The Impact of Supply Chain Finance on the Investment Efficiency of Publicly Listed Companies in China Based on Sustainable Development

Abstract

The development and utilization of supply chain finance play a pivotal role in both enhancing financial structures and delivering substantial support for the sustainable progress of the real economy. This assistance is essential for promoting high-quality economic growth and ensuring stable, long-term development. This study empirically examines the effects of supply chain finance on investment efficiency, exploring the underlying mechanisms involved. Additionally, it assesses whether financing constraints and information asymmetry serve as mediating variables in the relationship between supply chain finance and investment efficiency among enterprises. The analysis is based on data from publicly listed companies in China covering the period from 2013 to 2022. The results indicate that supply chain finance effectively addresses both overinvestment and underinvestment issues, leading to a notable improvement in overall investment efficiency. Utilizing a two-way fixed effects model to analyze the role of financing constraints and information asymmetry as mediating variables, the study demonstrates that both factors significantly mediate the relationship between supply chain finance and investment efficiency within enterprises. Supply chain finance improves investment efficiency by mitigating financing constraints and lessening information asymmetry between enterprises and external stakeholders. The heterogeneity analysis reveals that the positive impact of supply chain finance on investment efficiency is notably greater in non-state-owned enterprises and in regions with more advanced development.

1. Introduction

The rise of financial innovation has given birth to supply chain finance as a new and distinct financing mechanism. This model provides clear benefits and is progressively becoming essential for addressing financing challenges and optimizing capital allocation. Supply chain finance combines supply chain supervision with financial services to provide enterprises within the supply chain with more flexible and efficient capital solutions. This integration aligns logistics, information flow, and capital flow, enhancing overall operational effectiveness. Unlike traditional financial services that focus solely on evaluating the creditworthiness of individual enterprises, this model expands the evaluation to encompass the entire supply chain network. Supply chain finance capitalizes on the creditworthiness of core enterprises and the reliability of transaction data to offer credit enhancement services to enterprises within the supply chain. This approach effectively reduces the financing thresholds and costs for these enterprises.

The effective allocation of resources is crucial for the sustained growth of the real economy. As a key indicator for measuring resource allocation effectiveness, it has consistently been a core concern of corporate financial strategy [1]. In May 2022, the State Council of China released a document titled ‘Guiding Opinions on Further Activating Stock Assets to Expand Effective Investment’, emphasizing the importance of diversifying investment pathways and appropriately increasing effective investment. Rational resource allocation in investment decisions not only affects a firm’s competitiveness and future development potential but also has significant implications for the efficiency of macroeconomic resource allocation and its development level. However, managers are often constrained by bounded rationality, which may lead to suboptimal investment decisions, resulting in either overinvestment or underinvestment. Therefore, enhancing investment efficiency is crucial to promoting the structural optimization, transformation, and upgrading of Chinese enterprises.

In contrast to conventional bank lending, supply chain finance encompasses resource integration across all stages of the supply chain, utilizing both transaction and capital flow data to provide a comprehensive view of enterprise operations. This approach offers financial services not only to core enterprises but also to their upstream and downstream partners within the supply chain. As an integral component of the supply chain, supply chain finance facilitates enhanced information sharing, resource coordination, and organizational connectivity among partners. Research has demonstrated that the adoption of supply chain finance can expand credit financing options, strengthen bank relationships, boost external financing capabilities and performance, mitigate financing constraints, lower transaction costs, increase investment in innovation, and support specialization. Additionally, it fosters the development of robust supply chain relationships, allowing firms to more effectively utilize and integrate resources and knowledge within the supply chain.

Recent studies predominantly investigate the economic implications of supply chain finance. Primarily, this financial model addresses the credit deficiencies encountered by small and medium-sized enterprises (SMEs) by diminishing information asymmetry between these firms and financial institutions, which effectively eases financing constraints. Within the framework of digital empowerment, the innovative evolution of supply chain finance further bolsters the core competitiveness of enterprises, thereby enhancing productivity and specialization levels. Additionally, research has analyzed the influence of supply chain finance on corporate performance. In China, reforms promoting supply chain finance have led to a notable increase in the economic value added (EVA) by core enterprises, particularly within the digital economy. Furthermore, supply chain finance is instrumental in fostering corporate innovation. By optimizing the flow and sharing of resources across the supply chain, it significantly accelerates technological innovation output and yields substantial benefits for high-quality innovation performance.

However, the broader economic implications of how supply chain finance influences firm investment efficiency remain under explored. Investment efficiency, an essential metric for evaluating how effectively a firm allocates its capital, both reveals the effectiveness of capital resource utilization and provides a vital gauge of the firm’s growth potential and market competitiveness. This study investigates publicly traded companies in China over the period from 2013 to 2022, analyzing the impact of supply chain finance on enterprise investment efficiency and elucidating the underlying mechanisms through empirical methods. The contributions of this study are as follows. Firstly, this study contributes to the literature on the economic implications of supply chain finance. Although there is substantial research from both financial and supply chain perspectives, especially concerning its influence on financial decision-making, empirical studies exploring how supply chain finance impacts corporate financial behavior are relatively scarce. This paper provides empirical evidence on the effects of supply chain financing on firms’ investment efficiency. Secondly, existing studies often focus either on financial attributes or supply chain attributes; furthermore, there is limited research on the pivotal role of core enterprises in the interplay between supply chain financing and investment efficiency. This study addresses these gaps by integrating these perspectives. This study aims to fill this gap by examining whether supply chain finance could improve investment efficiency. It provides both theoretical insights and empirical evidence on its role in fostering the economic growth of micro-companies and contributing to the broader macroeconomic real economy.

