Original Research

Is digital transformation a catalyst or challenge for corporate financial flexibility? Evidence from China

Linrong Wu, Shi Liang, Ting Liang, Zhuqing Zheng

Received: 14 Jan. 2025; Accepted: 25 July 2025; Published: 20 Sept. 2025

Copyright: © 2025. The Author(s). Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose: In the contemporary landscape, where digital transformation and macroeconomic volatility converge, delineating strategies to augment financial flexibility has become a salient concern for both the corporate and academic realms. This article takes manufacturing firms listed on China’s A-share market as the research object and explores how digital transformation enhances financial flexibility through improving supply chain efficiency, considering digital transformation potential both as an enabler of financial flexibility and a formidable challenge.

Design/methodology/approach: Utilising an empirical framework that assesses manufacturing firms listed on the Shanghai and Shenzhen A-share markets over the period from 2007 to 2022, we deploy a two-way fixed effects model to dissect the interplay between the magnitude of digital transformation and financial flexibility. Robustness checks, including propensity score matching (PSM), the system generalised method of moments (system GMM) and alternative specifications for key variables, ensure empirical validity.

Findings/results: Our analysis yields evidence of a positive correlation between digital transformation and financial flexibility, particularly pronounced through enhancements in supply chain efficiency. Heterogeneity analysis further reveals that the beneficial effects are more markedly felt in non-state-owned enterprises.

Practical implications: This study provides empirical evidence to guide corporate managers and policymakers in leveraging digital transformation to fortify financial flexibility, particularly by enhancing supply chain efficiency and addressing challenges faced by non-state-owned enterprises.

Originality/value: This research contributes novel insights into the role of digital transformation in enhancing financial flexibility, offering empirical foundations to inform the development of targeted policy initiatives.

Keywords: digital transformation; financial flexibility; supply chain efficiency; ownership structure; enterprises.

Introduction

Within the current global milieu, a profound transformation is unfolding, marked by escalating geopolitical tensions and heightened uncertainty in global economic development, which collectively intensify the challenges confronting corporate operations. This complexity is particularly pronounced in China, the world’s second-largest economy, where the rapid economic growth and vast market potential simultaneously present firms with substantial opportunities and considerable risks. Companies with limited financial flexibility (FF) – defined as the ability of a firm to access and restructure its financing at a low cost (Gamba & Triantis, 2008) – often encounter difficulties in absorbing and withstanding pressures during downturns, and face exacerbated challenges in swiftly recovering and pursuing new growth trajectories post-adversity (Garmaise & Natividad, 2021; Ho et al., 2023), which can escalate to corporate insolvency and adverse macroeconomic repercussions. Financial flexibility, which empowers firms to tap into economic resources amid anticipated investment and expansion prospects and acts as a bulwark against financial hardship, is acknowledged as a critical intangible asset (Denis, 2011; Gamba & Triantis, 2008). Its strategic significance in maximising corporate value is highlighted by its capacity to alleviate investment strain during periods of capital scarcity and to minimise financial issue-related expenditures (Arslan-Ayaydin et al., 2014; Ferrando et al., 2017; Gamba & Triantis, 2008; Jiang et al., 2021; Ma & Jin, 2016). Financial flexibility operates as a buffer for companies in unfavourable conditions, essential for enhancing competitiveness and managing risk (Hu et al., 2023). Against this backdrop of intensifying volatility, both the Chinese government and businesses have taken proactive steps to strengthen FF, with digital transformation emerging as a pivotal enabler in navigating crises and ensuring strategic resilience. In the existing literature, ‘digital transformation’ is predominantly conceptualised at the organisational level, especially in the context of firms. It typically refers to how enterprises leverage digital technologies to restructure organisational architecture, optimise business processes and reinvent value creation mechanisms (Bharadwaj et al., 2013; Gong & Ribiere, 2021; Van Veldhoven & Vanthienen, 2023; Verhoef et al., 2021). When referring to government-led or public-sector transformations, scholars tend to explicitly specify such contexts (e.g. ‘government digital transformation’ or ‘e-government’). As our study focuses specifically on enterprises, we follow this convention and use the term ‘digital transformation’ throughout to denote firm-level transformation, for the sake of clarity and consistency. The concurrent advancement of enterprise globalisation and the state-led promotion of digital transformation initiatives provides a compelling context for investigating the dynamic relationship between digital transformation, FF and firm-level outcomes in China.

This study uses the resource-based view (RBV), dynamic capabilities perspective (DCP) and organisational learning theory (OLT) to explore digital transformation’s impact on FF. According to the RBV, digital transformation enables firms to acquire, integrate and leverage distinctive technological and informational resources, thereby improving resource allocation efficiency and reinforcing firms’ strategic adaptability and long-term competitive advantage (Wernerfelt, 1984). From the perspective of DCP, digital transformation is conceptualised as a strategic mechanism for adapting to environmental volatility, which allows firms to continuously sense, assimilate and reconfigure resources, thus strengthening their ability to develop and sustain competitive advantages in dynamic markets (Eisenhardt & Martin, 2000). Meanwhile, the OLT posits that digital transformation facilitates organisational knowledge acquisition, dissemination and institutionalisation, which collectively enhance a firm’s learning capabilities and its ability to manage financial resources adaptively in rapidly changing environments (Crossan et al., 1999). Yet, digital transformation carries risks, such as dependency, financial risks from disruptions, and challenges in investment, data security, and employee adaptability (Hasani et al., 2024; Saeed et al., 2023; Trenerry et al., 2021; Zhao et al., 2023), which could negatively affect FF. The study seeks to understand if digital transformation facilitates or challenges FF and how these dynamics vary across different contexts, prompting empirical inquiry.

