Building Sustainable Financial Capacity: How Supply Chain Digitalization Facilitates Credit Access by Adjustment Capability

Abstract

In the context of the deep restructuring of the global industrial chain and the concurrent pursuit of green and sustainable development, enterprises need to secure long-term, reliable supply chain competitiveness. The burgeoning wave of digitalization is simultaneously reshaping industry landscapes. Based on a sample of the Chinese manufacturing sector, this study explores how supply chain digital transformation enhances commercial credit financing performance by improving corporate adjustment capability. The research finds that supply chain digital transformation strengthens a firm’s commercial credit financing capacity through a dual-core mediating mechanism of corporate adjustment capability: (1) enhancing the adjustment capability of operational management, which mitigates the negative impact of cost stickiness on financing; (2) enhancing the adjustment capability of organizational management, which amplifies the positive effect of organizational resilience on financing. The study further reveals key moderating effects: (1) External Governance: Strong ESG performance strengthens the financing effect of digitalization by building reputational capital. High industry competition strengthens the financing effect by prompting firms to optimize operational efficiency. (2) Internal Endowments: Environmental risk aversion stemming from a firm’s polluting nature significantly weakens the credit supply effect of digitalization. The market-oriented foundation underpinning private ownership effectively activates the credit supply effect of digitalization. This study constructs an integrated pathway model of “Digital Transformation–Corporate Adjustment Capability–Supply Chain Credit Access.” It provides a research perspective for understanding how digitalization reshapes the logic of supply chain finance and offers empirical evidence for pathways empowering enterprises through digital transformation.

1. Introduction

Against the backdrop of profound evolution in the global political and economic landscape, supply chain security and strategic autonomy have emerged as core elements of national competitiveness. In recent years, intertwined geopolitical tensions and trade protectionism have driven the international supply chain system to evolve from a “low-cost” logic toward a “security-resilience” orientation [1]. Supply chain not only facilitates resource allocation and value transmission within global economic networks but also directly impacts the economic stability and industrial upgrading pathways of enterprises and nations alike [2].

As a global manufacturing powerhouse, China has consistently strengthened its dominant position in the global value chain through industrial digitalization and intelligentization while advancing its “Manufacturing Powerhouse Strategy.” The Made in China 2025 strategic blueprint emphasizes that future industrial development must focus on supply chain collaboration, industrial chain upgrading, and breakthroughs in core technologies. Research indicates that digital transformation serves as a key mechanism for enhancing supply chain resilience and manufacturing value creation capabilities [3]. On one hand, digital tools render supply chain management more responsive, transparent, and collaborative; on the other hand, they provide an unprecedented technological foundation for supply chain empowerment [4].

From a global governance and development perspective, supply chains play a pivotal role in achieving the Sustainable Development Goals (SDGs). UN 2030 Agenda targets, including “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation” (Goal 9) and “Ensure sustainable consumption and production patterns” (Goal 12), critically depend on green, secure, and financially inclusive supply chain systems [5]. Realizing this vision requires deepened development of Supply Chain Finance (SCF). SCF provides enterprises with timelier, more transparent, and accessible financial services by leveraging core enterprises’ creditworthiness and authentic transactions as foundations [6]. Research by Hofmann and Kotzab [7] demonstrates that SCF not only improves cash flow stability and financing structures across the industrial chain but also effectively enhances supply chain coordination efficiency.

Traditional SCF models face significant bottlenecks, including inefficient credit transmission mechanisms, severe information asymmetry, and financing cost dependency on core enterprises [8,9]. However, emerging digital technologies, such as big data, blockchain, and IoT, present opportunities for SCF reconstruction and iteration [10,11]. Studies reveal that supply chain digital transformation (SCDT) improves SCF coverage breadth and risk control capabilities by enhancing data transparency and transaction visibility, thereby facilitating dynamic credit assessment [12,13]. Furthermore, digital technologies drive the systemic restructuring of commercial credit financing, gradually shifting credit evaluation from a “financial-centric” to a “behavior-centric” paradigm. Indicators such as fulfillment records, invoice data, and order histories within the supply chain are increasingly becoming critical metrics for assessing financing capacity [14]. Concurrently, digital tools significantly boost operational efficiency. Integration of ERP, SCM platforms, e-invoicing, and blockchain smart contracts shortens capital circulation cycles and enhances overall supply chain operational efficiency [15].

Despite the accelerating global wave of digitalization, the systemic impact of enterprise digital transformation, particularly from a supply chain perspective, on supply chain financing capacity remains underexplored. Two research gaps persist: (1) Insufficient behavioral mechanism analysis: Limited research examines how digital technology deployment evolves into organizational behavioral changes through mediating mechanisms. (2) Lack of corporate finance embeddedness: Most studies focus on operational performance and supply coordination at the industry level, paying scant attention to how digitalization affects firm-level financing environments. While some literature preliminarily indicates that information technology enhances supply chain coordination and data visibility [16,17,18], the intrinsic mechanisms through which enterprises’ supply chain digital transformation influences information processing capabilities and resource allocation efficiency consequently shape SCF capacity, lacking in-depth discussion. Crucially, systematic modeling and empirical validation are absent regarding whether digital capabilities can enhance cost adjustment flexibility and resource reconfiguration capability when enterprises face revenue uncertainty or market competition, ultimately improving financing accessibility and credit assessment efficiency.

Addressing these gaps, this study adopts the perspective of supply chain digital transformation to investigate how it enhances firms’ dynamic capabilities in factor allocation through improving information processing efficiency and resource allocation flexibility, thereby strengthening upstream and downstream credit evaluations and ultimately improving firms’ supply chain financing capacity. Unlike the extant literature that primarily regards supply chain finance as a matter of financial product adaptation, this paper emphasizes its essence as a synergistic system of “information acquisition—resource reallocation—credit enhancement.” Through digital transformation, firms not only improve their capabilities in information processing and disclosure but also strengthen their resilience against cost stickiness and organizational inertia. On this basis, digital transformation enhances operational transparency and transactional collaboration, thereby fostering a more sustainable financing environment.

Relative to the existing academic focus on the theme of “digitalization and financing access,” the incremental contributions of this study lie in three dimensions. First, in terms of research perspective, this study shifts the focus from the digital transformation of the financial supply side to the digital practices within firms’ own supply chains, highlighting the proactive role of enterprises in commercial credit financing. Second, in terms of mechanism, this study innovatively proposes a dual mediating mechanism of “cost stickiness and organizational resilience,” thereby moving beyond the prevailing logic that emphasizes information transparency alone. It reveals that digital transformation improves financing efficiency through multiple pathways by enhancing both operational management capabilities and organizational management capabilities. Third, in terms of moderating effects, this study constructs a dual-dimensional framework of “external governance and internal endowments” to systematically examine the heterogeneous impacts of ESG performance, industry competition, ownership structure, and pollution attributes on the financing effects of digital transformation, thereby uncovering its contextual dependence. Overall, this study extends the theoretical understanding of the multidimensional pathways through which digital transformation promotes commercial credit financing, while also providing practical implications for differentiated credit evaluation and policy formulation by financial institutions.

2. Literature Review

2.1. Supply Chain Digital Transformation and Commercial Credit Financing

Against the backdrop of profound transformation in the global economic landscape, geopolitical conflicts, trade environment uncertainties, and financial market volatilities persistently undermine corporate supply chain operational stability [19]. Supply chain resilience and financial safeguarding capabilities have become critical elements for enterprises to maintain long-term competitiveness. As a crucial link connecting industry and finance, SCF significantly alleviates corporate financing constraints by optimizing the coordinated allocation of capital flows and information flows [6].

Furthermore, Trade Credit constitutes over 40% of corporate current liabilities [20]. As the dominant non-bank financing method among supply chain enterprises, it optimizes cash flow by facilitating credit extension and improving financing conditions, allowing enterprises to rely on deferred payment or prepayment terms provided by trading partners without immediate cash outlays. Trade credit thereby exerts broad-ranging effects reducing systemic financial risks by stabilizing supply chain capital circulation [21], providing core enterprises with liquidity management tools, creating alternative financing channels for Small and Medium-sized Enterprises (SMEs) [22,23,24].

Existing literature extensively examines the mechanisms influencing corporate commercial credit financing. Hill et al. [20] empirically investigated how firms’ financial capabilities and governance structures affect trade credit decisions. Financially constrained firms exhibit stronger reliance on trade credit as a substitute for bank loans [24]. Corporate financial management proficiency, cash flow status, and supply chain flexibility directly influence financing decisions [25,26]. Governance deficiencies significantly elevate financing risks. Duong et al. [27] found that overconfident CEO decisions increase trade credit default rates. From an external governance perspective, ESG performance is emerging as novel credit capital, with high-ESG-rated enterprises securing higher interest discounts and longer payment extensions [28,29].

