Does Digital Transformation Reflect the Adjustment of Capital Structure?

Abstract

This study investigates the effect of digital transformation on non-financial firms’ adjustment of the capital structure in European countries while controlling for firm characteristics (firm size, tangibility, profitability, and non-debt tax shields), board characteristics (board size, board gender diversity, and board meetings), and macroeconomic variables (GDP and inflation). Data were collated from the platform Refinitiv Eikon (LSEG). The final sample size was 514 companies during the 2010–2023 period. Panel GMM regression was used to thoroughly investigate the impact of digital transformation on the adjustment of capital structure. The results show that digital transformation improves capital structure adjustments. Based on the results of panel GMM regression, our results hold and confirm that there is a positive significant impact of digital transformation on the adjustment of capital structure. The main recommendation for businesses and policy makers is to successfully enter the digital age.

1. Introduction

Over the last few decades, digital transformation has redefined the operational and strategic frameworks of enterprises across all sectors (Vial, 2021). As technology advances, companies are progressively incorporating digital tools, automation, and data-driven decision-making into their operations to improve effectiveness and competitiveness (Rehan et al., 2023). This change has significantly impacted company models and productivity, and it is also pivotal in determining corporate financial decisions, especially regarding capital structure (Nguyen-Thi-Huong et al., 2023). The integration of digital technologies impacts risk management, investment plans, and financing requirements, ultimately affecting how companies reconcile debt and equity within their financial frameworks (Zhao et al., 2024).

The connection between digital transformation and capital structure modification has become notably significant in the European setting. European companies, propelled by swift technical progress and legislative structures that promote digital integration, have experienced substantial transformations in their finance approaches (Van Veldhoven & Vanthienen, 2022). Digital transformation strengthens companies’ access to diverse funding options, diminishes information asymmetry, and enhances operational efficiencies—elements that enable adjustments in capital structure (Luo & Jiang, 2022). Furthermore, in reaction to digitalization, companies in Europe may enhance their leverage ratios to maintain competitiveness and ensure sustained long-term growth, especially in sectors undergoing a swift technological transition.

This study’s empirical findings indicate there is a significant positive effect of digital transformation on capital structure modification. Companies that have proactively used digital technology generally modify their financial structures more efficiently, enhancing their debt ratios in accordance with market conditions. The findings demonstrate that digital adoption improves financial flexibility, enabling companies to reorganize their funding to correspond with technological investments and changing business models. This underscores the strategic significance of digital transformation in corporate finance, illustrating that technology-driven enterprises exhibit greater agility in managing their capital structures.

This research enhances the current literature by providing a thorough examination of the relationship between digital transformation and capital structure adjustment, particularly in the European market. This paper fills in the gap in several ways; firstly, this research diverges from prior studies that concentrated exclusively on the financial determinants of capital structure by including the impact of technical improvements, thereby offering new perspectives on how digitalization affects corporate finance decisions (Teo Piaw et al., 2024). The results provide significant insights for policymakers, corporate executives, and investors, highlighting the critical role of digital transformation in financial decision-making. Secondly, this is the first empirical evidence that the relationship between digital transformation and the adjustment of capital structure is controlled by firm characteristics (firm size, tangibility, profitability, and non-debt tax shields), board characteristics (board size, board gender diversity, and board meetings) and macroeconomic variables (GDP and inflation). The existing literature has not fully studied how these control variables interact with digital transformation and capital structure adjustment within non-financial firms. The lack of integration of these gaps creates a space for understanding the specific difficulties and opportunities for European non-financial organizations.

The primary objective of this research is to investigate the impact of digital transformation on the adjustment of the capital structures of European companies. Also, the secondary objective is to bridge the gap by investigating how digital transformation impacts capital structure adjustment, with a specific focus on the control variables that affect this relationship. Specifically, this study emphasizes how technology might revolutionize debt management, equity distribution, and overall non-financial conditions. The objective of this research is to determine whether digital transformation can boost capital structure adjustment, streamline processes, and foster a culture of continuous improvement in European nations.

The structure of this paper is as follows: A review of the literature and hypothesis development are presented in Section 2 along with the theoretical background. Section 3 presents the sample used and the development of the model. Section 4 analyzes the results, while Section 5 concludes this empirical paper and highlights the implications and limitations of this research.

2. Literature Review

2.1. Theoretical Background

2.1.1. Trade-Off Theory

According to this theory, businesses try to weigh the advantages of leverage against the disadvantages of possible non-financial hardship. By potentially lowering operational risks and increasing non-financial sustainability, digital transformation can have an impact on this balance and enable businesses to more dynamically modify their capital structure adjustment (Duwe & Duwe, 2022). According to the trade-off theory hypothesis, businesses try to weigh the advantage of debt against the disadvantages of possible non-financial difficulties. According to the trade-off theory, businesses may modify their capital structure adjustment to fund digital expenditures relating to digital transformation, balancing the possible advantages of better operating efficiency against the risk of higher debt (Serrasqueiro & Caetano, 2015). The trade-off argument states that businesses weigh the advantages of debt against tax benefits, possibly increasing the disadvantages of non-financial hardships. This balance can be affected by digital transformation, which may lower operational risks and expenses and increase the appeal of debt (Sdiq & Abdullah, 2022). In capital structure adjustment, companies with more leverage may be able to reduce their overall cost of capital and make use of tax shields, both of which have a beneficial effect on the trade-off theory (Cekrezi, 2013).

