Digitization and Internal Governance: The Power to Drive the Impact of Supply Chain Efficiency on Corporate Sustainability

Abstract

In the context of intensifying global competition and mounting environmental pressures, understanding how firms can improve corporate sustainability through supply chain efficiency remains a pressing and underexplored issue. This study addresses this gap by investigating the relationship between supply chain efficiency and corporate sustainability, with a particular focus on the moderating roles of digitalization and internal governance. Using panel data of 8000 firm-year observations from Chinese A-share listed companies over the 2010–2022 period, we adopt a two-way fixed effects model to empirically examine how inventory turnover days and the warehousing and transportation cost ratio affect firms’ sustainable growth. Our findings indicate that improving supply chain efficiency—manifested in shorter inventory turnover cycles and lower logistics cost ratios—significantly enhances corporate sustainability. Furthermore, we find that higher levels of digitalization and stronger internal governance significantly strengthen this positive relationship. These findings extend the resource-based view and dynamic capability theory by revealing how supply chain efficiency, supported by digital and internal governance, contributes to long-term sustainability. This study offers theoretical insights and practical implications for managers and policymakers aiming to optimize supply chain strategies in support of sustainable development goals.

1. Introduction

Given increasing global competition and rising resource and environmental pressures, corporate sustainable development, represented by the sustainable growth rate (SGR), has become an important criterion for measuring a firm’s long-term competitiveness and market adaptability. Sustainable development is related to corporate profitability and growth potential and affects the stability of the entire economic system in addition to social well-being [1]. Modern enterprises must pursue short-term profit maximization while ensuring long-term stable growth and improving resource utilization efficiency to cope with market volatility and external uncertainty [2]. Additionally, as global supply chain networks expand, corporate sustainability has gone beyond the scope of individual firms and has become an important issue for the global business system [3].

Supply chain management plays a pivotal role in achieving sustainability because its efficiency directly impacts corporate profitability, competitiveness, and long-term viability [4]. Enhancing supply chain efficiency optimizes resource allocation, reduces operational costs, and minimizes energy wastage, as well as logistics expenses, thereby improving financial performance and market stability [5]. In particular, reducing inventory turnover time and optimizing warehousing and transportation costs improve cash flow and mitigate supply chain risks, ultimately strengthening market resilience [6]. Recent studies underscore that effective supply chain practices not only enhance organizational agility but also facilitate environmental and social goals by reducing waste and promoting circular economy principles [7]. Clearly, examining the relationship between supply chain efficiency and corporate sustainability is important.

Among related factors, digital transformation has emerged as a key driver for enhancing supply chain efficiency and corporate sustainability. Technologies like information management systems, blockchain, artificial intelligence (AI), and the Internet of Things (IoT) enable firms to better predict market demand, optimize inventory management, and improve supply chain transparency [8]. A digitalized supply chain enhances a company’s ability to respond to market changes while reducing uncertainties, thereby strengthening its overall operational resilience [9]. Advanced digital management, through intelligent scheduling and data analytics, improves inventory efficiency, optimizes warehousing and transportation costs, and, ultimately, fosters long-term corporate growth [10]. Recent studies further demonstrate the value of integrating digital intelligence into supply chain systems. For example, rotation-aware deep learning models have been developed to improve maritime logistics performance, enabling real-time vessel detection and efficient data acquisition through decoupled orientation features [11]. In addition, hybrid digital twin-assisted models are being applied to optimize container terminal operations with multiple objectives, demonstrating the effectiveness of predictive analytics in sustainable logistics planning [12]. Moreover, blockchain-based optimization frameworks are increasingly used in closed-loop supply chain design, offering data integrity and operational transparency for circular and sustainable business models [13]. These innovations provide a technological foundation for further empirical analysis of how digital capabilities amplify the sustainability impact of supply chain efficiency.

Similarly, internal corporate governance plays a critical role in translating supply chain efficiency into sustainable development. An effective governance structure optimizes resource allocation, enhances operational transparency, and ensures supply chain efficiency and compliance [14]. High-quality internal governance fosters the adoption of more scientific and efficient supply chain strategies, like optimizing procurement management and improving logistics operations, which help mitigate supply chain disruption risks [15]. Additionally, a good governance structure improves firms’ ability to manage supply chain data and makes the supply chain system more resilient and adaptive, thus better supporting long-term growth [16]. Internal governance mechanisms are also important for promoting green innovation and enhancing corporate environmental performance, underscoring internal governance as a critical moderating factor in sustainability practices [17]. Strong governance frameworks promote stakeholder inclusivity, ethical accountability, and strategic alignment across functional departments, thereby institutionalizing sustainability principles into core business processes [18,19]. Internal governance can therefore be viewed as both an enabler and a catalyst of sustainable value creation, offering procedural legitimacy and internal safeguards for sustainability-oriented transformation.

Here, we analyze data on Chinese A-share listed companies from 2010 to 2022 to explore the impact of supply chain efficiency on corporate sustainable development, and how digitization and internal governance levels affect this relationship.

The innovations of this study are as follows: First, most studies on the impact of supply chain efficiency on corporate sustainable development focus only on the impact of supply chain management on corporate financial performance. Fewer studies explore the specific role of supply chain efficiency in corporate sustainable development [20,21]. Addressing this gap, we systematically analyze how supply chain efficiency affects firms’ sustainable growth in terms of profitability and long-term development. Second, differing from traditional research, we adopt an operational perspective to define supply chain efficiency as “inventory turnover days” and “storage and transport cost ratio”. These indicators can more accurately measure corporate operational efficiency in the process of supply chain optimization, thereby overcoming the limitations of using crude supply chain performance indicators (e.g., total cost) in previous studies [22].

Finally, our contributions are three-fold: First, we expand the application of the resource-based view (RBV) and dynamic capability theory (DCT) in supply chain management and sustainable development. Second, we explore the moderating roles of digitalization and internal governance, providing a solid theoretical basis for enhancing the sustainability of listed companies. Finally, in the context of China’s dual-carbon strategy and increasing digital transformation, understanding how sustainable corporate growth can be enhanced through supply chain efficiency optimization has become an important corporate strategic decision-making issue [23]. Therefore, our empirical results provide actionable management suggestions for corporate managers and evidence for regulators to formulate supply chain management as well as sustainable development policies.

2. Theoretical Background and Hypotheses

2.1. Supply Chain Efficiency and Corporate Sustainability

Firms in a highly competitive and resource-constrained market must rely on efficient supply chain management to optimize resource allocation, enhance market competitiveness, and achieve sustainable growth. RBV suggests that a firm’s long-term competitive advantage depends on effectively acquiring and utilizing key resources [1]. Within the supply chain management context, this implies that firms must improve supply chain efficiency by acquiring, producing, and distributing resources at lower costs, in shorter time frames, and with greater precision. DCT further emphasizes that firms must continuously adjust and refine their supply chain strategies to adapt to market changes and maintain sustainable competitiveness [24]. Therefore, supply chain efficiency is not only a foundation for operational performance but also a critical pathway through which firms achieve long-term sustainability goals. Furthermore, long-term development is a key dimension of sustainability that extends beyond immediate financial gains. Efficient supply chains contribute to long-term resilience by reducing operational uncertainties, enhancing resource utilization, and enabling firms to better withstand external shocks [25]. This systemic effect reinforces sustainable growth and strategic competitiveness.

