Commercial Credit Financing and Corporate Risk-Taking: Inhibiting or Facilitative?

Abstract

Improving the level of risk-taking is an important measure for enterprises to realize sustainable development; in this context, commercial credit financing has become an important type of transaction and an indispensable short-term financing method. In this work, we use a sample of A-share-listed companies listed from 2007 to 2021 to test the impact of commercial credit financing on corporate risk-taking. Research shows that commercial credit financing has a U-shaped relationship with corporate risk-taking, i.e., when there is a low level of commercial credit financing, it has an inhibitory effect on corporate risk-taking, and when the level of commercial credit financing is high, it has a promotional effect on corporate risk-taking. The main reason for this, based on substitute financing and buyer market theories, is that commercial credit financing has a “double-edged sword” effect. Further research has found that corporate financialization, debt default risk, and ownership form all have moderating effects on this U-shaped relationship. Heterogeneity analysis results show that among enterprises with good cash flow conditions, low financing constraints, and a low supply of commercial credit, commercial credit financing has a significant U-shaped impact on enterprise risk-taking. However, among enterprises with poor cash flow conditions, high financing constraints, and a high supply of commercial credit, commercial credit financing shows a solely inhibitory effect on enterprise risk-taking. This research innovatively clarifies the dual role of commercial credit financing in corporate risk-taking from the perspective of the supply chain, and these findings are pivotal in guiding enterprises to rationally allocate commercial credit financing and make informed risk investment decisions to realize the simultaneous sustainable development of enterprises and supply chains.

1. Introduction

Risk-taking is not only a prerequisite for enterprises to obtain a high return on investment but also an important factor in achieving sustainable economic growth [1]. In light of sustainable economic development goals, improving the level of corporate risk-taking is a common current point of focus in theoretical and academic circles. Corporate risk-taking refers to the extent to which firms are willing to take risks in order to achieve high returns. An increase in the level of expected returns is usually accompanied by an increase in their volatility. Corporate risk-taking is the result of a trade-off between expected returns and the risk of fluctuations in these expected returns when making investment decisions [2]. A higher level of risk-taking means that enterprises are less likely to surrender the expected net present value if it is high-risk but greater than zero, reflecting a preference for high-return, high-risk projects. Corporate risk-taking not only determines performance [3] but also significantly improves investment efficiency [4]; however, risky investment projects are strongly resource-dependent [5], and insufficient resource support or subsequent breaks in the capital chain often lead to decision-makers abandoning high-yield investment projects, which adversely affects sustainable enterprise development [6].

Financial support is the key to achieving sustainable economic development, but given that enterprise equity financing and credit financing are limited by an enterprise’s level of financial development and the market financing environment, commercial credit has become a vital source of enterprise financing [7]. This manifests as enterprises obtaining purchasing materials in advance delaying purchasing payments, occupying the funds of upstream suppliers, and acquiring short-term debt from suppliers [8]. Commercial credit financing is unsecured, low-cost, flexible-term, safe, and convenient, facilitating the flow of funds within the industrial supply chain. It plays a major role in broadening and diversifying financing channels for enterprises, preventing breaks in the capital chain, stabilizing production and operation, and maintaining the market shares and sales stability of products. Therefore, commercial credit financing crucially allows enterprises to maintain a sustainable competitive advantage, which may substantially impact corporate risk-taking.

Existing research on the factors influencing corporate risk-taking mainly focuses on external macro-factors [9,10,11] and internal governance factors [12,13]. Suppliers, which are upstream of enterprises in the supply chain, are impactful stakeholders within sustainable enterprise development. However, the impact of the supply chain, which is a meso-industry-level factor in corporate risk-taking, has not yet attracted the attention of the academic community. Enterprises with strong commercial credit are more likely to gain the trust of partners and customers, withstand risks so as to take advantage of market competition [11] and achieve sustainable development. They receive lower interest rates and more lenient loan conditions during financing [9], helping them to reduce costs, improve the efficiency of their capital use and investment in risky projects, and provide financial support for sustainable development. By optimizing their business credit, enterprises can attract more investors and partners [12], provide financial and technical support for R&D and innovation [10], and stimulate sustainable enterprise development.

Therefore, this study utilizes relevant data on Chinese A-share-listed companies from 2007 to 2021 to examine the dual impact of commercial credit financing on corporate risk-taking. Furthermore, it analyzes how and whether commercial credit financing mediates corporate risk-taking, as well as the variability in this relationship depending on corporate characteristics.