2.1.2. Pecking-Order Theory

According to this theory, businesses prioritize their funding sources based on what will lead to the lowest cost, prioritizing internal funding, followed by debt, and treating equity issuance as a last resort. By improving an organization’s capacity to generate internal revenue through higher efficiency and advancement, digital transformation can have an effect on the pecking order by reducing its dependence on external funding (Kiliç & Sakalsiz, 2023). Moreover, according to this theory, businesses favor internal funding over external financing and favor debt over equity when external non-financial support is required. Through its effects on the non-financial decisions made by firms, digital transformation affects this idea. Due to increased operational effectiveness, firms undergoing digital transformation may generate more internal revenue and become less dependent on outside finance (Adair & Adaskou, 2015). This research demonstrates the relationship between the pecking order of firms and different attributes in the context of digital transformation. The results show that larger businesses might still favor internal finances, and technological developments provide more flexibility and allow greater effectiveness, eliminating the need to seek outside capital (Castro et al., 2015). In this theory, because of information irregularity, businesses prefer inside finance and steer clear of external finance. This may result in reduced expenditure and more stable capital structure adjustment (Frank et al., 2020).

2.1.3. Agency Theory

Agency theory investigates the disputes regarding competing interests that exist between the shareholder and the leadership of a corporation. Enhancing openness and knowledge flow through digital transformation can help reduce agency difficulties and influence capital structure adjustment choice such that it better aligns with the needs of shareholders (Chen & Yu, 2011). The difference between the managers and shareholders is examined by agency theory. This theory suggests that businesses may experience a decrease in information asymmetries and agency costs if they implement digital innovation in the context of digital transformation. The resulting increased governance and openness may result in more effective decision-making and a possible decrease in the cost of capital (Fang et al., 2023). The premise that debt will help reduce agency costs among the managers and shareholders, aligning their interests and improving business performance, is supported by proof and agency theory. This is especially important since businesses use debt to fund their digital transformation, which can lower operational effectiveness and increase value overall (Hoang et al., 2021). In capital structure adjustment, debt can reduce the need to act in accordance with agency theory by aligning managers’ interests and reducing their diplomacy. This raises the corresponding company’s value and lessens the possibility of unnecessary administrative expenditure (Ahmed et al., 2023). The following table,

Table 1. Summary of expected impacts based on the previously mentioned theories.

Variables	Trade-Off Theory	Pecking-Order Theory	Agency Theory
Digital transformation	+	+	+/−

, provides a summary of the expected impact of digital transformation on the adjustment of capital structures based on the three previously discussed theories.

2.2. Previous Studies and Hypothesis Development

Digital Transformation and Capital Structure Adjustment

Digital transformation modifies operational structures, improves efficiency, and influences non-financial performance; it may substantially influence a firm’s capital structure adjustment. Research demonstrates that digitization expedites capital structure adjustments, allowing firms to swiftly adapt to market conditions and enhancing their financial management capabilities (Niu et al., 2023). The adoption of digital transformation can boost profitability, drive innovation, and enhance overall company performance. These outcomes may influence how enterprises allocate their non-financial resources and capital expenditure (Chen et al., 2023). Capital structure adjustment is highly influenced by digital transformation, which also alters businesses’ operational methods, capital structure, and profitability. This results in a dynamic capital structure adjustment emphasizing the shift from equity to debt as enterprises enhance operational efficiency and reduce expenditures (Xue & Zhang, 2024). Moreover, the digital transformation of non-financial firms has been strongly connected with enhanced operational performance, influencing these firms’ decisions about capital structure adjustment (Teng et al., 2022). Digital transformation significantly influences a firm’s capital structure adjustment since the transition from equity to debt is driven by enhanced operational performance and reduces the risk of bankruptcy due to digital transformation. Research has indicated that digital transformation expedites the alteration of capital structure adjustment, enabling organizations to respond more swiftly to both favorable and unfavorable changes (Olaniyi et al., 2024).