Inventory turnover days are a key indicator of a firm’s inventory management efficiency. A reduction in inventory turnover days implies faster inventory cycles, lower stockpiling, and less tied-up capital, ultimately enhancing financial flexibility [26]. Efficient inventory management also reduces warehousing costs, obsolete stocks, and waste, thereby mitigating resource inefficiencies and environmental burdens. These improvements not only strengthen a firm’s short-term operational performance but also contribute to its long-term corporate sustainability. By minimizing resource use and waste, firms align more closely with environmental goals; by improving cash flow and responsiveness, they enhance resilience and adaptability in uncertain markets. That is, accelerated inventory turnover improves a firm’s profitability while simultaneously enhancing environmental performance, yielding comprehensive benefits in economic, social, and environmental sustainability [5].

Thus, reducing inventory turnover days supports both economic efficiency and environmental responsibility, forming a tangible link between operational improvements and sustainable development. Accordingly, we propose the following hypothesis:

Hypothesis 1. 

A reduction in inventory turnover days improves corporate sustainability.

Next, controlling warehousing and transportation costs can also influence supply chain efficiency. These costs constitute a significant portion of logistics costs. Excessive logistics costs erode profitability and impose additional environmental burdens [27]. According to RBV, firms can optimize warehouse locations and enhance logistics network coordination to eliminate unnecessary costs while improving overall supply chain efficiency [28]. Meanwhile, DCT underscores that firms need to develop the agility to adjust logistics models and transportation strategies in response to market fluctuations, thereby ensuring long-term sustainable growth [29].

The widespread adoption of smart logistics technologies, like automated warehouse management systems, intelligent delivery networks, and data-driven transportation optimization, enables firms to manage warehousing resources with greater precision and enhances delivery efficiency. Together, this improves supply chain efficiency and strengthens firms’ sustainable competitive advantage by reducing energy consumption and carbon emissions [30]. Additionally, firms are increasingly adopting green logistics practices to lower their warehousing and transportation costs. Strategies like optimizing transportation modes, utilizing shared storage facilities, and implementing low-carbon transportation solutions help reduce energy consumption and environmental pollution while enhancing overall supply chain performance, thereby creating higher integrated value [31].

In summary, reducing warehousing and transportation costs not only enhances operational effectiveness but also plays a key role in environmental protection and resource conservation. This dual benefit reinforces a firm’s capacity to deliver sustainable value. Accordingly, we propose the following hypothesis:

Hypothesis 2. 

A reduction in the warehousing and transportation cost ratio improves corporate sustainability.

2.2. The Moderating Role of Digitalization

A firm’s digitalization level refers to the depth of digital technology application in its operations and management, as well as its ability in information integration, processing, and intelligent decision-making. This ability is mainly reflected in the degree of the firm’s adoption and integration of cutting-edge technologies, like the IoT, big data, cloud computing, and AI. Thus, the depth of digitalization directly influences a firm’s efficiency in information processing, resource allocation, and decision-making accuracy [32].

In the supply chain management context, higher digitalization enhances a firm’s agility and visibility, enabling it to respond more quickly to market demand and supply chain fluctuations. This accelerates the conversion of efficiency improvements into sustainability outcomes [23]. Moreover, digital tools like real-time inventory monitoring via the IoT, AI-driven demand forecasting, and blockchain-enabled supply chain transparency significantly enhance both environmental performance and operational capabilities [33,34]. Indeed, integrating AI technologies significantly boosts corporate green product innovation capabilities, implying digital capabilities’ potential to elevate supply chain sustainability through enhanced innovation outcomes [35].

Specifically, firms with high digitalization can better detect demand fluctuations, optimize inventory structures, and efficiently allocate transportation resources. These improvements contribute to lower carbon emissions and resource conservation, reinforcing corporate sustainability. Conversely, due to insufficient information integration or lagging system response, firms with lower digitization can hardly effectively promote sustainable development, even if they have improved their supply chain efficiency [32]. Accordingly, we propose the following hypotheses:

Hypothesis 3a. 

The level of digitization enhances the positive effect of reducing the inventory turnover days on sustainable development.

Hypothesis 3b. 

The level of digitization enhances the positive effect of reducing the warehouse and transportation expense ratio on sustainable development.

2.3. The Moderating Role of Internal Governance

Internal governance refers to the mechanisms and processes that regulate a firm’s operational management, decision-making oversight, and risk control. It reflects the effectiveness and standardization of a firm’s organizational structure and management system. A well-developed internal governance framework ensures the effective implementation of strategic objectives and the stable realization of corporate performance. When a firm has a robust governance structure, decision-making becomes more systematic and supervision mechanisms are more comprehensive. This enables firms to quickly identify, assess, and capitalize on sustainability opportunities arising from supply chain efficiency improvements, further amplifying the economic and environmental benefits of inventory optimization and logistics cost reductions. Indeed, strong internal governance enhances a firm’s strategic centrality within the supply chain, increasing its control and coordination over sustainability mechanisms. This helps in achieving sustainable development goals across the supply chain [36].

Internal governance also strengthens a firm’s ability to manage risks, improving its resilience and adaptability to supply chain fluctuations and uncertainty [37]. Effective governance structures foster communication and collaboration between internal and external stakeholders, mitigating the governance challenges caused by information asymmetry, compliance pressures, and regulatory gaps [38]. Importantly, governance mechanisms moderate the relationship between supply chain management and corporate sustainability, enhancing a firm’s ability to translate efficiency gains into sustainable outcomes [39]. Conversely, firms with weak governance structures are more prone to delayed decision-making and execution biases, limiting their ability to fully convert supply chain efficiency improvements into tangible sustainability outcomes. Firms can only achieve governance effectiveness and sustainability objectives if they establish governance mechanisms consistent with the complexity of their supply chain structures [40].

Accordingly, we propose the following hypotheses:

Hypothesis 4a. 

Internal governance strengthens the positive impact of reduced inventory turnover days on corporate sustainability.

Hypothesis 4b. 

Internal governance strengthens the positive impact of lowering the warehousing and transportation cost ratio on corporate sustainability.

Figure 1 is the research model of the study.

3. Methodology

3.1. Data and Sample

Our initial sample comprised A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2022. This was processed as follows: first, only companies listed before 2010 were retained and companies in the financial sector were excluded; second, firms classified as ST, *ST, PT, or delisted during the sample period were excluded; and third, observations with missing key variables were removed. The final dataset comprised 8000 firm-year observations.

Data were sourced from the China Stock Market and Accounting Research (CSMAR), China Research Data Service (CSMAR), and Choice databases. Digitalization-related word frequency data were extracted from annual reports by using text analysis. To mitigate the influence of extreme values, all continuous variables were winsorized at the 1% level on both tails. Data were processed and analyzed using Stata 16.0 and Python 3.8.

