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Article

Navigating Financial Risk in the Digital Age: The Mediating Role of Performance and Indebtedness

by
Siham Slassi-Sennou
1 and
Mourad Es-salmani
2,*
1
ERMOT Laboratory, FSJES—Fez, Sidi Mohamed Ben Abdellah University, Fez 30080, Morocco
2
Multidisciplinary Research Laboratory LAREM, HECF Business School, Fez 30000, Morocco
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(6), 325; https://doi.org/10.3390/jrfm18060325
Submission received: 16 May 2025 / Revised: 31 May 2025 / Accepted: 4 June 2025 / Published: 12 June 2025
(This article belongs to the Section Risk)
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Abstract

In the context of an increasingly digital economy, firms are rapidly adopting technological innovations to bolster financial resilience and competitiveness. However, the quantitative impact of digital transformation on key financial outcomes—specifically performance, indebtedness, and risk—remains underexplored. This study investigates the extent and pathways through which digital transformation influences financial structures and stability. Employing Structural Equation Modeling (SEM) on firm-level survey data, the analysis reveals that digital transformation significantly enhances financial performance (β = 0.538, p < 0.01). Improved performance, in turn, leads to substantial reductions in firm indebtedness (β = −0.591, p < 0.01) and financial risk (β = −0.124, p = 0.021). While digital transformation does not directly affect indebtedness, it mitigates financial risk indirectly through two mediating variables: financial performance and firm indebtedness (mediated effects: β = −0.221 and β = −0.318, respectively; both p < 0.01). These findings underscore the financial value of digital initiatives, highlighting their role in enhancing performance and reducing financial vulnerabilities. The study offers strategic insights for managers and policymakers aiming to leverage digital transformation for financial optimization.

1. Introduction

In the evolving global economic landscape, digital transformation has become a cornerstone of strategic renewal for firms facing increased competition, technological disruption, and mounting pressures to remain financially resilient. Particularly in emerging economies, where institutional modernization and financial system reforms are actively pursued, firms are increasingly leveraging digital tools to optimize operational efficiency and financial outcomes (Chen & Zhang, 2024). The post-COVID environment has further accelerated this shift, pushing firms across sectors to reconfigure their business models using digital technologies to ensure sustainability and competitiveness (Esamah et al., 2023; Troise et al., 2022). This transformation is not only a technological shift but also an economic imperative.
Despite growing interest in digital transformation, there remains limited empirical understanding of its financial consequences, especially concerning its impact on firm indebtedness and exposure to financial risk (Kidschun, 2024). While digitalization is often assumed to enhance performance, its broader effects on a firm’s capital structure and financial fragility remain ambiguous (Li & Li, 2025; Xue & Zhang, 2024). This gap is important in contexts where firms often operate under constrained financing conditions and volatile macroeconomic environments. Understanding whether digital initiatives can improve financial performance and, in turn, reduce financial vulnerability is important for firms and investors aiming to foster resilient and sustainable financial ecosystems.
Existing studies have largely focused on the relationship between digital transformation and firm productivity, innovation, or customer engagement, often overlooking its implications for financial structure and risk exposure (N. Xu et al., 2024; Zhou et al., 2023). Moreover, while financial performance has been studied as an outcome variable, its potential role as a mediator linking digital transformation to broader financial outcomes remains underexplored. This study addresses that gap by examining how digital transformation influences firm indebtedness and financial risk through the mediating role of financial performance.
This study empirically investigates the central quantitative research question: To what extent can the effects of digital transformation on firm indebtedness and financial risk be explained and measured through its impact on financial performance?
Structural equation modeling (SEM) is employed to quantify and test both direct and mediated relationships.
This research is theoretically anchored in the Resource-Based View (RBV), which argues that firms achieve sustained competitive advantage by leveraging strategic, inimitable resources (Bharadwaj et al., 2013). Within this framework, digital transformation enhances financial performance by improving operational efficiency and internal capital management, which in turn can reduce reliance on external debt and lower financial risk (Chen & Zhang, 2024). To deepen this understanding, the analysis also draws on Pecking Order Theory, which posits that firms prefer internal financing over external debt due to information asymmetry and financing costs (Bauweraerts & Colot, 2012). This perspective supports the idea that improved financial performance, driven by digitalization, diminishes the need for borrowing, thereby alleviating financial fragility. Together, these theoretical lenses provide a comprehensive foundation for examining the interconnected effects of the research constructs.
The remainder of this paper is structured as follows: The next Section reviews relevant literature and theoretical foundations. This is followed by the research methodology, presentation of empirical results, discussion of findings, and finally, implications, limitations, and avenues for future research.

2. Literature Review and Hypothesis Development

2.1. Conceptual Framework

2.1.1. Digital Transformation

Digital transformation refers to the broad integration of digital technologies into all areas of an organization’s operations, processes, and value creation (Lachova, 2021). Unlike digitization or digitalization, which only convert analog processes into digital formats, digital transformation is a strategic shift. It changes how technology is used and affects organizational structure and corporate culture (Narayanan, 2023).
This transformation uses advanced technologies such as artificial intelligence (AI), big data analytics, cloud computing, the Internet of Things (IoT), and blockchain (Lachova, 2021; Narayanan, 2023). It is often motivated by the need to improve efficiency, customer service, innovation, and adaptability to market changes.
More than adopting new tools, successful digital transformation connects digital efforts to corporate goals, changes work processes, and promotes a digital mindset among employees and leaders (Moatshe et al., 2024). It also requires investment in change management and training to support the organization’s digital growth.
The literature highlights its importance in enabling data-driven decisions and agility, especially in volatile, uncertain, complex, and ambiguous (VUCA) environments (Troise et al., 2022). Empirical studies increasingly link digital transformation with better financial and organizational outcomes, such as higher productivity, lower costs, and increased revenue.

2.1.2. Financial Performance

Financial performance is a broad concept that shows how well a firm meets its financial goals. It includes profit, return on investment, operational efficiency, and revenue growth (Lestari et al., 2023). In management and accounting research, it is seen as a measure of how efficient and sustainable an organization is (Othman & Ameer, 2011). It also reflects the financial outcomes of a company’s capabilities, strategies, and external relationships (Salamah, 2023).
Researchers measure financial performance in two ways. The first is objective, using indicators like Return on Assets (ROA), Return on Equity (ROE), and net income. The second is subjective, based on managers’ opinions about how their firm compares to competitors (Lestari et al., 2023). This twofold approach highlights the difficulty of measuring performance in different institutional settings, especially where reliable financial data are hard to access. In developing countries, for example, personal views often fill this gap when combined with context and expert input.
In digital transformation, financial performance helps show the value created through digital initiatives (Slassi-sennou & Elmouhib, 2025). As firms adopt new technologies and change their operations, improved financial performance is often one of the first and easiest benefits to observe (Bhilare, 2023). It is also closely linked to risk management and debt strategy. Firms in better financial health are more able to face uncertainty and build effective capital structures.