The principal theoretical contributions of this study are the following: Firstly, based on the perspective of the supply chain, it innovatively proposes that commercial credit financing can both inhibit and prompt corporate risk-taking, clarifying the “double-edged sword” mechanism of commercial credit financing within corporate risk-taking. This provides new perspectives for a comprehensive understanding of the advantages and disadvantages of commercial credit financing in terms of corporate risk-taking. Secondly, this study proposes that the level of commercial credit financing is jointly determined by motivation and ability and dialectically applies the “substitute financing theory” and “buyer market theory” to deconstruct the internal logic of commercial credit financing and enterprise risk-taking, providing new empirical evidence for these two theories. It makes up for the insufficiency of the existing literature, which has unilaterally researched either the motivations behind commercial credit financing based on substitute financing theory or the ability to utilize commercial credit financing based on buyer market theory. Thirdly, it enriches the existing research on the effect of commercial credit financing on corporate governance in periods of economic transition and on the growth of corporate earnings from the perspective of corporate financing constraints.

We organized this paper as follows: Following an introduction in Section 1, we provide a theoretical analysis and the research hypothesis in Section 2. In Section 3, we discuss the research methods. In Section 4, we report the research design and discuss the empirical results. In Section 5, we detail the use of endogeneity and robustness tests. In Section 6, we describe moderating factors and analyze heterogeneity. In Section 7, we provide a conclusion and indicate the contributions and limitations of our research, followed by an outline of future research directions.

2. Theoretical Analysis and Research Hypothesis

Studies on the motivations behind commercial credit financing are principally based on two theories, i.e., the substitute financing theory proposed by Petersen and Rajan in 1997 [14] and the buyer market theory proposed by Love in 2007 [15].

The alternative financing theory suggests that commercial credit financing is a form of alternative financing. That is to say, when it is difficult for enterprises to obtain financing through external formal channels, such as bank credit, they rely on delayed payments within commercial credit contracts to purchase credit from upstream enterprises in the supply chain in procurement transactions. At the same time, suppliers with stronger finances use commercial credit to act as an intermediary and provide these excess idle funds to downstream enterprises that are struggling to obtain external financing, providing them with financial support and credit rationing. Therefore, commercial credit financing has become a key alternative to bank borrowing [16]. The theory posits that enterprises obtain commercial credit financing from upstream suppliers with financing advantages in order to alleviate financing constraints when they face information asymmetry or an inadequate financial system.

Buyer market theory suggests that commercial credit exists primarily due to a strong buyer’s market and good customer credit. Downstream enterprises are in an advantageous position within a buyer’s market. There are more suppliers to choose from for procurement transactions upstream of enterprises in the supply chain, allowing them to reduce their inventory costs, sell their products as soon as possible, and smooth out the adverse effects of market fluctuations on their operations. Suppliers are willing to delay collection at the expense of providing downstream customers with commercial credit in order to finalize sales transactions; thus, enterprises obtain commercial credit financing in an advantageous position as buyers in transactions with suppliers, and suppliers have no choice but to delay collection to finalize transactions. This theory posits that downstream firms obtain commercial credit financing in an advantageous position as buyers as a result of upstream suppliers being forced to resort to delayed collection.

The research findings on the effect of commercial credit financing on the growth of corporate earnings are widely divergent. Some scholars believe that in an economic environment without a fully formed financial system, commercial credit financing can improve the efficiency of enterprise capital allocation [17] and promote the rapid growth of enterprises [18,19,20]; however, other scholars believe that the mechanism of reputation in commercial credit financing and the relational financing model do not have a significant positive effect on economic development and fail to support rapid growth [21]. Research on the impact of commercial credit financing on the investment efficiency of enterprises shows that commercial credit financing can play a two-way governing role in both overinvestment and underinvestment, two problematic types of investment inefficiency [22].

On the one hand, the amount of commercial credit financing an enterprise can access depends on its commercial credit financing ability, which is determined by whether it occupies an advantageous or disadvantageous position in the buyer’s market and its own commercial credit rating. On the other hand, it is contingent upon the enterprise’s motivation to obtain commercial credit financing, which is determined by the credit financing environment in which it operates and the degree of financing constraints it faces.

Capital accumulation and technological innovation are set to become power sources for sustainable economic growth in the future [23]. Meanwhile, accelerating capital accumulation and technological innovation requires a high level of risk-taking for microeconomic agents [2,9,24,25]. Under the assumption of a “perfect market”, enterprises will abandon projects with a negative net present value (NPV) and only choose investment opportunities with a positive NPV; under this investment decision criterion, enterprises will ultimately maximize value as one of their key micro-governance objectives. Based on this logic, neither the management’s will nor motivation nor an enterprise’s own ability to acquire resources will influence its investment decisions. However, the real economy is not a “perfect market” as far as the homogeneity of resource acquisition ability is concerned, so in their investment decisions, enterprises inevitably have to abandon certain risky investment projects with a positive net present value [3]. This means that resource acquisition ability affects enterprises’ choices of risky investment projects and therefore their corporate risk-taking level.