Digital transformation may accelerate capital structure adjustments by enhancing the stock market or modernizing the bond market. The idea here is that information asymmetry can be mitigated through digital transformation (Liu & Liu, 2023). In the realm of non-financial enterprises, digital transformation affects capital structure adjustment. Research analyzing companies listed from 2020 to 2023 revealed that factors such as accounts reliable percentages, managerial overhead ratios, and ownership structure substantially influence digital transformation initiatives. Consequently, these factors influence how these enterprises modify their capital structures (Bela et al., 2024). Researchers have found that the initial stage of a sound investment in research and development for digital transformation may not lead to significant progress, resulting in an excess of capital structure adjustment costs (Zhai et al., 2022). Non-financial firms generally require alterations to their financial structures to enable digital transformation (Khandelwal et al., 2023). To align with emerging digital activities, this even requires non-financial firms to restructure their resources and investments. For non-financial firms, effective capital structure adjustments enhance resource allocation efficacy, promote technological advancement, and mitigate hazards associated with digital transformation (Warner & Wäger, 2019). When the operational models of enterprises are subject to a digital transformation that significantly influences their capital structure adjustment, it not only entails technological innovation but also a revision of the enterprises’ overarching strategic frameworks (Libert et al., 2016). Digital transformation increases the dissemination of capital structure adjustment data. Reducing insight asymmetry reduces perceived risk for investors, consequently reducing the cost of capital funding for non-financial firms (João et al., 2023). Furthermore, digital transformation allows enterprises to offer more reliable and promptly available non-financial information. Increased transparency and improved shareholder plans help to reduce the cost of equity financing (Ren et al., 2023).

Based on the previous research, digital transformation enhances capital structure adjustment by allowing non-financial firms to refine their financial strategies and adapt more efficiently to evolving market conditions (Sarang et al., 2021). Prior studies have indicated that digitization improves the speed and efficacy of decision-making processes for capital allocation (Nuñez Huerta, 2021). By utilizing new technologies, non-financial firms can diminish information asymmetry, enhance transparency, and gain improved access to financial data (Culata & Gunarsih, 2012). These enhancements not only reduce the cost of equity borrowing but also augment a firm’s capacity to adjust its capital structure in reaction to external economic and completive forces (Elmoursy, 2020). Consequently, digital transformation enhances financial resilience and enables more agile adjustments to capital structures. Based on the previous discussion and the critical valuation of previous studies, the hypothesis of this empirical paper is as follows.

H1. 

Digital transformation has a positive relationship with capital structure adjustment.

3. Methodology

3.1. Sample Used

This paper investigates the impact of digital transformation on capital structure adjustment in all European countries. The data were collected from the platform Refinitiv Eikon (LSEG). The total number of listed nonfinancial companies in Europe is 8945, and after cleaning the data on missing observations, the final sample included 514 companies active during the 2010–2023 period.

Table 2. Sampling procedure.

Sampling Procedure	Number of Countries	Total Population	Sample Size
1. Western Europe	9	1925	205
2. Eastern Europe	10	1903	37
3. North Europe	15	2738	250
4. Southern Europe	17	2379	22
Total	51	8945	514

provides the details of the sampling procedure.

3.2. Research Model

We applied the panel GMM regression to investigate the impact of digital transformation on the adjustment of capital structures and deal with the endogeneity problem (Bajaj et al., 2020; Zhang et al., 2022; Zhu et al., 2024). Endogeneity may arise from measurement mistakes, omitted variable bias, or simultaneity, potentially resulting in biased and inconsistent estimates if inadequately addressed. To address these problems, this empirical investigation includes the variables firm size, tangibility, non-debt-tax shields, profitability, board size, board gender diversity, and board meetings as possibly endogenous variables. These variables may be affected by unseen factors or concurrently determined alongside the dependent variable. To resolve this problem, suitable instrumental variables (IVs) were employed within the GMM framework. The instruments chosen are lagged values of the endogenous variables, guaranteeing correlation with the endogenous regressors while remaining uncorrelated with the error term. The application of the GMM technique guarantees robust and consistent parameter estimations, addressing heteroskedasticity and probable autocorrelation in the panel data.

The economic model is given in Equation (1):

$$\begin{array}{ll}\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{S}\mathrm{t}\mathrm{A}\mathrm{d}\left(i,t\right)=& \alpha +\beta 1\mathrm{C}\mathrm{a}\mathrm{p}\mathrm{S}\mathrm{t}\mathrm{A}\mathrm{d}(i,t-1)+\beta 2\mathrm{D}\mathrm{T}(i,t)+\beta 3\mathrm{F}\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}\left(i,t\right)+\beta 4\mathrm{T}\mathrm{a}\mathrm{n}\mathrm{g}\left(i,t\right)+\beta 5\mathrm{N}\mathrm{T}\mathrm{S}\mathrm{h}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}\left(i,t\right)\\ & +\beta 6\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{f}\left(i,t\right)+\beta 7\mathrm{B}\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}\left(i,t\right)+\beta 8\mathrm{B}\mathrm{G}\mathrm{D}\left(i,t\right)+\beta 9\mathrm{B}\mathrm{M}\mathrm{e}\mathrm{e}\mathrm{t}\left(i,t\right)+\beta 10\mathrm{G}\mathrm{D}\mathrm{P}\left(t\right)+\beta 11\mathrm{I}\mathrm{n}\mathrm{f}\left(t\right)+\epsilon \end{array}$$

(1)

where

• CapStAd is the adjustment of capital structure toward the optimal capital structure;
• DT is digital transformation measured through data text mining via the ratio of keyword occurrences to the total word count in the annual report;
• FSize is firm size, which is measured by the natural logarithm of the total assets;
• Tang is tangibility, which is measured as fixed assets divided by total assets;
• NTShield denotes non-debt tax shield, which is measured by dividing accumulated depreciation by total assets;
• Prof is profitability, measured through return on assets;
• BSize is board size, measured by the number of members on a board;
• BGD is board gender diversity, measured by the percentage of females on a board;
• BMeet is board meeting, measured by the number of board meetings in a year;
• GDP is the growth in the Gross Domestic Product;
• Inf is inflation, measured by the percentage of the consumer price index.