Traditional supply chains typically comprise discrete, isolated steps. Digital transformation breaks down nodal barriers, integrating previously fragmented business chains into unified operating systems [30]. Supply chain digitalization is thus conceived as an interconnected business system empowered by modern digital technologies [31]. Unlike traditional firm-wide digitalization, which focuses on systemic internal transformation within a single enterprise—emphasizing the integration of functional departments into a unified IT architecture and data governance system to improve overall operational efficiency and innovation capacity [32]—supply chain digitalization emphasizes inter-organizational data and process coordination. It is grounded in the institutional environment shaped by trust mechanisms and contractual arrangements among supply chain partners, thereby enabling information sharing and collaborative decision-making among suppliers, manufacturers, and logistics providers. Although supply chain digital transformation depends on firms’ internal digital transformation, external digital practices across the supply chain will stimulate further digital upgrading of internal business functions [33]. The information value generated through supply chain digitalization reflects a dynamic process that empowers different stages of the supply chain, optimizing network structures, fostering ecosystem collaboration, and enhancing the reliability of supply chain financing.

Against this backdrop of digital convergence, digital technologies are reconstructing traditional credit relationships and driving commercial credit financing toward real-time, intelligent evolution. The Internet of Things (IoT) and cloud platforms dismantle supply chain data silos, enabling real-time information sharing across the entire chain [34]. Through intelligent risk management technologies—particularly Artificial Intelligence (AI) and machine learning algorithms—digital transformation facilitates real-time monitoring of supply chain entities, enabling early default prediction and thereby optimizing credit assessment and financing support [35]. Guan et al. [36], based on empirical evidence from Chinese SMEs, demonstrate that digital technologies promote supply chain finance and play a substantive role in alleviating the financing constraints faced by SMEs. Moreover, digital technologies reshape the foundations of credit evaluation by transforming information flows. Zhang et al.’s [37] empirical study demonstrates that digital transformation significantly promotes corporate commercial credit financing. Wang et al. [38] found that enhanced digitalization enables firms to obtain more trade credit even when facing lower credit constraints. Regarding supply chain digital transformation research, Akkermans et al. [39] revealed that ERP implementation substantially improves supply chain performance, including automated procurement, cost reduction, and shortened delivery cycles. Against this backdrop, enterprises leverage ERP-provided real-time data and inventory control to optimize bargaining strategies and extend payment terms. Specifically, information transparency constitutes a core factor influencing SCF financing efficiency. The degree of information sharing among supply chain members directly affects the difficulty of credit assessment, where information asymmetry may elevate financing costs [40,41]. With advancements in big data analytics and blockchain technologies, real-time information sharing across supply chain nodes reduces information asymmetry while enhancing financing decision accuracy and efficiency [12].

Consequently, corporate digital transformation drives fundamental business process reengineering internally while reintegrating the external supply–demand chain. This not only enhances supply chain transparency and collaboration efficiency but also positively impacts credit assessment through technological support and real-time risk monitoring. Leveraging technologies like big data, IoT, cloud platforms, and blockchain, supply chain digital transformation optimizes information flow timeliness, improving decision accuracy and accessibility in supply chain financing (particularly commercial credit). This establishes a digital infrastructure mechanism enabling sustainable improvement of the SCF environment. This study thus proposes the following hypothesis:

H1. 

Corporate supply chain digital transformation positively promotes commercial credit financing.

2.2. The Mediating Role of Adjustment Capability in the Process of SCDT Affecting SCF

Supply Chain Digital Transformation originates from widespread enterprise informatization and ERP system adoption. With advancements in cloud computing, the IoT, AI, and big data analytics, scholars increasingly focus on how these technologies reconfigure supply chain structures and processes to enhance dynamic adaptability and resilience [42]. During the COVID-19 pandemic, supply chain “resilience” and “agility” emerged as academic priorities, with digitalization recognized as a critical pathway to strengthen supply chain adjustment capability. Ning et al. [43] demonstrated that digital technologies significantly bolster enterprises’ resilience against COVID-19 disruptions by enhancing supply chain traceability and agility. Zhao et al. [44] found that during pandemic turbulence, firms implementing digital strategies exhibited superior supply chain resilience across shock absorption (absorptive), disruption response (responsive), and recovery phases.

The integration of big data and cloud platforms facilitates inventory optimization and intelligent logistics allocation, thereby improving operational efficiency and emergency response capabilities [45,46]. Ivanov and Dolgui [47] further examined resilience-building through digital transformation, noting that SCDT enhances rapid response to disruptions and strengthens corporate adjustment capability amid external volatility. Moreover, digital mechanisms—including dynamic inventory allocation—shorten cash cycles to indirectly improve financing capacity [46] while accelerating responsiveness to emergencies [47].

Adjustment Capability reflects an enterprise’s ability to navigate external shocks and internal changes. Here, Cost Stickiness characterizes operational adjustment under external pressures. Cost stickiness occurs when cost reductions lag behind declining sales revenue, revealing sluggishness in cost control and operational adaptation. Grounded in Agency Theory and Information Asymmetry Theory [48], corporate digitalization alleviates agency problems and managerial optimism bias through enhanced information sharing and internal controls [49,50], thus reducing cost adjustment inertia. Studies show that highly digitized firms cut costs faster during revenue declines, boosting operational management efficacy and external financing attractiveness [51]. Sun et al. [52] noted that stabilized cash flows reduce financing risks while increasing customers’ willingness to extend commercial credit.

Organizational Resilience reflects adaptive capability under external shocks, encompassing endurance, recovery, and adaptability [53]. Digitalization strengthens the supply chain’s ability to identify and respond to disruptions [47]. Specifically, guided by Dynamic Capabilities Theory [54], SCDT elevates risk-bearing capability for disruption identification, response, and recovery through technologies like cloud computing, IoT, and AI analytics [55,56,57]. Ivanov [3] examined how pandemic-era digitalization enhanced supply chain visibility and elasticity. Sriraman et al. [55] established that SCF mechanisms are integral to SMEs’ resilience-building, where organizational resilience reduces financing frictions. Crucially, heightened resilience lowers disruption risks, strengthening financiers’ trust across the supply chain [55]. Consequently, trading partners offer expanded credit lines and preferential terms based on enhanced commercial trust [57].

Specifically, the theoretical underpinnings of this study rest on the dynamic capability perspective and agency theory, both of which offer complementary explanations for how supply chain digitalization strengthens firms’ adaptive capacity. The dynamic capabilities framework conceptualizes firms’ ability to achieve and sustain competitive advantage in turbulent environments through the processes of sensing, seizing, and transforming [58]. This perspective emphasizes the continuous reconfiguration of organizational resources and routines to maintain strategic alignment with shifting environmental contingencies. Within this context, digital transformation is not merely the adoption of IT tools but a broader mechanism that facilitates capability-building and enables firms to reallocate resources dynamically. Supply chain digitalization, in particular, serves as an external catalyst that fosters such capability development. Empirical evidence supports this view: Sharma et al. [59] show that the interplay between digitalization and supply chain management enhances organizational resilience, while Zhang et al. [60] demonstrate that digital transformation improves firms’ agility and risk adaptability. Shi et al. [61] further reveal that highly digitalized manufacturing firms exhibit superior responsiveness in crisis scenarios, reflected in their ability to adjust production and orders with greater speed and precision. Complementing these findings, Mutascu [62] introduces the “operational-financial sustainability” framework, underscoring that long-term resilience requires translating technological investments into strategic adaptation capabilities. Similarly, Al-Moaid and Almarhdi [63] highlight the mediating role of dynamic capabilities in ensuring that digital initiatives are effectively transformed into tangible outcomes such as resource reconfiguration and process redesign. Taken together, these insights suggest that digital transformation strengthens firms’ adaptive capacity through the development of dynamic capabilities, thereby enhancing organizational resilience and facilitating supply chain resource reallocation.

In contrast, agency theory provides a behavioral and governance-oriented lens for understanding how supply chain digitalization mitigates agency costs. Rooted in the alignment of principal–agent interests, agency theory examines how contractual structures and incentive mechanisms reduce agency costs and improve coordination [64]. Within supply chain contexts, information asymmetry and incentive misalignment often manifest as cost stickiness and managerial optimism bias. Digitalization addresses these frictions by enabling real-time information sharing and reducing monitoring costs, thereby allowing principals—including supply chain partners—to detect and rectify deviations from optimal decision-making more efficiently. Recent empirical studies reinforce this logic: Li et al. [50] identify that client-side digitalization generates positive spillover effects by significantly reducing suppliers’ cost stickiness through enhanced information exchange. At the firm level, Chen and Xu [65] demonstrate that digitalization suppresses cost stickiness by improving transparency and lowering adjustment costs, while Li et al. [66] show that digital transformation curbs managerial short-termism by mitigating over-optimistic delays in cost control. Collectively, these findings underscore that digitalization enhances adaptive capacity from an operational management perspective by reducing managerial behavioral biases and improving cost adjustment efficiency, thereby alleviating coordination frictions within and across supply chains.

In synthesis, dynamic capabilities theory elucidates how digital transformation advances firms’ strategic adaptation and resource reconfiguration, thereby reinforcing organizational resilience under environmental turbulence. Agency theory, by contrast, explains how digital transformation improves governance mechanisms and mitigates managerial biases, thus enhancing operational flexibility and reducing cost stickiness. Together, these two theoretical perspectives provide a comprehensive explanatory framework for understanding how supply chain digitalization fosters firms’ adjustment capacity and financing potential.