2.1.3. Firm Indebtedness

Firm indebtedness refers to the extent to which a company uses borrowed capital to finance its operations, investments, and growth (Gajdosikova et al., 2023). It is a key part of a firm’s capital structure and one of the main factors affecting its financial strength, stability, and adaptability (Poljašević & Grujić, 2024). In finance, debt is usually measured using ratios such as the debt-to-equity ratio, debt-to-assets ratio, or interest coverage ratio (Budhiarjo et al., 2022; Sunaryo & Lestari, 2021). These ratios show how much the firm has borrowed and whether it can meet its financial obligations.
Theoretically, indebtedness is linked to the pecking order theory and the trade-off theory of capital structure (Bauweraerts & Colot, 2012; Correa et al., 2013). The first theory suggests that firms prefer to use internal funds and turn to debt only when necessary. The second argues that companies seek a balance between the advantages of debt, such as tax benefits, and its downsides, like financial distress and conflicts of interest. Therefore, the level of debt a company carries can reflect both its strategy and its constraints.
In empirical research, debt is commonly analyzed across different industries and firm sizes to understand how companies manage funding under various economic, legal, and organizational conditions (Tut, 2023). It is also widely studied in relation to corporate governance and risk management. In this context, it is often linked to challenges such as conflicts of interest, borrowing limits, and firm valuation.
For the purposes of this study, firm indebtedness is defined as the financial burden a company carries in the form of long-term and short-term debt. It is measured using recognized accounting and financial indicators.

2.1.4. Financial Risk

Financial risk refers to the uncertainty and potential loss an organization might face in its financial operations and obligations. It includes various exposures, such as the risk of being unable to repay debt, lacking sufficient liquidity, or dealing with unfavorable changes in interest rates, exchange rates, or credit terms (Anwar, 2017; Kulakovska et al., 2023). This type of risk is critical in corporate finance because it directly impacts a firm’s solvency and its ability to maintain stable finances. Theoretically, financial risk is divided into types like credit risk, market risk, liquidity risk, and interest rate risk (Isran et al., 2021; Sovilj, 2020).
These categories help identify specific sources of financial vulnerability within a company. For example, credit risk is the chance that a borrower will not repay their debt. Liquidity risk concerns a firm’s ability to meet short-term obligations without incurring major losses (Isran et al., 2021; Sovilj, 2020). Financial risk is often evaluated using quantitative tools such as the debt service coverage ratio, current ratio, and value-at-risk (VaR), as well as other financial ratios that indicate instability or overexposure. It can also be assessed through qualitative methods based on managerial judgment (Han & Ren, 2020; Kirina et al., 2019; Y. Yang, 2024).
Researchers study this concept using frameworks such as portfolio theory, agency theory, and financial distress models. These models help companies assess and manage their exposure to various financial risks (Mokoginta & Agung, 2022; Pryshchepa et al., 2015). Managing financial risk involves anticipating negative financial situations and taking steps to reduce their impact. Common approaches include diversification, hedging, and modifying the capital structure (Nabiyev, 2020).
In this study, financial risk is defined as the possibility that a firm may face financial losses or challenges due to its financial structure and obligations. This includes risks related to debt repayment, cash flow variability, and unexpected financial disruptions.

2.2. Hypothesis Development

2.2.1. Direct Effects Hypothesis

The relationship between digital transformation and financial performance is supported by both theory and evidence. From the resource-based view (RBV), many scholars argue that integrating digital technologies into business processes helps firms shape strategic resources to create value and gain competitive advantage (Tian et al., 2023; Turkcan, 2025; L. C. Yang & Ming, 2024). These digital capabilities lead to higher productivity, lower costs, and customer-driven innovation, which all contribute positively to financial performance (Liew et al., 2024).
Empirical studies support this link. Bharadwaj et al. (2013) show that firms with full digital strategies perform better financially. Kraus et al. (2021) find that digitalized firms are more likely to achieve higher profits, scalability, and adaptability in uncertain conditions. These improvements come from using real-time data, automating tasks, and making strategic decisions, which together boost efficiency and revenue.
Digital transformation also acts as a performance driver, especially in sectors facing structural or technological shifts (Turkcan, 2025). It promotes innovation in business models, reduces inefficiencies, and improves agility—key factors in achieving sustainable financial success in both the short and long term.
Based on this theoretical and empirical basis, we propose the following hypothesis:
H1. 
Digital transformation has a positive and direct effect on financial performance.
The adoption of digital transformation likely involves significant financial costs, especially in the early stages (L. C. Yang & Ming, 2024). These costs include investments in infrastructure upgrades, software purchases, process automation, security and compliance, employee training, and organizational redesign (G. Xu et al., 2023). Due to the scale of this spending, organizations—particularly those with limited internal liquidity—may seek external financing to support their digital transformation. As a result, digital transformation can lead to greater reliance on debt and increase overall firm indebtedness.
The pecking order theory provides a useful framework to analyze this relationship. According to this theory, companies prefer using internal funds first and turn to external debt only when internal resources are insufficient to finance investment opportunities (Li & Li, 2025). In the context of digital transformation, the urgent need to remain competitive and technologically up to date often pushes companies to invest, even when retained earnings are limited. This increases their debt exposure (Ren et al., 2024; P. Zhang & Wang, 2024).
Moreover, research shows that the long-term benefits of digital transformation can justify short- to medium-term financial burdens (He et al., 2024). In fast-changing industries, companies may consider debt-financed digital investment as a strategic necessity rather than a choice (P. Zhang & Wang, 2024). This is especially true in environments with strong institutional or competitive pressure, where failing to digitalize may risk market position or survival.
Based on these theoretical and empirical insights, we propose the following hypothesis:
H2. 
Digital transformation has a positive and direct effect on firm indebtedness.
A firm’s financial performance is important to its financing policy and capital structure choices. According to the pecking order theory, financially healthy firms tend to utilize their internal funds to fund their investments and operations (Guo et al., 2024; P. Zhang & Wang, 2024). In these instances, retained earnings and positive cash flows can reduce the company’s need for external debt, as they lower its overall indebtedness. On the other hand, financially troubled firms will more likely utilize debt. Moreover, financial performance tends to make a firm more credible when it comes to credit, enabling it to obtain more favorable loan conditions or even depend less on external borrowing (Kartika et al., 2022).
The trade-off theory of capital structure suggests that firms balance the costs and benefits of debt. In firms where profitability is high, the tax shield benefits of debt may be outweighed by the desire to maintain financial autonomy and avoid financial distress costs (Hackbarth et al., 2007; Mohammad et al., 2019). This further supports the view that better financial performance can lead to lower indebtedness, as firms choose to retain control and flexibility rather than increase leverage.
Thus, based on these theoretical considerations and financial behavior models, we deduce the following hypothesis:
H3. 
Financial performance has a negative and direct effect on firm indebtedness.
A firm’s financial performance plays a key role in shaping its financing policy and capital structure decisions. According to the pecking order theory, financially strong firms usually rely on internal funds to support their investments and operations (Guo et al., 2024; P. Zhang & Wang, 2024). In such cases, retained earnings and positive cash flows reduce the need for external debt, which lowers overall indebtedness. In contrast, firms facing financial difficulties are more likely to use debt financing.
Additionally, good financial performance improves a firm’s creditworthiness, allowing it to secure better loan terms or reduce its reliance on external borrowing (Kartika et al., 2022).
The trade-off theory of capital structure suggests that firms weigh the costs and benefits of using debt. In highly profitable firms, the tax advantages of debt may not outweigh the preference to stay financially independent and avoid the risk of financial distress (Hackbarth et al., 2007; Mohammad et al., 2019). This supports the idea that better financial performance can lead to lower debt levels, as firms prefer to retain control and flexibility instead of increasing leverage.
Based on these theoretical insights and financial behavior models, we propose the following hypothesis:
H4. 
Firm indebtedness has a positive and direct effect on financial risk.
The literature widely agrees that firms with strong financial performance are generally less exposed to financial risk. These firms benefit from greater liquidity, more stable cash flows, and stronger internal financing capacity (Karami et al., 2020). These factors act as protective buffers, helping firms absorb market shocks, meet financial obligations, and maintain the confidence of investors and creditors, even in difficult economic conditions (Slassi-sennou & Elmouhib, 2025).
Theoretically, this relationship is explained by financial distress theory, which suggests that poor financial performance increases the risk of instability, bankruptcy, or default (Mbuya, 2023; Sewpersadh, 2022). In contrast, firms that maintain strong profitability and manage costs efficiently are more resilient and less exposed to financial shocks. These firms also gain easier access to capital markets, benefit from lower borrowing costs, and rely less on short-term financing, all of which reduce their overall risk.
Based on these theoretical insights, we propose the following hypothesis:
H5. 
Financial performance has a negative and direct effect on financial risk.