This study investigates the impact of digital transformation on FF using a sample of manufacturing firms listed on China’s A-share market from 2007 to 2022. The potential contributions of this research are threefold. Firstly, by positioning digital transformation as a key antecedent of FF, this paper expands the scope of digital transformation research beyond its traditionally emphasised outcomes – such as operational efficiency, supply chain management and firm performance (Jiang & Li, 2024; Jiang & Wang, 2024; Li & Zhao, 2024; Shi et al., 2024; Verhoef et al., 2021; Yuan et al., 2024) – to incorporate its broader economic consequences. While FF has been widely examined as an outcome variable, existing studies have largely overlooked the systematic role of digital strategies in shaping it (Arslan-Ayaydin et al., 2014; Chang & Wu, 2022; Fahlenbrach et al., 2021; Feng et al., 2022; Kumar & Vergara-Alert, 2020). This study seeks to fill that gap by examining the nexus between digital strategy and FF. Secondly, this research introduces supply chain efficiency (SCE) as a mediating variable to explore its potential bridging role between digital transformation and FF. By doing so, it elucidates the operational mechanisms through which digital initiatives affect FF, thereby offering deeper insights into the practical implications of enterprise digitalisation. Thirdly, the paper constructs a comprehensive analytical framework grounded in the RBV, DCP and OLT, aiming to explain the impact of digital transformation on FF from the dimensions of resource endowment, capability development and knowledge evolution. In contrast to prior research that often relies on a single theoretical lens, this integrative approach enhances both explanatory power and analytical robustness.

Literature review

The relationship between digital transformation and financial flexibility

Digital transformation is a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication and connectivity technologies (Vial, 2019). Amid rapid technological advancement, digital transformation has emerged as a core strategic pathway for firms to cope with external uncertainty and enhance competitive advantage (Zhang et al., 2023). It not only fundamentally reshapes corporate business models (Bharadwaj et al., 2013; Verhoef et al., 2021) but also exerts a profound influence on FF. As a critical capability enabling firms to dynamically adjust financial strategies, ensure resource liquidity and maintain operational continuity in volatile environments (Fahlenbrach et al., 2021; Kumar & Vergara-Alert, 2020), FF has garnered increasing attention in both academic and managerial domains. Existing literature suggests that digital transformation enhances firm competitiveness and financial performance through improved operational efficiency, optimised resource allocation and strengthened market responsiveness (Bayo-Moriones et al., 2013; Jiang & Li, 2024; Li & Zhao, 2024). Moreover, it influences firms’ cash flow management, cost control and risk-taking behaviour under policy uncertainty (Gilch & Sieweke, 2021; Karimi & Walter, 2015). Particularly in areas such as payment systems, transaction processes and capital flow management, the integration of digital technologies has been shown to enhance financial agility and resource allocation efficiency (Chen et al., 2024), thereby reinforcing firms’ resilience and adaptive financial capacity (Nguyen & Dang, 2023).

However, digital transformation does not always yield uniformly positive outcomes. On one hand, excessive reliance on digital systems may amplify financial exposure in the face of supply chain disruptions or technical failures (Ning & Yuan, 2023). On the other hand, risks related to failed technological investments, data security breaches and workforce adaptation deficits may undermine a firm’s financial stability and liquidity management during the transformation process (Kane et al., 2017; Ning & Yuan, 2023; Pournader et al., 2020; Yeow et al., 2017), thereby weakening its ability to respond to external shocks. Furthermore, if organisations fail to achieve effective synergy between managerial practices, strategic execution and digital capabilities – reflecting an absence of dynamic capability for resource integration and reconfiguration – digital investments may devolve into misallocated resources, resulting in performance deterioration and strategic breakdowns, ultimately eroding FF and organisational resilience (Vidgen et al., 2017).

Theoretical integration: Resource-based view, organisational learning theory, and dynamic capabilities perspective

To systematically unveil how digital transformation influences FF, this study adopts an integrative theoretical framework that incorporates the RBV, DCP and OLT, offering complementary insights from the perspectives of resource endowment, capability development and learning evolution.

The RBV posits that firms can attain sustained competitive advantage by possessing rare, heterogeneous and inimitable strategic resources (Barney, 1991). Within the context of digital transformation, FF can be conceptualised as a strategic resource, encapsulating a firm’s capacity to mobilise cash flows, maintain capital redundancy and secure rapid financing (Chen et al., 2024; Garmaise & Natividad, 2021). Such a resource enables firms to preserve financial stability and agility in the face of external shocks. Li et al. (2025b) further argue that enterprises which strategically undertake risk and reconfigure their internal resources to enhance capital efficiency and financial responsiveness will likewise see strengthened FF. Hence, the RBV provides theoretical grounding for understanding the strategic role and foundational origin of FF.

However, RBV primarily emphasises the value of resource ‘stocks’, offering limited insight into how resources are reconfigured and activated within dynamic environments. To address this limitation, DCP is introduced. Dynamic capabilities perspective underscores the necessity for firms to develop the capacities of sensing, seizing and reconfiguring in response to environmental turbulence and technological disruptions (Teece, 2007). As a form of structural transformation, digitalisation compels enterprises to continuously refine their organisational architecture, operational workflows and resource allocation patterns (Li & Chan, 2019; Osmundsen & Bygstad, 2022; Tan & Pan, 2003). Empirical studies suggest that digital leadership, platform integration and responsiveness to technological trends significantly enhance a firm’s financial agility and resource orchestration capabilities (Faro et al., 2024; Osmundsen & Bygstad, 2022). Thus, DCP complements the RBV by illuminating the mechanisms through which FF is dynamically activated during digital transformation.

In addition, OLT emphasises the cognitive dimension of capability development, asserting that firms must foster mechanisms of knowledge acquisition, dissemination and institutionalisation to build experiential and adaptive capacity (Crossan et al., 1999). In the course of digital transformation, firms must establish ongoing learning routines to internalise the logic of emerging technologies and optimise strategic decision-making systems, including those related to financial management (Braojos et al., 2024). Organisational learning theory highlights that FF does not merely stem from resource possession or technological deployment, but from a firm’s capacity to learn and respond to environmental shifts. Its development is path-dependent, rooted in the accumulation and assimilation of digital knowledge, which in turn forms the foundation of forward-looking and adaptive financial strategies.