Thus, as firms pursue SCDT, operational management capability improves through cost stickiness mitigation, and organizational management capability strengthens via organizational resilience enhancement. With heightened adjustment capability, SCF risks decline while capital providers’ willingness increases. Beyond boosting supply chain transparency and collaborative efficiency, SCDT fundamentally elevates enterprises’ intrinsic adjustment capability, spanning operational management and organizational management capabilities, thereby promoting commercial credit financing efficiency. This study proposes:

H2. 

Supply chain digital transformation exerts a positive effect on commercial credit financing by enhancing corporate adjustment capability.

3. Research Design

3.1. Sample Selection and Data Source

In the context of China possessing the world’s most comprehensive manufacturing system, the Chinese government’s commitment to advancing the Digital China Strategy provides a stable and favorable institutional environment for manufacturing supply chain digital transformation. Within China’s manufacturing supply chain, commercial credit financing manifested through accounts payable, notes payable, and similar instruments, constitutes a prevalent and critical form of supply chain finance. This commercial ecosystem’s heavy reliance on commercial credit creates fertile ground for examining the interplay between digital transformation and commercial credit financing.

Specifically, Manufacturing represents a focal yet challenging domain for industrial digital transformation, with supply chain digitization serving as its core component. As the backbone of the national economy, manufacturing constitutes the central nexus of supply chain networks. Procurement, processing, and distribution activities form extensive supply chain characterized by tightly interconnected upstream/downstream enterprises, frequent trade credit transactions. An archetypal setting for studying SCF mechanisms represented by commercial credit financing.

Consequently, this study selects A-share listed companies (Shanghai/Shenzhen Stock Exchanges) from 2013–2023 as the initial research sample, with particular focus on manufacturing listed companies. All corporate data are sourced from the CSMAR database. ST and *ST companies were excluded, as were financial industry companies. Additionally, data missing samples were excluded.

3.2. Variable Definitions and Regression Model

3.2.1. Independent Variable: Supply Chain Digital Transformation

Amid global implementation of the UN 2030 Sustainable Development Goals (SDGs), supply chain digitalization has emerged as fundamental infrastructure for enhancing corporate operational performance. Consequently, measuring corporate supply chain digitalization levels constitutes a critical research question.

This study adopts the framework of Guidelines for Supply Chain Digitalization Management jointly issued by China’s State Administration for Market Regulation and Standardization Administration in 2022, and divides enterprise supply chain digitalization into five dimensions: Planning digitalization, Procurement digitalization, Production digitalization, Sales digitalization, Logistics digitalization. Leveraging textual analysis, this paper constructs an objective metric reflecting Chinese firms’ supply chain digitalization levels. To ensure keyword selection embodies both representativeness and comprehensiveness while capturing digitalization trajectories, this study follows methodological approaches by several scholars [67].

This study collected national-level policy documents, industry research reports, and corporate annual reports from the period 2010 to 2023, screened and extracted keywords related to supply chain digitalization to form a keyword list, as detailed in

Table 1. Supply Chain Digitalization Keyword List.

Dimension	Keyword
Planning digitalization	Intelligent Decision-Making, Intelligent Forecasting, Intelligent Planning, Intelligent Supply-Demand Matching, Demand Sensing, Demand Modeling, Planning Algorithm, Digital Planning, Planning Simulation Algorithm, Algorithm-Driven Supply Chain Planning, Agile Planning, Digital Decision-Making, Digital Business Planning, Digital Planning System, Digital Planning Platform, Automated Planning
Procurement digitalization	Digital Procurement, Intelligent Procurement, Procurement Platform, Procurement System, Online Procurement, Integrated Procurement, Procurement Visualization, Procurement Cloud, E-Procurement, Automated Procurement, Supplier Collaboration Platform
Production digitalization	Smart Manufacturing, Intelligent Equipment, Intelligent Control, Smart Production Line, Smart Workshop, Automatic Control, Automated Monitoring, Automated Production, Integrated System, Human–Machine Collaboration, Human-Machine Interaction, Industrial Intelligence, Industrial Cloud, Industrial Information, Industrial Automation, Industrial Robot, Industrial Internet, Virtual Manufacturing, Smart Factory, Unmanned Production, Future Factory, Digital Factory, Lighthouse Factory, Flexible Production, Intelligent Production Scheduling, Cloud Manufacturing
Sales digitalization	Internet Marketing, Smart Marketing, Digital Marketing, Unmanned Retail, E-Commerce, Customized Marketing, Big Data Marketing, Personalized Marketing, Digital Store, O2O, B2B, C2C, B2C, C2B, C2M
Logistics digitalization	Smart Transportation, Smart Warehousing, Smart Logistics, Digital Warehousing, Warehouse Management System (WMS), Logistics Automation, Digital Logistics, Online Logistics, Logistics Cloud, Digital Logistics Platform, Intelligent Fulfillment, Warehouse Robot, Inventory Counting Robot, Unmanned Truck, Autonomous Delivery Vehicle, Handling Robot, Logistics Robot, Smart Container, Online Freight Platform, Automated Sorting, Unmanned Warehouse

. Furthermore, this research conducted textual analysis on the Management Discussion and Analysis (MD&A) sections of listed companies’ annual reports. The cumulative frequency of keywords associated with the five dimensions of supply chain digitalization appearing in the MD&A sections was statistically measured. To account for variations in the length of the MD&A text across different annual reports, this study measured the extent of a company’s supply chain digitalization by dividing the total word frequency of supply chain digitalization-related terms by the length of the MD&A section.

3.2.2. Dependent Variable: Commercial Credit Financing

Trade credit, also termed commercial credit, refers to deferred payment or prepayment commitments extended between enterprises based on transactional trust. This credit form enables firms to obtain goods/services without immediate cash disbursement, thereby optimizing cash flow management. commercial credit financing (CCF) emerges from the temporal-spatial separation of goods and payments in commercial exchange. Its primary modalities include: Accounts Payable Financing, Notes Payable Financing, Advance Payment Financing.

These financing mechanisms enhance corporate capital flexibility. Following Huang et al. [68], this study measures CCF capacity through accessibility, operationalized as:

CCF = (Accounts Payable + Notes Payable)/Total Assets

(1)

3.2.3. Moderating Variable: Adjustment Capability

Amid the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) era in business environments, frequent “Black Swan” (low-probability, high-impact) and “Gray Rhino” (high-probability, high-impact) events profoundly impact manufacturing enterprises. Adjustment capability manifests as rapid resource reconfiguration capability, reflecting organizational adaptability and flexibility amid uncertainty, thus garnering extensive scholarly attention. Research perspectives on adjustment capability diverge as follows:

Firstly, about organizational-level adjustment capability. Organizational resilience examines how entities recover from disruptions, focusing on sustained growth through self-renewal and proactive adaptation. Resilience hinges on maintaining/enhancing performance in volatile environments. Scholars typically measure resilience via financial growth stability during external shocks [69,70]. Destructive changes—geopolitical conflicts, consumer preference shifts—erode established advantages and elevate business failure risks [71]. Specifically, following Ortiz-de-Mandojana & Bansal [72], this study incorporates two dimensions: stable growth is cumulative revenue growth over 3 years, and low volatility is the inverse of the monthly stock return standard deviation within 1 year. Given measurement unit incompatibility between dimensions, this paper applies the entropy weight method to construct a composite resilience index.

Secondly, for financial-level adjustment capability, cost stickiness denotes asymmetric cost responses to activity fluctuations: Resource adjustment mechanisms (e.g., capability/human capital reduction) during volume declines [73], Managerial expectation mechanisms (e.g., deliberate resource retention) [74]. Lower stickiness reflects superior intrinsic rigidity control, indicating timelier resource reallocation during downturns. While Anderson et al.’s [75] ABJ model measures industry-level stickiness, Weiss [76] quantifies firm-level stickiness using quarterly data. Thus, this paper adopts the Weiss model:

$${\mathrm{S}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{k}}_{\mathrm{i},\mathrm{t}}={\mathrm{l}\mathrm{o}\mathrm{g}\left({\displaystyle \frac{∆\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}{∆\mathrm{S}\mathrm{a}\mathrm{l}\mathrm{e}}}\right)}_{\mathrm{i},\mathsf{\omega }1}-{\mathrm{l}\mathrm{o}\mathrm{g}\left({\displaystyle \frac{∆\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}{∆\mathrm{S}\mathrm{a}\mathrm{l}\mathrm{e}}}\right)}_{\mathrm{i},\mathsf{\omega }2}$$

(2)

In the Weiss model, ω1: Revenue-declining quarter nearest to year-end for firm “i” in year “t”; ω2: Revenue-increasing quarter nearest to year-end; Sale: Quarterly operating revenue; Cost: Quarterly total costs (COGS + SG&A); Δ: Quarterly change rate. Negative Stick values indicate cost reduction magnitude during revenue declines is smaller than cost increase magnitude during revenue growth, with lower values denoting higher stickiness.