3.2. Definition and Measurement of Variables

3.2.1. Dependent Variable

Corporate sustainability refers to a firm’s ability to achieve long-term operations, maintain competitive advantage, and sustain stable growth. Following Wu et al. [41], we adopted Van Horne’s static model to measure corporate profitability and competitive sustainability [42] as follows:

$$SGR={\displaystyle \frac{Net sales interest rate\times total asset turnover\times income retention rate\times equity multiplier}{(1-net sales interest rate\times total asset turnover\times income retention rate\times equity multiplier)}}$$

3.2.2. Independent Variable

Enhancing supply chain efficiency requires effective supply chain management, which involves managing and coordinating the flow of products, information, and capital among various supply chain participants [43]. A slowdown in the flow of factors, like inventory, can cause conflicts of interest between upstream and downstream firms, which is not conducive to improving supply chain efficiency. As a result, an important aspect of improving supply chain efficiency is improving inventory management capabilities, as suggested by Zahran and Jaber [44]. Accordingly, following Chen et al. [45] and Feng et al. [46], we first selected listed companies’ inventory turnover days (inventory_day) to measure supply chain efficiency. Additionally, supply chain logistics and transport costs reflect supply chain efficiency to a large extent, as they account for a significant proportion of project costs and are significantly correlated with project efficiency [47]. Thus, the warehousing and transport cost ratio can be an appropriate proxy for supply chain efficiency [48], defined as the warehouse–transport-cost ratio (WTC).

$$WTC={\displaystyle \frac{ Warehousing and Transportation Costs }{Operating Revenue}}\times 100\%$$

3.2.3. Moderator Variable

Corporate digitalization is a complex and systematic process, making it challenging to quantify at the firm level. Most studies adopt a macroeconomic perspective, often relying on regional- or industry-level digital economy indicators to assess digitalization [49]. At the micro level, limited empirical research has primarily evaluated digitalization through measures like information capital and information technology (IT) system applications [50]. However, these methods have notable limitations.

Some studies use IT investment, telecommunications expenditure, and the proportion of intangible assets related to digitalization to measure information technology adoption. While these indicators are straightforward, they may be influenced by firms’ signaling behavior. Furthermore, IT investment levels do not necessarily reflect actual digital application [51]. Other studies rely on surveys or interviews [52]; however, the high cost of collecting comprehensive digitalization data poses challenges.

Currently, the most common approach is to measure digitalization (DT) based on the proportion of digitally related intangible assets [53]. However, these metrics often fail to provide a precise and comprehensive assessment of digitalization. Since digital transformation is a critical strategy for corporate development, it is frequently reflected in firms’ annual reports. The language used in annual reports conveys strategic priorities and future outlooks [54,55], providing insights into corporate management philosophies and growth trajectories. Because annual reports typically contain summaries and strategic discussions, they are likely to include details related to digitalization [56].

Following Wen et al. [57] and Guo et al. [58], we applied machine-learning-based text analysis to quantify the frequency of digitalization-related terms in annual reports and used it as a proxy for digital transformation.

Specifically, we first created a special list of keywords. This dictionary included two types of words: terms concerning digital technologies and words that describe how these technologies are used. We built this keyword list by looking at previous research studies, checking government websites, such as that of the Ministry of Industry and Information Technology, and reviewing other reports on technology.

After collecting the potential keywords, we used Python software to analyze them. We retained only the words that appeared at least five times. Through this process, 238 important digitalization terms were identified. These words became the official corporate digitalization dictionary for this study.

Third, we constructed a corporate digitalization index. To account for differences in the text length of the Management Discussion and Analysis (MD&A) section across firms, we measured digitalization by dividing the total frequency of digitalization-related keywords by the total length of the MD&A section. For ease of interpretation, this index was multiplied by 100. A higher index value indicates a greater level of corporate digitalization. The full list of keywords is provided in

Table 1. Keywords for digital transformation.

Information	Key Technologies	Information Security	Database	Information Networks	Network Coverage
Networking	Information Technology	Information Systems	Sensors	Public Data	Electric
Data	E-commerce	Data Centers	E-Government	Technology Development	Algorithms
Internet	Communications	Interconnectivity	Data Analysis	Hardware and Software	Communication Networks
Intelligence	Core Technologies	Cyberspace	Wireless	Information Industry	Cross-Media
Informatization	Industry Chain	Industry–Academia–Research Collaboration	Broadband Networks	Broadcasting and Television	Computers
Artificial Intelligence	Virtual Reality	Human–Computer Interaction	E-commerce	Broadcasting	Gateways
Digitalization	Networking	Data Sharing	Internet Security	Technological Transformation	Automation
Smart Technology	Digital Supply Chain	Data Security	Information Networks	Numerical Control	Television Networks
Supply Chain	Smart Supply Chain	Digital Currency	Integrated Circuits	Energy Networks	Service Networks
Investment Decision Support System	Autonomous Driving	Distributed Computing	Billion-Level Concurrent Access	Networked Connectivity	Smart Grids
Intelligent Data Analysis	Natural Language Processing	Differential Privacy Technology	Internet of Things (IoT)	Wearable Technology	Smart Marketing
Smart Robots	Big Data	Intelligent Financial Contracts	Cyber–Physical Systems	Smart Agriculture	Digital Marketing
Machine Learning	Text Mining	Cloud Computing	Mobile Internet	Smart Transportation	Unmanned Retail
Deep Learning	Data Visualization	Stream Computing	Industrial Internet	Smart Healthcare	Internet Finance
Semantic Search	Heterogeneous Data	Graph Computing	Mobile Connectivity	Smart Customer Service	Digital Finance
Biometric Technology	Credit Reporting	In-memory Computing	Internet Healthcare	Smart Homes	Fintech
Facial Recognition	Augmented Reality	Cognitive Computing	Mobile Payments	Smart Investment Advisors	Quantitative Finance
Voice Recognition	Mixed Reality	Converged Architecture	Third-Party Payments	Smart Tourism and Culture	Open Banking
Identity Verification	Blockchain	Network Equipment	Smart Energy	Smart Environmental Protection	Digital Technologies
Data Services	Data Management	Product Development	Smart Devices	Radio Frequency	Defense Technology
Data Flow	Information Management	Electronic Information	Smart Networks	Wireless Networks	Communication Satellites
Application Software	Online Education	Patents	Networking	Wireless Communication Networks	Information Flow
Service Network	Servers	High Technology	Navigation Systems	Information Ports	Virtualization
Connectivity	Computing Technology	Advanced Technology	Multimedia	Domain Names	Unified Network
Data Processing	Automatic Control	Monitoring Networks	Internet Protocols	End Products	Government Networks
Data Mining	Processors	Portal Networks	Base Stations	Bits	Intelligent Algorithms
Digital Television	Development Tools	Portal Websites	Agricultural Remote Sensing	Encoding	China Association for Science and Technology
Network Infrastructure	Control Technologies	Live Streaming	Human–Computer Interaction	Electronic Products	Business Intelligence
Broadband Access	Network Services	Internet Business	Satellite Communications	Management Information Systems	Image Understanding
Application Data	Digital Devices	Mobile Internet	Machine Learning Internet Business Models	Digital Business	Online
Digital	Cloud Ecosystem	Internet Applications	Cloud Storage	Digital Technology	Network Security
Digital Data Management	Cloud Services	Internet Marketing	Internet+	Data Empowerment	Online Retail
Data Network	Cloud Platforms	Internet Strategy	Relational Databases	New Industrialization	Multi-party Secure Computing
Data Platform	E-commerce Mobile Internet	Internet Platforms	Blockchain	Smart Manufacturing	Brain-like Computing
Data Science	Industrial Internet	Internet Models	Business Intelligence	Intelligent Technologies	Green Computing
Digital Control	Internet Solutions	Internet Business Models	Business Analytics	Smart Devices	Broadband
Digital Communication	Internet Technologies	Internet Ecosystem	Industrial 4.0	Robotics	Machines
Digital Network	Internet Thinking	E-commerce and Mobile Internet	Platform Economy	Ecological Collaboration
Digital Intelligence	Internet Actions	Internet Solutions	Digital Creativity	Knowledge Management

.