2. Theoretical Analysis and Research Hypothesis

2.1. Theoretical Analysis

Supply chain finance (SCF) represents an innovative integration of financial practices, supply chain management, and industrial processes. Currently, there is no consensus in the theoretical literature on the precise definition of supply chain finance, and no standardized definition exists. Research in this field predominantly explores two main perspectives: the “finance-oriented” and the “supply chain-oriented”. The finance-oriented perspective examines the financial aspects of supply chain finance, focusing on comprehensive financial solutions offered by financial institutions, with an emphasis on capital acquisition and cost management. The supply chain-oriented perspective highlights the importance of collaboration among various stakeholders within the supply chain, such as manufacturers, suppliers, retailers, distributors, and end users. This approach investigates how to optimize cooperation within a network framework by focusing on procurement, inventory management, transportation, financing, and other commercial activities. The goal is to maximize overall benefits and effectively plan, manage, and oversee the flow of funds. Klapper [2] identifies movable asset financing, including inventory financing, as a key method within supply chain finance. This strategy effectively mitigates the financing gap faced by small and medium-sized enterprises (SMEs) and reduces the financial costs associated with supply chain management. Sadlovska [3] highlights that supply chain financing allows financial institutions to access detailed transaction information and financing costs associated with supply chain enterprises. This capability facilitates the optimization of fund allocation throughout the supply chain. Comelli et al. [4] argue that supply chain finance involves the integration and utilization of funds and logistics information by core enterprises across the entire supply chain. This approach is designed to lower operational costs and stabilize production processes. According to Wuttke [5] and other researchers, supply chain finance is characterized as a self-repaying trade finance model rooted in actual transactions. Its primary aim is to streamline the flow of capital among supply chain enterprises, fostering a mutually beneficial ecosystem that includes core enterprises, upstream and downstream firms, and financial institutions. This approach seeks to enhance the overall competitiveness of the industry. Gelsomino et al. [6] provide an overview of the evolution and emerging trends in supply chain finance through a systematic review. Their analysis encompasses papers published from 2000 to 2014, focusing on key concepts and solutions in the field. Xu et al. [7] extended this research by incorporating bibliometrics, network analysis, and content analysis to complement Gelsomino et al.s’ work. Chakuu et al. [8] further explored the relationships between financing mechanisms, participants, and financial instruments within the background of supply chain finance.

Recent research demonstrates that supply chain finance has a beneficial impact on enterprises across various dimensions: it not only enhances shareholder value [9] but also promotes corporate innovation efficiency [10]. Furthermore, supply chain finance effectively alleviates financial pressures, improves efficiency of operation and market influence [11,12,13], and increases cash reserves [14,15]. However, existing research has not sufficiently addressed how supply chain finance impacts corporate investment efficiency. Investigating the application of supply chain finance tools in promoting sustainable growth and improving investment efficiency is of great practical significance in enhancing enterprise market value.

Academic research on corporate investment efficiency primarily focuses on analyzing the factors influencing this efficiency. On the one hand, some scholars argue that external conditions, such as improvements in the legal environment, financial market maturity, and the formulation of macro-industrial policies, significantly impact firms’ investment efficiency. For instance, Mclean et al. [16] demonstrate that the deepening of financial markets can markedly enhance companies’ investment efficiency. Additionally, some scholars focus on examining the internal factors that drive investment efficiency through the lens of corporate governance mechanisms, such as financial health, board composition, and agency concerns. Chen [17] discovered that high-quality accounting information improves investment efficiency. Shin [18] in a 2020 study noted that the presence of female managers on the board, due to their inherent conservatism and prudence, helps curb potential overinvestment by firms.

A thorough review of the current literature reveals that scholarly attention predominantly centers on the impact of supply chain finance on financing constraints, enterprise valuation, financial performance, and innovation capacity. However, there are few studies on how supply chain finance affects firms’ investment efficiency. An accurate assessment of the efficiency of using funds is critical after the enterprise has received financial support. Consequently, investigating the potential effects of supply chain finance on enterprise investment efficiency and understanding the underlying mechanisms constitutes a critical research priority.

2.2. Research Hypothesis

Supply chain finance, with its innovative financing mechanisms, optimizes the flow and allocation of capital by integrating upper and lower stream resources within the supply chain. It offers a more flexible and efficient means for enterprises to secure funding. The practice of supply chain finance reflects a more stable cooperative relationship among firms within the supply chain [5]. Supply chain finance facilitates efficient labor division and cooperation among supply chain participants, enhances the flow of information, and mitigates challenges such as information asymmetry, financing constraints, and moral hazard. Moreover, it bolsters internal control quality, integrates essential resources, and optimizes resource allocation, ultimately improving investment efficiency for enterprises.

Supply chain finance has the potential to enhance corporate investment efficiency, and its impact can be observed in several key areas. Firstly, by integrating information, supply chain finance improves enterprises’ access to critical investment data, providing abundant informational resources, significantly reducing preliminary investigation costs, and stimulating investment enthusiasm. Secondly, in a rapidly evolving market environment, the market data provided by supply chain finance platforms is crucial for investment and financing decisions as well as daily management. It enables managers to conduct in-depth analyses of investment projects, accurately assess cash flows and risks, and address contradiction between information asymmetry and decision-making needs. Finally, the implementation of supply chain finance facilitates more efficient management of capital and logistics, reduces financing costs, accelerates capital turnover, and improves the timeliness and accuracy of investment decisions, thereby enhancing overall investment efficiency. In light of the preceding analysis, the following hypothesis is advanced:

Hypothesis 1.

Supply chain finance can improve enterprise investment efficiency.

The issue of information asymmetry poses significant challenges for enterprises, not only increasing financing costs and risks but also heightening the uncertainty of investment decisions, which can ultimately lead to reduced investment efficiency. Additionally, information asymmetry between investors and management may result in managerial moral hazard, thereby inducing inefficient investment behavior. Therefore, reducing information asymmetry both within and outside the enterprise helps investors more accurately assess firm value while effectively supervising internal behaviors, thereby promoting improved investment efficiency [17]. Firstly, supply chain finance can establish platforms for information convergence, offering more informational channels for firms within the supply chain [19], thus facilitating the identification of high-quality investment opportunities. By pooling and allocating information and resources based on the principle of benefit distribution, supply chain finance fosters cooperation between information-advantaged and information-disadvantaged firms, alleviates adverse selection issues, enhances information acquisition efficiency across the entire supply chain, and consequently improves investment efficiency. Secondly, supply chain finance facilitates the flow and dissemination of information throughout the supply chain. It enables financial institutions to more accurately assess a firm’s creditworthiness and repayment capacity by establishing real-time data systems, including order, inventory, and transaction records. Enhancing information transparency helps firms secure financial support at lower costs, allocate more resources to valuable projects, and increase investment efficiency. Finally, supply chain finance strengthens trust and collaboration between firms and external investors. By leveraging advanced technologies such as third-party platforms or block chain, supply chain finance ensures the authenticity and immutability of information, reduces information falsification and fraud, and establishes a trust mechanism. This makes external investors more willing to provide financial support, reducing negotiation and transaction costs for firms when seeking external financing, effectively identifying investment opportunities, accelerating the investment decision-making process, and further improving firms’ investment efficiency. In light of these considerations, we present the following hypothesis:

Hypothesis 2.