Although this article integrates the RBV, dynamic capabilities theory and OLT at the theoretical level to explain how digital transformation enhances the FF of enterprises from a multidimensional perspective, the empirical tests mainly focus on the mediating path of SCE. This setting is not intended to independently validate the three theories, but rather regards SCE as a highly compatible and highly operational mediating variable that connects digital transformation and FF, serving as an empirical channel linking the two.

Specifically, from the RBV perspective, the improvement of SCE reflects the enterprise’s ability to optimise resource allocation and mobilisation, which is an external manifestation of financial redundancy and strategic resource integration capabilities; from the DCP perspective, the dynamic adjustment of the supply chain process reflects the enterprise’s ability to perceive the environment, seize opportunities and restructure the ability structure, directly enhancing its capital resilience; while within the OLT framework, the improvement of SCE reflects the enterprise’s ability to internalise technical logic, optimise decision-making systems and institutional arrangements through the learning mechanism, accumulating the ability to optimise.

Research gap

Although existing literature has initially explored the role of digital transformation in improving operational efficiency, enhancing organisational resilience and strengthening financial conditions (Jiang & Li, 2024; Li & Zhao, 2024; Verhoef et al., 2021), a systematic examination of its direct impact on FF remains limited, particularly with regard to the underlying mechanisms that drive this relationship. This gap is especially evident in the context of emerging economies, where environmental volatility renders FF increasingly critical. Theoretical development and empirical validation on this issue are still in their infancy. Moreover, current research primarily focuses on how digital transformation affects supply chain performance and organisational adaptability (Jiang & Wang, 2024; Shi et al., 2024; Yuan et al., 2024), but tends to overlook the potential mediating role of SCE in the relationship between digital transformation and FF. Few studies have attempted to construct and empirically validate a theoretical mechanism where improvements in SCE serve as the conduit through which digitalisation impacts FF.

More critically, much of the extant literature relies on a single theoretical lens, lacking an integrative analytical framework to holistically capture the causal chain of ‘Digital Transformation → Supply Chain Efficiency → Financial Flexibility’. This analytical fragmentation limits the explanatory power and applicability of prior findings. To address these gaps, this study introduces a unified framework that integrates RBV, DCP and OLT. By synthesising perspectives on strategic resource acquisition, capability mobilisation and knowledge absorption, we aim to delineate the pathways through which digital transformation, via improvements in SCE, enhances FF.

Theoretical framework and hypothesis development

Digital transformation and financial flexibility in manufacturing enterprises

In an increasingly volatile market environment, manufacturing enterprises are confronted with a multitude of challenges, including fluctuations in raw material prices, unstable orders, supply chain disruptions and mounting pressure for green transformation (Ghadge et al., 2012; Runtuk et al., 2024). These external pressures elevate the demands placed upon firms’ financial coordination capacities and the efficiency of their resource allocation. Digital transformation, widely recognised as a critical pathway for enterprise process re-engineering and organisational capability renewal (Warner & Wäger, 2019; Yeow et al., 2017), offers manufacturing firms novel tools for FF and decision-making. Given their high degree of technological dependence and capital-intensive characteristics (Van Alstyne & Brynjolfsson, 2005), manufacturers are particularly reliant on robust information systems, coordinated supply chains and the seamless flow of financial resources (Liao et al., 2017; Shi et al., 2024). As such, digital transformation not only restructures production systems but also lays a vital foundation for enhancing FF. To systematically examine this mechanism, this study draws upon three theoretical lenses: the RBV, the DCP, and the OLT.

From the RBV perspective, digital transformation enables manufacturing enterprises to acquire novel, heterogeneous and strategically valuable resources (Liang & Tian, 2024), such as intelligent manufacturing equipment, data-sensing capabilities and enterprise resource planning (ERP) systems – resources that are inherently difficult for competitors to replicate (Barney, 1991). By deeply integrating these digital assets into financial management systems, firms can streamline the flow and monitoring of financial data, thereby enhancing liquidity forecasting and resource reallocation efficiency (Shi et al., 2024; Zhao et al., 2023). Moreover, proprietary digital resources such as predictive analytics systems and cost-optimisation algorithms contribute to the development of distinctive financial resource endowments, strengthening firms’ capacity to weather economic uncertainty (Ghasemaghaei, 2019; Wamba et al., 2017). Once embedded into core operational routines, these digital resources become endogenous drivers of FF.

Dynamic capabilities perspective further asserts that organisations must cultivate the ability to sense, seize and reconfigure resources in response to external turbulence and technological shifts (Teece, 2007). Under digital conditions, manufacturers construct multilayered sensing infrastructures that provide real-time insights into supply chain risks, market fluctuations and policy changes (Li et al., 2025a; Liu, 2022). These dynamic capabilities enable firms to reconfigure capital budgeting processes, restructure financing portfolios and reprioritise expenditures, thereby reinforcing their ability to respond to financial shocks. For instance, digital workflows allow manufacturers to swiftly adjust procurement volumes and production schedules in response to sudden raw material price hikes, thus averting liquidity crises.

Organisational learning theory, with its emphasis on cognitive evolution and institutional learning, views digital transformation as an ongoing process of organisational knowledge development. Through iterative learning, experimentation and feedback, firms gradually craft forward-looking financial management models (Cheng et al., 2024). Manufacturing enterprises, in particular, rely heavily on the integration and dissemination of multidimensional data encompassing operating costs, capacity planning and sales forecasting (Thomé et al., 2012; Zhong et al., 2015). Digital tools accelerate the accumulation and circulation of such knowledge within the organisation, thereby constituting the informational substrate for adaptive financial strategies. Furthermore, digital transformation endows financial systems with dual capabilities of ‘memory’ and ‘responsiveness’ – not only enabling the tracking of historical financial behaviours, but also leveraging algorithms to enhance forecasting accuracy and fortify risk-hedging mechanisms (Chen et al., 2024; Faro et al., 2024; Ghasemaghaei, 2019; Nguyen & Dang, 2023). This path-dependent evolution of financial knowledge serves to stabilise and refine firms’ financial decision-making under uncertainty.