Next, for internal governance, following the literature [59,60], we selected eight corporate governance variables and employed principal component analysis to construct a comprehensive corporate governance quality index (CorpGov). A higher CorpGov value indicates better governance quality.

The eight corporate governance variables are as follows. First is the separation of the CEO and chairperson roles (CeoNotPresident). Agency theory suggests that combining these roles weakens board oversight, leading to poorer firm performance and lower transparency [61,62]. Second is the proportion of independent directors (IndDirectRatio). A higher proportion of independent directors increases voluntary disclosure [63,64]. Third and fourth are the shareholding ratios of directors (ShareDirectors) and executives (ShareManager), respectively. According to agency theory, higher ownership by directors and executives aligns managerial and investor interests [65]. Fifth is the largest shareholder’s shareholding ratio (ShareContHolder). Concentrated ownership mitigates the free-rider problem associated with dispersed ownership, thereby enhancing shareholder monitoring [66]. Sixth and seventh are board size (DirectorNumb) and supervisory board size (SupervisorNumb), respectively. Finally, we considered the total compensation of the top three executives (ManagerComp).

3.2.4. Control Variables

Following recent research [67,68,69,70], we controlled for firm size (Size), leverage (Lev), book/market ratio (Bm), Tobin’s Q (TobinQ), firm age (Firmage), cash flow ratio (Cashflow), revenue growth rate (Growth), and whether the firm is audited by a Big Four accounting firm (Big4).

Industry (Ind) and year (YEAR) dummy variables were also included. If a firm belongs to a specific industry or year, the corresponding dummy variable equaled 1 and was 0 otherwise.

Table 2. Variable definition.

Variable Types	Variable Name	Definition	Meaning and Description
Independent variable	Inventory turnover days	ITDs	ln(365/Inventory Turnover Ratio)
	Warehousing and transportation cost ratio	WTC	Warehousing and Transportation Costs/Operating Revenue × 100%
Dependent variable	Corporate sustainability	SGR	$={\displaystyle \frac{Net Profit Margin\times Total Asset Turnover\times Revenue Retention Rate\times Equity Multiplier}{(1-Net Profit Margin\times Total Asset Turnover\times Revenue Retention Rate\times Equity Multiplier}}$
Moderator variable	Digital transformation	DT	The total frequency of digital-related keywords divided by the length of the annual report MD&A section × 100
	Internal corporate governance	ICG	Select eight corporate governance variables and use principal component analysis to construct a comprehensive corporate governance quality index. A higher value indicates better corporate governance quality
Control variable	Firm size	Size	ln(Total Assets + 1)
	Debt/equity ratio	Lev	Total Liabilities at Year-End/Total Assets at Year-End
	Book/market ratio	Bm	Total Assets/Market Value
	Company age	Fimrage	ln(Current Year Company Establishment Year + 1)
	Tobin’s Q	TobinQ	(Market Value of Tradable Shares + Non-Tradable Shares × Net Asset Value per Share + Book Value of Liabilities)/Total Assets
	Cash flow ratio	Cashflow	Net Cash Flow from Operating Activities/Total Assets
	Revenue growth rate	Growth	Current Year Operating Revenue/Previous Year Operating Revenue 1
	Big Four auditor	Big4	A company audited by one of the Big Four (PwC, Deloitte, KPMG, and EY) is coded as 1, otherwise as 0
	Industry	Ind	The company belongs to this industry, coded as 1, otherwise as 0
	Year	Year	The company belongs to this year, coded as 1, otherwise as 0

provides the definitions and measurement details of all variables.

3.3. Model Design

To empirically test the proposed hypotheses, we constructed the following econometric models by using a two-way fixed effects panel regression framework that accounts for both industry and year effects. We gradually introduce key independent variables, control variables, and interaction terms across Models (1) through (6) to test baseline effects and moderation effects in line with our hypotheses.

The dependent variable in all models is the SGR; the independent variable in models is ITDs or WTC. ${DT}_{i,t}$ and ${ICG}_{i,t}$ represent the digitalization level and corporate governance quality of firm i in year t, respectively.

3.3.1. Baseline Model

To examine Hypotheses 1 and 2, we constructed Models (1) and (2). ${SGR}_{i,t}$ represents firm i’s sustainability level in year t. ITDs and WTC capture the firm’s inventory turnover days and warehousing and transportation cost ratio, respectively. ${Control}_{i,t}$ represents the control variables.

${\phi }_Y$ and ${\gamma }_I$ are dummy variables that account for Year and Ind fixed effects, respectively. ${ϵ}_{i,t}$ is the error term. The models analyze the relationship between inventory turnover days, the warehousing and transportation cost ratio, and corporate sustainability. If the ${\alpha }_0$ is negative and statistically significant, it suggests that reducing inventory turnover days and lowering warehousing and transportation costs (i.e., improving supply chain efficiency) enhance corporate sustainability, thereby supporting Hypotheses 1 and 2.

$${SGR}_{i,t}={\alpha }_0+{\alpha }_1{IT}_{i,t}+\sum {Control}_{i,t}+{\phi }_Y+{\gamma }_I+{ϵ}_{i,t}$$

(1)

$${SGR}_{i,t}={\alpha }_0+{\alpha }_1{WTC}_{i,t}+\sum {Control}_{i,t}+{\phi }_Y+{\gamma }_I+{ϵ}_{i,t}$$

(2)

3.3.2. Moderating Effects Model

We incorporated moderating variables in the baseline regression models, including the interaction terms of digitalization level [71] with inventory turnover days, and the warehousing and transportation cost ratio. To test Hypotheses 3a and 3b, we constructed Models (3) and (4), respectively. Model (3) builds on Model (1) by adding the digitalization level and its interaction with inventory turnover days. Model (3) predicts that if the coefficients ${\alpha }_1$, for inventory turnover days, and ${\alpha }_2$, for interaction terms, are statistically significant and negative, the digitalization level positively affects the reduction in inventory turnover days, thus contributing to corporate sustainability and supporting Hypothesis 3a:

$${SGR}_{i,t}={\alpha }_0+{\alpha }_1{IT}_{i,t}+{\alpha }_2{IT}_{i,t}\times {DT}_{i,t}+{\alpha }_3{DT}_{i,t}+\sum {Control}_{i,t}+{\phi }_Y+{\gamma }_I+{ϵ}_{i,t}$$