3.3. Measurement of Variables

Table 3. Means of measuring variables.

Variable	Measurements/Measures	Reference	Source
Dependent Variable
Capital structure adjustment	Net change in leverage (Act Levi,t − Act Levi,t−1)	(Khalaf, 2017)	Refinitiv Eikon
Independent Variable
Digital transformation	Content analysis (data text mining of annual reports), using the ratio of keyword occurrences to the total word count in the annual report	(Teo Piaw et al., 2024)	Annual Report
Control Variable (Firm Characteristics)
Firm Size	The natural logarithm of total assets	(Saldi et al., 2023)	Refinitiv Eikon
Tangibility	(Total assets − current assets)/total assets	(Jain et al., 2024)	Refinitiv Eikon
Profitability	Return on Assets	(Rachmawati, 2021)	Refinitiv Eikon
Non-debt tax shield	Accumulated depreciation to total assets	(Lebow & Rudd, 2006)	Refinitiv Eikon
Control Variable (Board Characteristics)
Board size	Number of directors on the board	(Joseph et al., 2023)	Refinitiv Eikon
Board gender diversity	Percentage of females on the board	(Martinez-Jimenez et al., 2020)	Refinitiv Eikon
Board meetings	Total number of board meetings	(López-Cabarcos et al., 2023)	Refinitiv Eikon
Control Variable (Macro-Economic Characteristics)
Inflation	Consumer price index (CPI)	(Marcoulides & Raykov, 2018)	World Bank
GDP	Growth in Gross Domestic Product	(Öztekin, 2015)	World Bank

Authors’ analysis.

provides all the variables selected based on the previous studies discussed in the previous Section. In addition, the measures suggested by several authors in previous studies and the sources of the data have been provided.

3.4. Model Development

3.4.1. Dependent Variable

Capital structure adjustment: (CapStAd) This variable constitutes a company’s leverage with respect to adjusting its structure towards the desired capital structure (Khalaf, 2017). The net change in leverage that a company uses to fund its operations and growth is referred to as its capital structure adjustment. It is critical for creating a comprehensive non-financial organizational strategy and greatly influences an organization’s value and success (Meshack et al., 2022). Thus, whenever the capital structure adjustment ratio (Act Lev_i,t − Act Lev_i,t−1) exceeds or falls short of the target, the aforementioned adjustment coefficient quantifies the magnitude of the response (Iqbal-Hussain et al., 2015). Consequently, it may be asserted that the costs associated with increasing leverage are less than those for reducing debt (Khalaf, 2017).

3.4.2. Independent Variable

Digital Transformation: This research’s digital transformation variable was developed using a data-mining methodology, particularly by examining the frequency of pertinent terms in organizations’ annual reports, specifically via the ratio of keyword occurrences to the total word count in the annual report (Gurumurthy et al., 2020). Natural language processing (NLP) was employed to identify and extract key terms related to digital transformation, including “big data”, “blockchain”, “artificial intelligence”, “data analytics”, “digital finance”, “IOT”, “digital innovation”, “cloud computing”, “multi-party security computing”, “automation”, and “cybersecurity”, from corporate annual reports. A term frequency (TF) model was utilized to assess the relative significance of these phrases within each company’s report, ensuring that frequently used words did not eclipse those explicitly representative of digital actions (Bharadwaj et al., 2013). This method provides an objective and quantitative assessment of a firm’s digital transformation initiatives, serving as a data-driven indicator of the degree to which organizations prioritize and incorporate digital strategies into their operations (Chen et al., 2023). The resultant variable functions as a significant input for evaluating the effects of digital transformation on the adjustment of the capital structures of non-financial companies in Europe.

3.4.3. Control Variables (Firm Characteristics)

Firm size: The size of a business significantly influences the capital structure adjustment process since larger organizations are often met with cheaper financing costs, enhanced access to capital markets, and less information asymmetry relative to smaller enterprises (Rasiah & Kim, 2011). Large corporations often possess more varied income sources, superior credit ratings, and robust affiliations with financial entities, enabling them to modify their capital structures more effectively in reaction to market dynamics (Wahhab et al., 2021). Conversely, smaller enterprises may face elevated borrowing expenses, financial limitations, and reduced adaptability in modifying their debt-to-equity ratios. In light of these dynamics, firm size is typically represented by total assets since this value offers a distinct and measurable indication of a company’s entire scale and resource capacity. The total assets value reflects a firm’s ability to generate capital and withstand financial disruptions, serving as a dependable metric for assessing capital structure modifications for enterprises of varying sizes (Nuñez Huerta, 2021).