Based on the aforementioned theoretical analysis, we propose Hypothesis 1:

H1: Digital transformation in manufacturing enterprises will have a positive impact on their FF.

The mediating role of supply chain efficiency

The enhancement of SCE is one of the pivotal outcomes of digital transformation (Shi et al., 2023; Zhao et al., 2023), which directly impacts a company’s operational costs and cash flow management by optimising logistics, reducing inventory and accelerating order processing (Rajaguru & Matanda, 2019; Wang et al., 2019). These improvements not only directly elevate the financial performance of the company but also indirectly foster FF by augmenting the company’s financial adaptability and agility. Specifically, digital transformation enhances the transparency and responsiveness of the supply chain (Li & Zhao, 2024; Wang et al., 2019; Zhang et al., 2025) through the implementation of advanced information technologies, such as real-time inventory management systems, automated logistics solutions and integrated supply chain platforms. This transparency enables companies to forecast demand more accurately, reduce excess inventory (Li & Zhao, 2024), thereby diminishing capital occupation and warehousing costs. Concurrently, automated and integrated supply chain management reduces human errors, enhances the precision and speed of order processing, and further optimises cash flow (Rai et al., 2006). Moreover, the enhancement of SCE facilitates companies to respond swiftly to market changes (Nandi et al., 2021; Zhang et al., 2025). For instance, through real-time data sharing, companies can promptly adjust production schedules and logistics routes to adapt to shifts in market demand. This rapid response capability enables companies to better adjust their financial strategies in the face of uncertainty, thereby strengthening FF.

Based on the aforementioned theoretical analysis, we can propose Hypothesis 2:

H2: Digital transformation in enterprises indirectly influences FF by improving SCE.

Ownership structure, digital transformation and financial flexibility

The ownership structure of a firm constitutes a critical institutional determinant of its strategic orientation and resource allocation efficiency (Liu & Lu, 2007; Peng, 2004). Within the institutional context of China, state-owned enterprises (SOEs) and non-state-owned enterprises (NSOEs) exhibit pronounced differences in financing channels, policy dependence, managerial objectives and risk preferences (Chen et al., 2006, 2009; Cull et al., 2009). These distinctions may profoundly shape the mechanisms through which digital transformation influences FF. To begin with, the financing advantages enjoyed by SOEs in the course of digital transformation may attenuate their intrinsic motivation to enhance FF. Owing to their policy-driven status and governmental backing, SOEs typically benefit from superior credit ratings and greater access to bank loans (Brandt & Li, 2003; Cull et al., 2009), leading to a financial strategy heavily reliant on external capital. This reliance potentially diminishes their incentives to improve flexibility and efficiency through internal resource integration and financial reconfiguration (Cull et al., 2009; Shleifer & Vishny, 1994). Moreover, extant research suggests that SOEs are more policy-oriented than efficiency-driven (Chen et al., 2011), further constraining their ability to translate digital transformation efforts into tangible improvements in FF.

In contrast, NSOEs often confront more stringent financing constraints (Lu et al., 2012), and thus rely heavily on internal cash flows and financial manoeuvrability to maintain operational stability and capital agility. This context compels them to prioritise the optimisation of internal resource allocation and the enhancement of financial resilience through digital transformation (Liu & Wang, 2023). In the process of digital transformation, NSOEs typically emphasise the deployment of technological tools to refine cash flow forecasting, cost control and financial monitoring systems, thereby strengthening FF more effectively (Hussain & Papastathopoulos, 2022). Furthermore, enhanced digital capabilities enable NSOEs to achieve higher marginal returns under constrained funding conditions, alleviating the rigidity imposed by limited access to external finance.

Based on the aforementioned theoretical analysis, we propose Hypothesis 3:

H3: Under the condition that all other factors remain constant, the positive relationship between digital transformation and FF will be more pronounced for NSOEs.

Methodology

Data

This study selects A-share listed manufacturing firms on the Shanghai and Shenzhen stock exchanges from 2007 to 2022 as the research sample. The classification of manufacturing enterprises follows the guidelines issued by the China Securities Regulatory Commission (CSRC) in 2012, incorporating all sub-industries under industry code C, including general equipment manufacturing, computer and communication equipment, pharmaceutical manufacturing, and food processing. To ensure the normality of business operations and the reliability of the data, firms designated as Special Treatment (ST) or Particular Transfer (PT) were excluded. Specifically, ST firms are those specially marked by the stock exchange because of consecutive losses or abnormal financial conditions, while PT firms face delisting risks – both categories typically suffer from operational sustainability issues that could distort the genuine relationship between digital transformation and FF. Furthermore, firms with missing financial data or severely abnormal indicators were removed. All continuous variables were winsorised at the 1st and 99th percentiles to mitigate the influence of outliers. Ultimately, the final sample comprises 21 155 firm-year observations. Data on digital transformation were extracted from the annual reports disclosed by listed companies, all of which were obtained from the China Securities Regulatory Commission-Designated Listed Company Information Disclosure Website (CNINFO). Financial data were drawn from both the China Stock Market & Accounting Research Database (CSMAR) and Wind Financial Terminal (Wind) to ensure consistency and credibility.

For the heterogeneity analysis, the sample was divided into two groups based on ownership structure: SOEs and NSOEs. The classification of ownership types was conducted as follows: equity ownership data were first retrieved from the CSMAR database; for missing values, supplementary identification was performed using information on ultimate controlling shareholders from the Wind database. According to China’s current enterprise classification standards, entities under state control – such as those affiliated with ‘the state’ or ‘the State-owned Assets Supervision and Administration Commission (SASAC)’ – were categorised as SOEs (coded as 1). All others, including collectively-owned, foreign-invested, privately-owned or individual-controlled firms, were categorised as NSOEs (coded as 0). For a small number of cases where both CSMAR and Wind lacked ownership data, the classification was manually verified using the list of top 10 shareholders disclosed in the annual reports to ensure the accuracy and comparability of the ownership categorisation.