(3)

Model (4) extends Model (2) by including the digitalization level and its interaction with the warehousing and transportation cost ratio. Model (4) predicts that if the coefficients ${\alpha }_1$, for inventory turnover days, and ${\alpha }_2$, for interaction terms, are statistically significant and negative, this suggests that the digitalization level positively impacts the reduction in warehousing costs, leading to enhanced corporate sustainability, thus supporting Hypothesis 3b:

$${SGR}_{i,t}={\alpha }_0+{\alpha }_1{WTC}_{i,t}+{\alpha }_2{WTC}_{i,t}\times {DT}_{i,t}+{\alpha }_3{DT}_{i,t}+\sum {Control}_{i,t}+{\phi }_Y+{\gamma }_I+{ϵ}_{i,t}$$

(4)

To test Hypotheses 4a and 4b, we constructed Models (5) and (6), respectively. Similarly, if the coefficients ${\alpha }_1$, for inventory turnover days, and ${\alpha }_2$, for interaction terms, are statistically significant and negative, Hypotheses 4a and 4b are supported:

$${SGR}_{i,t}={\alpha }_0+{\alpha }_1{IT}_{i,t}+{\alpha }_2{IT}_{i,t}\times {ICG}_{i,t}+{\alpha }_3{ICG}_{i,t}+\sum {Control}_{i,t}+{\phi }_Y+{\gamma }_I+{ϵ}_{i,t}$$

(5)

$${SGR}_{i,t}={\alpha }_0+{\alpha }_1{WTC}_{i,t}+{\alpha }_2{WTC}_{i,t}\times {ICG}_{i,t}+{\alpha }_3{ICG}_{i,t}+\sum {Control}_{i,t}+{\phi }_Y+{\gamma }_I+{ϵ}_{i,t}$$

(6)

3.3.3. Statistical Methods

First, we used descriptive statistics to observe the sample data’s distribution characteristics, ensuring that the data met the assumptions for linear regression. Second, we conducted a correlation analysis by calculating Pearson’s correlation coefficients to preliminarily assess the relationships between the variables. We also calculated the variance inflation factor (VIF) to check for multicollinearity issues. Third, before performing linear regression, we applied the Hausman test to determine whether to use a random or fixed effects model, ensuring the applicability of the model to the sample data. Finally, we performed linear regression analysis using alternative explanatory variables and two-stage least squares (2SLS) to ensure the robustness of the results.

4. Results

4.1. Descriptive Statistics

Table 3. Descriptive statistics.

Variable	N	Mean	p50	SD	Min	Max
SGR	8000	6.686	5.717	5.254	−8.939	45.40
ITDs	8000	4.576	4.569	0.767	−0.562	8.074
WTC	8000	1.145	0.872	1.082	0	10.64
DT	8000	0.824	0.544	0.827	0	5.615
ICG	8000	−0.0240	1.250	9.585	−27.31	25.52
Size	8000	22.16	22.01	1.168	18.95	27.57
Lev	8000	39.81	39.54	18.07	0.906	97.32
Cashflow	8000	0.492	0.474	0.618	−3.189	4.382
Growth	8000	0.177	0.119	0.376	−0.913	7.619
TobinQ	8000	1.912	1.586	1.078	0.699	14.82
Fimrage	8000	2.833	2.890	0.352	1.099	3.989
Big4	8000	0.0480	0.000	0.213	0	1
Bm	8000	0.631	0.631	0.233	0.0670	1.430

reports the descriptive statistics. The skewness and kurtosis of all data meet the requirements for a normal distribution. The mean and standard deviation of the corporate sustainability growth rate (SGR) are 6.686 and 5.254, respectively, indicating that sales growth rates vary across companies, with significant differences among them. The median SGR is 5.717, which is lower than the mean, suggesting that more than half of the Chinese listed companies exhibit relatively low levels of sustainability. This finding aligns with the literature [69] on the sustainability of Chinese companies. Clearly, improving sustainability through digital transformation is crucial for firms’ long-term development. The median ITDs is 4.569, which is close to the mean of 4.576, indicating that approximately half of the sample companies have moderate inventory turnover days. The standard deviation of 0.767 suggests minimal variation across companies, with relatively consistent efficiency in inventory management. The mean WTC is 1.145, while the median is 0.872. Thus, most companies have relatively low warehousing and transportation cost ratios, though some companies exhibit higher values. The standard deviation of 1.082 and range of 0 to 10.64 demonstrate significant variation across companies. The mean DT is 0.824, while the median is 0.544, suggesting that most companies have low digitalization. The standard deviation of 0.827 shows considerable variation in digitalization levels across companies. Overall, substantial disparity in digital transformation levels exists among Chinese listed companies, consistent with extant research [72]. The mean ICG is −0.0240, with a median of 1.250, indicating that most companies have high internal governance levels. The standard deviation of 9.585 reveals significant variability in governance levels across companies.

4.2. Correlation Analysis

As shown in

Table 4. Correlation analysis.

	SGR	ITDs	WTC	DT	ICG	Size	Lev	Cashflow	Growth	TobinQ	Fimrage	Big4	Bm
SGR	1
ITDs	−0.095 ***	1
WTC	−0.138 ***	0.050 ***	1
DT	0.051 ***	0.031 ***	−0.191 ***	1
ICG	0.022 **	−0.168 ***	−0.127 ***	−0.127 ***	1
Size	0.093 ***	−0.206 ***	−0.143 ***	0.0160	0.442 ***	1
Lev	0.001	−0.103 ***	−0.065 ***	−0.055 ***	0.321 ***	0.542 ***	1
Cashflow	0.261 ***	−0.160 ***	0.0110	−0.029 **	0.040 ***	0.044 ***	−0.129 ***	1
Growth	0.205 ***	−0.058 ***	−0.048 ***	0.020 *	−0.082 ***	0.000	0.020 *	−0.058 ***	1
TobinQ	0.199 ***	0.142 ***	−0.058 ***	0.092 ***	−0.079 ***	−0.327 ***	−0.287 ***	0.123 ***	0.034 ***	1
Fimrage	0.041 ***	−0.065 ***	−0.115 ***	0.078 ***	0.164 ***	0.266 ***	0.170 ***	0.096 ***	−0.053 ***	0.002	1
Big4	0.035 ***	−0.121 ***	−0.020 *	−0.023 **	0.159 ***	0.321 ***	0.140 ***	0.043 ***	−0.019 *	−0.084 ***	0.068 ***	1
Bm	−0.220 ***	−0.183 ***	0.053 ***	−0.118 ***	0.165 ***	0.473 ***	0.357 ***	−0.118 ***	−0.060 ***	−0.821 ***	0.053 ***	0.136 ***	1

Note: Robust t-statistics are shown in parentheses; * represents p < 0.1, ** represents p < 0.05, and *** denotes p < 0.01.