Tangibility: Tangibility significantly influences the capital structure adjustment process since enterprises with a greater ratio of tangible assets generally have improved access to secured loan funding (İltaş & Demirgüneş, 2020). Tangible assets, including property, plant, and equipment (PPE), function as collateral, mitigating risk for lenders and decreasing borrowing expenses. Consequently, companies with significant asset tangibility are more inclined to modify their capital structures by augmenting leverage as necessary since they may obtain loans with greater ease (Camisón et al., 2022). In contrast, companies with reduced tangibility—such as those in the technology or service industries—may be subject to elevated borrowing costs due to insufficient collateral, complicating their ability to change debt levels effectively (Vengesai, 2023). Tangible assets affect the pace of capital structure adjustment since businesses with substantial collateral can more rapidly realign their leverage in reaction to market changes or desired capital structures (Alves & Lourenço, 2022).

Profitability: Profitability considerably impacts a firm’s capital structure adjustment since more profitable enterprises often depend less on external loans and modify their capital structures at a slower rate than less profitable firms. The pecking-order hypothesis posits that enterprises with greater profits choose to finance their operations through internal funds (retained earnings) instead of incurring more debt, hence minimizing the necessity for frequent capital structure modifications (Jayathilaka, 2020). In contrast, less profitable enterprises, which may have greater financing limitations, are more inclined to modify their capital structures by augmenting leverage to satisfy funding requirements (Zambrano Farías et al., 2022). Furthermore, profitability influences the rate of adjustment, as organizations with robust revenues may swiftly diminish excessive debt levels while aiming for an optimal capital structure, whereas financially weaker enterprises may encounter difficulties in making prompt modifications due to liquidity limitations. Consequently, profitability serves a dual function in capital structure modification, affecting both the trajectory and pace of financial decision-making (Puspitaningtyas et al., 2018).

Non-Debt Tax Shield: Non-debt tax shields (NDTSs), including depreciation, diminish a firm’s taxable revenue without reliance on loan funding. Companies with elevated NDTSs are less motivated to depend on interest tax deductions from debt, potentially hindering capital structure modifications towards more leverage (Nasution et al., 2017). In contrast, enterprises with lower NDTSs may more actively modify their capital structures by raising debt to capitalize on interest tax shielding, hence expediting the adjustment process towards their target leverage ratio (Hristov et al., 2023).

3.4.4. Control Variables (Board Characteristics)

Board Size: The size of a board can impact the adjustment of capital structure by influencing the speed and efficacy of financial decision-making (Kalsie & Shrivastav, 2016). A large board may result in protracted capital structure revisions owing to coordination difficulties, varied perspectives, and possible bureaucratic hindrances in regard to achieving a consensus on financing choices. Conversely, smaller boards are often agiler and more stubborn, enabling the corresponding companies to modify their leverage more effectively in reaction to market dynamics and financial objectives (Elmoursy, 2020).

Board Gender Diversity: Gender diversity on boards can affect capital structure adjustments by improving risk evaluation and decision-making processes. Companies with more gender-diverse boards typically use more cautious financial strategies, resulting in gradual shifts towards increased debt, as female directors are generally linked to minimized risk tolerance. Gender-diverse boards may enhance governance quality and financial supervision, enabling more effective modifications to capital structures in alignment with long-term financial stability (Reddy & Jadhav, 2019).

Board Meetings: Board meetings are essential for capital structure modification as they enable strategic financial decision-making and supervision. An increased frequency of board meetings can facilitate expedited modifications to a company’s capital structure by allowing for prompt deliberations on financing strategies, risk management, and leverage optimization. Holding meetings rarely may hinder the adjustment process by causing delays in decision-making and diminishing board involvement in capital structure planning (Yilmaz et al., 2023).

3.4.5. Control Variables (Macro-Economic Variables)

Inflation: Inflation can profoundly affect capital structure modification by altering financing expenses and debt management tactics (Živkov et al., 2020). In times of elevated inflation, companies may modify their capital structures by augmenting leverage since the actual value of debt diminishes, rendering borrowing more appealing (Ali & Asfaw, 2023). Nevertheless, inflation may result in elevated interest rates and economic instability, thereby hindering capital structure modifications as companies exercise greater caution around incurring extra debt (Fitzgerald et al., 2020).

GDP: GDP growth impacts capital structure modification by influencing economic stability, loan accessibility, and corporate profitability (Cracolici et al., 2010). In times of robust GDP development, companies may modify their capital structures by augmenting leverage, as elevated economic activity enhances profitability and diminishes default risk (Cohen Kaminitz, 2023). Conversely, during economic recessions, companies may decrease debt levels or decelerate capital structure modifications due to heightened uncertainty and stricter lending conditions (Bernow et al., 2017).

4. Results and Analysis

4.1. Descriptive Statistics

Table 4. Descriptive statistics.