3.2.4. Control Variables

Drawing on literature regarding digital transformation and supply chain finance, this study selects the following control variables: Firm Size (Size), Listing Age (Age), Tobin’s Q (TobinQ), Asset–Liability Ratio (Lev), Ownership Type (State), Return on Equity (ROE), Proportion of Independent Directors (Indep), CEO/Chair Duality (Dual). Detailed variable definitions are presented in

Table 2. Variable Definitions.

Variable Type	Variable Name	Quantitative Standard
Dependent variable	CCF	With reference to preceding sections
Independent variable	SCDT	With reference to preceding sections
Moderating variable	Resilience	With reference to preceding sections
	Stick	With reference to preceding sections
Control variables	Size	Log value of corporate total assets
	Age	Listing Age
	TobinQ	Ratio of a company’s market value to its total assets
	Lev	Total Liability/Total Asset
	State	Ownership Type(State = 1)
	ROE	Return on Equity
	Indep	Proportion of independent directors on the board
	Dual	CEO/Chair Duality(Dual = 1)

. Additionally, industry-fixed effects and year-fixed effects were controlled.

3.2.5. Regression Model

To test Hypotheses 1 and 2, this article constructed regression models for empirical test:

$$\begin{gathered} {\mathrm{C}\mathrm{C}\mathrm{F}}_{\mathrm{i},\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\times {\mathrm{S}\mathrm{C}\mathrm{D}\mathrm{T}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_2\times {\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_3\times {\mathrm{A}\mathrm{g}\mathrm{e}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_4\times {\mathrm{T}\mathrm{o}\mathrm{b}\mathrm{i}\mathrm{n}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_5\times {\mathrm{L}\mathrm{e}\mathrm{v}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_6\times {\mathrm{S}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{i},\mathrm{t}} \\ +{\mathsf{\beta }}_7\times {\mathrm{R}\mathrm{O}\mathrm{E}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_8\times {\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{p}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_9\times {\mathrm{D}\mathrm{u}\mathrm{a}\mathrm{l}}_{\mathrm{i},\mathrm{t}}+\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{F}\mathrm{E}+\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{F}\mathrm{E}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}} \end{gathered}$$

(3)

4. Empirical Results

4.1. Descriptive Analysis

This paper conducted descriptive analysis of key variables in the empirical research, as presented in

Table 3. Descriptive Analysis.

Variable	Min	P50	Max	SD
CCF	0.00	0.10	0.46	0.10
SCDT	0.00	0.02	0.68	0.11
Resilience	0.09	0.80	3.36	0.63
Stick	−3.57	−0.01	2.77	0.95
Size	18.99	21.93	27.12	1.37
Age	0.00	2.20	3.37	0.97
TobinQ	0.84	1.59	9.20	1.32
Lev	0.05	0.40	0.97	0.21
State	0.00	0.00	1.00	0.46
ROE	−0.98	0.07	0.45	0.16
Indep	0.00	0.36	0.60	0.09
Dual	0.00	0.00	1.00	0.42

. Within the sample data: Commercial Credit Financing (CCF) and Supply Chain Digital Transformation (SCDT) exhibit substantial dispersion. CCF demonstrates relatively mature development, with a median value of 0.10 indicating higher aggregate levels. SCDT remains in early-stage development, showing lower aggregate levels with a median of 0.02. Cost Stickiness (reflecting cost adjustment capability) displays significant variation. Range: from −3.57 to 2.77. Standard Deviation: 0.95. Organizational Resilience (reflecting organizational adjustment capability) shows notable distributional spread. Range: from 0.09 to 0.80. Standard Deviation: 0.63. These differential distribution patterns establish a robust empirical foundation for subsequent analyses.

4.2. Correlation Analysis

This paper conducted a Pearson correlation analysis of key research variables, with results presented in

Table 4. Correlation analysis of main variables.

	CCF	SCDT	Resilience	Stick
CCF	1.00
SCDT	0.081 ***	1.00
Resilience	0.003 *	0.017 ***	1.00
Stick	−0.113 ***	−0.285 ***	−0.012 **	1.00

***, **, and * refer to significance at 1%, 5%, and 10%, respectively.

. Supply Chain Digital Transformation demonstrates a statistically significant positive correlation with Commercial Credit Financing (CCF) at the 1% level (p < 0.01), providing preliminary support for Hypothesis 1. Regarding mediating effects: Cost Stickiness (Stick) exhibits significant negative correlations with both SCDT and CCF at the 1% level; Organizational Resilience (Resilience) shows significant positive correlations with SCDT at the 10% level (p < 0.10) and with CCF at the 1% level; These findings align with prior theoretical analysis and provide empirical foundations for subsequent regression analyses. Furthermore, Variance Inflation Factor (VIF) tests for all independent variables (including controls) indicate no multicollinearity concerns, with all VIF values below 2.

4.3. Main Regression Analysis

Supply Chain Digital Transformation and Commercial Credit Finance

Regression analysis results examining the relationship between Supply Chain Digital Transformation and commercial Credit Financing are presented in

Table 5. Supply chain digital transformation and commercial credit finance.

	(1) CCF	(2) CCF	(3) CCF
SCDT	0.076 *** (21.47)	0.048 *** (13.97)	0.043 *** (11.00)
Size	−0.006 *** (−12.91)	−0.006 *** (−13.99)	−0.004 *** (−6.39)
Age	−0.013 *** (−25.28)	−0.006 *** (−13.74)	−0.010 *** (−17.57)
TobinQ	−0.003 *** (−10.20)	−0.004 *** (−14.10)	−0.004 *** (−12.21)
Lev	0.262 *** (98.35)	0.258 *** (100.26)	0.292 *** (91.17)
State	0.000 (0.12)	0.011 *** (11.73)	0.015 *** (11.43)
ROE	0.048 *** (13.88)	0.057 *** (17.67)	0.070 *** (15.77)
Indep	−0.003 (−0.56)	−0.014 *** (−2.90)	−0.013 ** (−2.12)
Dual	−0.001 (−0.65)	−0.003 *** (−3.20)	−0.001 (−1.41)
Cons	0.171 *** (17.95)	0.161 *** (18.93)	0.122 *** (10.45)
Year FE	NO	YES	YES
Ind FE	NO	YES	NO
Manufacture	NO	NO	YES
N	44,277	44,277	29,208
Adj R2	0.248	0.413	0.332

***, and ** refer to significance at 1% and 5%, respectively

: Columns 1 and 2: Non-financial listed firms; Column 3: Manufacturing listed firms (focal sample); Column 1: Baseline model excluding fixed effects; Columns 2 and 3: Models incorporating industry-year fixed effects. Robustness was enhanced using Stata 16.0’s vce(robust) option for heteroskedasticity-consistent standard errors.

Key findings indicate: SCDT exhibits a statistically significant positive relationship with CCF at the 1% level (p < 0.01). This positive effect is particularly pronounced within the manufacturing sector (Column 3). These results provide empirical support for Hypothesis 1. Collectively, they demonstrate that advancing supply chain digital transformation effectively enhances commercial credit financing capacity.

4.4. Mediating Effect Analysis

4.4.1. The Analysis of Mediating Effect of Adjustment Capability(Cost Stickiness)

As mentioned earlier, the adjustment capability of enterprises is of vital importance, not only concerning the improvement of operational management capabilities but also the enhancement of organizational management capabilities, and it further affects the financing situation of enterprises.

Table 6. Mediating effect of adjustment capability (cost stickiness).

	(1) Stick	(2) Stick	(3) Stick	(4) CCF	(5) CCF	(6) CCF
SCDT	−0.106 *** (−2.82)	−0.133 *** (−3.31)	−0.163 *** (−3.42)
Stick				−0.001 *** (−3.24)	−0.001 * (−1.88)	−0.001 ** (−2.04)
Size	−0.029 *** (−6.17)	−0.030 *** (−6.14)	−0.026 *** (−4.22)	−0.006 *** (−12.58)	−0.006 *** (−14.07)	−0.004 *** (−6.46)
Age	−0.001 (−0.19)	−0.004 (−0.81)	−0.011 * (−1.65)	−0.012 *** (−24.64)	−0.006 *** (−13.52)	−0.010 *** (−17.32)
TobinQ	0.009 ** (2.10)	0.005 (1.15)	0.000 (0.08)	−0.003 *** (−10.13)	−0.004 *** (−14.33)	−0.004 *** (−12.43)
Lev	0.235 *** (8.21)	0.206 *** (6.78)	0.211 *** (5.73)	0.262 *** (98.10)	0.259 *** (100.74)	0.294 *** (91.85)
State	0.064 *** (5.62)	0.056 *** (4.82)	0.062 *** (4.19)	−0.001 (−1.27)	0.011 *** (11.29)	0.014 *** (11.11)
ROE	0.947 *** (22.11)	0.935 *** (21.59)	0.900 *** (15.87)	0.049 *** (13.97)	0.059 *** (17.96)	0.072 *** (16.08)
Indep	−0.007 (−0.10)	−0.012 (−0.20)	−0.090 (−1.19)	0.000 (0.04)	−0.012 ** (−2.43)	−0.010 * (−1.70)
Dual	−0.008 (−0.73)	−0.005 (−0.46)	−0.001 (−0.09)	0.001 (0.92)	−0.002** (−2.26)	−0.001 (−0.77)
Cons	0.286 *** (2.83)	0.345 *** (3.24)	0.289 ** (2.15)	0.171 *** (17.89)	0.163 *** (19.20)	0.123 *** (10.60)
Year FE	NO	YES	YES	NO	YES	YES
Ind FE	NO	YES	NO	NO	YES	NO
Manufacture	NO	NO	YES	NO	NO	YES
N	44,277	44,277	29,208	44,343	44,343	29,248
Adj R2	0.022	0.026	0.022	0.241	0.411	0.330

***, **, and * refer to significance at 1%, 5%, and 10%, respectively.

and

Table 7. Mediating effect of adjustment capability (organization resilience).