Variables

Dependent variable

Financial flexibility: This study defines FF as the dependent variable, reflecting a firm’s financial adaptability. Given the lack of consensus on measuring FF, we use a composite metric (Zeng et al., 2013) that includes cash liquidity flexibility and debt financing flexibility. Cash liquidity flexibility measures a firm’s ability to manage cash reserves relative to industry norms, calculated as the difference between the firm’s and industry’s cash ratios. Debt financing flexibility assesses the capacity to adjust long-term debt relative to industry averages, expressed as the maximum of zero and the difference between the industry average debt ratio and the firm’s debt ratio, ensuring a positive value for practical financial analysis.

Independent variable

Digital transformation (digital): Unlike earlier, narrower definitions of ‘Digital Transformation’ (DT) that focused primarily on information technology (IT) investments and system implementation (Bharadwaj et al., 2013; Vial, 2019), this study ‘Digital Transformation’ adopts a broader conceptualisation on Digital Transformation, grounded in organisational behaviour and strategic change.

At the operational level, this study follows the methodology proposed by Wu et al. (2021), employing a text mining approach to quantify the degree of digital transformation, rather than relying on traditional indicators such as IT expenditure. Specifically, annual reports of A-share listed firms from 2007 to 2022 were collected via Python-based web scraping from CNINFO, and the textual content of PDF files was extracted using the Java PDFBox toolkit. Drawing on policy documents, the ‘Digital China’ development reports, and scholarly work on digital transformation pathways, a comprehensive keyword dictionary was constructed, encompassing core ‘ABCD’ technologies – Artificial Intelligence (AI), Blockchain, Cloud Computing and Big Data.

By calculating the frequency of digital-related keywords in each annual report and standardising the results, the study constructs firm-year-level digital transformation scores, thereby capturing the overall level of digitalisation. The adoption of text analysis over IT expenditure data is justified on several grounds. Firstly, it avoids the conceptual pitfall of equating input with transformation, thereby more accurately reflecting the actual transformation process. Secondly, it captures both the strategic narrative and the technological embedding pathways of firms, enhancing semantic recognition and cross-firm comparability.

Mediating variable

Supply chain efficiency: Following the methodologies of Ak and Patatoukas (2016) and Liu and Chi (2024), this study employs inventory turnover as a proxy for SCE. This metric is calculated by dividing a firm’s operating cost by the average inventory balance. A higher inventory turnover typically indicates greater efficiency in inventory management and faster asset liquidity, thereby reflecting the firm’s coordination capacity and execution efficiency across procurement, production and sales processes (Liu & Chi, 2024). Given the skewed distribution of the original indicator and to enhance the robustness and interpretability of the regression estimates, the natural logarithm of inventory turnover is taken.

Control variables

This research incorporates a range of corporate governance metrics and financial indicators as control variables to enhance the analytical framework’s rigour (Dimitropoulos & Koronios, 2021; Zhang & Liu, 2022). These variables account for extraneous factors affecting FF, enabling a more accurate evaluation of digital transformation’s impact. The model also features entity and year dummy variables to control for firm-specific and time-varying effects, reducing unobserved heterogeneity that might affect FF outside the scope of digital transformation. The definitions of the independent variable, dependent variable and control variables are presented in Table 1.

Empirical models

This research uses an econometric model with a two-way fixed effects regression to explore the effect of digital transformation on FF (Equation 1, Equation 2 and Equation 3).

Model (1) in this study is specifically crafted to evaluate the influence of digital transformation on FF. In this model, FF is designated as the dependent variable, serving as a metric for gauging a firm’s financial adaptability. The subscripts i and t within the model denote the city-level and year, respectively, and this notation will be maintained throughout the subsequent analysis. A regression coefficient for FF that is significantly greater than zero would corroborate Hypothesis 1 presented in this paper, which suggests a positive correlation between digital transformation and FF. Moreover, the model incorporates a suite of control variables to account for other factors that might affect FF. To address unobserved heterogeneity, the model includes entity-specific fixed effects (i) and time-invariant fixed effects (t). The random error term (ε) captures the unexplained variance within the model. To manage potential issues of serial correlation and heterogeneity, this paper employs cluster-robust standard errors clustered at the level of listed companies.

This research extends Model (1) to Models (2) and (3) to test Hypothesis 2, which proposes SCE as a mediator in the link between digital transformation and FF. The significance of coefficients in these models will reveal if SCE partially or fully mediates this relationship. Additionally, to test Hypothesis 3 regarding the varying impacts of digital transformation on FF across ownership types, a subsample analysis is performed, dividing the sample into state-owned and non-state-owned groups.

Results

Descriptive statistics

Table 2 presents the descriptive statistics for key variables. The mean FF is 0.0732, indicating a strategic focus on financial adaptability among listed Chinese manufacturers. Variability in FF is significant, likely because of factors such as firm size and management strategies. The mean for digital transformation (Digital) is 0.7066, with a standard deviation of 1.0651, reflecting diverse levels of digital progress influenced by resources and technology adoption. Supply chain efficiency has a mean of 4.7049 and a standard deviation of 0.8078, showing a generally high efficiency with some firm-specific differences, potentially linked to logistical capabilities. Control variables such as firm size (Size), financial leverage (Lev), growth (Growth), independent directors’ proportion (Indep), board size (Board), shareholding balance (Balance), and the dual role of chairman and CEO (Dual) exhibit heterogeneity across firms.

Correlation analysis

In this study, we used Pearson’s correlation coefficient to analyse the relationships between key variables (Ghasemi & Zahediasl, 2012), with results detailed in Table 3. The analysis shows significant positive correlations between digital transformation, FF and SCE, all below the 1% significance level, confirming a strong linear relationship. In addition, significant correlations among control variables were found, validating our variable selection. To check for multicollinearity, we calculated the variance inflation factor (VIF) for all variables, as shown in Table 4. The mean VIF for all variables is below 5, indicating no severe multicollinearity.