	CapStAd	DT	FSize	Tang	Prof	NTSheild	BSize	BGD	BMeet	GDP	Inf
Mean	0.199	0.015	22.583	0.633	0.069	0.184	10.492	0.259	8.044	0.014	0.022
St Dev	0.058	0.001	1.671	0.203	0.116	0.030	3.765	0.143	2.347	0.025	0.024
Min	−0.285	0.009	15.008	0.111	−0.140	0	3	0	2	−0.057	−0.062
Max	0.578	0.032	27.211	1.000	1.493	0.318	26	0.75	12	0.060	0.083

Authors’ analysis.

highlights the descriptive statistics and places of interest in regard to significant points that enhance one’s understanding of the data. The descriptive data offer insights into the capital structure adjustments and digital transformation levels of enterprises. The capital structure adjustment values vary from −0.285 to 0.578, signifying that certain enterprises have markedly decreased their leverage, while others have considerably augmented it. A standard deviation of 0.058 indicates substantial diversity in the adjustment process among enterprises.

The average value for non-financial enterprises in Europe concerning digital transformation is around 29 words counted in annual reports, indicating a moderate degree of digital adoption. The substantial standard deviation of 9.943 signifies significant variability among enterprises, indicating that although several companies have fully embraced digital transformation, others are still in the nascent phases of adoption. This variety underscores the varied strategies and investment levels employed in digital transformation among various organizations and sectors.

4.2. Correlation Matrix

Table 5. Correlation analysis.

	1	2	3	4	5	6	7	8	9	10	11
1. CapStAd	1
2. DT	0.007	1
3. FSize	−0.004	−0.033	1
4. Tang	0.213	−0.038	0.248	1
5. Prof	−0.101	0.053	−0.186	−0.171	1
6. NTSheild	0.008	−0.020	−0.050	−0.007	−0.048	1
7. BSize	−0.036	−0.005	0.437	0.057	−0.088	0.042	1
8. BGD	−0.003	−0.008	0.125	0.008	−0.022	0.014	0.045	1
9. BMeet	0.076	−0.006	−0.014	0.034	−0.052	0.058	−0.055	0.098	1
10. GDP	−0.017	−0.002	0.003	−0.015	0.036	−0.007	0.006	0.028	0.014	1
11. Inf	−0.014	−0.114	0.061	−0.002	−0.009	0.007	0.012	0.230	0.053	0.224	1
VIF	1.562	1.251	1.952	1.756	1.625	2.152	1.960	1.484	1.263	1.185

Authors’ analysis.

displays the correlation matrix, clarifying the connection between the variables. The positive link between capital structure adjustment and digital transformation indicates that enterprises modifying their capital structures are more inclined to invest in digital innovation and technology-driven initiatives. Organizations experiencing digital transformation frequently require significant financial resources to invest in digitalization, prompting them to modify their capital structures by optimizing their debt/equity ratios.

Table 5 (above) demonstrates a negative association of −0.004 between capital structure adjustment and firm size, suggesting that larger non-financial firms appear to participate in capital structure adjustment activities less, suggesting that having access to a sufficient quantity of resources does not inherently result in superior capital structure adjustment performance. Moreover, the impact of market conditions, including inflation and economic growth, is substantial, as they greatly affect non-financial firms’ capital structure adjustment performance, highlighting the importance of including these control factors in any thorough examinations of capital structure adjustment.

Additionally, there is a significant positive connection, equal to 0.213, between capital structure adjustment and tangibility. These results indicate that non-financial firms that efficiently utilize their tangible assets might have a superior capacity to acquire debt funding, hence fostering growth and financial performance. These findings highlight the intricacy of capital structure adjustment dynamics, where diverse firms adopt various financial strategies. Non-financial firms must comprehend this connection in order to refine their capital structure adjustment approaches to improve performance and sustainability.

4.3. Panel GMM Regression Analysis

According to the results of the panel GMM regression analysis shown in

Table 6. Panel GMM regression results.

Dependent Variable: Capital Structure Adjustment
Variables	Coefficient	p value
CapStAd(t−1)	0.035	0.000
DT	0.068	0.000
FSize	0.096	0.000
Tang	0.062	0.000
NTSheild	0.021	0.218
Prof	−0.147	0.000
BSize	0.078	0.028
BGD	−0.051	0.000
BMeet	0.044	0.010
Inf	−0.054	0.062
GDP	0.030	0.255
Intercept	0.235	0.000
Country Dummies	Yes
AR(1)	0.135
AR(2)	0.172
Hansen Test	0.404
Wald Chi2	1952.23 (0.000)

, digital transformation positively and significantly impacts the capital structure adjustments of non-financial firms. The capital structure adjustments of these companies becomes more stable and appealing to investors as digital transformation activities expand, according to the highly significant coefficient, and this is in line with the results reported by Santos (2023). More specifically, this result suggests that digitally advanced enterprises may have greater development potential, enhanced operational efficiency, and increased profitability as well as a better capacity to manage their leverage efficiently and adapt their capital structures in response to financial and market conditions (Tsou & Chen, 2023). This suggestion indicates that companies engaged in digital transformation exhibit greater dynamism in financial decision-making while also maintaining a capital structure that aligns with their changing strategic goals (Rothstein, 2024). According to these findings, non-financial companies that embrace digital transformation have superior operational savings, innovation potential, and overall market competitiveness. The findings suggest that these firms are better positioned to encourage investment (Bruszt & Langbein, 2020). The dynamic trade-off theory posits that enterprises modify their capital structures over time to maximize the tax advantages of debt while mitigating bankruptcy risks. Digital transformation reduces adjustment costs, increases capital accessibility, and promotes financial decision-making, resulting in expedited and less expensive adjustments to capital structures (Ahmed et al., 2023). Moreover, the pecking-order theory posits that firms favor internal finance over external financing because of asymmetric information. Digital transformation enhances financial openness and credibility, diminishes information asymmetry, and enables enterprises to acquire external finances on more advantageous terms (Bela et al., 2024).