	(1) Resilience	(2) Resilience	(3) Resilience	(4) CCF	(5) CCF	(6) CCF
SCDT	1.524 *** (66.80)	0.963 *** (45.56)	1.101 *** (45.73)
Resilience				0.024 *** (32.99)	0.015 *** (18.07)	0.025 *** (24.87)
Size	−0.268 *** (77.15)	0.238 *** (72.39)	0.221 *** (51.43)	−0.012 *** (−25.47)	−0.009 *** (−20.74)	−0.009 *** (−15.58)
Age	−0.101 *** (−29.20)	−0.076 *** (−26.22)	−0.090 *** (−24.54)	−0.010 *** (−19.58)	−0.006 *** (−11.72)	−0.008 *** (−13.71)
TobinQ	0.085 *** (35.03)	0.065 *** (30.78)	0.059 *** (22.15)	−0.005 *** (−16.25)	−0.005 *** (−16.53)	−0.006 *** (−15.79)
Lev	−0.457 *** (−29.08)	−0.242 *** (−18.24)	−0.153 *** (−9.22)	0.273 *** (101.03)	0.262 *** (100.78)	0.297 *** (92.13)
State	−0.071 *** (−11.12)	−0.015 *** (−2.65)	0.014 * (1.86)	0.001 (0.97)	0.011 *** (11.87)	0.015 *** (11.29)
ROE	−0.430 *** (−22.76)	−0.147 *** (−9.63)	−0.167 *** (−8.14)	0.057 *** (16.02)	0.059 *** (17.85)	0.073 *** (16.10)
Indep	0.280 *** (7.57)	0.246 *** (8.00)	0.250 *** (6.62)	−0.010 * (−1.82)	−0.018 *** (−3.77)	−0.019 *** (−3.25)
Dual	0.077 *** (11.47)	0.053 *** (10.03)	0.062 *** (9.75)	−0.002 ** (−2.24)	−0.003 *** (−4.05)	−0.003 *** (−3.12)
Cons	−4.950 *** (−65.75)	−4.373 *** (−61.00)	−4.061 *** (−43.44)	0.292 *** (29.33)	0.230 *** (25.14)	0.224 *** (18.74)
Year FE	NO	YES	YES	NO	YES	YES
Ind FE	NO	YES	NO	NO	YES	NO
Manufacture	NO	NO	YES	NO	NO	YES
N	43,130	43,130	28,365	43,915	43,915	28,404
Adj R2	0.264	0.518	0.370	0.259	0.416	0.343

***, **, and * refer to significance at 1%, 5%, and 10%, respectively.

, respectively, conduct the mediating effect analysis from the perspectives of organizational resilience and cost stickiness. Specifically, in Table 6, Columns 1, 2, 4, and 5 are for listed non-financial enterprises, while Columns 3 and 6 are based on listed manufacturing enterprises. Columns 1 and 4 do not consider the fixed effects of year and industry, while Columns 2, 3, 5, and 6 make supplementary considerations. Based on the regression results of Columns 1–3, there is a negative relationship between supply chain digital transformation and cost stickiness at the 1% significance level. And based on the regression results of Columns 4–6, cost stickiness and commercial credit financing show a negative relationship at least at the 10% significance level. Thus, it can be seen that the digital transformation of the supply chain promotes the improvement of the operational management ability of enterprises, thereby suppressing the phenomenon of cost stickiness and further mitigating the damage of cost stickiness to commercial credit financing.

4.4.2. The Analysis of Mediating Effect of Adjustment Capability (Organization Resilience)

Table 7 analyzes the corporate adjustment capability from the perspective of organizational resilience. Specifically, Columns 1, 2, 4, and 5 are listed for non-financial listed companies, while Columns 3 and 6 are based on listed manufacturing enterprises. Columns 1 and 4 do not consider the fixed effects of year and industry, while Columns 2, 3, 5, and 6 make supplementary considerations. Based on the regression results of Columns 1–3, there is a positive relationship between supply chain digital transformation and organizational resilience at the 1% significance level. And based on the regression results of Columns 4–6, organizational resilience and commercial credit financing also show a positive relationship at the 1% significance level. Thus, it can be seen that the digital transformation of the supply chain promotes the level of organizational resilience, enhances the adjustment capability of organizational management, and further strengthens the promoting effect of organizational resilience on commercial credit financing.

4.4.3. The Analysis of Cross Mediating Effect of Adjustment Capability (Organization Resilience & Cost Sticky)

This study further examined the interactive effect between organizational resilience and cost stickiness. As shown in

Table 8. Cross-mediating effect of adjustment capability.

	(1) Sticky	(2) Sticky	(3) CCF	(4) CCF
	High Resilience	Low Resilience	High Resilience	Low Resilience
SCDT	−0.177 *** (−3.25)	−0.184 * (−1.65)
Sticky			−0.001 (−1.58)	−0.001 (−1.19)
Size	−0.268 *** (77.15)	0.238 *** (72.39)	0.221 *** (51.43)	−0.012 *** (−25.47)
Cons	−4.950 *** (−65.75)	−4.373 *** (−61.00)	−4.061 *** (−43.44)	0.292 *** (29.33)
Year FE	NO	YES	YES	NO
Ind FE	NO	YES	NO	NO
Manufacture	NO	NO	YES	NO
N	15,391	13,817	15,400	13,848
Adj R2	0.022	0.022	0.356	0.296

***, and * refer to significance at 1%, and 10%, respectively.

, a partial synergistic effect exists between organizational resilience and cost stickiness. On the one hand, organizational resilience acts synergistically with digitalization to suppress cost stickiness. That is, in contexts with high organizational resilience, digitalization more effectively inhibits cost stickiness. On the other hand, the effect of organizational resilience on the relationship between cost stickiness and commercial credit financing is not statistically significant. The regression results indicate that organizational resilience, as an intrinsic adjustment capability of firms, can—when catalyzed by digital transformation—facilitate resource reallocation and process reengineering, thereby mitigating cost stickiness. Meanwhile, although organizational resilience serves as an implicit adjustment capacity that helps firms cope with long-term external risk shocks, its short-term impact on external financiers remains relatively limited compared to explicit financial information such as cost stickiness.

4.5. Moderating Effect Analysis

4.5.1. The Analysis of Moderating Effect of External Governance

The ESG Index (Environmental, Social, and Governance) has emerged as a critical metric for evaluating listed firms’ social responsibility, increasingly influencing stakeholder decisions and evolving into an external governance mechanism tied to corporate financing. By constructing sustainable reputational capital, ESG internalizes social responsibility, facilitating access to low-cost funding [28]. Concurrently, industry competition intensity (measured by the Herfindahl-Hirschman Index, HHI) serves as both a quantitative signal of market structure and an external governance tool. Fragmented industries exacerbate operational risks, prompting creditors to impose higher interest rates and restrictive covenants that compel governance improvements [77]. Therefore, this paper uses ESG and HHI as external governance variables to conduct a moderating effect study. The measurement method of ESG is borrowed from Mu et al. [78], specifically measured through the Hua Zheng ESG rating index. HHI is reflected by the square sum of the percentages of industry total revenue occupied by the main bodies of the industry enterprises, and the degree of industry competition is reflected by the dispersion of industry enterprise scale.

Columns 1 and 2 of

Table 9. Moderating effect of external governance.