2.2.2. Indirect Effects Hypothesis

Although digital transformation is often seen as a technological initiative, its broad impact on organizational risk profiles is increasingly acknowledged in academic literature (Ali & Govindan, 2023). One way digital transformation may influence financial risk is through its effect on financial performance. This perspective follows the logic of mediated causality, where financial performance acts as an intermediary between digital initiatives and financial vulnerability.
According to the resource-based view, digitalization strengthens a firm’s capabilities and operational efficiency, which improves financial performance (S. Yang et al., 2018). Strong financial performance, in turn, can give firms more liquidity, reserves, and access to capital. These factors help reduce exposure to financial risk (Civelek et al., 2023). This connection is also supported by dynamic capabilities theory, which suggests that a firm’s ability to absorb and reconfigure resources during digital transformation increases its resilience and adaptability in uncertain environments (T. Zhang et al., 2025).
Based on this theoretical reasoning, we propose the following hypothesis:
H6. 
Financial performance mediates the relationship between digital transformation and financial risk.
Digital transformation requires significant financial resources. For firms with limited internal funds, pursuing digital change often leads to greater reliance on external financing, especially debt (Xue & Zhang, 2024).
This raises an important theoretical point: digital transformation can indirectly affect a company’s financial risk by changing its capital structure (Xue & Zhang, 2024). According to the extended pecking order theory, when firms lack retained earnings to fund digital investments, they turn to debt (Bauweraerts & Colot, 2012). As debt increases, so does the firm’s exposure to financial risk.
The trade-off theory helps explain this mediation. While debt provides tax benefits, it also raises the risk of financial distress (Hackbarth et al., 2007). Therefore, the indirect link between digitalization and financial risk operates in two ways: first, digital transformation leads to more borrowing; second, the higher debt level increases financial vulnerability.
Based on this theoretical framework, we propose the following hypothesis:
H7. 
Digital transformation has an indirect positive effect on financial risk through the mediation of corporate indebtedness.
Firms with strong financial performance often gain easier access to external financing. However, this can paradoxically increase financial risk by raising debt levels. According to the trade-off theory, while debt can create value through tax benefits, too much debt increases the risk of financial distress (Loredana & Mirabela, 2015).
Evidence from Australian firms supports this idea. It shows that issuing new debt—although often linked to past performance—can lead to lower profitability after financing and a higher cost of capital, thereby increasing risk (Yeo-Hwan, 2011). Other studies confirm that indebtedness affects both solvency risk and profitability, highlighting its dual role as both a support and a threat to financial stability (Horobet et al., 2021). As a result, high-performing firms may take on more debt, which raises their exposure to financial risk (Horobet et al., 2021).
Based on the literature, we propose:
H8. 
Firm indebtedness mediates the relationship between financial performance and financial risk.
The relationships hypothesized above are illustrated in the conceptual model presented in Figure 1. This model captures the central idea that digital transformation is a strategic driver that influences both financial performance and firm indebtedness, which in turn shape the firm’s overall financial risk profile. It reflects a dual mediation structure: on the one hand, financial performance serves as a positive outcome of digital transformation and a protective buffer against financial risk; on the other hand, indebtedness may emerge as a consequence of investment in digital technologies and, in turn, elevate risk exposure.

3. Research Methodology

3.1. Measurement

To empirically test the proposed hypotheses, this research used existing measurement scales for each latent construct, thereby providing consistency and validity across the model. Digital transformation was measured according to a multi-item scale, based on extant literature, which reflects technology integration extent, innovation ability, and digital strategy implementation at the organizational level, as evidenced in works bridging the gap between digital development and organizational performance (Teichert, 2023). Financial performance was measured using validated perceptual scales based on managerial assessments, specifically focusing on return on assets (ROA), revenue growth, and profitability (Lestari et al., 2023). This approach aligns with prior research that employs managerial perceptions to assess firm outcomes when objective data may be unavailable or incomplete (Wati et al., 2022).
Firm indebtedness was operationalized according to financial leverage ratios, specifically debt-to-assets and debt-to-equity ratios, in line with previous literature examining the capital structure and risk profile of firms (Lopez-Valeiras et al., 2016). The perceptual measures used for financial risk were intended to reflect exposure to market risk volatility, default probability, and financial instability, which have frequently been employed as indicators to assess risk perception in empirical research (Gilliam et al., 2010).
The full list of measurement items for each construct is presented in Table 1. This table includes the specific item wording, source references, and a justification for the inclusion of each item, ensuring theoretical alignment and content validity.
To strengthen the robustness of the measurement strategy, this study combines both perceptual and objective indicators. The use of perceptual measures is justified by the nature of the data and the contextual constraints. In many emerging markets, including Morocco, access to timely and complete objective financial data at the firm level is often limited or inconsistent. Managerial perceptions, when measured using validated scales, provide a reliable proxy that captures not only quantitative outcomes but also strategic insights and contextual nuances that are not always visible through raw financial figures. Meanwhile, objective measures such as financial leverage ratios are used for firm indebtedness, offering a complementary quantitative anchor. The integration of both types of measures enhances the overall validity and realism of the model by balancing subjective assessments with hard financial data.

3.2. Sample and Data Collection

The research targets professionals who have strategic and financial roles in digitally transforming organizations. Respondent roles—like Chief Financial Officers (CFOs), Financial Directors, Risk Managers (CROs), General Managers or CEOs, and Heads of Digital Transformation—were chosen based on their competencies to measure digital maturity, financial performance, indebtedness, and financial risk.
A purposive sampling technique was employed, which is justified given the specific expertise needed to participate effectively with the questionnaire. The participants were reached via LinkedIn, email, WhatsApp, and physical contact at professional gatherings.
The sample size requirement was calculated utilizing Slovin’s formula, based on the estimated population in the relevant sectors. A target of 250 valid responses was established, where a minimum of 200 observations is recommended for structural equation modeling (Hair, 2010). Out of the 424 responses collected, 392 valid responses were retained following data cleansing, surpassing the SEM requirement and enhancing the validity of the findings.
Data were gathered using a standardized questionnaire with items identified in the literature. Four latent variables were measured: financial risk, digital transformation, financial performance, and corporate indebtedness. Each construct had reflective indicators rated on a 5-point Likert scale.
A pilot test among 20 participants was followed by adjustments in item clarity, instruction simplicity, and organization of sections. The ultimate form was validated internally prior to launching. Data were collected between November 2024 and March 2025, via Google Forms, along with hard copies where required. Informed consent was ascertained and ethical approval granted by the appropriate institutional authority.
The Moroccan context was chosen for its relevance as an emerging market actively engaged in digital transformation initiatives while simultaneously facing financial pressures and risk exposure. Morocco has launched several national strategies that underscore the intersection between digitalization and financial resilience. However, research exploring how digital transformation affects financial risk through mediating organizational variables remains limited in this context. This study, therefore, offers both theoretical and practical contributions by investigating these dynamics within a rapidly evolving economic environment, providing insights applicable to similar developing economies.
Data analysis was conducted with SmartPLS 4 using Partial Least Squares Structural Equation Modeling (PLS-SEM). Analysis proceeded in two stages: testing the measurement model (reliability, convergent, and discriminant validity) and testing the structural model (path coefficients, R2, and hypothesis testing).