In addition, there is a significant positive impact of firm size on the adjustment of capital structures, indicating that larger companies and those with more tangible assets are better able to manage their resources, maintain financial stability, and move toward their optimal capital structures (Chen et al., 2023). The beneficial effect of firm size on capital structure adjustment corresponds to trade-off theory, which posits that companies modify their capital structures to achieve an optimal debt ratio by weighing the tax benefits of debt against bankruptcy expenses (Ahmed & Abu Khalaf, 2025). Large corporations, owing to their financial robustness, adapt more effectively than smaller enterprises, which may encounter limitations. Moreover, dynamic trade-off models suggest that large enterprises have greater adjustment speed due to lower adjustment costs, including fewer constraints from lenders and decreased market friction in capital acquisition (Chen & Xu, 2023).

The size of a board and the frequency of its meetings have an important role in corporate financial decision-making, especially with respect to capital structure modifications. A larger board typically provides varied experience, improved governance, and enhanced oversight, hence augmenting the corresponding firm’s capacity to effectively modify its leverage ratio (Abu Khalaf, 2024). Likewise, regular board meetings facilitate ongoing oversight, prompt decision-making, and proactive reactions to financial discrepancies, guaranteeing that companies can modify their capital structures in accordance with strategic objectives (Elmoursy, 2020). An increased board size correlates positively with enhanced decision-making, as it encompasses directors possessing diverse forms of financial acumen, industry insights, and risk management competencies (Epong & Anom, 2019). This diversity allows companies to assess various financing alternatives, enhance debt/equity ratios, and promptly adjust their capital structures in response to market fluctuations. Moreover, an effectively organized board helps alleviate agency issues, guaranteeing that managers prioritize their firm’s best interests above inefficient financial approaches (João et al., 2023). Regular board meetings improve the speed and effectiveness of capital structure modifications by promoting active discourse on financial policies, debt management, and equity-financing methods (Alshaiba & Abu Khalaf, 2024). Holding more meetings enables boards to promptly address economic swings, interest rate variations, and investment prospects, thereby averting extended divergences from the ideal capital structure. Furthermore, regular meetings foster robust corporate governance, mitigating information asymmetry and improving access to capital markets (Joseph et al., 2023).

Profitability imposes a negative and significant influence on capital structure adjustment, as posited by the pecking-order hypothesis, which indicates that firms favor internal financing over external financing (Hristov et al., 2023). More profitable companies accumulate greater retained earnings, hence diminishing their need for debt or equity financing (Al-Kubaisi & Khalaf, 2023). Consequently, these organizations have less of an incentive to modify their capital structures, resulting in slower or less frequent adjustments in their leverage ratios (Jayathilaka, 2020). Moreover, highly profitable companies typically eschew superfluous financial risk and favor sustaining lower debt levels, further constraining the necessity for frequent capital structure modifications (Kim et al., 2023).

Likewise, gender diversity on boards may adversely affect capital structure adjustment because of variations in risk perception and decision-making approaches. Studies indicate that female board members are generally more risk-averse, favoring financial stability over bold financing modifications (Khalaf, 2017). This prudent strategy may result in postponed or conservative modifications to capital structures, as diverse boards prioritize long-term sustainability over swift reconfiguration of debt and equity (Reddy & Jadhav, 2019). Furthermore, heightened variety may prolong decision-making procedures since several perspectives must be evaluated prior to implementing substantial finance alterations. Thus, while gender diversity improves governance and ethical decision-making, it may impede a firm’s capacity to optimize its capital structure effectively (Sarang et al., 2021).

Inflation adversely and substantially affects the modification of capital structure because of its influence on borrowing expenses, investment unpredictability, and corporate valuation. Elevated inflation results in increased interest rates, rendering debt financing more costly and deterring companies from modifying their capital structures via further borrowing (Zhang et al., 2022). Moreover, inflation heightens economic uncertainty, prompting companies to choose a more conservative strategy for financial restructuring, as variable prices and expenses complicate the forecasting of future cash flows and repayment abilities (Ali & Asfaw, 2023). Furthermore, inflation can diminish the actual value of liabilities, prompting enterprises to postpone capital structure modifications in expectation of additional inflationary effects (Ktit & Abu Khalaf, 2024). Consequently, companies in high-inflation contexts may find it challenging to optimize their capital structures, choosing instead to reduce financial risk and preserve liquidity, thereby impeding the adjustment process (Zou et al., 2024).