Summarizing the above analysis, the risk-taking level of an enterprise implies the extent to which it is willing to pay for value enhancement, which is the key guarantee of its success and sustainable development. Enterprises’ decisions on risky investment projects vary according to the scale and financing capacity of commercial credit financing, which leads to different levels of risk-taking. Based on substitute financing theory and buyer market theory, this study explores the dual effects of commercial credit financing on enterprise risk-taking from the perspectives of “inhibition” and “facilitation”, respectively.

2.1. The Inhibitory Effect of Commercial Credit Financing on Corporate Risk-Taking

When enterprises have little commercial credit financing ability, they usually take short-term performance improvements as their business objectives, and the limitation of their financing constraints leads to a high risk of credit contract defaults, with an insufficient willingness to take risks on the part of management. Therefore, risky investment projects lack core resources and the necessary support, and in this context, commercial credit financing has an “inhibitory effect” on corporate risk-taking.

Based on the theory of finite resources, the risk-taking activities of enterprises are highly dependent on resources. A lower commercial credit financing capacity represents fewer opportunities for enterprises and their weaker ability to obtain all kinds of resources from suppliers, and this lack of sufficient resource support is the main reason why enterprises have no choice but to give up when faced with high-yield, high-risk investment opportunities. According to signaling theory, enterprises are in a disadvantageous position as buyers. As a result of the information received by investors, they may come to have no confidence in an enterprise’s ability to invest or efficiency in investing in risk and then reduce capital investment in this enterprise. Downturns in investor sentiment are often accompanied by banks and other financial institutions tightening their credit policies, with increases in the cost of external financing forcing a conservative investment strategy. Enterprises then must give up high-yield, high-risk investment projects and reduce their level of risk-taking [26].

From the perspective of business performance objectives, a lower level of commercial credit financing means that an enterprise’s financing ability is weak, its performance is poor, and its market share is low. Its objectives are mainly focused on maintaining production and operation and maintaining the market share of its products, and an increase in commercial credit financing will prompt it to invest more resources in production and operation, which will have a “crowding-out” effect on venture capital. At the same time, in order to improve short-term performance targets and cope with the pressure of optimizing their performance on the market [27], managers will not choose high-yield long-term investment projects, inhibiting corporate risk-taking.

In terms of managers’ willingness to invest, on the one hand, suppliers with financing advantages provide commercial credit to enterprises with a lower financing capacity. In order to reduce the risk to the financier due to an inability to pay for the goods in accordance with the stipulated credit period, additional insurance premiums and default premiums are likely required to obtain a certain amount of financial returns. In this case, if an enterprise defaults on repayment, the higher cost of commercial credit financing will force enterprises to pay more money and could even lead to legal disputes over credit defaults, reducing or even eliminating the willingness of managers to make risky investments [28]. On the other hand, a lower scale of commercial credit financing represents a weaker enterprise in terms of operating ability, and in the case of prevalent managerial power, for prudential purposes or to seek private gain and stabilize their existing positions, management tends to show a strong tendency to avoid risks with high short-term costs or long lags and high fluctuations in expected returns [29]. In addition, based on the reputation mechanism, managers of enterprises with weaker financing ability may pay more attention to maintaining their own prestige and reputation [5], avoiding investment failure, losing their personal wealth, or risking their employment. Thus, they may be more inclined to avoid risky investments or even use their own influence to oppose risky investment projects, which, in turn, reduces the level of risk-taking in enterprises.

From the perspective of credit contract defaults, enterprises with a weaker financing capacity, despite the smaller scale of commercial credit financing, may face multiple pressures, such as being unable to fulfill credit contracts or make payments on time. Under such circumstances, enterprises make more prudent financial decisions, and stable and predictable returns will be the primary considerations in their investment decisions. As their financing constraints increase, their incentive to take risks is inhibited [30]. In addition, if a supplier’s credit rating system is not mature and it sells goods for inventory management purposes, this will lower the credit audit “threshold” for downstream enterprises, resulting in an inflow of resources that does not match an enterprise’s own management, production, or credit capacity. When the credit period expires, the enterprise will repay the payable amount or redeem the bonds payable in accordance with the provisions of the contract, which will inevitably cause a large cash outflow, increasing financial and bankruptcy risks. A mismatch between the cash outflow and operating capacity will exacerbate principal-agent conflicts in the enterprise, cause internal control problems, lead to over-consumption and wastage of the inflow of resources, and even incentivize managers to engage in self-interested behaviors, weakening enterprise risk-taking levels. Based on the above analysis, we propose Hypothesis H1a, as follows:

H1a: 

Lower levels of commercial credit financing inhibit corporate risk-taking.

