtova Michalikova, 2023). Nevertheless, Martín-Peña et al. (2019) found that, in addition to its mediating role in the relationship between servitization and performance, digitalization has a direct, strong positive relationship with the sales performance of industrial firms. In contrast, Kharlamov and Parry (2021) conclude that digitization does not have a direct impact on the financial performance of British publishing firms; however, it has an effect in combination with servitization. Therefore, previous research has shown mixed results regarding the relationship between digitization and performance measures.

Globally, there is increasing attention on how digital capabilities, not just technology acquisition, drive business value. As Teece (2018) argues, dynamic capabilities such as the ability to reconfigure resources and adapt business models are crucial to leveraging digital tools effectively. Slovak firms, especially SMEs, may benefit from capacity-building programs aimed at fostering digital leadership and agile management practices, a strategy that has proven effective in countries like Singapore (IMDA, 2020).

Moreover, emerging technologies such as AI, IoT and digital twins are no longer confined to high-tech industries. Retail, agriculture and logistics sectors are seeing strong returns on digital investments. For example, Australian agritech firms have adopted IoT for precision farming, resulting in measurable productivity gains (Finger, 2023). Such cross-sectoral learning could inform digitalization strategies across Slovak enterprises as well. In addition, according to Volker (2014), the incorporation of computational data into the physical environment is considered to be an advanced manufacturing technique. It is possible for this information to be conveyed to visual components that are integrated into the environment of the organization. Real-time augmented reality provides employees with assistance in completing difficult tasks in an environment that is always changing (Krulicky and Horak, 2021). AI (Goel and Gupta, 2020) is a cutting-edge technical development that offers a beneficial addition to the manufacturing process. It does this by enabling robots or computers to carry out jobs that are similar to those that are performed by humans. A definition of AI provided by Pisar and Bilkova (2019) describes it as the ability of robots to simulate human intellect through the application of synthetic knowledge. It is necessary to understand the relevance of IoT-based robotic systems in automated manufacturing operations to comprehend processes that are enabled by intelligent learning capability. According to Dzedzickis et al. (2022), diversity in the production utilization of robots may be ascribed to the fact that robots are suitable for a variety of different industrial sectors. Industry 4.0, which includes business operations such as manufacturing, shipping and office administration, is simultaneously having an impact on the development of robot capabilities (Kubickova et al., 2021). Through the utilization of intelligently linked sensors, cognitive decision-making algorithms and real-time process monitoring, robot palletizing has the potential to greatly impact both the amount of time it takes to produce a product and the amount of output it generates (Lamon et al., 2020). It is vital that businesses implement Industry 4.0 because technology plays a crucial role in overcoming economic competitiveness (Popescu et al., 2022). This is the fundamental reason why businesses must adopt Industry 4.0. Additionally, the adoption of Industry 4.0 spans all services and activities along the full value chain (Vinerean et al., 2022). This is not restricted to changes in manufacturing or production processes. It is quite likely that companies will aim to make use of the technologies that are associated with Industry 4.0 in conjunction with the resources that they already possess. For the purpose of conforming to this new paradigm, it is important to design a way for precisely changing technologies and gadgets that are generally available (Hamilton, 2022; Nica, 2021).

Digitization is rapidly changing the way companies operate and compete in today’s global economy. It is no longer a question of whether a business should implement digital tools and technologies, but rather how to use them effectively to achieve a competitive advantage. Digitization offers companies a wide range of benefits, from process automation to data analysis and connecting with customers. However, the journey to digitization is not without challenges, from a lack of skills and resources to resistance to change. Digitization means the integration of digital technologies into all aspects of business, including operations, processes, products and services. Embracing digitization has become a key strategy for businesses that want to remain competitive in today’s digital economy. By leveraging these technologies, businesses can improve efficiency, productivity and customer engagement, and gain insight into market trends and customer behavior. Digitization also allows companies to expand their reach and penetrate new markets, creating new sources of income and business models.

For companies, the digitization process requires significant investment in technology, skills and culture change. And it can be difficult to know where to start and how to measure the impact of digitization on their performance. Therefore, it is important for companies to have a clear and well-defined digitization strategy, considering the specific needs and goals of the organization and involving all stakeholders in the process. The outputs of the analysis in conditions of Slovak enterprises reveal that some specific firm features influence the level of digitalization, which supports the significance of sector, size and ownership and the industry’s general image in the national context. The industry sector is the most significant factor influencing digital transformation. Enterprises in the industrial sector (sectors B, C, D and E according to NACE) have the highest level of digitalization (more than 75% of processes). Service sectors (H to S according to NACE) show a digitalization level of 50–75%, while agriculture and trade sectors digitalize up to 50% of their processes. The construction sector shows the lowest level of digitalization, despite potential productivity and quality benefits from digitalization. The size of the enterprise also significantly impacts the level of digital transformation. Large enterprises tend to digitalize more than 75% of their processes, medium-sized enterprises digitalize 50–75% and micro and small enterprises only digitalize up to 50% of their processes. The ownership structure influences digitalization levels. Public limited companies exhibit the highest digitalization (more than 75% of processes), while private limited companies tend to digitalize less than 25% of their processes. General partnership companies tend to digitalize 50–75% of their processes. The financial performance of Slovak enterprises is significantly impacted by the level of digitalization in terms of total assets, earnings after taxes, and shareholder funds. These financial parameters are strongly influenced by the degree of digital transformation. Total Liabilities did not show a significant relationship with the level of digitalization, indicating that liabilities may not be as strongly impacted by digital transformation.

