FinTech-Driven Corporate Sustainability: A Technology–Organization–Environment Framework Analysis

Abstract

At the intersection of the digital economy and sustainable development, FinTech emerges as a pivotal force reshaping corporate operations. However, existing research lacks a systemic analysis of how technology, organizational synergy, and environmental factors jointly drive corporate sustainability. Building on this, this study employs the technology–organization–environment (TOE) framework to analyze panel data from China’s A-share non-financial listed companies (2012–2022). Our findings reveal the following: (1) FinTech directly enhances corporate sustainability and indirectly does so through supply chain finance (33.30% mediation effect). (2) Digital infrastructure and marketization level amplify FinTech’s impact, with effects 52.27% stronger in high-marketization regions and 48.84% stronger in regions with advanced digital infrastructure. (3) Heterogeneity analysis indicates the positive impact is more pronounced for enterprises with higher digital transformation maturity, those in technology-intensive industries, and those located in eastern China. These results offer policymakers and enterprises a systemic framework and empirical evidence to co-design FinTech-enabled sustainable development strategies, emphasizing cross-sector collaboration and region-specific interventions.

1. Introduction

The global economy is undergoing a profound transformation, shifting from a paradigm of scale expansion to one of quality enhancement. Against this backdrop, sustainable development has become a core strategy for nations to address pressing growth bottlenecks and structural contradictions [1,2]. This shift is evident not only at the national policy level but also extends to the corporate sphere. The concept of corporate sustainability has emerged as a critical determinant of long-term competitiveness, resilience, and value creation for firms.

For corporations, embracing sustainability is no longer merely a moral choice but a strategic imperative. It helps enhance brand reputation, reduce operational and regulatory risks, access new markets, and attract investors and consumers who are increasingly focused on sustainability [3,4]. However, achieving meaningful sustainability outcomes presents significant challenges. Companies often grapple with the high costs of adopting green technologies and circular economy models, difficulties in accurately measuring and reporting ESG performance, the inherent tension between short-term financial pressures and long-term sustainability investments, and the complexity of ensuring sustainability across multi-tier supply chains [3,5,6]. These challenges create a critical “sustainability implementation gap,” highlighting the urgent need for innovative tools and approaches to support companies on their sustainability journey.

In this context, FinTech, as a product of the integration between the digital technology revolution and the financial system, is evolving into a key lever for corporate sustainability by reshaping resource allocation efficiency, reducing market friction, and expanding service boundaries [7,8]. This trend exhibits differentiated pathways between developed economies and emerging markets: developed economies, dominated by original technological breakthroughs, prioritize optimizing capital market efficiency through algorithmic trading and robo-advisory services [9]; correspondingly, emerging markets focus on inclusive leapfrogging, relying on mobile payments and digital credit to fill gaps in traditional finance [10,11,12].

However, global practices reveal a common challenge—the misalignment between technological applications and institutional environments. On one hand, disputes over data sovereignty and algorithmic ethical risks constrain the release of technological dividends [13,14]; on the other hand, lagging infrastructure and regulatory frameworks exacerbate the “digital divide” [15,16].

Within this context, China, as the largest emerging economy and a global FinTech pioneer, demonstrates both typicality and uniqueness in its development path [17]. China’s economy has entered a new stage of high-quality development, where the systemic enhancement of enterprises’ capabilities in innovation-driven growth, green transformation, coordination, and sharing is critically important [18]. Although existing studies have confirmed the positive role of FinTech in alleviating corporate financing constraints and optimizing risk management [19,20,21], significant limitations remain in understanding the systematic mechanisms and boundary conditions through which FinTech enables corporate sustainability.

To address the aforementioned research gaps, we leverage a comprehensive dataset comprising China’s A-share non-financial listed companies from 2012 to 2022. Our methodological approach integrates fixed-effect, mediation-effect, and moderation-effect models to ensure robust identification of causal mechanisms and boundary conditions. This research design enables a nuanced analysis of the tripartite interactions between technological applications, organizational synergies, and environmental factors in shaping corporate sustainability outcomes.

The findings of this study provide profound theoretical and empirical insights into FinTech-empowered sustainability of the real economy. Our analysis delivers actionable implications for practical pathways—including promoting supply chain platform co-construction, optimizing regional digital infrastructure layouts, and implementing differentiated supportive policies—thereby offering significant reference value for deepening FinTech innovation and policy optimization under the “Digital China” strategy.

2. Literature Review, Theoretical Analysis, and Hypotheses

2.1. Literature Review

2.1.1. FinTech and Corporate Performance: The Direct “Technology–Firm” Pathway

Numerous studies have confirmed the positive impact of FinTech on corporate development. Grounded in information asymmetry theory and transaction cost economics, scholars have demonstrated that FinTech, through capabilities such as big data analytics and blockchain, significantly alleviates financing constraints—especially for SMEs—enhances information transparency, and reduces credit risk assessment costs [19,20,21]. Furthermore, research indicates it can also lower operational risks [15] and promote innovation by easing funding constraints for R&D activities [22].

However, this body of research primarily focuses on direct bilateral “technology–firm” relationships, largely neglecting the more complex organizational mechanisms through which FinTech’s influence might be transmitted. This oversight prompts the investigation into the mediating processes that translate technological empowerment into sustainable performance.

2.1.2. The Underexplored Organizational Pathway: The Role of Supply Chain Finance

Supply Chain Finance (SCF) has emerged as a critical organizational innovation for enhancing supply chain resilience and corporate sustainability [23].The Resource-Based View (RBV) posits that effective SCF optimizes the allocation of financial resources within supply chain networks, creating a valuable, rare, and difficult-to-imitate capability that enhances the overall competitiveness and sustainability of all members [24]. Recent research has begun to explore the digitalization of SCF, suggesting that technologies like blockchain and big data can improve its efficiency and transparency [8].

Despite progress in both FinTech and SCF research, there is a notable lack of empirical studies examining SCF as a key mediating mechanism connecting FinTech to broader corporate sustainability outcomes. The pathway of “technological empowerment → organizational synergy (SCF) → performance improvement” is theoretically appealing but remains empirically underexplored.

2.1.3. Boundary Conditions: The Moderating Role of the External Environment

The effectiveness of technological and organizational innovations is always contingent on the external environment. Institutional theory suggests that a region’s marketization level, which reflects its formal institutional quality, can significantly reduce transaction costs and regulatory uncertainty [25]. This institutional environment can facilitate or hinder the effective adoption and impact of FinTech applications. Simultaneously, according to General Purpose Technologies (GPTs) theory, digital infrastructure is considered a GPT, providing the necessary foundation for technology diffusion. It creates powerful network and spillover effects, greatly amplifying the productivity and impact of applications like FinTech [26,27].

While some studies have explored firm-level or industry-level heterogeneity [28], few have systematically examined how these critical contextual factors—marketization level and digital infrastructure—moderate the effectiveness of FinTech. Neglect of this contextual level limits the practical relevance and generalizability of existing findings.

2.1.4. Summary of Research

In summary, while providing valuable insights, the existing literature suffers from three interrelated limitations: (1) a predominant focus on direct effects, neglecting organizational mediating pathways; (2) a lack of contextual analysis, overlooking the crucial moderating role of the external environment; and (3) consequent theoretical fragmentation, lacking an integrated framework to explain the interactions between technology, organization, and environment.

To address these gaps, this study adopts the technology–organization–environment (TOE) framework as an overarching theoretical lens. This framework allows us to systematically consider the interactions between technological innovation (FinTech), organizational structure and processes (SCF), and the external environment (marketization, digital infrastructure) in which firms operate. Building on this framework, we develop hypotheses to test direct effects alongside mediating and moderating mechanisms, thereby advancing an integrated theoretical model.