Supply chain finance enhances investment efficiency for enterprises by mitigating information asymmetry between them and external stakeholders.

In the current business environment, financing costs are a critical factor that enterprises must consider when making investment decisions. High financing costs not only increase the financial pressure on enterprises but also weaken their liquidity, ultimately affecting their investment capacity and efficiency. Stulz [20] investigates financing constraints and identifies a positive correlation between free cash flow and investment opportunities. Companies encountering projects with potential investment returns may often have to forgo these opportunities due to financing difficulties, which affects their capital allocation and results in inefficient investments. Firms facing stronger financing constraints experience insufficient investment more frequently than those with weaker constraints [21]. Therefore, alleviating financing constraints is crucial in optimizing enterprises’ strategic layout [22], promoting investment in technological transformation [23], and enhancing investment efficiency. Firstly, it can mitigate enterprises’ external financing constraints and provide the financial support necessary for improving investment efficiency and achieving higher economic returns. By integrating the upper and lower stream resources, supply chain finance facilitates the centralized management and optimal allocation of financing needs, helping to reduce enterprises’ transaction costs during the financing process, including those related to information search, negotiation, and supervision. Supply chain finance functions as a self-liquidating trade financing mechanism that capitalizes on the influence of core enterprises and the principles of value co-creation. It provides flexible short-term loan services, allowing businesses to swiftly convert short-term receivables and inventory into working capital. This conversion process boosts internal free cash flow and, as a result, reduces inefficient investments, thereby enhancing overall investment efficiency [5]. Secondly, supply chain finance can activate liquid assets within the supply chain. With the supply chain financing pattern, the collateral requirements of financial institutions for enterprises have expanded from traditional real estate to liquid assets such as accounts receivable, prepayments, and inventory. This expansion mitigates the financing constraints encountered by enterprises that lack collateral, thereby further enhancing investment efficiency. Finally, in the supply chain finance pattern, the creditworthiness of the core enterprise can be transmitted to its upper and lower stream mates, resulting in higher credit ratings and lower financing costs for these firms. This credit transmission mechanism addresses the financing challenges encountered by enterprises, lowers financing costs, mitigates funding difficulties, and improves overall investment efficiency. Accordingly, the following assumptions are made:

Hypothesis 3.

Supply chain finance enhances enterprise investment efficiency by mitigating financing constraints.

3. Research Design

3.1. Sample Selection and Data Sources

This study uses China’s listed companies from 2013 to 2022 as the research sample. The sample chosen excluded financial institutions, ST and PT companies, and entities with anomalous or incomplete data. Economic data for this research were obtained from the CSMAR repositories, and analysis was performed using STATA software.

3.2. Definition of Variables

3.2.1. Definition of Dependent Variables

In this study, we construct a model of firm investment efficiency based on Richardson’s research [24]. Model (1) is employed to obtain the dependent variable of this study, namely, firm investment efficiency. Regression analysis is then conducted to derive the residuals of the model. The absolute values of these residuals are used to evaluate the efficiency of a firm invest. Specifically, a larger absolute value of the residual indicates greater inefficiency in the company’s investment; i.e., lower investment efficiency. The model is defined as follows:

$$\begin{gathered} Inves{t}_{i,t}={\beta }_0+{\beta }_{1}Growt{h}_{i,t-1}+{\beta }_{2}Cas{h}_{i,t-1}+{\beta }_{3}Ag{e}_{i,t-1}+{\beta }_{4}Siz{e}_{i,t-1}+ \\ {\beta }_{5}Retu{n}_{i,t-1}+{\beta }_{6}Inves{t}_{i,t-1}+{\lambda }_i+{\delta }_i+{\epsilon }_{i,t} \end{gathered}$$

(1)

In model (1), the variable Invest denotes the investment spending of a publicly listed company. It is computed as follows: the total cash outflows for acquiring fixed assets, invisible assets, and other long-term property, plus the net cash spent on acquiring subsidiaries and business units, minus the net cash inflows from the sale of fixed assets, invisible assets, and other long-term assets, as well as the net cash received from the divestiture of branches and business units, all divided by the company’s total assets for the corresponding period. $Growt{h}_{t-1}$ means the growth chance of the company in T − 1 year, expressed as the growth rate of operating income; $Cas{h}_{t-1}$ means the corporation cash flow for T − 1, using net cash flow from operating activities/total assets at the beginning of the year; $Ag{e}_{t-1}$ is the age of the company, in terms of number of years on the market; $Siz{e}_{t-1}$ is the asset size of the company in T − 1, expressed in the natural logarithm of total assets; $Retur{n}_{t-1}$, is the stock return of a company in T − 1 years, expressed as an annual return on individual stocks that takes into account the reinvestment of cash dividends; $Inves{t}_{t-1}$ represents new investment expenditures in T − 1. In addition, industry virtual variables (λ) and year fictitious variables (δ) are included in the model. When the residual value is positive, it signifies overinvestment, labeled as Over_INV. Conversely, a negative residual value reflects underinvestment, which is quantified by taking its absolute value and is denoted as Under_INV.

3.2.2. Explanatory Variables

Supply chain finance (SCF) is a comparative newly financial product, so there is no dedicated database for it. However, based on available query data, measurement methods for SCF are primarily categorized into micro and macro indicators. The paper adopts the approach used by Yao et al. [25] to construct micro indicators. This measure of supply chain finance is quantified by dividing the combined value of short loans and notes payable by the total property of the enterprise.

3.2.3. Control Variables

The control variables utilized in this study are detailed as follows (

Table 1. Calculation method of control variable.