, Pearson’s correlation coefficients were used to represent the relationships between all variables. The correlation coefficients of inventory turnover days and the warehousing and transportation cost ratio with corporate sustainability are −0.095 and −0.138, respectively. Both coefficients were statistically significant and negatively correlated with corporate sustainability. These two variables, representing supply chain efficiency, suggest that reducing inventory turnover days and lowering warehousing and transportation costs (i.e., improving supply chain efficiency) can enhance corporate sustainability. Thus, Hypotheses 1 and 2 have some preliminary support. The VIF values are lower than three, indicating no significant multicollinearity issues.

4.3. Regression Results

A Hausman test was conducted before the regression analysis to ensure the accuracy of the regression results. The p-values of the test were all 0.000, indicating that the fixed effects model is more appropriate. Therefore, we used a two-way fixed effects model that includes fixed effects for both industry and year.

Table 5. Regression results.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	SGR	SGR	SGR	SGR	SGR	SGR
ITDs	−0.399 ***		−0.407 ***		−0.408 ***
	(−5.32)		(−5.41)		(−5.44)
WTC		−0.247 ***		−0.280 ***		−0.294 ***
		(−4.72)		(−5.33)		(−5.21)
ICG			−0.047 ***	−0.049 ***
			(−6.01)	(−6.25)
ITDs × ICG			−0.015 **
			(−2.07)
WTC × ICG				−0.010 **
				(−2.00)
DT					0.040	−0.100
					(0.55)	(−1.18)
ITDs × DT					−0.188 **
					(−2.22)
WTC × DT						−0.198 **
						(−2.29)
Size	1.173 ***	1.154 ***	1.325 ***	1.307 ***	1.166 ***	1.153 ***
	(17.58)	(17.19)	(18.61)	(18.29)	(17.47)	(17.17)
Lev	0.003	0.003	0.006	0.006 *	0.003	0.003
	(0.75)	(0.89)	(1.53)	(1.73)	(0.74)	(0.83)
Cashflow	1.887 ***	1.982 ***	1.878 ***	1.984 ***	1.893 ***	1.986 ***
	(21.08)	(22.44)	(21.03)	(22.51)	(21.10)	(22.43)
Growth	2.680 ***	2.713 ***	2.615 ***	2.640 ***	2.683 ***	2.715 ***
	(19.13)	(19.40)	(18.62)	(18.85)	(19.15)	(19.41)
Top1	0.006	0.006 *	0.021 ***	0.022 ***	0.006	0.007 *
	(1.50)	(1.65)	(4.57)	(4.82)	(1.51)	(1.72)
Fimrage	−0.199	−0.190	0.052	0.076	−0.205	−0.192
	(−1.16)	(−1.11)	(0.30)	(0.43)	(−1.19)	(−1.12)
Big4	−0.243	−0.153	−0.310	−0.192	−0.223	−0.141
	(−0.95)	(−0.60)	(−1.21)	(−0.75)	(−0.87)	(−0.55)
Bm	−8.919 ***	−8.499 ***	−9.206 ***	−8.725 ***	−8.871 ***	−8.484 ***
	(−20.06)	(−19.04)	(−20.66)	(−19.54)	(−19.90)	(−18.98)
Constant	−13.117 ***	−14.634 ***	−17.671 ***	−19.314 ***	−12.987 ***	−14.515 ***
	(−8.89)	(−10.37)	(−10.71)	(−12.08)	(−8.78)	(−10.25)
Observations	8000	8000	8000	8000	8000	8000
R-squared	0.238	0.238	0.242	0.242	0.239	0.238
year FE	YES	YES	YES	YES	YES	YES
industry FE	YES	YES	YES	YES	YES	YES

Note: The t-values are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

presents the regression results for ITDs and WTC in relation to corporate sustainability. After controlling for multiple variables, the regression coefficients for ITDs and WTC with the SGR are −0.399 and −0.247, respectively, and significantly negative at the 1% level. Thus, fewer inventory turnover days and lower warehousing costs are associated with higher sustainability, indicating that higher supply chain efficiency leads to greater corporate sustainability. Hence, Hypotheses 1 and 2 are supported.

An increase in digitalization strengthens the relationship between supply chain efficiency and corporate sustainability. After controlling for multiple variables, in Model (5), the coefficient of ITDs is −0.408, while that for the interaction term with digitalization (ITDs × DT) is −0.188. Both coefficients are significantly negative at the 5% level. This indicates that digitalization amplifies the negative effect of ITDs on sustainability; that is, increased digitalization enhances the positive impact of reducing inventory turnover days on sustainability. Thus, Hypothesis 3a is supported.

In Model (6), the coefficient for WTC is −0.294, while that for the interaction term with digitalization (WTC × DT) is −0.198. Both coefficients are significantly negative at the 5% level. Thus, the lower the warehousing and transportation cost ratio, the greater the potential for corporate sustainability. Moreover, when digitalization levels increase, this positive effect is further amplified; that is, increased digitalization enhances the positive impact of reducing warehousing costs on sustainability. Thus, Hypothesis 3b is supported.

In summary, digitalization positively impacts both supply chain efficiency and corporate sustainability.

A firm’s internal governance strengthens the relationship between supply chain efficiency and corporate sustainability. After controlling for multiple variables, in Model (3), the coefficient for ITDs is −0.407, while that for the interaction term with internal corporate governance (ITDs × ICG) is −0.015. Both coefficients are significantly negative at the 5% level. Thus, fewer ITDs are associated with greater corporate sustainability, while higher internal governance strengthens this effect. Therefore, improving internal governance increases the positive impact of reducing inventory turnover days on sustainability. Thus, Hypothesis 4a is supported.

Similarly, in Model (4), the coefficient for WTC is −0.280, while that for the interaction term with internal governance (WTC × ICG) is −0.010. Both coefficients are significantly negative at the 5% level. This indicates that internal governance amplifies the negative effect of WTC on corporate sustainability. Therefore, improving internal governance amplifies the positive effect of reducing warehousing and transportation costs on sustainability. Thus, Hypothesis 4b is supported.

In summary, enhanced internal governance positively affects both supply chain efficiency and corporate sustainability.

4.4. Robustness Tests

We employed some robustness checks on the main findings to ensure their reliability. First, we used an alternative independent variable. Second, we employed 2SLS regression to address issues of endogeneity and reciprocal causality [73].

4.4.1. Replacing the Dependent Variable

We replaced the SGR with the long-term sustainable growth rate (LSGR) from the CSMAR database.