2.2. The Mechanism of Action Within the TOE Framework

The “technology–organization–environment” (TOE) framework, proposed by Tornatzky and Fleischer, systematically analyzes the interactive influences of technological, organizational, and environmental factors on enterprises or organizations during technological innovation adoption [27]. Initially focused on information technology innovation, its high flexibility and systematicity have enabled its expansion into interdisciplinary research as a universal analytical tool [29]. The TOE framework categorizes factors affecting organizational technology adoption and innovation into three dimensions:

• Technological Dimension: Encompasses the characteristics of the technology itself and the organization’s existing technological foundation. Technological capability determines an organization’s capacity to absorb and apply new technologies.
• Organizational Dimension: Includes organizational structure, scale, management style, human resources, and other factors. Flexible organizational structures adapt more readily to technological transformations.
• Environmental Dimension: Covers external market conditions, policies, regulations, industry competition, and socio-cultural factors. The environment may provide resource support (e.g., policy incentives) or impose constraints (e.g., regional development limitations) [30].

This study examines FinTech (technological layer), supply chain finance (organizational layer), and marketization level/digital infrastructure (environmental layer). The TOE framework’s comprehensiveness and adaptability make it an ideal tool for analyzing FinTech’s mechanisms. We thus integrate three pathways:

• Technology Empowerment (T): As the core technological driver, FinTech lowers information barriers, enhances capital allocation efficiency, and extends service coverage. This technological potential translates into accessible “inclusive financial resources” for enterprises, converting regional digital financial ecosystem advantages into firm-level application capabilities.
• Organizational Synergy (O): Improvements in supply chain finance rely on resource integration between core enterprises and upstream/downstream partners. Grounded in the Resource-Based View, FinTech optimizes capital allocation efficiency, strengthening overall supply chain competitiveness and thereby driving corporate sustainability.
• Environmental Support (E): External environments critically moderate FinTech’s impact. Digital infrastructure provides essential physical and network foundations for deploying FinTech solutions. Following General Purpose Technology theory, robust infrastructure amplifies FinTech’s spillover effects and productivity gains. Concurrently, marketization level reflects regional institutional quality. Higher marketization—as Institutional Theory posits—reduces transaction costs, mitigates regulatory uncertainty in FinTech applications, and incentivizes efficiency-seeking innovation through competitive pressure. Thus, digital infrastructure (hardware support) and marketization level (institutional software) constitute indispensable environmental factors.

2.3. Research Hypotheses

FinTech represents technology-driven financial innovation that reshapes traditional financial services through big data, blockchain, AI, and other technologies [31]. Its core value lies in achieving inclusive, precise, and intelligent financial services via efficiency enhancement and resource reconfiguration.

Sustainability’s essence is fulfilling societal aspirations for better living through efficient operations, equitable distribution, and green sustainability [32]. It entails coordinated advancement across economic, political, cultural, social, and ecological civilization domains [33,34]. While improving product/service quality is foundational, the crux lies in achieving holistic, harmonious development of these systems [35]. Corporate sustainability, as the fundamental building blocks of national economies, critically determines the quality enhancement of the aggregate economy.

As a deep integration of digital technology and financial services, FinTech provides core momentum for corporate sustainability by reshaping traditional finance [36,37]. Strategically, FinTech is positioned in China’s 14th Five-Year Plan as a key lever for advancing new quality productive forces [38], serving as a “digital engine” for corporate sustainability by empowering technological innovation and industrial upgrading [39,40,41].

FinTech enables multifaceted direct effects on corporate sustainability through three core mechanisms: information asymmetry reduction, resource allocation optimization, and risk management upgrading [35,42]. First, at the information processing level, by harnessing big data analytics and artificial intelligence, FinTech alleviates information asymmetry between financial institutions and enterprises, specifically resolving the “credit visibility dilemma” prevalent among small and medium-sized enterprises [43,44]. This is achieved through integrating multi-dimensional alternative data—including supply chain transactions and real-time operational cash flows—to construct precise credit risk assessment models. These models enable financial institutions to identify creditworthy firms beyond conventional financial metrics, thereby expanding financial inclusion while reducing financing costs [45]. Second, regarding resource allocation, FinTech optimizes resource allocation efficiency through algorithm-driven intelligent matching mechanisms that enhance capital supply–demand alignment [46]. Technologies such as mobile payment systems and blockchain enable instantaneous fund settlement and cross-regional capital mobility, significantly shortening financing cycles and optimizing working capital management efficiency [47]. Concurrently, digital platforms dismantle geographical constraints in financial services, strategically channeling resources toward innovation-driven sectors and green transition initiatives to accelerate productive reconfiguration. Finally, in risk management, risk management capabilities are upgraded through dynamic monitoring systems and predictive early-warning models that bolster enterprise risk resilience. Machine learning-powered risk data platforms detect operational anomalies in real-time, autonomously triggering intervention protocols to mitigate financial volatility and external shock vulnerability [48]. The tamper-proof execution architecture inherent in blockchain-based smart contracts institutionally constrains moral hazard while ensuring transactional security. The synergistic operation of these mechanisms—mediated through organizational coordination in supply chain finance and moderated by environmental enablers including digital infrastructure and marketization level—establishes FinTech as a structural catalyst for corporate sustainability. Consequently, we postulate that

H1. 

FinTech exerts a significant positive effect on corporate sustainability.

Supply chain finance is a systemic financial service model centered on industrial supply chains, integrating logistics, capital, and information flows to serve core enterprises and upstream/downstream entities [24,49]. Its core value lies in leveraging supply chain credit penetration to transform traditional financing models—which rely solely on core enterprises’ creditworthiness—into transaction data-based credit systems, thereby alleviating financing constraints for small and medium-sized enterprises, optimizing working capital efficiency, and enhancing supply chain resilience [8,50].

FinTech provides a critical organizational synergy pathway for corporate sustainability by reconstructing supply chain finance’s credit assessment paradigms and transaction execution efficiency [51,52]. According to the Resource-Based View (RBV) [53,54], FinTech serves as a scarce strategic resource that empowers enterprises to convert the “three flows” (logistics, capital, information) into collateralizable digital assets. This shift breaks traditional reliance on entity credit, establishing a data-centric financing model. Such transformation significantly reduces financing constraints for upstream/downstream firms, particularly mitigating credit rationing stemming from information asymmetry among small and medium-sized enterprises, thus optimizing capital allocation efficiency across the entire chain [55]. Transaction Cost Theory further elucidates the mechanism: blockchain and smart contracts drastically reduce verification costs, negotiation costs, and moral hazards in supply chain finance through immutable data authentication and automated execution [56]. For instance, enhanced accounts receivable verification shortens cash conversion cycles, directly improving working capital flexibility [33].

Concurrently, supply chain finance drives multidimensional corporate sustainability through optimized resource allocation and strengthened industrial resilience [57]. Specifically, it enhances innovation by easing financing constraints for innovation-intensive firms, thereby accelerating R&D capital turnover [55]. Simultaneously, operational coordination is improved through tools such as dynamic discounting and reverse factoring, which shorten cash conversion cycles and elevate asset turnover rates, while reducing supply chain “bullwhip effects” to mitigate inventory redundancy [58]. Ultimately, the green transition is advanced as specialized green supply chain finance products strategically channel funds toward low-carbon projects, boosting corporate ESG performance.

Based on the “Technology Empowermen → Organizational Synergy → Performance Enhancement”, we postulate Hypothesis 2:

H2. 

FinTech promotes corporate sustainability by enhancing supply chain finance efficiency.

Digital infrastructure, as a quintessential General Purpose Technology (GPT) [59], exhibits strong penetrability, complementarity, and technological spillover effects. As conceptualized by Bresnahan & Trajtenberg’s General Purpose Technology theory, digital infrastructure amplifies FinTech’s technical efficacy by reducing marginal application costs and lowering barriers to technological diffusion [26]. A representative example is high-speed networks and computing resources enabling efficient deployment of FinTech functionalities—such as blockchain verification and real-time risk control—which significantly compress data processing latency [60]. Transaction Cost Theory further elucidates its moderating mechanism: robust hardware facilities harden data authenticity and credibility, thereby reducing information verification costs and moral hazards in credit approval and movable asset pledging. Complementing this perspective, Network Externality Theory demonstrates that mature regional digital ecosystems enhance compatibility between FinTech platforms and enterprise systems, diminishing technological adaptation costs while reinforcing scale economies in technology spillovers through cross-network effects [61].