Variable	Method of Measurement
ROA	Net profit/balance of total assets
Lev	Total liabilities/total assets
Far	Net fixed assets/total assets
Board	Natural logarithm of the total number of board members
Growth	Operating income for the year/last year’s operating income
Age	Natural logarithm of the establishment years of the enterprise
SOE	State-owned enterprises are recorded as 1, and non-state-owned enterprises as 0
Size	The natural logarithm of total assets
Cash	Cash flow/total assets
Salary	Executive compensation level
Dual	Manager and chairman of the enterprise are the same person, 1 is taken, otherwise 0 is taken

): (1) return on assets (ROA); (2) asset-liability ratio (Lev); (3) fixed assets ratio (Lev); (4) growth rate of operating income (Growth); (5) age of enterprise (Age); (6) nature of property rights (SOE); (7) number of directors (Board); (8) size of the enterprise (Size); (9) cash ratio (Cash); (10) executive compensation level (Salary); (11) two in one (Dual).

3.3. Construction of the Model

To test Hypothesis 1, which posits that supply chain finance improves enterprise investment efficiency, Model (2) is specified with supply chain finance (SCF) as the explanatory variable and the degree of inefficient investment (INV) as the dependent variable. The design of this model is as follows:

$$IN{V}_{i,t}={\alpha }_0+{\alpha }_{1}SC{F}_{i,t}+\mathsf{\Sigma }{\alpha }_{i}Control{s}_{i,t}+\mathsf{\Sigma }Year+\mathsf{\Sigma }Industry+{\epsilon }_{i,t}$$

(2)

In this study, INV is designated as the dependent variable, representing investment efficiency as assessed using Richardson’s method. SCF is identified as the independent variable, which measures supply chain finance as previously described. The analysis also controls for various factors, including the net interest rate on total assets, the proportion of fixed assets, the asset-liability ratio, and executive compensation. Additionally, both year fixed effects and industry fixed effects are incorporated into the regression model. Hypothesis 1 will be supported if the coefficient α1 is found to be significantly negative.

To test Hypotheses 2 and 3, this paper employs the method proposed by Jiang [26] to investigate the causal relationship between supply chain finance and firm investment efficiency. The regression equation is as follows:

$$M_{i,t}={\gamma }_0+{\gamma }_{1}SC{F}_{i,t}+\mathsf{\Sigma }{\gamma }_{i}Control{s}_{i,t}+\mathsf{\Sigma }Year+\mathsf{\Sigma }Industry+{\epsilon }_{i,t}$$

(3)

Firstly, this study investigates the effect of supply chain finance on the investment efficiency of enterprises. Subsequently, it examines the effect of supply chain finance on channel variables. If the regression coefficient α1 in Model (2) is significantly negative and the coefficient of supply chain finance on channel variables in Model (3) aligns with the hypothesis, then the hypothesis will be considered supported. Among them, $M_{i,t}$ representing the information asymmetry (ASY) and financing constraints (SA) channel variables.

4. Empirical Results

4.1. Descriptive Statistical Analysis

Table 2. Descriptive statistics of variables.

Variable	Mean	Standard Deviation	Min	Max
INV	0.042	0.054	0	0.963
Over_INV	0.056	0.078	0	0.963
Under_INV	0.033	0.030	0	0.473
SCF	0.880	0.255	0	1.076
ROA	0.038	0.076	−1.681	0.969
Lev	0.453	0.210	0.008	9.699
Far	0.234	0.172	0	0.960
Growth	0.188	0.453	−0.558	3.005
Age	2.279	0.633	0.693	3.497
SOE	0.473	0.499	0	1
Board	2.261	0.188	0	2.996
Size	22.23	1.328	15.58	28.64
Cash	0.152	0.230	−0.060	33.93
Salary	14.41	0.961	0	18.20
Dual	0.240	0.427	0	1

presents the descriptive statistics for the primary variables. The mean value for the underinvestment sample is 0.033, which is lower than the 0.056 mean observed for the overinvestment sample. This suggests that while underinvestment is a more prevalent issue among listed companies in China, the severity of overinvestment is greater. The average value of supply chain finance stands at 0.880, with a notable range between the maximum and minimum values. This disparity indicates significant variation in the development of supply chain finance among listed companies in China.

4.2. Benchmark Regression Results

Table 3. Benchmark regression results.

	(1)	(2)
	Over_INV	Under_INV
SCF	−0.019 ***	−0.008 ***
	(−2.922)	(−4.605)
ROA	0.099 ***	−0.013 **
	(5.409)	(−2.471)
Lev	0.027 ***	0.000
	(3.349)	(0.127)
Far	−0.017 *	0.022 ***
	(−1.925)	(8.727)
Growth	0.004 ***	0.005 ***
	(14.412)	(9.321)
Age	−0.013 ***	−0.006 ***
	(−6.644)	(−9.702)
SOE	−0.015 ***	−0.004 ***
	(−5.554)	(−4.726)
Board	−0.007	−0.005 **
	(−1.109)	(−2.528)
Size	0.002 **	−0.001 ***
	(1.984)	(−4.277)
Cash	−0.049 ***	0.019 ***
	(−3.502)	(5.256)
Salary	−0.007 ***	−0.001
	(−4.838)	(−1.543)
Dual	0.002	−0.001
	(0.969)	(−1.274)
Constant term	0.161 ***	0.106 ***
	(5.737)	(11.324)
N	5123	7599
Ind and Year	Yes	Yes
Adj. R2	0.143	0.116

Note: The values in parentheses represent t-values. Significance levels are denoted as *, ** and *** for the 10%, 5%, and 1% levels, respectively. All of the following tables are interpreted in the same way.

displays the fundamental regression results concerning the effect of supply chain finance on corporate investment efficiency. The variables Over_INV and Under_INV denote two categories of investment inefficiency: overinvestment and underinvestment, respectively. Columns (1) and (2) analyze the influence of supply chain finance on overinvestment and underinvestment, respectively. It is important to note that both overinvestment and underinvestment are negative indicators; thus, higher values correspond to lower investment efficiency.