4. Research Results

4.1. Descriptive Statistics

Table 2 presents the descriptive statistics of the 392 respondents involved in the study. With respect to the economic classification of firms, small and medium enterprises (SMEs) represent the largest portion of the sample, with 280 responses (71%), which is in line with the dominant role SMEs play in Morocco’s business landscape. Very small enterprises (VSEs) and big enterprises (BEs) accounted for 40 (10%) and 72 (18%) responses, respectively, reflecting a balanced yet SME-focused sample composition. These proportions are consistent with national economic figures showing that SMEs dominate the Moroccan economic fabric, while VSEs and BEs fulfill supporting or complementary roles.
As for the sectoral distribution, the textiles sector stands out as the most represented, with 90 responses (23%), reflecting its historical importance in Moroccan exports and employment. This is followed by the healthcare (86 responses, 22%) and finance (60 responses, 15%) sectors, which are key players in public service delivery and economic intermediation, respectively. Other sectors such as tourism (48 responses, 12%), automotive (30 responses, 8%), and a grouped “Other” category (78 responses, 20%) complete the picture, capturing a broad array of industries without any overconcentration.
Finally, the analysis by function reveals a diverse respondent pool in terms of roles within the organization. ICT Managers are the most represented with 110 responses (28%), highlighting the relevance of their input in digital transformation initiatives. Risk Managers and Digital Transformation Leaders follow closely with 90 (23%) and 85 (22%) responses, respectively, showing strong representation of key decisionmakers in risk and innovation. Executives such as CEOs and CFOs make up 70 responses (18%), while other roles account for 37 responses (9%). This functional diversity reinforces the robustness of the sample and its relevance to investigating the strategic dynamics of digital transformation and risk within Moroccan organizations.

4.2. Assessment of Measurement Model

As presented in Table 3, all constructs demonstrate strong internal consistency and reliability. Specifically, the Composite Reliability (CR) and Cronbach’s Alpha (CA) values for each construct exceed the recommended threshold of 0.7 (Hair, 2010), thereby confirming the reliability of the measurement model. For example, the Financial Performance construct shows the highest reliability values, with CR = 0.921 and CA = 0.909, while Financial Risk and Digital Transformation also exhibit high reliability with CR values of 0.910 and 0.897 and CA values of 0.907 and 0.896, respectively. Firm Indebtedness also meets the acceptable criteria with CR = 0.859 and CA = 0.854.
All factor loadings are above the recommended threshold of 0.7, indicating that each observed variable adequately reflects its corresponding latent construct. The loadings range from 0.782 (FI1) to 0.960 (FP2), with most indicators clustering around the 0.84–0.86 range, further supporting the convergent validity of the items. Notably, the highest indicator loading (0.960) is found in FP2 under Financial Performance, indicating a particularly strong association with its construct.
Convergent validity is further confirmed by the Average Variance Extracted (AVE), which in all cases exceeds the minimum acceptable value of 0.5. For instance, Financial Performance demonstrates excellent convergent validity with AVE = 0.787, followed by Digital Transformation (AVE = 0.706), and Firm Indebtedness (AVE = 0.695). Even the lowest AVE, observed for Financial Risk (0.643), remains comfortably above the minimum, ensuring that a substantial proportion of variance is captured by the indicators of each construct.
Overall, these results support the robustness of the measurement model in terms of both reliability and convergent validity, enabling confidence in subsequent structural model assessments.
To assess discriminant validity, Table 4 presents the Heterotrait–Monotrait (HTMT) ratio matrix for the constructs included in the model. All values are well below the conservative threshold of 0.90, supporting the assertion that each construct is conceptually distinct from the others. For example, the HTMT value between Digital Transformation and Financial Performance is 0.593, and between Financial Performance and Financial Risk, it is 0.441. These moderate correlations confirm that the constructs do not suffer from conceptual overlap and maintain clear boundaries in their measurement.
Similarly, the HTMT ratio between Digital Transformation and Firm Indebtedness (0.427) is sufficiently low to establish discriminant validity. The relatively lower value of 0.169 between Digital Transformation and Financial Risk further strengthens the case for construct distinctiveness. Among the highest observed values, Firm Indebtedness and Financial Performance present an HTMT of 0.698. While this value is relatively higher than others, it still remains well below the 0.90 threshold. This moderately strong correlation may be attributed to the theoretical and empirical proximity of these two constructs, as financial performance often influences or is influenced by a firm’s level of indebtedness. Nonetheless, the value does not compromise discriminant validity and reflects an expected, yet distinct, conceptual relationship. Likewise, the HTMT between Firm Indebtedness and Financial Risk (0.585) remains within acceptable bounds.
These findings collectively confirm that the model satisfies the HTMT criterion for discriminant validity. This validation ensures that the constructs used in the structural model are not only conceptually different but also measured independently. The HTMT results reinforce the structural soundness of the research model in explaining the relationships among digital transformation, firm indebtedness, financial performance, and financial risk.

4.3. Direct Effect Analysis

The direct effect analysis based on Structural Equation Modeling (SEM) is summarized in Table 5. As anticipated, Digital Transformation has a significant and positive impact on Financial Performance (β = 0.538; p < 0.01), thus confirming hypothesis H1. This result highlights the strategic role digital initiatives play in improving organizational financial outcomes. However, the effect of Digital Transformation on Firm Indebtedness is negative but not statistically significant (β = −0.057; p = 0.195), rendering H2 unsupported. This suggests that digital transformation, in this study’s context, does not necessarily influence the firm’s level of debt.
In contrast, Financial Performance has a strong and negative effect on Firm Indebtedness (β = −0.591; p < 0.01), confirming H3. This implies that higher financial performance helps firms reduce their debt burden, likely due to improved cash flow and capital structure. Similarly, Firm Indebtedness positively and significantly affects Financial Risk (β = 0.412; p < 0.01), supporting H4 and reinforcing the notion that debt increases exposure to financial uncertainty.
Finally, hypothesis H5 is also supported, as Financial Performance negatively and significantly influences Financial Risk (β = −0.124; p = 0.021). This means that organizations with stronger financial results are more resilient to financial instability, highlighting the protective effect of good performance.
Together, these findings shed light on the centrality of financial performance in mediating the impacts of digital transformation and debt on financial risk. They confirm the relevance of the proposed model in capturing the financial dynamics associated with digital transformation in organizational contexts.