Consequently, we propose Hypothesis 3:

H3. 

Digital infrastructure development significantly strengthens the positive impact of FinTech on corporate sustainability.

As a core institutional indicator, marketization level critically moderates FinTech’s efficacy in enabling corporate sustainability by optimizing institutional arrangements and reducing transaction costs [62,63,64]. Institutional Theory posits that higher marketization level strengthens property rights protection and contract enforcement, significantly lowering compliance risks and policy uncertainty (such as legal recognition of blockchain evidence), thereby providing stable institutional safeguards for technology diffusion [65]. Complementarily, the Resource-Based View elaborates that marketization level—as a scarce “institutional complementary asset”—enhances FinTech’s resource allocation efficiency by activating competition mechanisms (non-state sector development forcing efficiency gains) and optimizing factor mobility (capital reallocation to high-productivity sectors) [66].

Therefore, we formulate Hypothesis 4:

H4. 

Higher marketization levels significantly amplify FinTech’s positive effects on corporate sustainability.

3. Research Design

3.1. Sample Selection and Data Sources

This study selects China’s A-share non-financial listed companies spanning the 2012–2022 period as its research sample. This timeframe strategically encompasses the pivotal developmental phase of FinTech acceleration in China, while deliberately excluding the 2008 global financial crisis to minimize data contamination from exogenous shocks. Sample screening adheres to the following sequential criteria: (1) exclusion of ST, * ST, and delisted companies; (2) removal of financial sector firms; (3) elimination of observations with missing critical variables. Following this protocol, 19,740 firm-year observations were retained for analysis.

Data acquisition leveraged multiple authoritative sources: FinTech metrics derive from the Peking University Digital Inclusive Finance Index; supply chain finance indicators were extracted from listed companies’ annual reports using keyword frequency analysis; digital infrastructure variables originate from the China Statistical Yearbook, an official publication of the National Bureau of Statistics of China; patent counts were sourced from CNRDS (China Research Data Services (CNRDS): Provided by Shenzhen Gildata Co., Ltd. (Shenzhen, China). CNRDS is a prominent research database platform widely used for academic studies in China.); ESG scores were obtained from the Wind database (Wind Economic Database (Wind): Provided by Nanjing Wind Information Co., Ltd. (Nanjing, China). Wind is a major supplier of financial data and analytics tools in China, serving both academic and professional investment communities.); with all remaining data systematically collected from the CSMAR database (China Stock Market & Accounting Research Database (CSMAR): Provided by Shenzhen Gildata Co., Ltd. (Shenzhen, China). CSMAR is a comprehensive database extensively applied in scholarly research covering Chinese listed companies and macroeconomic analysis.).

3.2. Variable Selection and Measurement

3.2.1. Explained Variables: Corporate Sustainability (CS)

(1)

Indicator Design Rationale

Corporate sustainability encompasses rich and evolving connotations that continually expand through practical implementation [67,68]. Consequently, constructing a scientifically sound evaluation framework is pivotal for guiding enterprises toward the realization of sustainability. Based on the definition of high-quality development outlined in China’s 14th Five-Year Plan and aligned with the United Nations Sustainable Development Goals (SDGs) [69,70,71], this study constructed a corporate sustainability evaluation system comprising five dimensions: innovation-driven, coordinated development, green transition, open collaboration and shared outcomes [72,73,74].

To enhance the international relevance and theoretical grounding of our framework, we have explicitly aligned these five dimensions with the United Nations Sustainable Development Goals (SDGs).

• Innovation-driven dimension corresponds to SDG 9 (Industry, Innovation, and Infrastructure).
• Coordinated development aligns with SDG 8 (Decent Work and Economic Growth) and SDG 10 (Reduced Inequalities).
• Green transition directly supports SDG 7 (Affordable and Clean Energy), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action).
• Open collaboration resonates with SDG 17 (Partnerships for the Goals).
• Shared outcomes contribute to SDG 1 (No Poverty) and SDG 8 (Decent Work and Economic Growth).

The complete framework of the 17 SDGs that informed this mapping is presented in Figure 1.

This dual anchoring in both national strategy and global sustainability frameworks ensures our metric is both contextually specific for Chinese enterprises and universally comprehensible within international academic discourse.

Drawing on the relevant literature, we measure firm innovation-driven using the natural logarithm of the number of patent applications filed by a firm in a given year [22,75,76,77,78]. These patents represent a concrete, verifiable output of a firm’s inventive activity and are particularly relevant in the Chinese context, where fintech firms actively engage in patenting to protect intellectual property and signal technological capability. This metric aligns with our focus on innovation as a key sub-dimension of sustainable development.

The green transition dimension is measured using the Wind ESG environmental (E) score. Wind Information Co., Ltd. (Wind) is a leading Chinese financial data provider, widely recognized as the Bloomberg equivalent in China. Its ESG rating system is extensively adopted in both academic research and investment practices focusing on Chinese firms. The methodology for the Wind ESG score integrates internationally recognized frameworks with China-specific contexts. The environmental (E) pillar, specifically, is derived through a systematic process:

• Data Collection: Wind systematically gathers publicly available data from corporate annual reports, sustainability reports, regulatory filings, and news media.
• Core Issue Assessment: Assessment is conducted across key environmental issues, including climate change, pollution and waste, and biodiversity, based on a framework of over 100 specific metrics.
• Weighting and Calculation: Scores are calculated using industry-specific materiality weighting to ensure cross-sector comparability. A higher score indicates superior environmental performance.

This methodology ensures a high degree of objectivity and comparability, making the Wind ESG score a robust and reliable metric for evaluating the environmental performance of listed companies in China.

To avoid subjectivity, the entropy TOPSIS weight method was employed to calculate indicator weights. The methodology involves five steps:

Step 1: Data standardization

$$z_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{{\sum }_{i=1}^m{x}_{ij}^2}}},(positive indicators)$$

(1)

$$z_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{{\sum }_{i=1}^m{x}_{ij}^2}}},(negative indicators)$$

(2)

Notes: For negative indicators: Debt-to-Asset ratio, inverse transformation ensures higher values indicate better sustainability performance.

Step 2: Entropy value and weight calculation

Entropy value formula:

$$e_j= -{\displaystyle \frac{1}{lnm}}\sum _{i=1}^m{p}_{ij}{lnp}_{ij}$$

(3)

$$p_{ij}={\displaystyle \frac{z_{ij}+\epsilon }{{\sum }_{i=1}^m{(z}_{ij}+\epsilon )}}$$

(4)

Weighting coefficient formula:

$$w_j={\displaystyle \frac{1-e_j}{\sum _{j=1}^n(1-e_j)}}$$

(5)

Notes: Entropy$e_j$measures data dispersion: Higher dispersion yields lower $e_j$ and higher weight $w_j$.

Step 3: Weighted decision matrix

$$V_{ij}=w_j·{ z}_{ij}$$

(6)

Notes: Weights $w_j$ are objectively derived from data entropy, eliminating subjective bias.

Step 4: Ideal solutions

Positive ideal solution$V^{+}$: Maximum values across indicators max ($V_{1j},V_{2j},\dots ,V_{mj}$).

Positive ideal solution$V^{-}$: Minimum values across indicators min ($V_{1j},V_{2j},\dots ,V_{mj}$).

Notes: $V^{+}$ represents the theoretical optimum.

Step 5: Relative closeness

Distance metric:

$$D_i^{+}=\sqrt{\sum _{j=1}^n{\left(v_{ij}-V_j^{+}\right)}^2 }$$

(7)

$$D_i^{-}=\sqrt{\sum _{j=1}^n{(v_{ij}-V_j^{-})}^2 }$$

(8)

Relative closeness coefficient:

$$C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}$$

(9)

$$\theta \le C_i\le 1$$

(10)

Notes: $C_i\to 1$ indicates high sustainability.

(2)

Indicator System Composition and Weights

The structure and weights of the corporate sustainability indicator system, derived through entropy weight calculations, are presented in

Table 1. Corporate sustainability indicator weighting table.