After accounting for time and industry effects, column (1) reports a regression coefficient of −0.019 for the relationship between supply chain finance and corporate overinvestment, which is statistically significant at the 1% level. This finding suggests that supply chain finance may help mitigate overinvestment, thereby enhancing investment efficiency. In a similar vein, column (2) displays a regression coefficient of −0.008 for the relationship between supply chain finance and corporate underinvestment, also significant at the 1% level. This result shows that supply chain finance can reduce underinvestment and further improve overall investment efficiency, thus corroborating research Hypothesis 1.

4.3. Robustness Test

4.3.1. Replace of Dependent Variables

This paper uses the Biddle [27] model to re-measure the degree of inefficient investment, and the basic model is regressed accordingly.

Table 4. Robustness test.

	(1)	(2)
	OverINV_Biddle	UnderINV_Biddle
SCF	−0.017 **	−0.010 ***
	(−2.535)	(−6.322)
ROA	0.172 ***	−0.007
	(8.476)	(−1.622)
Lev	0.054 ***	0.012 ***
	(6.174)	(6.131)
Far	−0.014	−0.018 ***
	(−1.556)	(−7.755)
Growth	0.003 ***	−0.000
	(11.730)	(−0.123)
Age	−0.013 ***	0.006 ***
	(−6.076)	(10.347)
SOE	−0.013 ***	0.000
	(−4.009)	(0.127)
Board	−0.007	−0.007 ***
	(−1.062)	(−4.015)
Size	0.001	−0.002 ***
	(1.150)	(−7.541)
Cash	−0.061 ***	0.008 **
	(−4.041)	(2.534)
Salary	−0.005 ***	−0.001 ***
	(−3.512)	(−2.712)
Dual	0.004	−0.001
	(1.530)	(−1.320)
Constant term	0.159 ***	0.117 ***
	(5.210)	(14.683)
N	5123	7599
Ind and Year	Yes	Yes
Adj. R2	0.110	0.122

demonstrates that the regression coefficient for the relationship between supply chain finance and overinvestment is statistically significant at the 5% level. Additionally, the regression coefficient for the association between supply chain finance and corporate underinvestment is significant at the 1% level. These results underscore the robustness of the findings.

4.3.2. Tail Reduction Test

To mitigate the influence of outliers, the sample has been reduced by 1%. The regression results, presented in

Table 5. The robustness test.

	(1)	(2)
	Over_INV	Under_INV
SCF	−0.016 ***	−0.007 ***
	(−3.003)	(−4.694)
ROA	0.007	−0.016 ***
	(0.361)	(−2.918)
Lev	0.013 *	0.001
	(1.839)	(0.293)
Far	0.006	0.024 ***
	(0.785)	(10.668)
Growth	0.052 ***	0.008 ***
	(24.412)	(9.292)
Age	−0.009 ***	−0.006 ***
	(−5.656)	(−10.152)
SOE	−0.013 ***	−0.003 ***
	(−5.826)	(−4.560)
Board	−0.004	−0.005 ***
	(−0.661)	(−3.054)
Size	0.001	−0.002 ***
	(1.211)	(−5.205)
Cash	−0.038 ***	0.021 ***
	(−3.263)	(6.208)
Salary	−0.004 ***	−0.001 **
	(−2.893)	(−2.227)
Dual	0.001	−0.001
	(0.647)	(−0.881)
Constant term	0.122 ***	0.111 ***
	(4.838)	(12.949)
N	5123	7599
Ind and Year	Yes	Yes
Adj. R2	0.206	0.134

, indicate that the coefficient for the relationship between supply chain finance and corporate overinvestment is significantly negative at the 1% level. Similarly, the coefficient for the relationship between supply chain finance and corporate underinvestment is also significantly negative at the 1% level. These findings confirm the robustness of the results.

4.3.3. Changing the Time Window

The empirical results presented cover the period from 2013 to 2022. We narrowed the time window for the robustness test to 2017–2022, as supply chain finance has been widely promoted since 2017. To minimize the impact of other macroeconomic factors, we retained only the samples from 2017 onward. Table 3 illustrates the regression results, which affirm a negative relationship between supply chain finance and both overinvestment and underinvestment. Specifically, columns (1) and (2) show that supply chain finance effectively curbs excessive corporate investment and reduces underinvestment, thereby improving overall investment efficiency. The robustness of these results is further corroborated by the findings in

Table 6. The robustness test.

	(1)	(2)
	Over	Under
SCF	−0.134 ***	−0.029 ***
	(−4.619)	(−2.649)
ROA	0.098 ***	−0.014 **
	(6.190)	(−2.227)
Lev	0.045 ***	0.001
	(5.506)	(0.165)
Far	0.022 ***	0.020 ***
	(2.708)	(5.219)
Growth	0.005 ***	0.007 ***
	(12.911)	(8.105)
Age	−0.014 ***	−0.006 ***
	(−7.893)	(−6.721)
SOE	−0.011 ***	−0.004 ***
	(−4.536)	(−3.558)
Board	−0.005	−0.007 **
	(−0.930)	(−2.440)
Size	−0.000	−0.000
	(−0.292)	(−0.891)
Cash	−0.035 **	0.015 **
	(−2.492)	(2.562)
Salary	−0.000	−0.001
	(−0.303)	(−1.420)
Dual	0.000	−0.000
	(0.153)	(−0.225)
Constant term	0.226 ***	0.127 ***
	(5.873)	(7.177)
N	3093	3899
Ind and Year	Yes	Yes
Adj. R2	0.167	0.088

.

4.3.4. Endogeneity Analysis

To address the endogeneity concerns in the regression model, due to factors such as omitted variables, selection bias, and reverse causality, this study employs the instrumental variable (IV) approach. Specifically, the annual mean MSCF value of the industry in which the enterprise operates, excluding the enterprise itself, serves as the instrumental variable. The results of this analysis are presented in

Table 7. Endogeneity test.