	(1) CCF	(2) CCF	(3) CCF	(4) CCF
	High ESG	Low ESG	High HHI	Low HHI
SCDT	0.054 *** (9.21)	0.031 *** (4.06)	0.027 *** (4.81)	0.056 *** (9.90)
Size	−0.006 *** (−7.26)	−0.001 (−0.85)	−0.005 *** (−6.07)	−0.002 ** (−2.10)
Age	−0.009 *** (−10.96)	−0.012 *** (−9.68)	−0.008 *** (−9.07)	−0.013 *** (−15.95)
TobinQ	−0.005 *** (−8.78)	−0.005 *** (−7.05)	−0.005 *** (−10.10)	−0.003 *** (−6.83)
Lev	0.308 *** (64.36)	0.263 *** (45.61)	0.276 *** (56.76)	0.304 *** (72.13)
State	0.010 *** (5.57)	0.022 *** (9.17)	0.010 *** (4.93)	0.019 *** (11.41)
ROE	0.083 *** (9.96)	0.053 *** (8.18)	0.063 *** (9.46)	0.076 *** (12.84)
Indep	−0.004 (−0.44)	−0.020 * (−1.65)	−0.018 ** (−1.98)	−0.009 (−1.13)
Dual	0.000 (0.18)	−0.006 *** (−2.80)	−0.001 (−0.47)	−0.002 (−1.59)
Cons	0.155 *** (9.73)	0.087 *** (3.33)	0.159 *** (9.46)	0.073 *** (4.60)
Year FE	YES	YES	YES	YES
Manufacture	YES	YES	YES	YES
N	13,544	8303	14,066	15,142
Adj R2	0.348	0.295	0.287	0.379

***, **, and * refer to significance at 1%, 5%, and 10%, respectively.

present the moderating effect results of the ESG index. Based on the median ESG score of the aforementioned sample of listed companies, they were categorized into high-ESG and low-ESG companies. Although both show a positive and significant relationship, the statistical significance coefficient of the high ESG group (Column 1) is 9.21, which is significantly higher than that of the low ESG group (Column 2), which is 4.06. This can reflect the positive promoting effect of ESG on the digital transformation of the supply chain and commercial credit financing. Therefore, good ESG performance enhances the trust of stakeholders and strengthens the promoting effect of digital transformation on commercial credit financing. Digital transformation enhances the transparency of enterprise information (such as supply chain data sharing), and ESG, as an external governance signal, proves the sustainable development ability and performance reliability of the enterprise to suppliers, reduces transaction risk perception, and makes suppliers more willing to provide lenient commercial credit terms [79].

Columns 3 and 4 of Table 9 present the moderating effect results of HHI. Based on the median HHI score of the aforementioned sample of listed companies, they were categorized into high-HHI and low-HHI companies. Although both show a positive and significant relationship, the statistical significance coefficient of the low HHI group, that is, the high competition group (Column 4), is 9.90, which is significantly higher than that of the high HHI group, that is, the low competition group (Column 3). This can reflect the positive promoting effect of HHI on the digital transformation of the supply chain and commercial credit financing. Therefore, a high degree of industry competition (low HHI) amplifies the positive impact of digital transformation on commercial credit financing through intensified market disciplinary pressure. In high-competition industries, enterprises have a stronger motivation to utilize digital capabilities (such as intelligent supply chain management) to optimize operational efficiency and convey cooperation stickiness signals to the supply chain financing party. Competitive pressure forces enterprises to reduce opportunistic behavior, protect supplier rights, and thereby obtain more credit financing [80].

As shown in

Table 10. Moderating effect of ESG Sub-dimension.

	(1) CCF	(2) CCF	(3) CCF	(4) CCF	(5) CCF	(6) CCF
	High E-Rate	Low E-Rate	High S-Rate	Low S-Rate	High G-Rate	Low G-Rate
SCDT	0.056 *** (9.65)	0.031 *** (5.75)	0.038 *** (7.16)	0.048 *** (7.99)	0.045 *** (8.38)	0.044 *** (7.66)
Size	−0.004 *** (−6.26)	−0.002 ** (−2.14)	−0.004 *** (−5.02)	−0.004 *** (−4.38)	−0.005 *** (−6.72)	−0.001 (−1.20)
Age	−0.010 *** (−12.19)	−0.011 *** (−12.66)	−0.011 *** (−12.76)	−0.009 *** (−11.74)	−0.009 *** (−11.96)	−0.012 *** (−12.31)
TobinQ	−0.004 *** (−7.78)	−0.004 *** (−8.81)	−0.004 *** (−8.70)	−0.004 *** (−8.67)	−0.004 *** (−7.88)	−0.005 *** (−8.69)
Lev	0.292 *** (65.60)	0.293 *** (63.19)	0.310 *** (68.46)	0.276 *** (61.40)	0.291 *** (62.99)	0.286 *** (61.60)
State	0.013 *** (7.21)	0.017 *** (9.09)	0.013 *** (6.67)	0.017 *** (9.62)	0.004 ** (2.24)	0.029 *** (14.26)
ROE	0.075 *** (12.05)	0.064 *** (10.07)	0.079 *** (11.59)	0.062 *** (10.54)	0.087 *** (10.87)	0.063 *** (11.62)
Indep	−0.023 *** (−2.74)	−0.000 (−0.01)	−0.025 *** (−2.97)	−0.001 (−0.06)	0.011 (1.49)	−0.027 *** (−2.79)
Dual	0.000 (0.07)	−0.003 ** (−2.10)	−0.001 (−0.61)	−0.002 (−1.44)	0.001 (0.59)	−0.004 *** (−2.82)
Cons	0.145 *** (9.63)	0.083 *** (4.37)	0.131 *** (8.14)	0.119 *** (6.93)	0.134 *** (8.97)	0.083 *** (4.45)
Year FE	YES	YES	YES	YES	YES	YES
Manufacture	YES	YES	YES	YES	YES	YES
N	16,028	13,180	14,383	14,825	15,677	13,531
Adj R2	0.316	0.350	0.342	0.324	0.328	0.308

***, and ** refer to significance at 1%, and 5%, respectively.

, the ESG performance was decomposed into three sub-dimensions—environmental (E-rate), social (S-rate), and governance (G-rate)—for further examination. Columns 1 and 2, 3 and 4, 5 and 6 present the test results for the corresponding sub-indicators, respectively. The regression results indicate that the moderating effect of the environmental dimension is more pronounced, while the moderating effects of the social and governance dimensions are not statistically significant.

4.5.2. The Analysis of Moderating Effect of Internal Attribute

In addition to the external governance influences on enterprises, the internal endowment of enterprises also plays a significant role in the digital transformation of the supply chain and commercial credit financing. On one hand, polluting enterprises face stronger environmental regulatory pressure, which is more likely to lead to an increase in loan costs and financial distress risks [81]. For heavily polluting enterprises, according to the 2012 version of the industry classification guidelines of listed companies by the China Securities Regulatory Commission, they are defined by selecting secondary industries under manufacturing, including textiles, leather, papermaking, petroleum processing, chemical products, chemical fibers, rubber and plastic products, metal smelting processing, non-metallic mineral products, etc. [81]. On the other hand, the nature of property rights, as a key to interpreting China, is a core institutional variable for Chinese enterprises’ research. Due to the lack of state-owned guarantees, private enterprises are more likely to be discriminated against by the traditional financial system and seek to deeply develop commercial credit. Allen et al. [82] found that the proportion of commercial credit of private enterprises in total liabilities is higher than that of state-owned enterprises. Therefore, this study adopts polluting enterprises and property rights nature as external governance variables to conduct a moderation effect study.

Columns 1 and 2 of

Table 11. Moderating effect of internal attribute.

	(1) CCF	(2) CCF	(3) CCF	(4) CCF
	Pollute firm	Green firm	State-owned	Private
SCDT	0.030 *** (3.22)	0.030 *** (6.84)	0.064 *** (5.68)	0.040 *** (9.71)
Size	−0.006 *** (−6.33)	−0.002 *** (−3.02)	−0.004 *** (−3.81)	−0.003 *** (−4.67)
Age	−0.007 *** (−6.51)	−0.011 *** (−15.75)	−0.003 ** (−2.26)	−0.012 *** (−17.94)
TobinQ	−0.005 *** (−7.10)	−0.005 *** (−13.21)	−0.004 *** (−5.48)	−0.004 *** (−9.98)
Lev	0.221 *** (38.98)	0.323 *** (85.24)	0.301 *** (43.75)	0.291 *** (80.31)
ROE	0.031 *** (3.91)	0.081 *** (15.14)	0.081 *** (9.51)	0.065 *** (12.44)
Indep	−0.013 (−1.24)	−0.014 ** (−2.00)	−0.083 *** (−5.42)	0.009 (1.33)
Dual	−0.002 (−1.01)	−0.003 ** (−2.23)	−0.011 *** (−3.01)	−0.001 (−0.71)
Cons	0.186 *** (9.58)	0.086 *** (6.22)	0.145 *** (6.75)	0.106 *** (7.67)
Year FE	YES	YES	YES	YES
Manufacture	YES	YES	YES	YES
N	8002	21,200	7172	22,036
Adj R2	0.249	0.385	0.284	0.334

***, and ** refer to significance at 1%, and 5%, respectively.

present the moderation effect results of polluting enterprises and green enterprises. According to the above secondary industry definitions, both are presented as positive and significant relationships. However, in the polluting enterprise group (Column 1), the statistical significance coefficient is 3.22, which is significantly lower than that of the green enterprise group (Column 2), which is 6.84. This can reflect the constraining effect of the internal endowment of polluting enterprises on the digital transformation of the supply chain and commercial credit financing. Therefore, polluting enterprises face an indirect trust crisis caused by suppliers’ avoidance of environmental joint risks. Although digital transformation improves operational transparency, suppliers will worry about sudden production suspension or fines due to environmental violations, weakening their willingness to provide credit.