Table 6. Robustness test: changing explanatory variable.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	LSGR	LSGR	LSGR	LSGR	LSGR	LSGR
ITDs	−0.372 ***		−0.374 ***		−0.379 ***
	(−5.58)		(−5.58)		(−5.68)
WTC		−0.217 ***		−0.242 ***		−0.258 ***
		(−4.65)		(−5.17)		(−5.13)
ICG			−0.032 ***	−0.034 ***
			(−4.61)	(−4.81)
ITDs × ICG			−0.014 **
			(−2.07)
WTC × ICG				−0.011 **
				(−2.32)
DT					0.028	−0.093
					(0.42)	(−1.24)
ITDs × DT					−0.152 **
					(−2.02)
WTC × DT						−0.171 **
						(−2.23)
Size	1.022 ***	1.007 ***	1.126 ***	1.111 ***	1.017 ***	1.006 ***
	(17.22)	(16.85)	(17.76)	(17.45)	(17.12)	(16.83)
Lev	0.002	0.003	0.004	0.005	0.002	0.002
	(0.66)	(0.80)	(1.26)	(1.47)	(0.65)	(0.74)
Cashflow	1.820 ***	1.908 ***	1.815 ***	1.912 ***	1.825 ***	1.911 ***
	(22.86)	(24.27)	(22.81)	(24.34)	(22.85)	(24.25)
Growth	2.022 ***	2.054 ***	1.979 ***	2.005 ***	2.024 ***	2.055 ***
	(16.21)	(16.50)	(15.82)	(16.07)	(16.23)	(16.51)
TobinQ	0.039	0.039	0.053	0.054	0.042	0.042
	(0.51)	(0.51)	(0.70)	(0.71)	(0.55)	(0.55)
Fimrage	0.014	0.024	0.186	0.208	0.008	0.021
	(0.09)	(0.16)	(1.18)	(1.33)	(0.06)	(0.14)
Big4	−0.198	−0.116	−0.249	−0.143	−0.182	−0.106
	(−0.87)	(−0.51)	(−1.09)	(−0.63)	(−0.80)	(−0.46)
Bm	−7.855 ***	−7.477 ***	−8.063 ***	−7.640 ***	−7.819 ***	−7.466 ***
	(−19.86)	(−18.82)	(−20.31)	(−19.21)	(−19.71)	(−18.76)
Constant	−11.556 ***	−13.030 ***	−14.676 ***	−16.221 ***	−11.445 ***	−12.916 ***
	(−8.81)	(−10.37)	(−9.98)	(−11.38)	(−8.69)	(−10.24)
Observations	8000	8000	8000	8000	8000	8000
R-squared	0.230	0.229	0.232	0.232	0.230	0.229
year FE	YES	YES	YES	YES	YES	YES
industry FE	YES	YES	YES	YES	YES	YES

Note: The t-values are in parentheses. **, and *** indicate significance at the 15%, and 1% levels, respectively.

shows that both the ITD and WTC coefficients are negative and statistically significant in the first and second rows. Thus, fewer inventory turnover days and a lower warehouse and transport cost ratio positively impact corporate sustainability, further supporting Hypotheses 1 and 2. As shown in the fourth and fifth rows of Table 6, the interaction term coefficients are negative and statistically significant. Thus, higher digitalization levels and good internal governance positively moderate the effect of reducing inventory turnover days and warehousing costs on corporate sustainability. Thus, internal governance and digitalization levels actively enhance the impact of supply chain efficiency on corporate sustainability. This further validates Hypotheses 3a, 3b, 4a, and 4b.

4.4.2. Two-Stage Least Squares Regression

To address potential reverse causality issues, we selected the one-period lagged values of inventory turnover days and warehousing transportation costs (L.ITDs and L.WTC) as instrumental variables, and performed a regression analysis using 2SLS.

Table 7. Robustness test: 2SLS results.

	(1)	(2)	(3)	(4)
	Stage 1	Stage 2	Stage 1	Stage 2
Variables	ITDs	SGR	WTC	SGR
L.ITDs	0.950 ***
	(152.32)
ITDs		−0.758 ***
		(−7.41)
L.WTC			0.873 ***
			(57.97)
WTC				−0.189 **
				(−2.50)
Size	−0.001	1.127 ***	−0.009	1.160 ***
	(−0.19)	(11.93)	(−1.04)	(12.27)
Lev	−0.000	0.011 **	−0.001	0.010 **
	(−0.25)	(2.23)	(−1.39)	(2.07)
Cashflow	−0.068 ***	1.901 ***	−0.011	2.075 ***
	(−11.30)	(13.45)	(−1.00)	(14.87)
Growth	−0.316 ***	3.391 ***	−0.154 ***	3.521 ***
	(−7.67)	(8.73)	(−6.26)	(8.84)
TobinQ	−0.005	0.070	0.022	0.062
	(−0.96)	(0.50)	(1.55)	(0.47)
Fimrage	−0.022 *	−0.305	−0.006	−0.263
	(−1.77)	(−1.36)	(−0.29)	(−1.17)
Big4	0.003	−0.603 *	−0.050	−0.468
	(0.22)	(−1.81)	(−1.42)	(−1.41)
Bm	−0.094 ***	−8.790 ***	0.214 ***	−8.329 ***
	(−3.52)	(−14.80)	(3.35)	(−14.18)
Constant	0.485 ***		0.198
	(5.05)		(1.00)
Observations	4505	4505	4505	4505
R-squared	0.921	0.837	0.241	0.240
year FE	YES	YES	YES	YES
industry FE	YES	YES	YES	YES
Kleibergen–Paap rk LM statistic	Chi-sq (1) = 975.65 p-val = 0.0000		Chi-sq (1) = 509.868 p-val = 0.0000
Kleibergen–Paap rk Wald F statistic	23201.97		3360.342
10% maximal IV size	16.38		16.38

Note: The t-values are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

shows that, in the first stage, L.ITDs and L.WTC are significantly positive (L.ITDs = 0.950, L.WTC = 0.873) at the 1% significance level. In the second stage, the coefficients of ITDs and WTC on corporate sustainability are significantly negative (ITDs = −0.758, WTC = −0.189) at the 1% significance level. Thus, even after controlling for endogeneity issues, the core variables still have a significant negative impact on corporate sustainability.

Moreover, the Kleibergen–Paap rk LM statistics are 984.816 and 509.868, with p-values of 0.000, refuting the null hypothesis of “insufficient instrument identification.” The Kleibergen–Paap rk Wald F statistics are 23,201.97 and 3360.342, both exceeding the 10% critical value (16.38) in the Stock–Yogo weak identification test. This further confirms that the chosen instrumental variables do not suffer from weak identification issues.

In conclusion, the direction and significance of the effects of the ITDs and WTC remain robust after controlling for endogeneity, further validating the empirical findings.

5. Discussion and Conclusions

5.1. Discussion

Supply chain management plays a crucial role in enhancing corporate competitiveness and achieving long-term development goals. It also serves as an essential tool to boost a firm’s adaptability and flexibility while strengthening strategic resilience [74]. Well-executed supply chain management has two important aspects. First, it enhances a company’s capacity to handle unexpected external challenges. Second, and perhaps more crucially, it is vital for maintaining competitive advantages that last [75].

While extant research has examined how supply chain efficiency is connected to business growth, most studies concentrate on immediate benefits, such as higher profit margins or improved cash flow situations [76]. A thorough investigation of how supply chain efficiency systematically influences a company’s long-term sustainability remains elusive.

Another issue is how researchers measure supply chain efficiency. Several studies used broad-based indicators to provide an overall picture, including general cost ratios and subjective performance ratings [77,78]. However, these methods do not reveal what actually happens in the most important supply chain operations. Consequently, they may not accurately reflect the real effectiveness of supply chain processes. The lack of targeted and actionable measurement methods means that the pathway through which supply chain efficiency influences a company’s sustainable strategic goals remains unclear, thus creating a gap in the research on how supply chains contribute to corporate sustainability.