Primary Dimension	Secondary Dimension	Tertiary Indicator	Attribute	Entropy Weight
Innovation-Driven	Innovation Output	Ln (Number of Patent Applications + 1)	+	14.23%
Coordinated Development	Financial Health	Debt-to-Asset Ratio (%)	–	9.75%
	Operational Efficiency	Total Asset Turnover (times)	+	10.82%
Green Transition	Environmental Performance	Wind ESG Environmental (E) Score	+	17.12%
Open Collaboration	Internationalization	Overseas Revenue Ratio (%)	+	8.31%
	Supply Chain Coordination	Top 5 Supplier Concentration Ratio (%)	–	7.64%
Shared Outcomes	Employee Welfare	Per Capita Salary Growth Rate (%)	+	12.05%
	Social Contribution	Tax Payment Ratio (%)	+	10.08%

Notes: “+” indicates a positive indicator; “–” indicates a negative indicator.

. The structure and weights of the corporate sustainability indicator system, derived through entropy weight calculations, are presented in Table 1. This data-driven weighting methodology minimizes subjectivity, thereby offering policymakers a more precise and targeted foundation for evidence-based decision-making. This data-driven weighting methodology minimizes subjectivity, thereby offering policymakers a more precise and targeted foundation for evidence-based decision-making.

3.2.2. Explanatory Variables

Financial Technology (FinTech): FinTech is measured using the Peking University Provincial Digital Inclusive Finance Index subjected to standard logarithmic transformation [11]. This multi-dimensional index quantitatively assesses regional FinTech development levels and has been extensively validated in China-context studies, thereby effectively mitigating corporate self-selection biases inherent in firm-level metrics.

3.2.3. Mediating Variables

Supply Chain Finance (SCF): Supply chain finance is operationalized through contextual keyword frequency analysis within corporate annual reports, building upon prior scholarship [24,50,79,80]. Specifically, we compute logarithmic-transformed keyword occurrence frequencies (detailed in

Table 2. Supply chain finance keywords table.

Category	Standardized Keywords
Receivables Financing	Accounts Receivable Financing; Factoring Financing; Reverse Factoring; Dynamic Discounting; Receivables Securitization
Prepayment Financing	Prepayment Financing; Future Goods Rights Financing; Goods Rights Pledge Financing; Confirmed Warehouse Financing
Inventory Financing	Movable Asset Pledge Financing; Inventory Pledge Financing; Stock Financing; Inventory Financing; Spot Pledge Financing; Warehouse Receipt Financing; Purchase Order Financing; Raw Material Financing
Integrated Solutions	Supply Chain Finance; Supply Chain Financing; Supply Chain Fund; Supply Chain Investment; Supply Chain Loan; Supply Chain Management; Trade Credit; Financial Supply Chain; Supplier Financing; Buyer Financing; Vendor Managed Inventory; Buyer Investment; Distributor Financing; Working Capital Management; Logistics Finance; Unified Credit Financing; Financial Value Chain; Working Capital Optimization

) to objectively proxy enterprise-level SCF sophistication, ensuring methodological consistency with established text-based measurement approaches.

Through specifically designed keywords capturing supply chain finance activities proactively engaged by core enterprises, the keyword frequency metric can empirically verify how FinTech promotes digitalized supply chain finance, which further facilitates corporate sustainability—thereby more accurately reflecting FinTech’s technological enablement effects within this framework [24].

3.2.4. Moderating Variables

(1) Digital Infrastructure (Digital_Infra): Drawing on methodologies validated in prior studies [81,82], we construct the digital infrastructure index using three standardized indicators: broadband access ports per 10,000 population, registered domain names per 10,000 units, and IPv4/IPv6 addressing resources per 10,000 units. The entropy weight method was rigorously applied to determine individual indicator weights, followed by weighted summation to derive the composite digital infrastructure index. Detailed metric specifications and calculated weights are systematically presented in

Table 3. Digital infrastructure indicator weighting table.

Primary Dimension	Indicator Description	Attribute	Entropy Weight
Broadband Access Ports	Proxies’ hardware coverage density	+	39.00%
Registered Domain Names	Measures digital resource abundance	+	26.60%
IPv4/IPv6 Addressing Resources	Quantifies network addressing resources, directly determining enterprise network access capabilities	+	34.40%

Notes: IPv4: Internet Protocol version 4; IPv6: Internet Protocol version 6. “+” indicates a positive indicator.

.

(2) Marketization Level (Market): Marketization level is gauged using Fan Gang’s Marketization Index [83], a comprehensive framework encompassing five dimensions: government-market relationship; development of non-state sectors; product market maturity; factor market maturity; and market intermediary development with legal institutional environment. This index quantitatively reflects regional market economy sophistication, where higher values denote advanced marketization levels and economic systems approximating mature market paradigms.

Crucially, all variables are theoretically grounded in the technology–organization-environment (TOE) framework, systematically covering technological application (core variables), organizational capacity (mediating pathways), and external environmental support (moderating contexts). Specifically, the mediating variable elucidates how technological enablement effects propagate through organizational synergy, while moderators reveal how environmental factors amplify or constrain technological impacts.

3.2.5. Control Variables

Informed by the thematic imperatives of FinTech and corporate sustainability, this study designed a theoretically justified control variable selection protocol, ultimately incorporating five critical controls: firm size (Size), capital expenditure (Capex), firm age (Age), ownership structure (SOE), and government subsidies (Subsidy). Collectively, these variables account for heterogeneity across operational scale, investment intensity, organizational maturity, ownership type, and policy intervention intensity—thus systematically mitigating potential confounding factors.

In addition, this paper introduces an individual dummy variable (Id), an industry dummy variable (Industry) and a year dummy variable (Year) for control.

In summary, the detailed definitions of the variables are listed in

Table 4. Variable definitions and symbol descriptions.

Variable Name	Symbol	Measurement Approach	Variable Name
Explained Variable	Corporate Sustainability	CS	Composite index constructed via Entropy-weighted TOPSIS method
Explanatory Variable	FinTech Development Level	Fintech	Peking University Digital Inclusive Finance Index
Mediating Variable	Supply Chain Finance	SCF	Natural logarithm of (total keyword frequency + 1)
Moderating Variables	Digital Infrastructure	Digital_Infra	Weighted composite index using Entropy Weight Method
	Marketization Level	Market	Fan Gang’s Marketization Index
Control Variables	Firm Size	Size	Natural logarithm of total assets
	Capital Expenditure	Capex	Ratio of capital expenditure to total assets
	Firm Age	Age	Natural logarithm of (years since incorporation + 1)
	Ownership Structure	SOE	Dummy variable (1 for state-owned enterprises; 0 otherwise)
	Government Subsidies	Subsidy	Ratio of government subsidies to operating revenue

.

3.3. Modeling

3.3.1. Baseline Regression Model

To test Hypothesis 1, that is, the direct impact of financial technology (Fintech) on corporate sustainability (CS), we construct the following baseline regression model using a panel regression method controlling for the triple fixed effects of individual, industry, and year:

$${CS}_{i,t}={\alpha }_0+{\alpha }_1{Fintech}_{i,t}+{\alpha }_2{Control}_{it}+\sum Id+\sum Industry+\sum Year+{\epsilon }_{i,t }$$

(11)

where the subscript $i$represents firm and $t$ represents year; $CS$ represents the corporate sustainability index. $\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{h}$denotes the degree of fintech, $\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}$ denotes firm-level control variables; $\sum Id$, $\sum Industry$ and$\sum Year$ represent individual, industry, and year fixed effects, respectively, and $\epsilon$ is the error term. To ensure the robustness of the estimation results, all regressions employ firm-clustered robust standard errors to ensure estimator robustness. If the estimated coefficient of FinTech ${\alpha }_1$ is positive and significant, it indicates that FinTech can effectively promote corporate sustainability.