Regression results

In the baseline regression presented in Column (1) of Table 5, digital transformation (Digital) exerts a significantly positive effect on firms’ FF, with a regression coefficient of 0.0072, significant at the 1% level, thereby providing robust support for Hypothesis H1. This result suggests that, within the manufacturing sector, a higher degree of digitalisation contributes meaningfully to enhancing a firm’s capacity for financial manoeuvring and strategic adaptability amid complex market environments. The finding aligns with the theoretical framework articulated earlier in the paper, drawing upon the RBV (Wernerfelt, 1984), dynamic capabilities theory (Eisenhardt & Martin, 2000) and OLT. Specifically, the infusion of novel resources enabled by digitalisation – such as ERP systems and intelligent financial data analytics – and the resulting reinforcement of organisational learning and resource reconfiguration capabilities, collectively enhance the responsiveness and agility of financial systems, thereby strengthening overall FF (Chen et al., 2024; Gamba & Triantis, 2008; Li et al., 2025b). This is particularly critical in dynamic environments, where FF is regarded as a vital safeguard for sustaining investment capacity and crisis resilience (Fahlenbrach et al., 2021; Garmaise & Natividad, 2021).

To further test Hypothesis H2 – that digital transformation enhances FF indirectly through improved SCE – this study conducts a stepwise regression to examine the mediating effect. Firstly, in Column (2) of Table 5, digital transformation is shown to have a significantly positive impact on SCE, with a coefficient of 0.0091, significant at the 1% level. This indicates that digital capabilities enhance inventory turnover and operational coordination, a finding consistent with Zhao et al. (2023), who argue that supply chain digitalisation strengthens resilience and performance. Secondly, Column (3) demonstrates that SCE also positively affects FF, with a coefficient of 0.0396, significant at the 1% level, affirming the foundational role of supply chain capability in supporting financial manoeuvrability (Ferrando et al., 2017). Lastly, after controlling for the mediating variable SCE, the direct effect of digital transformation on FF remains significant (coefficient = 0.0069), albeit slightly reduced compared to the baseline model. This indicates a partial mediation effect, further validating the theoretical soundness of Hypothesis H2. This mechanism pathway reveals that digitalisation not only enhances financial systems directly by increasing the flexibility of resource allocation but also indirectly strengthens financial resilience by improving supply chain responsiveness, lowering inventory costs, and facilitating cash flow coordination (Gamba & Triantis, 2008; Shi et al., 2024). These findings substantiate the core proposition of the RBV and dynamic capabilities theory that organisational agility is achieved through process integration and digital reinvention (Eisenhardt & Martin, 2000; Wernerfelt, 1984).

We also control for factors such as firm size (Size), financial leverage (Lev), growth (Growth) and dual role (Dual). For example, the significant coefficient for Lev at the 1% level implies that higher leverage could limit FF, reflecting the multifaceted influences on a firm’s FF.

Robustness and endogeneity checks

To ensure the robustness of our research findings, we have employed some methods for robustness checks:

• Propensity score matching (PSM): In this study, we employed the PSM method for robustness checks to address potential sample selection bias. Because a firm’s engagement in digital transformation is unlikely to be random and is instead influenced by a range of factors such as firm size, profitability and industry characteristics, systematic differences may exist between treated and untreated samples prior to intervention, potentially giving rise to endogeneity issues (Heckman et al., 1997; Li, 2013). If left unaddressed, such biases may result in either an overestimation or underestimation of the true effect of digital transformation on FF. We first calculated the propensity scores using logistic regression, considering covariates that influence digital transformation. Then, we matched the most similar control group samples to the treated group using nearest neighbour matching. This process controlled for selection bias and endogeneity (Rosenbaum & Rubin, 1983). Post-matching regression analysis (Table 6, Column 1) revealed that digital transformation has a statistically significant positive effect on FF, with a coefficient of 0.0078 (p < 0.001), corroborating the baseline regression findings and confirming the positive impact of digital transformation on FF even after controlling for sample selection bias.
• System generalised method of moments (GMM): In this study, recognising the potential impact of past FF on the present (Denis, 2011; Wintoki et al., 2012), we utilised the system GMM to establish a dynamic panel model (Blundell & Bond, 1998). The system GMM addresses endogeneity by incorporating lagged dependent variables, avoiding the biases that Ordinary Least Squares (OLS) might introduce. The SYS-GMM technique, introduced by Arellano and Bover (1995), is particularly effective in overcoming small sample bias and weak instrument issues, enhancing the reliability of the estimates by employing lagged values as instruments (Wintoki et al., 2012). As depicted in Table 6, Column (2), the SYS-GMM estimation results show no evidence of second-order serial correlation in the difference equation residuals (p > 0.1), passing the autocorrelation test. The Hansen test also supports the validity of the instrumental variables used (p > 0.1). Adhering to Roodman’s (2009) advice, we restricted the number of instruments to 48. The SYS-GMM estimation meets the requirements for GMM and provides consistent and reliable model estimates. The coefficient for digital transformation on FF is 0.0012, highly significant statistically (p < 0.001), corroborating the baseline findings.
• Alternative independent variables: To further verify the robustness of the main variable measurement, this study follows the methodology of Wu et al. (2021) and remeasures the level of digital transformation using alternative text-mining-based word frequency indicators. In the baseline regression, digital transformation is defined as the logarithmic transformation of the total frequency of terms associated with keywords such as ‘artificial intelligence technology’, ‘Big Data technology’, ‘cloud computing technology’ and ‘blockchain technology’ appearing in firms’ annual reports, expressed as ln (1 + total word frequency). For robustness checks, the scope of the indicator is broadened by incorporating the total frequency of keywords related to the application of digital technologies – such as mobile internet, intelligent manufacturing and internet finance – and constructing a substitute variable using the natural logarithm of this expanded term count. Column (3) of the table presents the regression results with these alternative variables, yielding a coefficient of 0.0035 that is significant at the 10% level. This outcome substantiates our main conclusion, indicating that the positive effect of digital transformation on FF is consistent and robust across various measurement approaches.
• Alternative dependent variables: In this study, we explored an alternative measure of FF based on Gregory (2020), using a binary dummy variable that equals 1 if a company has unused debt capacity over three consecutive years, and 0 otherwise, identified with the model by Marchica and Mura (2010). The regression analysis with this revised independent variable, as shown in column (4) of Table 6, confirms that the positive effect of digital transformation on FF remains significant (β = 0.0069, p < 0.01), aligning with our earlier findings.