Addressing these gaps, we advance supply chain management and sustainability research in three ways. First, we clearly define and precisely measure the critical dimensions of supply chain efficiency by constructing a refined measurement framework centered on inventory turnover days and the warehousing transportation cost ratio. Second, we reveal the mechanisms through which supply chain efficiency influences corporate sustainability at the micro level and empirically demonstrate the positive impact of supply chain efficiency on corporate sustainability. In particular, better inventory management and reduced logistics costs directly improve corporate sustainability. Third, this study reveals the moderating roles of digital transformation and internal governance as critical capability-based resources in the relationship between supply chain efficiency and corporate sustainability. The findings indicate that both digitalization and internal governance significantly enhance the positive impact of supply chain efficiency on corporate sustainability, aligning with most existing research. For instance, Yao et al. [79] emphasize that the application of digital technologies substantially improves firms’ responsiveness and resource allocation efficiency, thereby strengthening the link between supply chain efficiency and sustainable performance. Similarly, Govindan et al. [39] highlight that a sound internal governance structure provides effective decision-making, oversight, and risk management mechanisms, which ensure that supply chain efficiency improvements are effectively implemented, ultimately amplifying their contribution to long-term sustainable development.

Moreover, our findings are consistent with related studies in other countries. For instance, studies in Mexico and Europe confirm that digital tools enhance supply chain flexibility and that strong internal governance supports sustainable development [80,81]. These comparisons suggest that the mechanisms identified in this study have broader applicability across different institutional settings.

To achieve real, long-term sustainability, businesses must simultaneously develop three key capabilities: they must maintain efficient operations while also building digital intelligence and organizational resilience. Only by combining these elements can companies create supply chain systems that are truly “efficient, smart, and adaptable” to changing market conditions. This integrated approach represents the path toward sustainable competitive advantage.

5.2. Conclusions

As globalization and digitalization accelerate, businesses must operate in more complex environments. They also face stricter sustainability rules. The supply chain is the central part that connects companies to the market. In particular, digital transformation and better governance can help improve supply chain efficiency. By drawing on longitudinal data from A-share listed companies in China (2010–2022), this research contributes novel empirical evidence from an emerging economy, thereby expanding the global relevance of sustainability [82]. We examine how supply chain efficiency, digitalization, and corporate governance affect corporate sustainability in Chinese publicly listed companies.

While prior literature has largely focused on the short-term financial benefits of supply chain management—such as improved profitability or cash flow [83,84]—this study shifts the focus to long-term corporate sustainability. We find that supply chains significantly impact sustainability. When companies reduce their inventory turnover days, their sustainability improves. Cutting warehouse and transportation costs is also helpful. Better supply chain efficiency does more than just improve profits; it also reduces waste and carbon emissions, which are beneficial to the environment and society, supporting Hypotheses 1 and 2. Thus, making supply chains more efficient is key to comprehensively achieving sustainability.

Moreover, this study introduces a novel dual-moderator model by examining the roles of digitalization and internal governance. While previous studies have considered these elements separately [85,86], this research integrates both into a unified framework, providing empirical support for their interactive effects on the relationship between supply chain efficiency and sustainability.

The empirical results support Hypothesis 3a, indicating that digitalization significantly amplifies the positive impact of inventory turnover efficiency on corporate sustainability. Firms have improved supply chain intelligence and transparency and strengthened their resource utilization and market responsiveness through technologies like big data analytics, the IoT, and blockchain. Digital transformation improves firms’ operational efficiency and helps them remain competitive in a rapidly changing market. Hypothesis 3b is also supported, showing that digitalization enhances the effect of warehousing and transportation cost reduction on sustainability, further confirming its critical role in enabling environmentally and economically efficient supply chain practices.

Furthermore, internal corporate governance plays a significant moderating role, supporting both Hypothesis 4a and Hypothesis 4b. Specifically, Hypothesis 4a shows that internal governance strengthens the positive relationship between inventory turnover efficiency and sustainability, while Hypothesis 4b confirms that governance also enhances the effect of logistics cost efficiency on sustainable outcomes. Governance optimization enables firms to pay more attention to environmental and social responsibilities in supply chain management, thereby promoting sustainable development while achieving long-term strategic goals. A good governance mechanism is essential for firms to achieve efficient resource utilization and responsibility fulfillment.

As such, firms should improve their supply chain efficiency to achieve more comprehensive sustainable development. Meanwhile, firms need to actively improve their level of digitalization and internal governance to better adapt to market changes and policy requirements and provide good conditions for improving supply chain efficiency.

5.3. Implications

Theoretically, firstly, this study enriches the theory of supply chain management. Specifically, we analyze supply chain efficiency in a multidimensional manner, providing a new perspective for research on the intersection of supply chain management and sustainable development. This introduces a systematic and multidimensional analysis paradigm to supply chain management research. Secondly, we reveal digitalization and internal corporate governance as moderating variables in the relationship between supply chain efficiency and sustainability, thereby providing a theoretical foundation for future studies and deepening supply chain efficiency research. Finally, we contribute evidence from developing countries using data from publicly listed companies in China, thereby highlighting the unique impact of supply chain efficiency on sustainability in various economic contexts. This provides both empirical support for supply chain management theory in the context of globalization and offers guidance for developing countries to explore more efficient supply chain management models.

Practically, firstly, this study can guide companies to optimize their supply chains. Companies should focus on inventory management and the optimization of warehouse logistics, reducing costs, and minimizing resource waste by improving supply chain efficiency, thus achieving an economic–environmental win–win. This can help companies achieve their short-term goals while supporting long-term sustainable development. Secondly, digital transformation should be promoted. Companies should actively adopt digital technologies to enhance supply chain transparency and collaboration, enabling them to adapt better to market changes and policy requirements. Digital technology application can further enhance firms’ ability to cope with complex supply chain challenges. Thirdly, governance structures should be strengthened, including internal governance mechanisms, and integrate environmental and social responsibilities into strategic planning to better meet sustainable development challenges. A sound governance structure can improve firms’ efficiency and sustainability performance in supply chain management. Fourthly, firms should not only seek efficiency gains for immediate benefits but also embed supply chain strategies within long-term development frameworks. This is particularly critical in an era of heightened global uncertainty and sustainability-driven regulation, where long-term resilience defines competitive success.

5.4. Limitations and Future Research Directions

We only used a sample of Chinese listed companies, limiting the generalizability to other countries or regions. Future studies should explore different economies and industries.

Further, this study does not filter out firms that issued new equity or took on significant new debt during the study period. Such changes in capital structure may affect corporate financial decisions and indirectly influence sustainability outcomes. Although control variables and fixed effects were included to reduce bias, the lack of direct screening of capital structure changes remains a limitation. Future research could improve upon this by incorporating capital issuance events or leverage changes as exclusion criteria or control variables to more precisely isolate the causal impact of operational efficiency on sustainability.

Finally, we did not fully investigate external influences, like government policies and industry trends, which can affect how supply chain efficiency influences sustainability. Examining how policies, industry features, and economic situations impact supply chains can help create a more complete understanding. Adding these elements to future studies will improve the overall research approach and provide deeper insights.