3.3.2. Mediated Effects Model

In order to test hypothesis 2, that is, financial technology (FinTech) affects corporate sustainability (CS) through the mediation path of supply chain finance (SCF), this paper constructs the following mediation effect model:

First, estimate the impact of FinTech on supply chain finance:

$${\mathrm{S}\mathrm{C}\mathrm{F}}_{i,t}={\alpha }_0+{\alpha }_1{Fintech}_{i,t}+{\alpha }_2{Control}_{it}+\sum Id+\sum Industry+\sum Year+{\epsilon }_{i,t}$$

(12)

Second, the impact of supply chain finance on corporate sustainability is estimated while controlling for FinTech:

$${\mathrm{C}\mathrm{S}}_{i,t}={\beta }_0+{\beta }_1{Fintech}_{i,t}+{\beta }_2{SCF}_{i,t}+{\beta }_3{Control}_{it}+\sum Id+\sum Industry+\sum Year+{\epsilon }_{i,t}$$

(13)

If both ${\alpha }_1$ and ${\beta }_2$are significantly positive, it indicates that FinTech promotes corporate sustainability by enhancing supply chain finance.

3.3.3. Moderating Effects Model

To test hypotheses 3 and 4, how digital infrastructure (Digital_Infra) and marketization level (Market) moderates the role of FinTech on corporate sustainability, this paper constructs the following moderating effect model:

• Digital Infrastructure (Digital_Infra) Moderation

$${CS}_{i,t}={\alpha }_0+{\alpha }_1{Fintech}_{i,t}+{\alpha }_2{Digital\_Infra}_{i,t}+{\alpha }_3{Fintech}_{i,t}\ast {\mathrm{D}\mathrm{i}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}\_\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}}_{i,t}+{\alpha }_4{Control}_{it}+\sum Id+\sum Industry+\sum Year+{\epsilon }_{i,t}$$

(14)

If the estimated coefficients${ \alpha }_1$, ${\alpha }_3$ of Fintech and Fintech*Digital_Infra are significantly positive, it indicates that the improvement of digital infrastructure can enhance the positive impact of FinTech on corporate sustainability.

2.

Marketization Level (Market) Moderation

$${CS}_{i,t}={\alpha }_0+{\alpha }_1{Fintech}_{i,t}+{\alpha }_2{Market}_{i,t}+{\alpha }_3{Fintech}_{i,t}\ast {Market}_{i,t}+{\alpha }_4{Control}_{it}+\sum Id+\sum Industry+\sum Year+{\epsilon }_{i,t}$$

(15)

If the estimated coefficients ${ \alpha }_1$, ${\alpha }_3$ of Fintech and Fintech*Market are significantly positive, it suggests that increased marketization level enhances the positive impact of FinTech on corporate sustainability.

4. Empirical Testing and Analysis of Results

4.1. Descriptive Statistics

As presented in

Table 5. Table of descriptive statistics and multicollinearity tests.

Variant	Ave	SD	Min	Max	VIF
CS	0.62	0.15	0.25	0.92	-
FinTech	5.48	0.35	4.59	5.95	1.28
SCF	1.72	0.85	0.00	4.61	1.37
Digital_Infra	6.75	1.85	2.30	9.80	1.76
Market	7.89	2.11	3.50	10.20	1.89
Size	22.34	1.56	18.90	26.70	2.01
Capex	0.08	0.05	0.01	0.25	1.14
Age	2.85	0.72	0.00	4.50	1.10
SOE	0.35	0.48	0.00	1.00	1.26
Subsidy	0.03	0.02	0.00	0.10	1.07

, the mean value of corporate sustainability (CS) is 0.62 (standard deviation 0.15), indicating moderately high comprehensive development levels among sample firms with notable heterogeneity. The FinTech development index averages 5.48 (range: 4.59–5.95), reflecting regional disparities consistent with China’s East–West digital divide. Supply chain finance (SCF) shows a mean of 1.72 (SD = 0.85), suggesting significant efficiency variations across firms, with some still in early-stage adoption. All variables exhibit variance inflation factors (VIF) below 5.0, with the highest value being 2.01, which is well within the acceptable threshold of 10, confirming no severe multicollinearity issues.

4.2. Correlation Analysis

Table 6. Correlation analysis test results table.

Variable	CS	Fintech	SCF	Digital_Infra	Market	Size	Capex	Age	SOE	Subsidy
CS	1.000
Fintech	0.31 ***	1.000
SCF	0.26 ***	0.38 ***	1.000
Digital_Infra	0.23 ***	0.60 ***	0.26 ***	1.000
Market	0.18 ***	0.58 ***	0.32 ***	0.71 ***	1.000
size	0.15 **	0.11 *	0.08	0.10	0.09	1.000
Capex	0.19 **	0.08	0.10 *	0.06	0.04	0.23 **	1.000
Age	0.06	−0.03	−0.01	−0.02	−0.01	0.18 **	0.10	1.000
SOE	−0.13 *	−0.20 **	−0.17 **	−0.18 *	−0.25 ***	0.30 ***	−0.08	0.05	1.000
Subsidy	0.07	0.02	0.03	0.01	−0.01	0.05	0.10	0.03	0.12	1.000

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1.

shows that FinTech is significantly and positively correlated with corporate sustainability (CS), which tentatively supports hypothesis H1, indicating that the higher the level of FinTech development, the stronger the level of corporate sustainability. Supply chain finance (SCF) is significantly correlated with FinTech, suggesting that it may act as a mediating variable to transmit the technology-enabling effect. Marketization Level is highly synergistic with digital infrastructure (r = 0.71, p < 0.01), providing a basis for environmental moderating effects. The level of FinTech development is moderately correlated with the moderating variables, but the previous VIF test shows a manageable covariance.

4.3. Baseline Regression

In column (1) of

Table 7. Baseline regression results table.

Variable	(1)	(2)
	CS	CS
FinTech	0.021 ***	0.018 ***
	(0.003)	(0.003)
Size		0.004 **
		(0.002)
Capex		0.012 **
		(0.005)
Age		0.001
		(0.003)
SOE		−0.003 **
		(0.001)
Subsidy		0.007 *
		(0.004)
Id	YES	YES
Year	YES	YES
Industry	YES	YES
N	19,740	19,740
Adj. R2	0.292	0.348

Notes: Clustered robust standard errors at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, controlling for individual, year, and industry effects, but the model does not incorporate any control variables, the regression results show that FinTech has a significant positive impact on corporate sustainability. In column (2), the paper further introduces control variables to control for other factors that may affect corporate sustainability. The regression results show that the coefficient of FinTech is 0.018, which is significant at the 1% level, indicating that the positive impact of FinTech remains robust with the addition of control variables. Specifically, for every 1-unit increase in the FinTech index, corporate sustainability index increases by 1.8% on average. This result not only verifies the direct promotion effect of FinTech on corporate sustainability, but also indicates that the effect remains significant after controlling for other factors, which enhances the credibility of the results. The regression results support hypothesis H1: FinTech exerts a direct and significant positive effect on corporate sustainability.

4.4. Robustness Tests

4.4.1. Endogeneity Test

(1)

Instrumental Variable Approach (IV)

To mitigate the potential endogeneity bias caused by reverse causality and omitted variables between financial technology (Fintech) and corporate sustainability (CS), we refer to the related literature [84], and adopt provincial Internet penetration rate (Internet) as an instrumental variable to construct a two-stage least squares (2SLS) model. Among them, Internet penetration directly affects the level of regional FinTech (relevance condition), but is not directly related to the micro-mechanism of corporate sustainability (exclusivity constraint).

The results of the first stage regression show (

Table 8. Table of endogeneity test results.