	(1)	(2)
	SCF	INV
MSCF	0.505 ***
	(10.073)
SCF		−0.066 **
		(−2.150)
ROA	0.007	0.052 ***
	(0.259)	(6.500)
Lev	0.329 ***	0.033 ***
	(29.627)	(3.163)
Far	−0.038 ***	0.003
	(−3.070)	(0.807)
Growth	−0.000	0.004 ***
	(−0.436)	(22.004)
Age	−0.017 ***	−0.010 ***
	(−5.520)	(−9.049)
SOE	−0.003	−0.011 ***
	(−0.794)	(−8.399)
Board	0.028 ***	−0.006 **
	(2.943)	(−2.061)
Size	0.019 ***	0.002 *
	(11.584)	(1.890)
Cash	−0.414 ***	−0.034 **
	(−23.108)	(−2.421)
Salary	−0.005 **	−0.004 ***
	(−1.982)	(−4.931)
Dual	0.002	0.002
	(0.507)	(1.442)
Constant term	0.432 ***	0.149 ***
	(9.264)	(7.762)
N	12,722	12,722
Ind and Year	Yes	Yes
Adj. R2	0.366	0.074

. In the first stage, Column (1) demonstrates a significant positive correlation between the instrumental variable MSCF and the SCF of supply chain finance. Following this, the second stage of regression was conducted using the fitted values of supply chain finance, as shown in Column (2). Here, the regression coefficient for the effect of SCF on corporate inefficiency investment (INV) is −0.066, with statistical significance at the 0.05 level, confirming the robustness of the results.

4.4. Analysis of Mediator Effect

4.4.1. Mediator Effect of Information Asymmetry

Capital investment is a fundamental aspect of corporate strategic decision-making, yet information asymmetry can lead to inefficient investment and adversely affect firms. In this context, providing high-quality accounting information is crucial. It not only facilitates investors’ accurate assessment of enterprise value but also enhances oversight of internal corporate behaviors, thereby motivating firms to increase the efficiency of their investment decisions. For instance, Zhang and Lv [28] demonstrated that reducing information asymmetry through enhanced information disclosure can mitigate both excessive and insufficient investment behaviors. Building on their research, this study utilizes external audit quality as a proxy for the information environment. Specifically, audit quality is assessed based on whether the company is audited by one of the four major accounting firms, leading to the establishment of a virtual audit variable [29]. The implementation of supply chain finance can facilitate more informed investment decisions by enhancing the transparency of corporate accounting information and standardizing management practices. Therefore, this paper posits that alleviating information asymmetry serves as an intermediary mechanism through which supply chain finance improves investment efficiency.

Table 8. Results of the mediating effect test of information asymmetry.

	(1)	(2)	(3)
	Over	Asy	Under
SCF	−0.019 ***	−0.010 ***	−0.008 ***
	(−2.922)	(−3.967)	(−4.605)
ROA	0.099 ***	−0.157 ***	−0.013 **
	(5.409)	(−20.522)	(−2.471)
Lev	0.027 ***	0.037 ***	0.000
	(3.349)	(10.897)	(0.127)
Far	−0.017 *	−0.043 ***	0.022 ***
	(−1.925)	(−11.465)	(8.727)
Growth	0.004 ***	0.002 ***	0.005 ***
	(14.412)	(11.158)	(9.321)
Age	−0.013 ***	−0.001	−0.006 ***
	(−6.644)	(−1.064)	(−9.702)
SOE	−0.015 ***	−0.005 ***	−0.004 ***
	(−5.554)	(−4.126)	(−4.726)
Board	−0.007	−0.009 ***	−0.005 **
	(−1.109)	(−3.170)	(−2.528)
Size	0.002 **	−0.004 ***	−0.001 ***
	(1.984)	(−9.101)	(−4.277)
Cash	−0.049 ***	0.006	0.019 ***
	(−3.502)	(1.160)	(5.256)
Salary	−0.007 ***	0.002 ***	−0.001
	(−4.838)	(2.648)	(−1.543)
Dual	0.002	−0.000	−0.001
	(0.969)	(−0.450)	(−1.274)
Constant term	0.161 ***	0.173 ***	0.106 ***
	(5.737)	(13.375)	(11.324)
N	5123	12,722	7599
Ind and Year	Yes	Yes	Yes
Adj. R2	0.143	0.107	0.116

displays the results of the regression analysis for testing the mediating effect of information asymmetry. Models (1)–(3) depict the channel test results for information asymmetry. The regression coefficients for supply chain finance are significantly negative in columns (1) and (3), indicating that increased levels of supply chain finance are associated with a reduction in inefficient investment among firms. Additionally, the coefficient in column (2) is also significantly negative, reflecting a decrease in information asymmetry. These results are consistent with theoretical predictions and suggest that information asymmetry serves as a mechanism through which supply chain finance influences firms’ investment efficiency. The impact of supply chain finance extends beyond merely alleviating the information asymmetry faced by firms; it also facilitates multi-dimensional cross-validation of information. This mechanism enhances firms’ capabilities to acquire and analyze information, thereby providing more accurate and comprehensive data support for investment decisions. Consequently, supply chain finance contributes to improving the quality of firms’ investment decisions and, in turn, enhances their overall investment efficiency. The findings of the two-step method generally support the assertion that supply chain finance enhances investment efficiency by reducing information asymmetry among firms, thereby confirming Hypothesis 2.

4.4.2. Mediator Effect of Financing Constraints

Examining external financing constraints reveals a significant relationship between financing and investment activities, as financing provides the essential capital base for investment and is crucial for enterprise value growth. Supply chain finance effectively mitigates these external financing constraints by improving cash flow turnover efficiency, unlocking current assets, and reducing financing risks. These improvements lead to better cash flow, which allows firms to develop more accurate and effective investment strategies. Therefore, investigating the intercession role of financing constraints in the connection between supply chain finance and enterprise investment efficiency represents a crucial area of research.

Table 9. Results of the mediating effect test of financing constraints.