4.4. Indirect Effect Analysis

Table 6 presents the analysis of the indirect effects, offering further insight into the mediating mechanisms within the proposed model. The findings confirm the mediating roles of both Financial Performance and Firm Indebtedness in the relationship between Digital Transformation and Financial Risk. Specifically, Digital Transformation indirectly reduces Financial Risk through Financial Performance (Std. Beta = −0.221, T-Value = 7.49, p < 0.01), supporting H6. This indicates that when digital initiatives enhance financial outcomes, they also contribute to mitigating financial instability.
Similarly, H7 is supported, showing that Digital Transformation also exerts an indirect negative effect on Financial Risk via Firm Indebtedness (Std. Beta = −0.318, T-Value = 9.841, p < 0.01). This result suggests that digital transformation can contribute to lowering financial risk by reducing a firm’s debt burden.
Finally, H8 confirms that Financial Performance indirectly affects Financial Risk through its impact on Firm Indebtedness (Std. Beta = −0.243, T-Value = 6.421, p < 0.01). This mediation emphasizes the sequential relationship between performance, capital structure, and exposure to financial risk.

5. Discussions and Conclusions

5.1. Theoretical Implications

The findings of this study offer significant theoretical contributions at the intersection of digital transformation, financial performance, corporate indebtedness, and financial risk. The confirmed positive and substantial impact of digital transformation on financial performance (H1: β = 0.538) supports the Resource-Based View (RBV), which emphasizes the strategic value of rare and inimitable capabilities. In this case, digital capabilities act as performance-enhancing resources that improve operational efficiency and long-term profitability (Chen & Zhang, 2024; N. Xu et al., 2024). These findings are consistent with bibliometric evidence (Zhou et al., 2023), further validating the performance benefits of digital adoption across diverse economies.
However, the absence of a statistically significant direct relationship between digital transformation and firm indebtedness (H2: β = −0.057, p = 0.195) challenges the assumption that digitalization inherently reduces debt exposure. This suggests that the financial benefits of digital initiatives may not translate directly into capital structure adjustments, particularly in the short term (Liu & Dang, 2025). Instead, these benefits appear to operate indirectly through improved financial performance—a key mediating construct in the model.
The negative effect of financial performance on indebtedness (H3: β = −0.591) aligns with the pecking order theory, indicating that firms with strong internal resources are less reliant on external financing. The positive link between indebtedness and financial risk (H4: β = 0.412) supports agency theory and risk amplification frameworks, emphasizing that debt remains a central source of financial vulnerability (You & Zhao, 2023). Furthermore, the protective role of financial performance against financial risk (H5: β = −0.124) confirms that strong profitability not only improves internal financing capacity but also buffers firms against market instability (You & Zhao, 2023; Zhou et al., 2023).
The mediation analysis brings additional theoretical depth. Notably, digital transformation exerts a meaningful indirect effect on financial risk via both performance (H6: β = −0.221) and indebtedness (H7: β = −0.318). While both paths are statistically significant, the magnitude of the effect through indebtedness suggests that capital structure is a critical mechanism through which digital investments influence risk exposure. This finding contributes a novel insight into the literature: digitalization not only enhances earnings but can also structurally reduce financial fragility when leveraged effectively (Liu & Dang, 2025; N. Xu et al., 2024).
Lastly, the sequential mediation pathway—from digital transformation to performance, then indebtedness, and finally financial risk (H8: β = −0.243)—highlights the interdependence between technological strategy, financial management, and risk governance. This holistic view reinforces the value of integrating digital transformation frameworks into financial risk models to capture cascading organizational effects (Chen & Zhang, 2024; Liu & Dang, 2025).

5.2. Practical Implications

The findings of this study provide actionable insights for business leaders, financial managers, and policymakers. The strong relationship between digital transformation and financial performance (H1) highlights the strategic importance of investing in technologies. Given that financial performance reduces both firm indebtedness and financial risk (H3, H5, H6, H8), it should be considered not merely an outcome, but a key strategic lever.
Crucially, the indirect effects also carry meaningful economic implications. The path from digital transformation through firm indebtedness to financial risk (H7: β = −0.318) is not just statistically significant—it is also economically relevant, especially for firms operating in highly leveraged contexts or under strict regulatory capital requirements. For such firms, digital tools that improve forecasting, compliance, and internal control can mitigate risk exposure by lowering reliance on external debt. In sectors like banking, telecom, or utilities—where leverage is structurally embedded—this insight should guide targeted digital investment aligned with financial restructuring.
Similarly, the sequential mediation (H8) suggests that firms cannot reduce financial risk simply by upgrading technology—they must also achieve tangible performance gains and strategically manage their debt levels. This calls for integrated digital-financial strategies: for example, linking ERP or fintech adoption to KPI-based performance goals and debt optimization programs.
Moreover, for firms in volatile environments, such as startups, SMEs, or emerging-market companies, the findings offer reassurance: while digital transformation may not reduce debt overnight, it can indirectly fortify the firm’s financial stability over time, through performance improvement and better capital structuring.
In conclusion, managers and policymakers must recognize that the value of digital transformation extends beyond efficiency—it can reshape financial health and risk posture when embedded into a broader strategic vision. Future digital investment decisions should thus be evaluated not only in terms of immediate ROI but also in terms of their cumulative effect on financial resilience.

5.3. Research Limitations

Despite providing insights into the financial implications of digital transformation, this study is subject to several limitations.
First, the use of Structural Equation Modeling (SEM) with a cross-sectional design restricts the ability to infer causal relationships over time. SEM captures associations but cannot fully confirm causality without longitudinal data (Kline, 2016).
Second, the exclusion of external macroeconomic and regulatory factors may limit the generalizability of the findings, as financial risk and indebtedness are often influenced by market volatility and policy changes beyond firm-level variables (Esamah et al., 2023).
Third, the sectoral and geographic context of the sample may constrain broader applicability. The dynamics of digital transformation and financial performance can vary significantly across industries and national contexts, and similar models might yield different results elsewhere. In particular, the study focuses on data collected from Moroccan companies, which may limit the generalizability of the results to other economic or institutional environments. To assess the cross-contextual relevance of the model, future research could replicate the study in other emerging economies—such as Egypt, South Africa, or Tunisia—where digital transformation is underway but subject to different institutional, infrastructural, and regulatory dynamics. Comparative studies across these contexts could help validate the robustness and adaptability of the proposed framework.
Fourth, potential measurement bias may arise from self-reported survey data, which could affect the reliability of the constructs, particularly in assessing subjective elements like perceived performance or transformation readiness (Takeuchi et al., 2024). Additionally, due to limitations in data access, the study relies on a combination of objective and perceptual measures, which may introduce variability in interpretation and consistency across respondents.
Future research should consider longitudinal designs, multi-source and verified datasets, and the integration of broader contextual variables to enhance the validity, depth, and applicability of the proposed model.