Variable	IV Regression		GMM Estimation
	(1)	(2)	(3)	(4)
	First Stage	Second Stage	One-Step	Two-Step
	FinTech	CS	CS	CS
Internet	0.721 ***
	(0.087)
FinTech	-	0.016 ***	0.017 ***	0.018 ***
	-	(0.003)	(0.003)	(0.005)
Size	0.012	0.004 **	0.003 *	0.004 **
	(0.015)	(0.002)	(0.003)	(0.004)
Capex	−0.003	0.011 **	0.009 *	0.010 **
	(0.008)	(0.005)	(0.005)	(0.006)
Age	−0.005	0.001	0.001	0.001
	(0.007)	(0.003)	(0.003)	(0.004)
SOE	−0.023 **	−0.003 **	−0.002 *	−0.003 **
	(0.011)	(0.001)	(0.002)	(0.002)
Subsidy	0.004	0.007 *	0.005	0.006
	(0.006)	(0.004)	(0.004)	(0.005)
L.FinTech			0.015 ***	0.016 ***
			(0.004)	(0.005)
Id	YES	YES	YES	YES
Year	YES	YES	YES	YES
Industry	YES	YES	YES	YES
N	19,740	19,740	19,740	19,740
Adj. R2	0.285	0.342	0.345	0.343
F-stat (Weak IV)	28.740 ***
Hansen J (p-value)		0.540	0.620	0.580
AR (1) p-value			0.032	0.028
AR (2) p-value			0.250	0.220

Notes: Clustered robust standard errors at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, column 1) that the coefficient estimate of the instrumental variable (Internet) on FinTech is 0.721, significant at the 1% level, and the F-statistic of the weak instrumental variable test is 28.740 (much greater than 10), indicating that the instrumental variable is strongly correlated with the endogenous variable.

In the second-stage regression, the promotion effect of FinTech on corporate sustainability is still significant, and the p-value of Hansen J test is 0.540, which cannot reject the original hypothesis of “exogenous instrumental variables”, and further supports the validity of the model setting. The above results show that the positive effect of FinTech on corporate sustainability is still robust after controlling endogenous interference.

(2)

System GMM test

To further verify the reliability of the findings, this paper adopts a systematic GMM test, taking the independent variables lagged by one period as instrumental variables and controlling for individual, industry and year fixed effects. First, the Hansen over-identification test p-value is 0.620 (one-step method) and 0.580 (two-step method), respectively, which are both greater than 0.1, indicating that the instrumental variables satisfy the exogeneity assumption; the AR (1) test p-value is less than 0.05, which supports the existence of first-order autocorrelation in the residuals, while the AR (2) test p-value is greater than 0.1, which indicates that the model does not have the second-order autocorrelation, and that the dynamic panel setting is reasonable.

The one-step GMM and two-step GMM estimation results show (columns 3–4 of Table 8) that the FinTech coefficients are 0.017 and 0.018, respectively, which are both significant at the 1% level, reflecting the robustness of the findings.

4.4.2. Alternative Explanatory Variable

In this paper, we refer to the related study [85,86], and select the county-level Peking University Mathematical Financial Inclusion Index and replace the explanatory variables to test the baseline regression model (1) again. As can be seen from column (1) of

Table 9. Robustness checks: alternative specifications, lagged effects, and subsample analyses.

Variable	Replacement	Lag 1 to 3			Sub-Interval Estimation
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Explanatory Variable	Lag 1	Lag 2	Lag 3	(2012–2019)	(2012–2020)	(2012–2021)
FinTech1	0.016 ***
	(0.004)
L. FinTech		0.015 **
		(0.006)
L2. FinTech			0.013 **
			(0.006)
L3. FinTech				0.012 **
				(0.005)
FinTech					0.017 ***	0.016 ***	0.015 ***
					(0.004)	(0.004)	(0.004)
Size	0.003 *	0.003	0.002	0.002	0.004 *	0.003	0.003
	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
Capex	0.011 *	0.010 *	0.008	0.007	0.011 **	0.010 *	0.009
	(0.006)	(0.006)	(0.006)	(0.006)	(0.005)	(0.006)	(0.006)
Age	0.002	0.001	0.001	0.001	0.001	0.001	0.001
	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)
SOE	−0.002 *	−0.002 *	−0.002	−0.001	−0.003 **	−0.002 *	−0.002 *
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Subsidy	0.006	0.005	0.004	0.003	0.006	0.005	0.005
	(0.004)	(0.004)	(0.004)	(0.004)	(0.004)	(0.004)	(0.004)
Id	YES	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES	YES
N	19,740	17,850	16,210	14,760	15,430	17,620	18,950
Adj. R2	0.335	0.327	0.319	0.312	0.341	0.332	0.329

Notes: Clustered robust standard errors at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, the coefficients of the explanatory variables are significantly positive at the 1% level, showing the robustness of the findings.

4.4.3. Lagged Effects Analysis

There may be a lag effect when analyzing the impact of FinTech on corporate sustainability. If this effect is not considered in the estimation process, it may affect the accuracy of the current results and lead to bias. Therefore, this part of the analysis is based on the benchmark regression model, which further controls the impact of the lagged effect. In this paper, we control for FinTech lagging one period to three periods, respectively, and the results are shown in columns (2) to (4) of Table 9, the regression coefficients of FinTech are all significantly positive at 5% level, which is consistent with the conclusion of the benchmark regression.

4.4.4. Subsample Analysis by Year

The outbreak of the COVID-19 pandemic in 2020 profoundly impacted Chinese enterprises’ operational and financial environments. In response, both the government and the central bank implemented a series of policies to mitigate the economic shock. These interventions exerted persistent effects on corporate technological progress and corporate sustainability trajectories [87,88].

To isolate potential distortions from the pandemic, we conducted subsample analyses for the periods 2012–2019 (pre-pandemic), 2012–2020 (initial outbreak phase), and 2012–2021 (recovery phase). Regression results for these subsamples (Columns 5–7 in Table 9) demonstrate coefficients broadly consistent with our core findings—indicating that the pandemic shock did not systematically alter the fundamental mechanism through which FinTech enables corporate sustainability. This consistency robustly validates our conclusions against exogenous systemic disruptions.

4.5. Mechanism Analysis

The regression analysis in

Table 10. Mechanism test: mediating role of supply chain finance.

Variable	(1)	(2)	(3)
	CS	SCF	CS
FinTech	0.018 ***	0.042 ***	0.012 ***
	(0.003)	(0.008)	(0.002)
SCF			0.141 ***
			(0.019)
Size	0.004 **	0.002	0.003 **
	(0.002)	(0.003)	(0.001)
Capex	0.012 **	0.007 ***	0.010 **
	(0.005)	(0.004)	(0.004)
Age	0.001	−0.001	0.001
	(0.003)	(0.002)	(0.002)
SOE	−0.003 **	−0.004 **	−0.002 **
	(0.001)	(0.002)	(0.001)
Subsidy	0.007 *	0.005	0.006 *
	(0.004)	(0.003)	(0.003)
Id	YES	YES	YES
Year	YES	YES	YES
Industry	YES	YES	YES
N	19,740	19,740	19,740
Adj. R2	0.348	0.283	0.357

Notes: Clustered robust standard errors at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

shows that FinTech has a significant positive impact on supply chain finance (SCF), with a coefficient estimate of 0.042, which is significant at the 1% level, indicating that for every 1% increase in the level of FinTech development, the intensity of disclosure of supply chain finance (SCF) increases by 0.042 units. Further, the contribution of supply chain finance (SCF) to corporate sustainability (CS) is equally significant, implying that every 1-unit increase in supply chain finance, the high corporate sustainability index increases by 14.1% on average. The value of indirect effect is 0.006 (0.042 × 0.141 = 0.006), which accounts for 33.3% of the total effect (0.018).

The 95% confidence interval by Bootstrap method (repeated sampling 1000 times) is [0.004, 0.008], which does not contain zero, suggesting that the mediating role of supply chain finance between FinTech and corporate sustainability is statistically robust. This empirically validates Hypothesis 2 derived from Resource-Based View theory: FinTech drives corporate sustainability by augmenting supply chain finance efficiency—embodying the “technology empowerment → organizational synergy → performance enhancement” pathway that constitutes the core “technology–organization” transmission mechanism within the TOE framework.

4.6. Moderating Effect Analysis

4.6.1. Digital Infrastructure

As a General Purpose Technology, digital infrastructure has a wide range of technological complementarities and spillover effects. Its degree of perfection directly affects the application cost and efficiency of FinTech.

Table 11. Moderating effect analysis table.