Columns 3 and 4 of Table 11 present the moderation effect results of state-owned enterprises and private enterprises. According to the above secondary industry definitions, both are presented as positive and significant relationships. However, in the state-owned enterprise group (Column 3), the statistical significance coefficient is 5.68, which is significantly lower than that of the private enterprise group (Column 4), which is 9.71. This can reflect the constraining effect of the internal endowment of state-owned enterprises on the digital transformation of the supply chain and commercial credit financing. Therefore, due to the difficulty for private enterprises to rely on administrative support, they mainly rely on the market-based trust mechanism to improve the efficiency of resource allocation [83]. Specifically, the digital transformation of private enterprises activates commercial credit by enhancing the credibility of the supply chain. As Ge and Qiu [84] found, private enterprises using ERP have a faster growth in commercial credit financing due to the enhancement of production information transparency by the application of digital systems. Therefore, the digital transformation promoted by private enterprises is conducive to improving the efficiency of commercial credit financing, thereby better alleviating financing constraints.

Furthermore, considering the matrix-like interactive moderating effects of internal corporate endowments, this study further examines the differential impact of supply chain digital transformation on commercial credit financing across green and polluting firms with different ownership attributes. The regression results are presented in

Table 12. Cross-moderating effect of internal attribute.

	(1) CCF	(2) CCF	(3) CCF	(4) CCF
	State-Owned		Private
	Pollute Firm	Green Firm	Pollute Firm	Green Firm
SCDT	0.081 *** (3.05)	0.014 (1.13)	0.019 * (1.96)	0.034 *** (7.46)
Size	−0.003 ** (−2.32)	−0.002 * (−1.80)	−0.008 *** (−6.51)	−0.001 * (−1.86)
Age	−0.008 *** (−3.63)	0.000 (0.17)	−0.005 *** (−4.15)	−0.013 *** (−17.34)
TobinQ	−0.003 ** (−2.03)	−0.009 *** (−9.35)	−0.006 *** (−7.18)	−0.004 *** (−8.61)
Lev	0.221 *** (21.38)	0.347 *** (40.40)	0.224 *** (32.49)	0.315 *** (75.09)
ROE	0.009 (0.72)	0.114 *** (9.74)	0.049 *** (4.54)	0.070 *** (11.72)
Indep	−0.061 *** (−2.76)	−0.092 *** (−4.94)	0.009 (0.71)	0.006 (0.84)
Dual	−0.005 (−0.69)	−0.020 *** (−4.77)	−0.002 (−0.81)	−0.002 (−1.25)
Cons	0.144 *** (4.77)	0.114 *** (4.17)	0.225 *** (8.62)	0.069 *** (4.36)
Year FE	YES	YES	YES	YES
Manufacture	YES	YES	YES	YES
N	2535	4637	5467	16,563
Adj R2	0.238	0.365	0.248	0.367

***, **, and * refer to significance at 1%, 5%, and 10%, respectively.

. State-owned enterprises (SOEs) exhibit a policy-driven mechanism: state-owned polluting firms are more likely to benefit from policy support in promoting digital transformation, thereby facilitating access to financing. On the other hand, private enterprises demonstrate a market-driven initiative: environmentally friendly private firms rely on their own digital construction to obtain commercial credit financing.

4.6. Robustness Test

First, this study employs an instrumental variable (IV) approach to address endogeneity concerns and enhance the robustness of the conclusion. China’s postal system, as a historical communication infrastructure, facilitated early fixed-line telephone and internet deployment through its distribution network. Regions with higher post office density likely experienced earlier internet penetration, satisfying the relevance condition for using postal service metrics as an IV for regional supply chain digital transformation. Critically, the historical distribution of post offices exhibits diminishing impact on contemporary corporate financing outcomes amid rapid internet development, fulfilling the exclusion restriction when controlling for other variables. Additionally, the 1984 pilot reform of public–private separation in China’s Ministry of Posts and Telecommunications marked the inception of market-oriented telecommunication transformation. Following Huang et al. [85], this study adopts the number of post offices per million residents in each Chinese city in 1984 as the IV for enterprise supply chain digital transformation.

As shown in

Table 13. Instrumental variable test.

	CCF
SCDT	1.635 *** (3.67)
Cons	0.073 ** (2.53)
Control variables	Control
Anderson canon. corr. LM statistic (p-Value)	15.274 (0.00)
Cragg-Donald Wald F statistic (p-Value)	15.270 (0.00)
Year FE	YES
Ind FE	NO
Manufacture	YES
N	26,697
Adj R2	0.340

***, and ** refer to significance at 1%, and 5%, respectively.

, the results of the two-stage instrumental variable (2SLS) regression confirm the research hypothesis, indicating that supply chain digital transformation has a significantly positive effect on commercial credit financing at the 1% significance level.

Furthermore, the Anderson LM statistic value of 15.274 (p < 0.01) suggests a significant correlation between the instrumental variable and the endogenous variable. The Cragg-Donald Wald F statistic exceeds 10, indicating that weak instrument bias is not a concern. Therefore, the instrumental variable—1984 post office density—is validated as effective in addressing endogeneity.

Within China’s political context of strong policy enforcement, this study further incorporates a policy shock variable to mitigate potential endogeneity issues arising from policy influences. In April 2018, the Ministry of Commerce of China issued the Notice on Conducting Pilot Programs for Supply Chain Innovation and Application, which ultimately designated 266 pilot enterprises and 55 pilot cities. These pilot initiatives emphasize “supply chain digitalization,” focusing particularly on the development of smart supply chain systems—an objective highly consistent with digital transformation in supply chains and reflective of the deep integration of modern digital technologies with supply chain management [86]. The Notice requires pilot enterprises to prioritize supply chain digitalization as a core task and to promote the integration of supply chains with the internet and IoT. Accordingly, the implementation of this pilot policy can be regarded as a quasi-natural experiment. Based on this, firms located in pilot cities or identified as pilot enterprises are assigned to the treatment group (Treat = 1), while others serve as the control group (Treat = 0). The year 2018 and subsequent years are defined as the post-policy period (After = 1). An interaction term, Policy = Treat × After, is constructed to examine the impact of the pilot policy on commercial credit financing.

Additionally, a dynamic effects analysis was conducted by including interaction terms between Treat and year dummies—Policy2019 and Policy2020, defined as Treat × Year2019 and Treat × Year2020, respectively—where Year2019 = 1 indicates the year 2019, and similarly for Year2020. Regression results presented in

Table 14. Policy effect test.

	(1) CCF	(2) CCF	(3) CCF
Policy	0.005 *** (3.95)
Policy2019		0.000 *** (2.77)
Policy2020			0.000 * (1.83)
Cons	0.125 *** (10.72)	0.139 *** (3.26)	0.111 *** (3.03)
Control variables	Control	Control	Control
Year FE	YES	YES	YES
Ind FE	NO	NO	NO
Manufacture	YES	YES	YES
N	29,247	2266	2539
Adj R2	0.330	0.324	0.339

***, and * refer to significance at 1%, and 10%, respectively.

show that the coefficients of the interaction terms in Columns 1–3 are significantly positive, indicating that the effect of supply chain digital transformation on firms’ commercial credit financing is sustainable.

To further mitigate potential endogeneity issues arising from sample selection, this study employed the Propensity Score Matching (PSM) approach. Firms with a level of digital transformation above the sample median were assigned to the treatment group, while those below the median served as the control group. The study then matched each treated firm with the most comparable control firm based on observable characteristics. Specifically, firm size, listing age, and return on equity (ROE) were selected as covariates. A Logit model was used to estimate the propensity scores, and nearest-neighbor matching within a caliper of 1:4 was performed. After matching, the standardized biases of all covariates were below 10%. The matched sample was subsequently re-estimated using the regression model. The results, as presented in

Table 15. Post-PSM Regression Analysis.

	(1) CCF	(2) CCF	(3) CCF
SCDT	0.066 *** (17.45)	0.046 *** (12.38)	0.039 *** (9.07)
Cons	0.162 *** (15.66)	0.156 *** (16.78)	0.116 *** (9.25)
Control variables	Control	Control	Control
Year FE	NO	YES	YES
Ind FE	NO	YES	NO
Manufacture	NO	NO	YES
N	38,624	38,624	25,852
Adj R2	0.259	0.413	0.335

*** refer to significance at 1%.

, remain consistent with the main findings, supporting the robustness of the original conclusions.

Furthermore, to further mitigate potential endogeneity concerns, this study introduced a one-period lag of the independent variable to address possible reverse causality. The regression results, as shown in

Table 16. Lagged supply chain digital transformation (L.SCDT) and CCF.

	(1) CCF	(2) CCF1	(3) CCF1
L.SCDT	0.074 *** (19.30)	0.048 *** (12.61)	0.046 *** (10.47)
Cons	0.187 *** (18.60)	0.168 *** (18.70)	0.128 *** (10.39)
Control variables	Control	Control	Control
Year FE	NO	YES	YES
Ind FE	NO	YES	NO
Manufacture	NO	NO	YES
N	39,137	39,137	25,621
Adj R2	0.243	0.412	0.325

*** refer to significance at 1%.

, remain consistent with the original findings, supporting the robustness of the conclusions.

To ensure measurement robustness, this study further validated the findings by re-measuring both the independent and dependent variables. About the dependent variable, beyond prior measurement using commercial credit receivables (accounts payable + notes payable), this study reconstructs commercial credit financing capacity as the net position: CCF = (accounts payable + notes payable) − (accounts receivable + notes receivable), which captures bidirectional credit flows [85]. As shown in

Table 17. Supply chain digital transformation and net commercial credit finance (CCF1).