Author Contributions

Conceptualization, S.S.-S. and M.E.-s.; methodology, S.S.-S. and M.E.-s.; software, S.S.-S. and M.E.-s.; validation, S.S.-S. and M.E.-s.; formal analysis, S.S.-S. and M.E.-s.; investigation, S.S.-S. and M.E.-s.; resources, S.S.-S. and M.E.-s.; data curation, S.S.-S. and M.E.-s.; writing—original draft preparation, S.S.-S. and M.E; writing—review and editing, S.S.-S. and M.E.-s.; visualization, S.S.-S. and M.E.-s.; supervision, S.S.-S. and M.E; project administration, S.S.-S. and M.E.-s.; funding acquisition, S.S.-S. and M.E.-s. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of HECF Business School.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All data used in this article can be found at the following link https://drive.google.com/file/d/1TboFLlQ72x34I7YKXzssTHqzKrXabNFL/view?usp=sharing.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ali, I., & Govindan, K. (2023). Extenuating operational risks through digital transformation of agri-food supply chains. Production Planning & Control, 34(12), 1165–1177. [Google Scholar] [CrossRef]
  2. Anwar, M. (2017). Financial risk management practices in financial and non-financial firms; Survey of Pakistani firms. SSRN Electronic Journal, 14(4), 1–28. [Google Scholar] [CrossRef]
  3. Bauweraerts, J., & Colot, O. (2012). Pecking-order or static trade-off theory in family firms? Evidence from Belgium. International Business Research, 5(11), p1. [Google Scholar] [CrossRef]
  4. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. V. (2013). Digital business strategy: Toward a next generation of insights (SSRN Scholarly Paper 2742300). Available online: https://papers.ssrn.com/abstract=2742300 (accessed on 2 May 2025).
  5. Bhilare, M. (2023). Adoption of fintech solutions and their role in enhancing corporate financial growth. ShodhKosh: Journal of Visual and Performing Arts, 4(1), 1078–1095. [Google Scholar] [CrossRef]
  6. Budhiarjo, I. S., Nurfitriana, N., Dewi, W. C., Nurcahayati, S., & Cay, S. (2022). The effect of debt to assets ratio (DAR) and debt to equity ratio (DER) on return on assets (ROA) at PT phapros Tbk. Journal Ad’ministrare, 9(2), 721. [Google Scholar] [CrossRef]
  7. Chen, Y., & Zhang, Y. (2024). The impact of digital transformation on firm’s financial performance: Evidence from China. Industrial Management & Data Systems, 124(5), 2021–2041. [Google Scholar] [CrossRef]
  8. Civelek, M., Krajčík, V., & Fialova, V. (2023). The impacts of innovative and competitive abilities of SMEs on their different financial risk concerns: System approach. Oeconomia Copernicana, 14(1), 327–354. [Google Scholar] [CrossRef]
  9. Correa, C. A., Basso, L. F. C., & Nakamura, W. T. (2013). A estrutura de capital das maiores empresas brasileiras: Análise empírica das teorias de Pecking Order e trade-off, usando panel data. RAM. Revista de Administração Mackenzie, 14(4), 106–133. [Google Scholar] [CrossRef]
  10. Esamah, A., Aujirapongpan, S., Rakangthong, N. K., & Imjai, N. (2023). Agile Leadership and Digital Transformation in Savings Cooperative Limited: Impact on Sustainable Performance Amidst COVID-19. Journal of Human, Earth, and Future, 4(1), 36–53. [Google Scholar] [CrossRef]
  11. Gajdosikova, D., Lăzăroiu, G., & Valaskova, K. (2023). How Particular Firm-Specific Features Influence Corporate Debt Level: A Case Study of Slovak Enterprises. Axioms, 12(2), 183. [Google Scholar] [CrossRef]
  12. Gilliam, J., Chatterjee, S., & Grable, J. (2010). Measuring the perception of financial risk tolerance: A tale of two measures (SSRN Scholarly Paper 2061313). Social Science Research Network. Available online: https://papers.ssrn.com/abstract=2061313 (accessed on 4 May 2025).
  13. Guo, L., Xu, L., Wang, J., & Li, J. (2024). Digital transformation and financing constraints of SMEs: Evidence from China. Asia-Pacific Journal of Accounting & Economics, 31(6), 966–986. [Google Scholar] [CrossRef]
  14. Hackbarth, D., Hennessy, C. A., & Leland, H. E. (2007). Can the trade-off theory explain debt structure? Review of Financial Studies, 20(5), 1389–1428. [Google Scholar] [CrossRef]
  15. Hair, J. F. (Ed.). (2010). Multivariate data analysis (7th ed.). Pearson Prentice Hall. [Google Scholar]
  16. Han, D., & Ren, X. (2020). Financial risk assessment based on factor analysis model. Journal of Physics: Conference Series, 1616(1), 012056. [Google Scholar] [CrossRef]
  17. He, J., Du, X., & Tu, W. (2024). Can corporate digital transformation alleviate financing constraints? Applied Economics, 56(20), 2434–2450. [Google Scholar] [CrossRef]
  18. Horobet, A., Curea, S. C., Smedoiu Popoviciu, A., Botoroga, C.-A., Belascu, L., & Dumitrescu, D. G. (2021). Solvency risk and corporate performance: A case study on European retailers. Journal of Risk and Financial Management, 14(11), 536. [Google Scholar] [CrossRef]
  19. Isran, M. A., Hussain, A., Aslam, H. M. S. I., & Bahoo, S. (2021). Effect of liquidity, credit, and solvency risk on corporate innovation: Empirical evidence from emerging economies & developed countries. Studies of Applied Economics, 39(1). [Google Scholar] [CrossRef]
  20. Karami, M., Samimi, A., & Jafari, M. (2020). The impact of effective risk management on corporate financial performance. Advanced Journal of Chemistry-Section B, 2(3). [Google Scholar] [CrossRef]
  21. Kartika, I., Sulistyowati, S., Septiawan, B., & Indriastuti, M. (2022). Corporate governance and non-performing loans: The mediating role of financial performance. Cogent Business & Management, 9(1), 2126123. [Google Scholar] [CrossRef]
  22. Kidschun, F. (2024). A literature review: The impact of digital transformation on financial performance. European Conference on Innovation and Entrepreneurship, 19(1), 331–338. [Google Scholar] [CrossRef]
  23. Kirina, L., Nazarova, N., Takhumova, O., Semenova, L., & Goryacheva, A. (2019). Method development to assess the enterprise financial risks. International Journal of Innovative Technology and Exploring Engineering, 9(2), 1540–1543. [Google Scholar] [CrossRef]
  24. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press. [Google Scholar]
  25. Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2021). Digital transformation: An overview of the current state of the art of research. Sage Open, 11(3), 21582440211047576. [Google Scholar] [CrossRef]
  26. Kulakovska, T., Petkova, D., & Odesa National University of Technology. (2023). Genesis of scientific thought regarding the definition of «financial risk». Ukrainian Journal of Applied Economics and Technology, 2023(3), 383–387. [Google Scholar] [CrossRef]
  27. Lachova, K. (2021). Digital transformation as a strategy of value creation in industry. Acta Mechanica Slovaca, 25(3), 52–56. [Google Scholar] [CrossRef]
  28. Lestari, E., Setyawati, Y., & Kabora, P. M. T. (2023). Financial performance assessment based on financial ratio analysis. PENANOMICS: International Journal of Economics, 2(3). [Google Scholar] [CrossRef]
  29. Li, J., & Li, Z. (2025). Mechanisms of corporate digital transformation on asymmetric capital structure adjustment—The mediating role of information asymmetry and financial stability. Heliyon, 11(3). [Google Scholar] [CrossRef]