Variable	(1)	(2)
	Digital_Infra	Market
FinTech	0.015 ***	0.014 ***
	(0.003)	(0.003)
Digital_Infra	0.008 **
	(0.004)
Market		0.021 ***
		(0.005)
FinTech ×Digital_Infra	0.006 ***
	(0.001)
FinTech × Market		0.005 ***
		(0.001)
Size	0.003 *	0.004 **
	(0.002)	(0.002)
Capex	0.011 **	0.010 **
	(0.004)	(0.007)
Age	0.001	0.001
	(0.003)	(0.003)
SOE	−0.003 **	−0.002 *
	(0.001)	(0.001)
Subsidy	0.006 *	0.007 *
	(0.004)	(0.006)
Marginal Effects
Low Moderation	0.044	0.043
High Moderation	0.067	0.064
Id	YES	YES
Year	YES	YES
Industry	YES	YES
N	0.361	0.358
Adj. R2	19,740	19,740

Notes: Clustered robust standard errors at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

Column (1) shows that the coefficient of FinTech is 0.015, and the coefficient of the interaction term between FinTech and digital infrastructure is 0.006, both of which are significant at the 1% level, indicating that digital infrastructure significantly enhances the role of FinTech in promoting corporate sustainability. The marginal effect analysis further reveals that when the level of digital infrastructure rises from a low value (mean − 1 standard deviation) to a high value (mean + 1 standard deviation), the marginal effect of FinTech jumps from 0.044 (0.015 + 0.006 × 4.90) to 0.067 (0.015 + 0.006 × 8.60), an increase of 52.27%.

This result corroborates theoretical hypothesis 3: Digital infrastructure significantly magnifies FinTech’s positive impact on corporate sustainability.

4.6.2. Marketization Level

Marketization level, as the core manifestation of regional institutional environments, moderates the FinTech–corporate sustainability relationship through dual mechanisms. First, an institutional safeguarding effect operates in high-marketization regions, where strengthened rule of law and enhanced contract enforcement significantly reduce compliance risks associated with FinTech applications. This is evidenced by robust property rights protection minimizing data misuse disputes and transparent regulatory frameworks ensuring the execution of digital finance contracts. Second, a competition-driven efficiency effect emerges: intensified market competition compels firms to adopt FinTech more efficiently, wherein accelerated factor mobility promotes technology diffusion, while a rising share of non-state enterprises heightens “innovate-or-perish” pressure, driving firms to leverage digital technologies to alleviate financing constraints.

As Column (2) of Table 11 shows, both the main effect of FinTech (β = 0.014, p < 0.01) and its interaction with the marketization level index (β = 0.005, p < 0.01) are statistically significant. This confirms that higher marketization level amplifies FinTech’s positive impact on corporate sustainability—each unit increase in the marketization level elevates FinTech’s marginal effect by 35.7%. Marginal effect analysis further demonstrates this amplification: at low marketization level (mean − 1 SD: 5.78), the effect is 0.043, whereas at high marketization level (mean + 1 SD: 9.98), it increases to 0.064, representing a 48.84% enhancement.

These results robustly validate the “institutional leverage effect” of marketization level—its significant positive moderation of the FinTech-corporate sustainability relationship—thereby confirming Hypothesis 4.

Synthesizing these findings, digital infrastructure and marketization level collectively form the essential “twin pillars” of environmental support for FinTech-driven development. Digital infrastructure reduces technology diffusion thresholds through hardware economies of scale, while marketization level accelerates technology assimilation via institutional adaptive efficiency.

4.7. Heterogeneity Test

4.7.1. Digital Transformation Stage Heterogeneity

The stage of digital transformation reflects heterogeneity in the ability of firms to assimilate digital technologies. Drawing on Cohen & Levinthal’s absorptive capacity theory, enterprises require priori knowledge bases to effectively identify, internalize, and deploy new technologies. High-digitalization firms have completed infrastructure investments, possess sophisticated data integration capabilities, and use financial technology (FinTech) as supply chain collaboration tools. Conversely, low-digitalization firms are in the early stages of digitization, which have weaker technological foundations that hinder effective technology implementation and exacerbate resource allocation inefficiencies due to the “digital divide.” In this paper, we adopt the digital transformation index constructed Wu Fei et al., [89], selecting the top and bottom 30% of enterprises based on this index for grouped regression analysis.

The empirical results, as shown in columns (1) and (2) of

Table 12. Heterogeneity test results table.

Variable	Digital Transformation		Industry		Regional
	(1)	(2)	(3)	(4)	(5)	(6)
	High-Digitalization	Low-Digitalization	Tech-Intensive	Traditional	Eastern	Central-Western
FinTech	0.028 ***	0.005 ***	0.025 ***	0.008 **	0.022 ***	0.009 **
	(0.007)	(0.002)	(0.006)	(0.003)	(0.005)	(0.004)
Size	0.006 ***	0.001	0.005 **	0.003	0.004 **	0.002
	(0.002)	(0.001)	(0.002)	(0.002)	(0.002)	(0.001)
Capex	0.016 ***	0.003	0.015 **	0.007	0.013 **	0.006
	(0.006)	(0.002)	(0.006)	(0.004)	(0.005)	(0.003)
Age	0.003	0.000	0.002	0.001	0.002	0.001
	(0.003)	(0.001)	(0.003)	(0.002)	(0.002)	(0.001)
SOE	−0.005 ***	−0.000	−0.004 **	−0.002	−0.003 **	−0.001
	(0.002)	(0.001)	(0.002)	(0.001)	(0.001)	(0.001)
Subsidy	0.009 ***	0.002	0.008 **	0.005	0.007 *	0.004
	(0.004)	(0.001)	(0.003)	(0.003)	(0.003)	(0.002)
Id	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
Industry	YES	YES	YES	YES	YES	YES
N	5922	5922	10,250	9490	11,250	8490
Adj. R2	0.402	0.278	0.375	0.302	0.368	0.291

Notes: Clustered robust standard errors at the firm level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

, show that the FinTech coefficient of high-digitization firms is 0.028, which is significantly higher than that of low-digitization firms, 0.005, both of which are significant at the 1% level. This underscores the decisive role of digital maturity in technology enablement. Mature digital capabilities determine firms’ capacity to absorb FinTech, enabling high-digitalization firms to convert technological advantages into productivity gains more efficiently.

Thus, digital transformation stages constitute a “capability threshold” for FinTech-enabled effects. Low-digitalization firms lack absorptive capacity, resulting in disruptions to the “Technology (T) → Organization (O)” transmission pathway within the TOE framework.

4.7.2. Industry Heterogeneity

Considering the significant differences in technology levels among Chinese industries, FinTech may have a heterogeneous impact on corporate sustainability across different sectors. Therefore, this paper classifies enterprises into two categories: technology-intensive industries and traditional manufacturing.

Group regression analyses were conducted separately for the sample enterprises, with estimation results shown in columns (3) and (4) of Table 12. The FinTech coefficient for technology-intensive enterprises is 0.025 (significant at the 1% level), which is significantly higher than that of traditional manufacturing industries. This indicates that FinTech plays a stronger role in promoting corporate sustainability in technology-intensive enterprises.

This divergence occurs because technology-intensive industries are highly dependent on external financing to support innovation and exhibit high supply chain complexity. In these sectors, FinTech enables the precise matching of financing needs through supply chain finance while strengthening innovation synergy. In contrast, traditional manufacturing industries exhibit standardized production processes, where financing primarily supplements working capital. Consequently, financial technology (FinTech) exerts a comparatively limited impact on this sector.

The essence of industry heterogeneity lies in the differential matching of “technology attributes—organizational needs”—technology-intensive industries, with their high complexity and strong innovation demand, are better positioned to unlock FinTech’s deep value.

4.7.3. Regional Heterogeneity

China’s economic development has long exhibited regional imbalances. The eastern region boasts a developed economy, advanced digital infrastructure, and high marketization, whereas the central and western regions face constraints from infrastructure and institutional environments, creating greater resistance to technological diffusion. Consequently, FinTech’s impacts vary significantly across regions.