	(1)	(2)	(3)
	Over	SA	Under
SCF	−0.019 ***	−0.134 ***	−0.008 ***
	(−2.922)	(−12.630)	(−4.605)
ROA	0.099 ***	−0.321 ***	−0.013 **
	(5.409)	(−10.233)	(−2.471)
Lev	0.027 ***	−0.066 ***	0.000
	(3.349)	(−4.726)	(0.127)
Far	−0.017 *	0.017	0.022 ***
	(−1.925)	(1.126)	(8.727)
Growth	0.004 ***	0.000	0.005 ***
	(14.412)	(0.430)	(9.321)
Age	−0.013 ***	−0.209 ***	−0.006 ***
	(−6.644)	(−57.353)	(−9.702)
SOE	−0.015 ***	0.001	−0.004 ***
	(−5.554)	(0.199)	(−4.726)
Board	−0.007	−0.050 ***	−0.005 **
	(−1.109)	(−4.325)	(−2.528)
Size	0.002 **	0.106 ***	−0.001 ***
	(1.984)	(52.517)	(−4.277)
Cash	−0.049 ***	0.021	0.019 ***
	(−3.502)	(0.969)	(5.256)
Salary	−0.007 ***	−0.018 ***	−0.001
	(−4.838)	(−6.420)	(−1.543)
Dual	0.002	0.010 **	−0.001
	(0.969)	(2.174)	(−1.274)
Constant term	0.161 ***	−5.014 ***	0.106 ***
	(5.737)	(−95.116)	(11.324)
N	5123	12,722	7599
Ind and Year	Yes	Yes	Yes
Adj. R2	0.143	0.399	0.116

displays the regression results for channel tests concerning financing constraints. The data reveal that the coefficient for supply chain financing is significantly negative across columns (1) through (3), indicating that an increased level of supply chain financing effectively reduces ineffective investments and notably decreases financing costs, which aligns with theoretical predictions. This outcome further implies that financing constraints serve as an intermediary in the relationship between supply chain finance and investment efficiency. Additionally, supply chain financing has proven to be a pivotal factor in enhancing business relationships with banks. Firms with strong ties to banks are more inclined to leverage banking resources to alleviate financing constraints. As an innovative financing tool, supply chain financing significantly enhances the external financing capacity of enterprises, helping them overcome financing challenges and address issues related to insufficient investment. In the supply chain financing model, financial institutions have broadened their collateral requirements from stable, fixed-value assets to include more liquid assets, such as accounts receivable, prepaid accounts, and inventory. This expansion reduces collateral-related financing constraints and further improves investment efficiency. The results from the two-step tests broadly support the presence of financing channels, confirming that supply chain financing enhances investment efficiency by mitigating external financing constraints.

5. Heterogeneity Analysis

5.1. Heterogeneity Analysis of Property Rights

The diversity within a firm affects how management assesses the costs and benefits of capital expenditures, which subsequently influences the firm’s investment choices. To investigate how supply chain finance impacts enterprise investment efficiency, this study analyzes variations in enterprise characteristics, specifically focusing on geographic location and the nature of property rights.

Compared to private enterprises, state-owned enterprises generally have greater access to banks and other financial institutions, whereas private enterprises often face higher financing costs and limited access to information, making it more challenging for them to secure necessary credit resources. This study divided the research samples into state-owned and non-state-owned enterprises for empirical analysis, with the findings detailed in

Table 10. Findings related to the heterogeneity in property rights.

	(1)	(2)	(3)	(4)
	Over	Over	Under	Under
	State-Owned Enterprises	Non-State-Owned Enterprises	State-Owned Enterprises	Non-State-Owned Enterprises
SCF	−0.006	−0.017 ***	−0.004 **	−0.009 ***
	(−0.937)	(−2.655)	(−2.255)	(−5.245)
ROA	0.057 **	0.110 ***	0.001	−0.003
	(2.169)	(5.942)	(0.127)	(−0.470)
Lev	−0.008	0.033 ***	−0.003	0.001
	(−0.825)	(4.872)	(−1.387)	(0.518)
Far	0.021 **	−0.010	0.031 ***	0.021 ***
	(2.447)	(−0.982)	(12.687)	(6.658)
Growth	0.005 ***	0.003 ***	0.002 ***	−0.000
	(7.542)	(13.931)	(6.876)	(−0.685)
Age	−0.008 ***	−0.015 ***	−0.007 ***	−0.006 ***
	(−2.989)	(−6.956)	(−8.718)	(−8.383)
Board	0.006	−0.009	−0.005 **	−0.005 **
	(0.881)	(−1.268)	(−2.404)	(−2.275)
Size	−0.001	0.005 ***	−0.002 ***	−0.001 **
	(−0.859)	(3.660)	(−5.272)	(−2.372)
Cash	−0.031 **	−0.049 ***	0.024 ***	0.001
	(−1.985)	(−3.328)	(6.137)	(0.758)
Salary	−0.000	−0.008 ***	−0.000	−0.001 **
	(−0.148)	(−5.675)	(−0.529)	(−2.074)
Dual	−0.001	0.002	0.001	−0.001
	(−0.139)	(0.790)	(0.525)	(−1.573)
Constant term	0.030	0.190 ***	0.069 ***	0.099 ***
	(0.427)	(2.992)	(3.506)	(4.120)
N	3090	4265	5015	6335
Ind & Year	Yes	Yes	Yes	Yes
Adj. R2	0.084	0.144	0.134	0.082

. The regression results reveal a significant negative correlation between supply chain finance and overinvestment in non-state-owned enterprises, at the 1% significance level. Conversely, the coefficient for supply chain finance in state-owned enterprises is negative but does not achieve statistical significance. Furthermore, the impact of supply chain finance on addressing insufficient investment is notably more substantial in non-state-owned enterprises.

The possible explanation for the above findings is twofold: first, state-owned enterprises (SOEs) typically dominate with a central role in the supply chain and benefit from advantages in logistics, capital flow, and information flow. As a result, they are less responsive to the positive impacts of supply chain finance compared to non-state-owned enterprises. However, non-SOEs often encounter constraints in capital sources and face challenges in accessing information. For these enterprises, supply chain finance provides an effective solution to issues related to capital constraints and information asymmetry, thereby more directly enhancing investment efficiency. Second, from an investment decision-making perspective, SOEs generally pursue a more stable development strategy, leverage their institutional advantages to secure high-quality investment opportunities, and exercise caution with long-term and high-risk investments. Meanwhile, non-SOEs, driven by the need to remain competitive, must improve their capital allocation efficiency. Supply chain finance offers these firms essential financial support and information resources, significantly boosting their investment efficiency.

5.2. Heterogeneity Analysis of Region

Following the National Bureau of Statistics’ classification standards, this study segments the total sample into three groups: east, middle, and west regions. This segmentation facilitates an examination of regional variations in how supply chain finance affects enterprise investment efficiency. According to the regression results shown in

Table 11. Results of geographic location heterogeneity.