  30. Liew, C. F., Prakash, J., & Ong, K. S. (2024). Equipment cost efficiency: A comprehensive metric for evaluating operational and financial performance in manufacturing. Manufacturing Letters, 39, 16–19. [Google Scholar] [CrossRef]
  31. Liu, E. X., & Dang, L. (2025). Digital transformation and debt financing cost: A threefold risk perspective. Journal of Financial Stability, 76, 101368. [Google Scholar] [CrossRef]
  32. Lopez-Valeiras, E., Gomez-Conde, J., & Fernandez-Rodriguez, T. (2016). Firm size and financial performance: Intermediate effects of indebtedness. Agribusiness, 32(4), 454–465. [Google Scholar] [CrossRef]
  33. Loredana, L. M., & Mirabela, F. I. M. (2015). Analysis of the influence of a company’s indebtedness on the financial risk. Annals—Economy Series, Special, 0, 193–200. [Google Scholar]
  34. Mbuya, M. A. (2023). Effect of financial risk on financial performance of insurance companies in Kenya [Ph.D. Thesis, University of Nairobi]. [Google Scholar]
  35. Moatshe, D. F., Ramdass, K. R., & Sukdeo, N. (2024, December 15–18). Investigating the impact of digital transformation on organizational strategy performance. 2024 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1194–1198), Bangkok, Thailand. [Google Scholar] [CrossRef]
  36. Mohammad, H., Bujang, I., & Abd Hakim, T. (2019). Capital structure and financial performance of Malaysian construction firms. Asian Economic and Financial Review, 9(12), 1306–1319. [Google Scholar] [CrossRef]
  37. Mokoginta, D. D., & Agung, T. R. (2022). Pengawasan manajemen risiko dan financial distress Dalam agency theory. E-Jurnal Akuntansi, 32(7), 1675. [Google Scholar] [CrossRef]
  38. Nabiyev, M. A. (2020). Methodological basis of firm financial risk management. Technium Social Sciences Journal, 11, 159–164. [Google Scholar] [CrossRef]
  39. Narayanan, V. (2023). The transformative AI initiative: From process digitization to AI strategic digitalization. Strategy & Leadership, 51(2), 24–30. [Google Scholar] [CrossRef]
  40. Othman, R., & Ameer, R. (2011). Financing capabilities and sustainability strategy of global corporations: Implications for sustainability-corporate performance relationship. SSRN Electronic Journa l, 1, 1–26. [Google Scholar] [CrossRef]
  41. Poljašević, J., & Grujić, M. (2024). Capital structure determinants of listed private and state companies: Evidence and lessons from Bosnia and Herzegovina. Review of Innovation and Competitiveness, 9(1), 7–29. [Google Scholar] [CrossRef]
  42. Pryshchepa, O., Aretz, K., & Banerjee, S. (2015). In the path of the storm: Does distress cause non-financial firms to risk-shift? SSRN Electronic Journal, 23(6), 1115–1154. [Google Scholar] [CrossRef]
  43. Ren, L., Liu, J., & Hao, Q. (2024). How digital transformation affects the cost of equity capital: The role of information disclosure quality and stock liquidity. Industrial and Corporate Change, 33(5), 1098–1122. [Google Scholar] [CrossRef]
  44. Salamah, S. N. (2023). Financial management strategies to improve business performance. Journal of Contemporary Administration and Management (ADMAN), 1(1), 9–12. [Google Scholar] [CrossRef]
  45. Sewpersadh, N. S. (2022). An econometric analysis of financial distress determinants from an emerging economy governance perspective. Cogent Economics & Finance, 10(1), 1978706. [Google Scholar] [CrossRef]
  46. Slassi-sennou, S., & Elmouhib, S. (2025). Managing financial and operational risks through digital transformation: The mediating influence of information and communication technologies’ adoption and resistance to change. Journal of Risk and Financial Management, 18(3), 128. [Google Scholar] [CrossRef]
  47. Sovilj, R. (2020). The challenges of credit risk management in the investment firms in crisis. Strani Pravni Zivot, 3, 43–56. [Google Scholar] [CrossRef]
  48. Sunaryo, D., & Lestari, E. P. (2021, November 18). The effect of debt to asset ratio and debt to equity ratio against return on assets. 2nd Borobudur International Symposium on Humanities and Social Sciences, BIS-HSS 2020, Magelang, Central Java, Indonesia, Magelang, Indonesia. [Google Scholar] [CrossRef]
  49. Takeuchi, M., Niimi, J., & Hoshino, T. (2024). Handling the inconsistency between self-report and the actual behavior: Validity of excluding survey participants with insufficient effort responding. International Journal of Market Research, 66(4), 451–472. [Google Scholar] [CrossRef]
  50. Teichert, R. (2023). A model for assessing digital transformation maturity for service provider organizations. European Journal of Business Science and Technology, 9(2), 205–230. [Google Scholar] [CrossRef]
  51. Tian, M., Chen, Y., Tian, G., Huang, W., & Hu, C. (2023). The role of digital transformation practices in the operations improvement in manufacturing firms: A practice-based view. International Journal of Production Economics, 262, 108929. [Google Scholar] [CrossRef]
  52. Troise, C., Corvello, V., Ghobadian, A., & O’Regan, N. (2022). How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era. Technological Forecasting and Social Change, 174, 121227. [Google Scholar] [CrossRef]
  53. Turkcan, H. (2025). How to improve financial performance through sustainable manufacturing practices? The roles of green product innovation and digital transformation. Journal of Manufacturing Technology Management, 36(3), 577–596. [Google Scholar] [CrossRef]
  54. Tut, D. (2023). Debt dynamic, debt dispersion and corporate governance. International Journal of Managerial Finance, 19(4), 744–771. [Google Scholar] [CrossRef]
  55. Wati, C. R., Sumiati, S., & Andarwati, A. (2022). The effect of financial knowledge on firm performance: The role of financial risk attitude as moderation. International Journal of Research in Business and Social Science (2147-4478), 10(8), 236–249. [Google Scholar] [CrossRef]
  56. Xu, G., Li, G., Sun, P., & Peng, D. (2023). Inefficient investment and digital transformation: What is the role of financing constraints? Finance Research Letters, 51, 103429. [Google Scholar] [CrossRef]
  57. Xu, N., Lv, W., & Wang, J. (2024). The impact of digital transformation on firm performance: A perspective from enterprise risk management. Eurasian Business Review, 14(2), 369–400. [Google Scholar] [CrossRef]
  58. Xue, Y., & Zhang, X. (2024). Digital transformation and corporate capital structure: Evidence from China. Pacific-Basin Finance Journal, 84, 102299. [Google Scholar] [CrossRef]
  59. Yang, L. C., & Ming, C. W. (2024). Digital transformation and its effects on the financial performance of Malaysian SMEs. Journal of Digitainability, Realism & Mastery (DREAM), 3(08), 21–32. [Google Scholar] [CrossRef]
  60. Yang, S., Ishtiaq, M., & Anwar, M. (2018). Enterprise risk management practices and firm performance, the mediating role of competitive advantage and the moderating role of financial literacy. Journal of Risk and Financial Management, 11(3), 35. [Google Scholar] [CrossRef]
  61. Yang, Y. (2024). Research on financial investment risk assessment techniques and applications. Science, Technology and Social Development Proceedings Series, 2, 117–123. [Google Scholar] [CrossRef]