Heterogeneity test

To further test Hypothesis H3, this study examines the heterogeneous effects of digital transformation on FF across different ownership structures. Specifically, within China’s unique institutional environment, the ownership structure of a firm profoundly shapes its financing constraints, strategic orientation and resource allocation efficiency, which in turn may lead to substantial differences in the mechanisms through which digital transformation impacts FF (Chen et al., 2006, 2009). Accordingly, the sample is divided into two subgroups – SOEs and NSOEs – with separate regression analyses conducted for each. The empirical results reported in Table 7 reveal that, among NSOEs, digital transformation exerts a significantly positive effect on FF, with a coefficient of 0.0083, significant at the 1% level. In contrast, for SOEs, the corresponding coefficient is 0.0056 and fails to reach statistical significance. This finding supports Hypothesis H3, indicating that the positive effect of digital transformation on FF is more pronounced among non-state-owned firms.

State-owned enterprises, because of their policy-driven nature and greater access to financing, tend to rely more heavily on external capital support (Brandt & Li, 2003), thereby reducing their dependence on internal FF for resource reallocation. Moreover, SOEs are often oriented towards fulfilling policy mandates rather than pursuing efficiency optimisation, which limits their ability to fully harness the transformative potential of digital technologies to enhance financial agility (Chen et al., 2011). When external environments become volatile, their heavy reliance on capital markets may, in fact, exacerbate financial risks (Lu et al., 2012). In contrast, NSOEs – facing more constrained access to financing – must rely more on effective cash flow management and financial coordination to sustain operational stability (Cull et al., 2009). Consequently, such firms place greater emphasis on leveraging digital tools to enhance financial data processing, risk forecasting and resource allocation efficiency during their digital transformation processes (Liu & Wang, 2023).

Discussion

Compared with the existing literature, this study contributes to the digital transformation discourse in three key aspects: Firstly, we shift the outcome variable from general performance to FF, which has been largely overlooked in prior research. Existing studies typically view digital transformation as a driver of enhanced operational efficiency or firm performance. For instance, Verhoef et al. (2021) emphasise improvements in customer engagement and operational processes, while Li and Zhao (2024) highlight supply chain optimisation and overall enterprise performance. Similarly, Bayo-Moriones et al. (2013) focus on digital transformation’s role in enhancing resource allocation efficiency. Departing from this performance-centric focus, our study introduces FF as a distinct outcome variable, thereby extending the scope of digital transformation outcomes in the literature.

Secondly, we move beyond black-box theorisation by articulating an explicit mechanism linking digital transformation to FF. While existing research often establishes a direct link between digital transformation and improved resilience or adaptability (e.g. Shi et al., 2024; Yuan et al., 2024), it rarely unpacks the underlying transmission paths. Studies by Karimi and Walter (2015) and Gilch and Sieweke (2021) examine digital transformation’s influence on cash flow and cost structures, yet stop short of connecting these elements to a broader construct such as FF. In contrast, we empirically validate a sequential mediation mechanism –‘digital transformation → supply chain efficiency → financial flexibility’ – and situate it within a unified theoretical framework that integrates RBV, dynamic capabilities and organisational learning theories.

Thirdly, we engage in constructive dialogue with several seminal and recent works. Our study builds upon Bharadwaj et al. (2013), who underscored the strategic value of digital transformation but did not address its financial consequences. We also complement Zhang et al. (2023), who identified digital transformation as a source of competitive advantage, by further demonstrating how such advantage can be translated into enhanced FF through improved supply chain performance. Moreover, we respond to the concerns raised by Ning and Yuan (2023), who cautioned against the potential risks and uncertainties associated with digital transformation. Our findings suggest that, when strategically managed, digital transformation yields substantial financial benefits and improves firms’ adaptive capacity under uncertainty.

Policy implications

This study puts forward several policy implications. Firstly, policymakers should prioritise supporting enterprises in enhancing their FF through digitalisation, in order to improve their ability to allocate funds in the face of external shocks. In today’s volatile and uncertain market environment, it is imperative for enterprises to possess agile capital allocation capabilities. It is therefore recommended that governments encourage the construction of ‘low-leverage, high-liquidity’ financial structures by offering tax incentives, interest subsidies and other fiscal tools, thereby strengthening firms’ self-adjustment capacities in response to shocks such as policy shifts, raw material price surges or demand contractions.

Secondly, for NSOEs, targeted policy support for digital transformation should be introduced, with a particular focus on improving SCE. In practice, two complementary policy pathways may be considered: (1) establishing dedicated funds to support private enterprises in adopting ERP systems, automated warehousing and smart logistics platforms, thereby reducing the cost burden of supply chain digitalisation and (2) incentivising leading firms to spearhead the development of regional industrial coordination platforms that enable data sharing and logistics collaboration among upstream and downstream enterprises.

Thirdly, given the potential risks associated with digital transformation – such as investment failure, data breaches and technological mismatch – it is imperative to develop enterprise-level digital risk management capabilities. At present, many small and medium-sized enterprises lack the technical discernment and risk identification mechanisms necessary during their digital transition, often resulting in resource misallocation and financial strain. To address this, governments are advised to provide pre-emptive support through instruments such as ‘digital transformation risk assessment toolkits’ and ‘digital capability evaluation frameworks’, which can help firms identify risks and issue early warnings. Furthermore, the establishment of a dedicated risk compensation fund should be considered to offer a financial buffer for enterprises that experience setbacks in their digital investments.