	(1) CCF1	(2) CCF1	(3) CCF1
SCDT	0.028 *** (6.00)	0.025 *** (5.37)	0.035 *** (6.59)
Cons	−0.369 *** (−36.97)	−0.369 *** (−35.50)	−0.450 *** (−32.17)
Control variables	Control	Control	Control
Year FE	NO	YES	YES
Ind FE	NO	YES	NO
Manufacture	NO	NO	YES
N	44,277	44,277	29,208
Adj R2	0.175	0.228	0.169

*** refer to significance at 1%.

, the regression results remain robust.

Moreover, considering that the CCF variable did not account for industry heterogeneity, which may lead to biases in cross-industry comparisons, this study re-evaluated commercial credit financing using industry-adjusted CCF. As shown in

Table 18. SCDT and industry-adjusted commercial credit finance (CCF2).

	(1) CCF2	(2) CCF2	(3) CCF2
SCDT	0.056 *** (17.40)	0.041 *** (12.44)	0.033 *** (8.64)
Cons	0.022 *** (2.86)	0.023 *** (2.77)	0.008 (0.76)
Control variables	Control	Control	Control
Year FE	NO	YES	YES
Ind FE	NO	YES	NO
Manufacture	NO	NO	YES
N	44,277	44,277	29,208
Adj R2	0.229	0.267	0.295

*** refer to significance at 1%.

, the regression results remain robust.

About the independent variable, expanding from supply chain-specific to enterprise-wide digital transformation. Constructed a lexicon encompassing AI, big data, cloud computing, blockchain, and digital applications. Measured by aggregated term frequency in annual reports [87]. As shown in

Table 19. Digital transformation(DT) and commercial credit finance.

	(1) CCF	(2) CCF	(3) CCF
DT	0.008 *** (28.15)	0.006 *** (17.44)	0.008 *** (21.06)
Cons	0.198 *** (20.79)	0.176 *** (20.65)	0.143 *** (12.29)
Control variables	Control	Control	Control
Year FE	NO	YES	YES
Ind FE	NO	YES	NO
Manufacture	NO	NO	YES
N	44,301	44,301	29,218

*** refer to significance at 1%.

, Regression outcomes under these specifications consistently support the positive impact of supply chain digital transformation on commercial credit financing.

To verify the reliability of the independent variable measurement, this study further decomposed the variable based on the five sub-dimensions of supply chain digitalization. As shown in

Table 20. Sub-dimension of supply chain digitalization and CCF.

	(1) CCF	(2) CCF	(3) CCF	(4) CCF	(5) CCF
DT_plan	1.165 *** (3.39)
DT_purc		0.242 ** (2.17)
DT_prod			0.116 *** (18.89)
DT_sale				−0.045 *** (−5.73)
DT_logi					0.341 *** (6.13)
Cons	0.125 *** (10.67)	0.125 *** (10.73)	0.119 *** (10.30)	0.124 *** (10.63)	0.124 *** (10.67)
Year FE	YES	YES	YES	YES	YES
Manufacture	YES	YES	YES	YES	YES
N	29,208	29,208	29,208	29,208	29,208
Adj R2	0.330	0.330	0.338	0.330	0.330

***, and ** refer to significance at 1%, and 5%, respectively.

, four of these dimensions remain consistent with the original findings. However, marketing digitalization demonstrates an adverse relationship with commercial credit financing. As enterprises advance their marketing digitalization and comprehensively disclose operational information to supply chain partners—particularly customers—the high degree of information transparency may potentially reduce the willingness of customers to extend credit.

5. Conclusions

5.1. Research Conclusions

This paper takes the importance of supply chain in the current global context as the starting point and explores the core role of digital transformation in the sustainable development of supply chain, especially in the improvement in collaboration levels in supply chain finance. The paper specifically focuses on the digital transformation of the supply chain in enterprises from a research perspective and explores how it can improve the credit enhancement among enterprises in the supply chain, enhance the commercial credit financing ability of enterprises, and ultimately achieve the goals of reducing financing costs and improving financing efficiency by improving the efficiency of enterprise information processing and resource allocation flexibility.

This paper reveals that enterprises promoting the digital transformation of the supply chain can effectively improve the level of commercial credit financing. On the one hand, the digital transformation of the supply chain promotes the improvement of the enterprise’s operational management capability, suppresses the phenomenon of cost stickiness, and reduces the damage of cost stickiness to commercial credit financing. On the other hand, the digital transformation of supply chain promotes the improvement of organizational resilience, enhances organizational management capability, and strengthens the promoting effect of organizational resilience on commercial credit financing. Therefore, the digital transformation of the enterprise supply chain can bring about the improvement of supply chain information transparency and external collaboration efficiency, and more importantly, bring about the improvement of the enterprise’s adjustment ability including operational management capabilities and risk-bearing capability, thereby promoting the efficiency of commercial credit financing.

This paper studies the moderating effect of external governance of enterprises. ESG, by building sustainable reputation capital, proves that the digital transformation of enterprises is more reliable and makes the supply chain financiers more willing to provide financial support. In highly competitive industries, enterprises are under greater pressure to optimize operational efficiency through digital transformation and convey sustainable cooperation signals to the supply chain financiers.

This paper also studies the moderating effect of internal endowments of enterprises. Polluting enterprises face an indirect trust crisis caused by the avoidance of environmental-associated risks by funding suppliers, which counteracts the enhancing effect of information transparency from digital transformation, thereby weakening the willingness to provide credit. Private enterprises, based on the market-based trust mechanism, have more motivation to enhance the credibility of supply chain transactions through digital transformation, thereby activating the willingness to provide credit.

5.2. Theoretical Implications

This study constructs a research pathway model of "Digital Transformation–Corporate Adjustment Capacity–Supply Chain Financing Performance," extending the theoretical understanding of how digitalization reshapes the logic of supply chain finance. Unlike existing literature that often focuses on the financial institution supply side, this paper emphasizes the proactive role of corporate digital practices in commercial credit financing. By proposing a dual mediation mechanism of "cost stickiness–organizational resilience," it moves beyond the traditional emphasis on information transparency and more comprehensively reveals how digital transformation enhances credit through multiple pathways—such as improving operational and organizational management capacities. Furthermore, by developing a two-dimensional framework of "external governance–internal endowment," the study uncovers the context dependency of digital financing effects, thereby enriching the analytical framework of supply chain finance research.

5.3. Policy Implications

The findings indicate that ESG performance and industry competition play significant roles in the relationship between digital transformation and financing performance, suggesting that reputational capital and market mechanisms can enhance financing credibility. Policymakers should refine green finance standards and ESG disclosure requirements, encouraging enterprises to integrate digitalization with sustainability strategies. Meanwhile, environmental compliance imposes a "rigid constraint" on the effectiveness of digital transformation; governments should use green credit policies and environmental regulations to guide polluting firms toward improved transparency and better financing conditions. The study also finds that firms in highly competitive industries are more motivated to enhance operational efficiency through digitalization, providing a policy basis for fostering a healthy competitive environment and incentivizing corporate digital transformation.

5.4. Practical Value

For corporate management, digital transformation not only improves information processing efficiency but also enhances operational and organizational adjustment capacities, helping to overcome cost stickiness and organizational inertia, thereby improving financing conditions. For financial institutions, this study reveals differences in digital financing effects across firms with varying ownership structures and industry attributes: private enterprises are more likely to activate credit supply through digitalization, while polluting firms are constrained by environmental risks. These insights provide empirical support for differentiated financing evaluation and optimized credit assessment. For industrial development, digital transformation increases transparency and trust in collaboration across supply chains, strengthening financing synergies. This offers practical guidance for core enterprises to promote digital transformation in supply chains and build a sustainable supply chain finance ecosystem.

5.5. Limitations and Future Research

In conclusion, the digital transformation of the supply chain plays a significant role in enhancing the commercial credit financing of enterprises. Digital transformation provides new technological support for supply chain finance by improving information transparency and transaction collaboration, optimizing the overall financing efficiency of the supply chain. However, the research on commercial credit financing from the perspective of digital transformation is still insufficient. Future research can further deepen and expand the analysis from the following research perspectives: (1) How does digital transformation specifically affect the financing relationships of each enterprise in the supply chain? (2) How do different digital technologies specifically improve the transparency of supply chain financing? (3) How can small and medium-sized enterprises improve their financing accessibility through digital tools? (4) Differential impacts of sub-dimensions of supply chain digitalization; (5) Effectiveness of centrality and network analysis measurement methods; (6) Distinctive differences between private enterprises and state-owned enterprises in the context of supply chain digital transformation.

In the context of the deep restructuring of the global industrial chain and the concurrent pursuit of green sustainable development, the integration of the digital transformation of the supply chain and supply chain finance is not only a key path for enterprises to enhance their competitiveness and operational efficiency, but also a core pivot for achieving the improvement of the credit mechanism and the construction of an inclusive financial system. The in-depth study of this topic has important theoretical value and practical significance.