Despite the steadily expanding amount of research on the relationship between digitalization and the success of businesses, there are still major gaps, particularly in some geographical situations. As an illustration, there are not a lot of academic studies that concentrate on the Slovak Republic, despite the fact that there has been a considerable development in digital transformation, particularly regarding the service sector. This constitutes a wasted chance to gain a better understanding of how digitalization influences the performance of firms in an economy that is fast-growing, particularly in comparison to its neighbors in Central and Eastern Europe. Therefore, it is of the highest significance to concentrate on doing more in-depth research into the ways in which digitalization impacts the performance of businesses in a variety of contexts and industries. The processes that drive the success of an organization through digital transformation would be illuminated by this, which would give useful insights. In Slovakia, for instance, where the service industry plays a significant role, digitalization may present itself in a manner that is distinct from how it would appear in economies that are more focused on manufacturing. When it comes to designing policies and strategies that are adapted to the specific features of each business, it is necessary to have a thorough understanding of these intricacies.

It is becoming increasingly important for firms to digitalize their operations to differentiate themselves from their competitors and acquire a competitive edge. It makes it possible for businesses to simplify their processes, cut expenses and increase customer satisfaction by providing personalized services and a quicker response time, among other benefits. How successfully businesses are able to incorporate digital technology into their fundamental operations and strategy is becoming an increasingly important factor in shaping the competitive environment. Those who are unable to keep up with the rapid pace of digital change may find themselves at a considerable disadvantage in the global market. The combination of these contradictory findings raises significant issues regarding the circumstances in which digitalization results in enhanced performance. Others may have difficulty realizing the same advantages from digital transformation, particularly if their efforts to digitalize are not linked with their overall company strategy. While some businesses may see large gains from digital transformation, others may struggle to realize the same benefits. In order for digitalization projects to be successful, a number of elements must be taken into consideration. These factors include the size of the firm, the industry, the circumstances of the market and the particular digital technologies that are deployed. Digital technologies have a tremendous amount of potential to revolutionize company structures and operations at any given time. However, in order for businesses to successfully integrate their digital activities, they must first match such projects with their larger strategic goals. It will be vital for firms in the Slovak Republic and abroad to develop a comprehensive and strategic approach to digitalization to maintain their competitive edge in a global economy that is becoming increasingly digital. However, in order for businesses to fully realize the potential benefits of digitalization, they need to address the challenges associated with digital transformation. These challenges include the requirement for skilled employees, the need to overcome resistance to change, and the requirement to make the necessary financial investments in digital infrastructure. Because of this, they are able to flourish in the digital era and make the most of the potential presented by Industry 4.0 technologies.

The study’s findings underscore the complexity of digital transformation and its heterogeneous impact on firms based on their size, sector and ownership structure. This study contributes to the ongoing discourse on digital transformation theory by empirically validating the connection between firm-specific features (sector, size and ownership) and the degree of digitalization. It supports the idea that digital transformation is not a uniform process but one that is contingent on the context of the firm. These findings reinforce the notion that digitalization is an ongoing, multidimensional process with varying trajectories across different industries and firm types. For practitioners, the results highlight the importance of developing tailored digital strategies that account for a firm’s size, industry and ownership structure. Enterprises should not only consider the technological aspect of digital transformation but also align it with broader strategic objectives to maximize its financial impact. For instance, by expanding the use of advanced technologies like AI, IoT and data analytics, large enterprises can gain even greater operational efficiency and competitive differentiation. Medium-sized enterprises should prioritize technologies that offer a high return on investment and can be integrated with existing systems without requiring a complete overhaul (e.g. invest in the most impactful digital technologies, such as cloud computing and data analytics tools, that can help improve decision-making and operational efficiency). Small and micro enterprises may form partnerships with larger firms or industry consortia to gain access to digital tools, knowledge and financial resources because mutual collaboration and resource-sharing can help mitigate the barriers smaller firms face, such as a lack of internal expertise and capital. As digital technologies continue to evolve, enterprises should align their digital initiatives with broader business strategies to secure long-term success in an increasingly competitive, digital-first economy. While this study provides valuable insights into the relationship between digital transformation and financial performance in Slovak enterprises, several limitations must be acknowledged that may affect the generalizability and scope of the findings. First, the analysis is based exclusively on firm-level data from Slovakia, a small economy with unique structural, institutional and cultural characteristics. While this localized focus offers depth and context-specific insight, it inherently limits the external validity of the results. Enterprises in other Central and Eastern European countries, or in more digitally advanced economies, may experience different patterns of digitalization and financial outcomes due to varying levels of technological infrastructure, policy support and managerial capabilities. Second, the data used were collected via a self-reported questionnaire, which introduces potential biases related to perception and social desirability. Third, the study does not fully capture external variables that may influence financial outcomes, such as market competition, supply chain integration, government digitalization incentives or macroeconomic volatility. These contextual factors could moderate or mediate the impact of digital strategies and should be accounted for in future analyses. Nonetheless, to enhance the robustness and relevance of future research, several pathways are recommended. A comparative cross-country study involving similar-sized economies in the Central and Eastern European region would allow for benchmarking and uncovering regional digitalization patterns. Furthermore, the adoption of a longitudinal research design could reveal how digital transformation unfolds over time and how sustained investments in digital capabilities contribute to financial resilience and growth. By addressing these limitations through broader geographic scope, time-based designs and more diversified data sources, future studies can provide richer, more actionable insights for both the academic community and decision-makers in the digital economy.

This paper was presented during the 10th Innovative Economic Symposium 2024 Trends in transformation of business and economy Ceske Budejovice, Czech Republic (October 16–18 2024).

This research was financially supported by the Slovak Research and Development Agency Grant VEGA 1/0494/24: Metamorphoses and causalities of indebtedness, liquidity and solvency of companies in the context of the global environment.