4.4. Robustness of Results

In this part of the study, we employed the Panel Generalized Method of Moments (GMM) estimation model to validate the robustness of the results by integrating various proxies for the chosen variables. We employed alternative measures to guarantee that the conclusions would not be influenced by a particular variable’s definition and maintain consistency across many specifications. The GMM methodology adeptly mitigates any endogeneity issues while enhancing the dependability of estimations. The outcomes derived from these robustness assessments substantiate the credibility of the principal findings, illustrating that the associations recognized by the basic model persist even when other proxies are employed. The results provided by the panel GMM regression hold, as evident in

Table 7. Panel GMM regression results.

Dependent Variable: Capital Structure Adjustment
Variables	Coefficient	p value
CapStAd(t−1)	0.022	0.000
DT	0.059	0.000
FSize	0.088	0.000
Tang	0.057	0.000
NTSheild	0.020	0.352
Prof	−0.177	0.000
BSize	0.085	0.032
BGD	−0.047	0.000
BMeet	0.049	0.025
Inf	−0.053	0.081
GDP	0.035	0.284
Intercept	0.288	0.000
Country Dummies	Yes
AR(1)	0.121
AR(2)	0.198
Hansen Test	0.452
Wald Chi2	1845.25 (0.000)

below, suggesting that digital activities have a favorable impact on capital structure adjustment.

5. Conclusions

This empirical investigation of the association between digital transformation and capital structure adjustment and control variables in regard to non-financial firms across four European regions (Western Europe, Eastern Europe, North Europe, and Southern Europe) provides significant illumination. By evaluating data from 8945 listed non-financial organizations, this research addresses a significant gap in the previous literature, which has mainly disregarded the relationship between digital transformation and capital structure adjustment in the European environment. According to the empirical results, digital transformation is vital to non-financial firms’ ability to optimize their capital structures, increase financial flexibility, and lessen financing limitations. By highlighting the effects, the inclusion of control variables further improved our knowledge of how different factors affect capital structure adjustment. The findings highlight how crucial it is for non-financial companies to incorporate digital transformation into their strategic planning to improve financial stability and make long term investments. By lowering perceived risks and financing costs, digital tools and technologies not only boost access to financial markets but also streamline operations. Businesses can use these insights to match technological developments with their financial goals, guaranteeing long-term growth and competitiveness in the European market.

The beneficial effects of digital transformation on capital structure modification for non-financial enterprises in Europe have considerable implications for corporate financial strategies and investment choices. As companies progressively incorporate digital technologies and processes like automation, artificial intelligence, and big data analytics, they improve their operational efficiency, revenue generation, and overall financial stability. This enhanced financial performance enables enterprises to modify their capital structures more efficiently, optimizing their debt and equity composition in accordance with market conditions. Furthermore, digital transformation mitigates information asymmetry by augmenting transparency and financial reporting, resulting in heightened investor trust and enhanced access to external finance. Consequently, companies that actively engage in digital transformation are more likely to be met with advantageous financing conditions, allowing them to modify their leverage in a more strategic and timely fashion. The relationship between digital transformation and capital structure adjustment highlights the significance of technology investment for long-term financial stability. Policymakers and corporate leaders in non-financial industries throughout Europe must acknowledge that digital transformation serves not just as a mechanism for improving competitiveness but also as a crucial factor for attaining financial flexibility. Companies that do not react to digital innovations may face increased funding limitations, complicating the efficient adjustment of their capital structures. Consequently, promoting digital adoption via government incentives, corporate training initiatives, and infrastructure expenditures can enhance enterprises’ capacity to manage financial difficulties, optimize capital structures, and maintain long-term development in a progressive digital economy.

This study does have certain limitations: Firstly, although the sample size is representative, it is limited to 514 non-financial firms, which might not adequately represent the diversity seen in all European industries and geographical areas. Second, the scope may be constrained by the exclusive use of Refinitiv Eikon as the data source, which leaves out qualitative information or data from smaller, unlisted companies. Furthermore, the data’s cross-sectional nature limits the capacity to examine causality or long-term consequences. This study makes important contributions to our understanding of digital transformation and how it affects non-financial organizations’ financial decision making. The findings highlight the significance of digital transformation in promoting financial stability and growth, making them especially important for stakeholders in the non-financial sector, policy makers, and financial management.

Additionally, this research contributes to the growing conversation on digitalization in financial strategies by laying the foundation for future investigations into dynamic and wider industrial applications. For researchers, this research provides a rigorous framework for studying the substantial consequences of digital transformation on financial decision-making. It gives a platform from which future studies can study dynamic connections between digital transformation and other economic or industry-specific factors in diverse geographical and regulatory environments. Additionally, this research emphasizes the need for more detailed evaluations on topics such as how a different degree of digital adoption affects capital structure adjustment or how sector disparities within the non-financial sector affect them. Overall, this research highlights how digitalization can revolutionize non-financial firms’ financial practices and lays the groundwork for future research on digital transformation in a more complex economic environment.