Practical implications

Our findings hold significant practical implications for both the corporate sector and policymakers. Firstly, firms can leverage the insights from this research to adjust their digital strategies, optimise resource allocation, enhance SCE and maintain FF when necessary, to respond to market changes and potential risks. Secondly, policymakers can utilise the conclusions of this study to design and implement policies that support digital transformation, particularly in promoting the improvement of SCE and the development of non-state-owned holding enterprises. Thirdly, firms can draw upon the risk management strategies presented in this research to mitigate the negative impacts of digital transformation, ensuring financial stability while pursuing efficiency gains.

Limitations and future studies

This study examines the effects of digital transformation on FF, with findings suggesting positive impacts. However, this study is not without limitations. Firstly, the scope of the sample remains constrained. Because of data availability and comparability considerations, the analysis is limited to A-share listed manufacturing firms in China, excluding non-listed small and medium-sized enterprises as well as firms in other sectors. This may limit the generalisability of the findings. Future research should extend the analysis to other industries – such as services or energy – and conduct cross-country or regional comparative studies to assess the external validity of the conclusions.

Secondly, although this study addresses endogeneity and selection bias through two-way fixed effects, system GMM, and PSM, it cannot entirely eliminate the influence of potential omitted variables or dynamic adjustment processes. Future studies could enhance causal identification by incorporating stronger instrumental variable designs or adopting quasi-experimental approaches.

More importantly, the present study primarily emphasises the positive effects of digital transformation while offering limited discussion on the financial and operational risks that may arise during the transformation process. In real-world scenarios, firms undertaking digital transformation often confront a variety of challenges, including failed technology investments, system integration breakdowns, data breaches and workforce misalignment – factors that may exert downward pressure on FF.

Therefore, future research should adopt a risk-based perspective to investigate how various forms of digital risks mediate or moderate the relationship between digital transformation and financial outcomes. It should also systematically evaluate firms’ risk response capabilities under different governance structures.

Conclusion

Based on panel data of A-share listed manufacturing firms in China from 2007 to 2022, this study systematically investigates the impact of digital transformation on FF, with a particular focus on the mediating role of SCE and the heterogeneous effects of ownership structure. The sample includes both SOEs and NSOEs, ensuring the empirical identification of ownership-based disparities.

Firstly, the baseline regression results provide strong support for Hypothesis H1, indicating that digital transformation significantly enhances firms’ FF. This finding suggests that digital transformation enables manufacturing firms to improve information processing and resource coordination capabilities, thereby strengthening their financial resilience in the face of market uncertainty. The result is consistent with the RBV (Barney, 1991) and DCP (Eisenhardt & Martin, 2000), which emphasise that the acquisition and integration of digital resources activate a firm’s adaptive mechanisms, enhancing the flexibility of financial responses.

Secondly, the mediation analysis confirms Hypothesis H2, revealing that SCE partially mediates the relationship between DT and FF. Digital transformation enhances firms’ supply chain systems by accelerating inventory turnover, reducing logistical redundancies, and improving order responsiveness, thereby improving cash flow and financial coordination. The validation of this mediating path echoes the propositions of DCP and OLT (Crossan et al., 1999), which emphasise the importance of continuous capability building and experiential learning in the dynamic integration of processes and resources.

Furthermore, heterogeneity analysis supports Hypothesis H3, showing that the positive impact of digital transformation on FF is more pronounced among NSOEs. Given their greater financing constraints, NSOEs rely more heavily on internal resource optimisation and structural FF to maintain operational stability, and are thus more motivated to leverage digital tools to enhance financial coordination and risk management. In contrast, although SOEs enjoy advantages in digital resource investment, their policy-driven access to external financing diminishes the urgency to optimise internal financial systems, resulting in an insignificant marginal effect of digital transformation on FF. This finding is consistent with the studies of Brandt and Li (2003) and resonates with Liu and Wang’s (2023) analysis of the technological adoption incentives in non-state-owned firms.

To further validate our conceptual framework, we conducted a series of robustness and endogeneity checks. Firstly, we employed PSM to address potential selection bias, ensuring comparability between digitally transformed and non-transformed firms. The results remained consistent, confirming the positive effect of digital transformation on FF. Secondly, we applied system GMM to account for possible dynamic endogeneity and reverse causality, with the estimations passing key diagnostic tests and yielding significant coefficients. Moreover, the findings were robust to alternative measures of both the independent variable (e.g. expanded keyword sets) and the dependent variable (e.g. unused debt capacity).

Acknowledgements

The authors would like to express their sincere gratitude to the anonymous reviewers and the editors for their truly valuable comments.

The author reported that they received funding from Guangxi Philosophy and Social Science Self-financed Project and the Research on the Cooperation Mechanism and the Path to Development of the Digital Trade Cooperation between Guangxi and ASEAN of the Counterpart Supporting Scientific Research Project of Guangxi Normal University, which may be affected by the research reported in the enclosed publication. The author has disclosed those interests fully and has implemented an approved plan for managing any potential conflicts arising from their involvement. The terms of these funding arrangements have been reviewed and approved by the affiliated university in accordance with its policy on objectivity in research.

Conceptualisation, L.W.; methodology, S.L.; formal analysis, S.L.; investigation, Z.Z.; writing – original draft preparation, S.L., T.L.; writing – review and editing, L.W. and Z.Z.; visualisation, T.L.; supervision, Z.Z. All authors have read and agreed to the published version of the manuscript.

This article followed all ethical standards for research without direct contact with human or animal subjects.

This study was supported by Guangxi Philosophy and Social Science Self-financed Project ‘Research on the Path and Mechanism of the Development of Digital Economy in Guangxi Driven by the China-ASEAN Digital Trade Cooperation’ (2023FGL025), and the Research on the Cooperation Mechanism and the Path to Development of the Digital Trade Cooperation between Guangxi and ASEAN of the Counterpart Supporting Scientific Research Project of Guangxi Normal University.

The data that support the findings of this study are available from the corresponding author, Z.Z. upon reasonable request.

The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.

References