  62. Yeo-Hwan, K. (2011). The relationship between default risk and financial performance from australian firms. Korea International Accounting Review, 39, 91–108. [Google Scholar] [CrossRef]
  63. You, Z., & Zhao, S. (2023). Enterprise digital transformation and financial risk. Advances in Economics and Management Research, 4(1), 114. [Google Scholar] [CrossRef]
  64. Zhang, P., & Wang, Y. (2024). Digital transformation: A systematic review and bibliometric analysis from the corporate finance perspective (Version 1). arXiv, arXiv:2412.19817. [Google Scholar] [CrossRef]
  65. Zhang, T., Said, J., Zakaria, N. B., & Li, X. (2025). Enhancing financial resilience in chinese public hospitals: Role of dynamic capabilities. Environment-Behaviour Proceedings Journal, 10(31), 179–186. [Google Scholar] [CrossRef]
  66. Zhou, Y., Ock, Y.-S., Alnafrah, I., & Dagestani, A. A. (2023). What aspects explain the relationship between digital transformation and financial performance of firms? Journal of Risk and Financial Management, 16(11), 479. [Google Scholar] [CrossRef]
Jrfm 18 00325 g001
Figure 1. Research conceptual model.
Figure 1. Research conceptual model.
Jrfm 18 00325 g001
Table 1. Measurement Items for Key Constructs.
Table 1. Measurement Items for Key Constructs.
ConstructItem CodeMeasurement StatementSourceJustification
Digital TransformationDT1Our organization has a clearly defined and well-communicated roadmap for digital transformation in service operations.(Teichert, 2023)Reflects the strategic planning dimension of digital transformation maturity.
Digital TransformationDT2We regularly analyze the market to identify opportunities for digitalization within our service domain.(Teichert, 2023)Captures the external opportunity scanning capacity crucial to digital strategy.
Digital TransformationDT3We systematically assess our employees’ digital competencies and implement targeted development programs to address skill gaps.(Teichert, 2023)Measures organizational readiness and internal capability building for digital initiatives.
Digital TransformationDT4The use of digital technologies in financial operations has contributed to measurable improvements in our Return on Investment (ROI).(Teichert, 2023)Explicitly links digital transformation to financial performance, aligning with study objectives.
Financial RiskFR1Our organization is willing to accept short-term financial volatility in pursuit of long-term returns.(Gilliam et al., 2010)Reflects risk–return tradeoff aligned with investment theory.
Financial RiskFR2We frequently engage in financial decisions that carry a moderate to high level of uncertainty.(Gilliam et al., 2010)Captures organizational risk perception and tolerance behavior.
Financial RiskFR3Financial risks are evaluated against our company’s strategic goals before committing to major investments.(Gilliam et al., 2010)Introduces strategic evaluation of risk as part of corporate governance.
Financial performanceFP1Our organization leverages digital tools to improve the accuracy and timeliness of Return on Assets (ROA) tracking and analysis.(Lestari et al., 2023)ROA is a key metric reflecting operational efficiency and profitability.
Financial performanceFP2The use of digital technologies in financial operations has contributed to measurable improvements in our Return on Investment (ROI).(Lestari et al., 2023)ROI connects investments to outcomes and reflects strategic effectiveness.
Financial performanceFP3Digital transformation has enabled better decision-making processes that positively influence our company’s ROA.(Lestari et al., 2023)Introduces a relative benchmark to assess perceived financial standing.
Financial performanceFP4Investments in digital systems are evaluated based on their impact on ROI, ensuring strategic alignment with financial goals.(Lestari et al., 2023)Introduces a relative benchmark to assess perceived financial standing.
Table 2. Respondents’ characteristics (n = 392).
Table 2. Respondents’ characteristics (n = 392).
AttributesCharacteristicsNumber of ResponsesPercentage
Firm economic classificationVSE (very small enterprise)4010%
SME (small and medium enterprises)28071%
BE (Big enterprises)7218%
SectorFinance6015%
Tourism4812%
Textiles9023%
Automotive308%
Healthcare8622%
FunctionOther7820%
ICT Managers11028%
Risk Managers9023%
Digital Transformation Leaders8522%
Executives7018%
Others379%
Table 3. Convergent validity (n = 392).
Table 3. Convergent validity (n = 392).
ConstructsLoadingsCACR AVEConstructsLoadingsCACR AVE
Digital transformation 0.8960.8970.706Financial performance 0.9090.9210.787
DT10.840 FP10.862
DT20.845 FP20.960
DT30.842 FP30.862
DT40.838 FP40.860
DT50.835
Firm indebtedness 0.8540.8590.695Financial risk 0.9070.9100.643
FI10.782 FR10.851
FI20.852 FR20.835
FI30.853 FR30.865
FI40.846
Table 4. Heterotrait–Monotrait ratio (HTMT).
Table 4. Heterotrait–Monotrait ratio (HTMT).
Digital TransformationFinancial PerformanceFinancial RiskFirm Indebtedness
Digital transformation
Financial performance0.593
Financial risk0.1690.441
Firm indebtedness0.4270.6980.585
Table 5. Direct effect.
Table 5. Direct effect.
HypothesesRelationshipsFindingsResults
H1Digital Transformation → Financial PerformancePositive and statistically significant
(β = 0.538; p < 0.01)		Supported
H2Digital Transformation → Firm IndebtednessNegative and not statistically significant
(β = −0.057; p = 0.195)		Not Supported
H3Financial Performance → Firm IndebtednessNegative and statistically significant
(β = −0.591; p < 0.01)		Supported
H4Firm Indebtedness → Financial RiskPositive and statistically significant
(β = 0.412; p < 0.01)		Supported
H5Financial Performance → Financial RiskNegative and statistically significant
(β = −0.124; p = 0.021)		Supported
Table 6. Indirect effect.
Table 6. Indirect effect.
HypothesesIndependent VariableMediatorDependent VariableStd. BetaT-Valuep-ValueDecision
H6Digital TransformationFinancial PerformanceFinancial Risk−0.2217.49p < 0.01Supported
H7Digital TransformationFirm IndebtednessFinancial Risk−0.3189.841p < 0.01Supported
H8Financial PerformanceFirm IndebtednessFinancial Risk−0.2436.421p < 0.01Supported
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MDPI and ACS Style

Slassi-Sennou, S.; Es-salmani, M. Navigating Financial Risk in the Digital Age: The Mediating Role of Performance and Indebtedness. J. Risk Financial Manag. 2025, 18, 325. https://doi.org/10.3390/jrfm18060325

AMA Style

Slassi-Sennou S, Es-salmani M. Navigating Financial Risk in the Digital Age: The Mediating Role of Performance and Indebtedness. Journal of Risk and Financial Management. 2025; 18(6):325. https://doi.org/10.3390/jrfm18060325

Chicago/Turabian Style

Slassi-Sennou, Siham, and Mourad Es-salmani. 2025. "Navigating Financial Risk in the Digital Age: The Mediating Role of Performance and Indebtedness" Journal of Risk and Financial Management 18, no. 6: 325. https://doi.org/10.3390/jrfm18060325

APA Style

Slassi-Sennou, S., & Es-salmani, M. (2025). Navigating Financial Risk in the Digital Age: The Mediating Role of Performance and Indebtedness. Journal of Risk and Financial Management, 18(6), 325. https://doi.org/10.3390/jrfm18060325

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