	(1)	(2)	(3)	(4)	(5)	(6)
	Over	Over	Over	Under	Under	Under
	East	Middle	West	East	Middle	West
SCF	−0.016 ***	−0.001	−0.002	−0.008 ***	−0.005	−0.003
	(−3.012)	(−0.062)	(−0.155)	(−5.705)	(−1.414)	(−0.750)
ROA	0.098 ***	0.135 ***	0.081	−0.005	0.011	−0.013
	(5.909)	(2.837)	(1.626)	(−1.065)	(0.981)	(−1.000)
Lev	0.022 ***	0.024 **	0.033	−0.003	−0.002	0.004
	(2.950)	(2.188)	(1.531)	(−1.574)	(−0.316)	(0.680)
Far	−0.003	−0.004	0.022	0.025 ***	0.026 ***	0.015 ***
	(−0.325)	(−0.208)	(1.087)	(10.707)	(5.037)	(2.611)
Growth	0.003 ***	0.003 ***	0.012 ***	0.003 ***	0.002 ***	−0.000
	(15.514)	(4.234)	(6.077)	(8.646)	(4.102)	(−0.969)
Age	−0.014 ***	−0.011 **	−0.014 **	−0.006 ***	−0.009 ***	−0.006 ***
	(−7.799)	(−2.005)	(−2.370)	(−9.857)	(−6.430)	(−3.724)
Board	−0.005	−0.009	0.012	−0.004 **	−0.015 ***	0.003
	(−0.860)	(−0.665)	(0.747)	(−2.178)	(−3.556)	(0.660)
SOE	−0.009 ***	−0.013 **	−0.021 ***	−0.004 ***	−0.002	−0.004 **
	(−3.619)	(−2.261)	(−3.377)	(−4.493)	(−1.272)	(−2.406)
Size	0.002 *	0.004	−0.001	−0.001 ***	0.000	−0.004 ***
	(1.949)	(1.447)	(−0.365)	(−4.425)	(0.299)	(−4.476)
Cash	−0.038 ***	−0.054 *	−0.043	0.001	0.014 *	0.016 *
	(−3.034)	(−1.726)	(−1.192)	(1.287)	(1.840)	(1.648)
Salary	−0.007 ***	−0.004	0.000	−0.001 *	−0.001	−0.000
	(−5.112)	(−1.342)	(0.091)	(−1.689)	(−0.637)	(−0.047)
Dual	0.003	0.006	−0.008	−0.001	−0.001	0.002
	(1.526)	(1.028)	(−1.057)	(−1.252)	(−0.740)	(1.060)
Constant term	0.232 ***	0.077	0.033	0.081 ***	0.051	0.132 ***
	(4.109)	(1.189)	(0.338)	(3.766)	(1.548)	(3.879)
N	5508	1043	802	8410	1633	1301
Ind & Year	Yes	Yes	Yes	Yes	Yes	Yes
Adj. R2	0.132	0.120	0.205	0.112	0.123	0.146

, a significant negative correlation between supply chain finance and both overinvestment and underinvestment is observed in the eastern region. In contrast, no significant relationship is detected in the middle and west regions. In the east, where the financial sector is more developed, financial institutions can establish cooperative relationships with enterprises at a lower cost. The integration of information and digital technology into supply chain finance enhances its adaptability and iterative capabilities. This integration not only facilitates the merging of critical resources such as capital, human resources, and logistics information but also enables efficient coordination of resource allocation, thereby significantly improving enterprise investment efficiency.

6. Conclusions and Implications

Supply chain finance is essential in promoting the sustainable and robust development of industrial and supply chains, optimizing enterprise production and operations, and refining investment decisions. This study investigates the effects of supply chain finance on corporate investment efficiency using data from listed companies between 2013 and 2022. The empirical findings reveal the following: (1) Supply chain finance reduces inefficient investments, thereby enhancing overall investment efficiency. (2) It improves investment efficiency by alleviating financing constraints and diminishing information asymmetry between enterprises and external stakeholders. (3) The negative correlation between supply chain finance and investment inefficiency is more pronounced in non-state-owned enterprises and those located in the eastern region, resulting in higher investment efficiency for these firms.

The conclusions of this study offer several implications for policy-making. First, it is essential for the government to enact policies that bolster the advancement of supply chain finance. This could include offering preferential interest rates to reduce financing costs for enterprises and incentivizing financial institutions to increase their involvement in supply chain financial services. Additionally, it is advisable to reduce the risk weight of inclusive small and microloans to lower capital requirements for financial institutions, thereby increasing their incentive to participate in supply chain finance. Secondly, given the rapid development of supply chain finance and the associated legal challenges, the government needs to strengthen the legal and regulatory framework. This includes clarifying the rights and obligations of all parties, standardizing operational procedures, mitigating legal risks, and adjusting credit restrictions for core enterprises to facilitate their role in providing credit enhancements for other enterprises within the supply chain. Finally, to advance the development of professionals in supply chain finance, it is essential to enhance their theoretical knowledge and overall skills. This involves establishing effective development channels, improving practitioners’ comprehensive competencies, and fostering a high-quality, cohesive team with a unified vision. Such efforts will contribute to the rapid advancement of the supply chain finance sector.

From a theoretical standpoint, we offer significant and novel insights into the framework of supply chain finance. Although existing literature extensively addresses factors affecting enterprise investment efficiency, this study introduces a new dimension by incorporating supply chain finance into the analysis. The practical implications of our findings are also noteworthy; they are expected to motivate enterprises to more actively participate in supply chain finance and to leverage financial innovations to improve their investment efficiency. Additionally, the findings can help banks better understand market demands and business operations, leading to more effective fund allocation and contributing to broader social and economic development. While this study has some limitations, it offers valuable insights for future research. Notably, we focus on the current landscape of supply chains in China, emphasizing the role of supply chain finance for private enterprises. Given China’s distinctive institutional context, private enterprises often face greater financing challenges compared to their state-owned counterparts. Future research should consider examining supply chain finance’s global impact on investment efficiency by extending studies to other countries. Furthermore, some factors will produce unexpected events in the supply chain and will have a potential impact, so it is crucial for future studies to incorporate these factors to provide a thorough assessment of supply chain finance’s effectiveness.