We divided the sample into eastern and central/western sub-samples and conducted separate baseline model regressions. Empirical results (Table 12, columns 5–6) show that FinTech’s coefficient in the eastern region is 0.022 (significant at 1% level)—144% higher than the central/western region’s coefficient of 0.009 (p < 0.05). This regional heterogeneity test confirms that FinTech’s effectiveness depends critically on “technology-environment” synergy, with regional imbalance reflecting a “double break” between digital infrastructure and institutional environments.

5. Conclusions and Policy Recommendations

5.1. Conclusions

Based on the technology–organization–environment (TOE) framework, this paper constructs a multi-dimensional corporate sustainability indicator system—covering innovation-driven growth, coordinated development, green transformation, open cooperation, and outcome sharing—using the entropy-weighted TOPSIS method and data from China’s A-share non-financial listed companies (2012–2022). We systematically test the influence mechanisms and boundary conditions of FinTech. The main findings are as follows:

First, the direct and indirect driving effects of FinTech. FinTech exerts a significant direct effect on corporate sustainability (elasticity coefficient 0.018, p < 0.01), alongside an indirect mediating effect through improved supply chain finance efficiency (33.3%). These results remain robust after mitigating endogeneity using instrumental variables and systematic GMM methods. This confirms the “technology empowerment → organizational synergy → performance enhancement” transmission logic within the TOE framework, demonstrating that supply chain finance is the critical hub for releasing FinTech’s value.

Second, environmental factors decisively moderate technological empowerment effects, exhibiting significant economies of scale. Increased marketization and improved digital infrastructure significantly strengthen FinTech’s enabling effect. Specifically, FinTech’s facilitation effect in high-marketization areas is 100% stronger than in low-level areas; similarly, the marginal effect of digital infrastructure is 133% higher in well-developed areas versus weak areas. This verifies the “technology-environment” synergy hypothesis—institutional optimization reduces transaction costs while hardware upgrading amplifies technological spillovers—which jointly constitute necessary conditions for technology diffusion.

Third, firms’ technological endowments, industry attributes, and regional endowments jointly shape heterogeneous responses. FinTech’s facilitating effect is more pronounced in highly digitized firms, technology-intensive industries, and eastern regions. Technology endowment: Digitization level constitutes the threshold for technology absorption capacity. Notably, FinTech’s elasticity coefficient for highly digitized enterprises (0.028) is 5.6 times higher than for low-level enterprises (0.005), underscoring the critical “capacity–technology” match. Industry attributes: Technological intensity strengthens demand responsiveness. The elasticity coefficient for technology-intensive industries (0.025) significantly exceeds that of traditional manufacturing (0.008), reflecting FinTech’s differentiated value in innovation synergy and risk pricing. Regional endowment: Development gradient determines technology penetration depth. The eastern region’s elasticity coefficient is 144% higher than central/western regions (0.009), revealing that the “double disconnection” between digital infrastructure and institutional environment is the core cause of regional imbalance.

5.2. Policy Recommendations

The “technology–organization–environment” synergistic mechanism revealed in this study necessitates policy designs that transcend fragmented intervention approaches and establish systematic frameworks aligned with corporate sustainability objectives. This will facilitate deep integration of FinTech with the real economy and advance sustainable economic progress. Accordingly, this study proposes the following recommendations:

First, strengthen organizational synergy to activate the pivotal function of supply chain finance. Establish mandatory data-sharing and risk-sharing platforms requiring leading enterprises to provide real-time transaction data to supply chain finance platforms, thereby eliminating information silos. Concurrently, governments should create fiscal guidance funds to compensate financing risks for SMEs and lower market entry barriers.

Second, optimize the regional digital infrastructure layout to narrow the technological divide. Launch a “Digital West Initiative” special fund to prioritize deployment of new infrastructure (e.g., 5G base stations and cloud computing centers) in central and western regions while reducing technology adoption costs. Promote cross-regional digital resource sharing by encouraging developed eastern regions to support central and western China through technical assistance and co-construction of data platforms, thus enabling balanced allocation of digital production factors.

Third, deepen market-oriented reforms to unleash institutional dividends. Implement pilot “regulatory sandboxes” in low-marketization areas to relax FinTech access restrictions and stimulate regional financial innovation. Enhance data property rights regimes and trading mechanisms by clarifying data usage boundaries, providing legal safeguards for inter-enterprise data sharing, and reducing collaboration friction.

Fourth, implement differentiated policies to enhance technology inclusion. Provide targeted support for technology-intensive industries through measures such as expanding R&D expense deduction ratios and piloting intellectual property pledge financing, thereby amplifying FinTech’s innovation-driven effects. Strengthen technological empowerment of low-digitalization enterprises through tax incentives, digital transformation subsidies, and skill development programs to enhance technical application capabilities and prevent digital marginalization.

6. Discussions

6.1. Theoretical and Practical Implications

This study provides nuanced insights into how FinTech shapes corporate sustainability by empirically validating and extending the technology–organization–environment (TOE) framework. Our findings offer four key theoretical contributions:

• Theoretical Integration and Extension: We pioneer the systematic integration of FinTech into the TOE framework, moving beyond its traditional application in information systems. By conceptualizing supply chain finance (SCF) as a critical mediating mechanism and marketization/digital infrastructure as boundary conditions, we provide a more granular and testable model for understanding technology-driven sustainability transitions. This addresses the theoretical gap of how technology diffusion translates into sustainable performance through organizational adaptation.
• Quantification of Mechanisms: We move from qualitative association to quantitative causal pathways. By precisely estimating SCF’s mediation effect (33.30%) and the strength of environmental moderators (52.27% and 48.84%), we provide empirical benchmarks for future theoretical models aiming to predict the impact of digital finance. This responds to calls for more precise measurements in sustainability governance research.
• Re-contextualizing Heterogeneity: We introduce the digital transformation stage as a key heterogeneity factor, shifting the theoretical discourse from “whether” FinTech matters to “for whom” and “under what conditions” it matters most. Our findings challenge the assumption of uniform effects and provide a theoretical basis for resource-allocation and policy-design theories in digital transformation.
• Metric System Innovation: Our multidimensional sustainability index, aligned with China’s Five-Year Plan and UN SDGs, offers an operationalizable theoretical construct for future research. It bridges the gap between macro-level sustainability goals and micro-level corporate activities, providing a balanced measurement tool that captures the interplay between economic, environmental, and social dimensions.

6.2. Research Limitations and Future Directions

While our study provides valuable insights, several limitations should be acknowledged, which also present opportunities for future research.

• Limitation: Innovation Measurement

Description: Although patent data is widely used, it primarily captures technological innovation outputs. It may inadequately reflect non-technological innovations (e.g., business model, service process, or organizational structure innovations) often spurred by FinTech, potentially leading to an underestimation of its full impact.

Future Direction: Future studies could develop a more comprehensive innovation assessment framework. This could incorporate metrics like the proportion of revenue from new services, text analysis of annual reports to capture management innovation, or survey-based measures of process innovation to better capture FinTech’s multifaceted influence.

2.

Limitation: Model Explanatory Power

Description: Our model explains 34.8% of the variance, leaving a substantial portion unexplained. This may stem from unobserved firm-level heterogeneities (e.g., managerial philosophy, corporate culture, human capital quality) and dynamic macro-institutional factors (e.g., sudden policy shifts, regional incentives) not fully captured by our fixed effects.

Future Direction: Research could employ mixed-methods approaches. Qualitative case studies or interviews could delve into the “unobserved” firm-specific factors. Quantitatively, integrating time-varying policy variables (e.g., green finance incentives, carbon trading policies) or using natural language processing (NLP) on management discussion and analysis (MD&A) reports could better control for these dynamic complexities.

3.

Limitation: Generalizability of Findings

Description: Our focus on China’s A-share listed companies, while providing internal validity, may limit the generalizability of our findings to other contexts (e.g., SMEs, startups, or other countries with different financial systems and regulatory environments).

Future Direction: A promising avenue is cross-national comparative studies. Replicating this research in other emerging economies or developed markets could test the boundary conditions of our theoretical framework and elucidate the role of different institutional arrangements in FinTech-driven sustainability.