2.2. The Facilitation Effect of Commercial Credit Financing on Corporate Risk-Taking

The “facilitation effect” of commercial credit financing on corporate risk-taking is mainly realized through access to abundant core resources and key information, incentives for managers to invest with confidence, improvements in returns alongside reductions in investment risks, and the positive governing effect of credit financing.

From the perspective of resource dependence, enterprise risk-taking activities rely on the consumption of substantial resources. Through the supply chain, as a carrier of social capital embedded with rich resources and information [23], and commercial credit financing, as an important source of capital in the supply chain, in allocating resources, suppliers can help downstream enterprises obtain them at a lower cost in the form of direct resource provision and indirect signaling. At the same time, the information advantage suppliers impart helps decision makers in downstream enterprises predict future uncertainty more cogently [24], enhances their preference for high-risk investments, and boosts their enthusiasm for risk-taking. In addition, based on signaling theory, a higher commercial credit financing ability can also guide the external capital market, attract advantageous attention, further optimize enterprise resource allocation, and allow them to reserve various resources for high-risk investment projects.

From the perspective of agency costs on the one hand, based on incentive constraint theory, an increase in the scale of commercial credit financing motivates enterprise managers to work harder in order to improve the future liquidity of funds, prompting the convergence of manager and shareholder interests. To reduce agency costs, the two parties will work together to achieve long-term value in line with the enterprise objectives and reach an agreement on high-yield, high-risk investment decisions. On the other hand, based on stakeholder theory, commercial credit financing represents the supplier’s investment in the dedicated assets of the enterprise, incentivizing the supplier to participate in corporate governance over the enterprise so as to safeguard the security of these assets and the funds allocated for the commercial credit. Thus, to realize joint value for the enterprise and the supplier, the enterprise becomes inclined to invest in high-yield, high-risk projects. Moreover, based on the hypothesis of the rational economic man, managers, and shareholders are incentivized to tap into benefits for external stakeholders such as suppliers [31], and managerial risk aversion and short-term thinking are mitigated when they are faced with return and risk trade-offs, which, in turn, lend themselves to high-risk and high-return investment projects.

From the perspective of investment risk, the higher its commercial credit financing ability, the more advantageous the position of an enterprise in transactions with suppliers. It can obtain more productive resources through credit purchases, reserve more funds for risky investments, and avoid falling into financial difficulties due to breaks in the capital chain in project investment, and its risk-bearing ability is enhanced. In addition, commercial credit financing can also be regarded as a specialized investment on the part of the supplier [32], locking it into a long-term contractual relationship with an enterprise, providing liquidity insurance within the cycle of economic downturns, and thus adding an “insurance lock” to high-risk enterprise investments. Based on the mechanism of reputation, higher commercial credit financing enhances “reputation” by increasing the transparency of enterprise information and improving business connections within the industry chain, and banks usually regard having commercial credit financing as a positive sign. Moreover, enterprises with buyer’s market advantages tend to have higher prestige and better reputations, which, in turn, aids them in obtaining financing through formal channels and predisposes them to be less risk-averse and to expand their investment in high-risk projects.

From the perspective of governance effects, commercial credit financing increases the liquidity of enterprises through the delayed payment of goods, and the amount of cash flow and the ability of enterprises to obtain resources considerably affect the confidence of managers when investing. When managers are more confident in their performance, they are more inclined to invest in high-risk projects. Further, a higher level of commercial credit financing can alleviate financing constraints, increase the proportion of interest-free liabilities, and reduce the cost of financing, which, in turn, enhances the level and efficiency of enterprise investment and improves the enthusiasm of enterprises toward high-risk and high-yield investments such as R&D investment. Based on the above analysis, we propose Hypothesis H1b and Hypothesis H1, as follows:

H1b: 

Higher levels of commercial credit financing facilitate corporate risk-taking.

H1: 

As the level of commercial credit financing increases, the level of corporate risk-taking first decreases and then increases, and the two show a U-shaped relationship.

2.3. Moderating Effects

2.3.1. Moderating Effects of Corporate Financialization

Enterprise financialization is the process of non-financial enterprises continuously increasing their investments in financial assets, which plays into sustainable enterprise development. Real enterprises mainly allocate financial assets in pursuit of their capital management needs and to gain from financial investment income [33]. Enterprise investment in financial assets can have two diametrically opposed effects, that is, the “reservoir” effect or the “crowding-out” effect. Obviously, the financialization of enterprises will affect the relationship between commercial credit financing and enterprise risk-taking. The economic effect of enterprise investment in financial assets is determined by different levels of commercial credit financing; that is, when their level of commercial credit financing is low, enterprises mainly focus on production and the operation of the business, and access to commercial credit financing is based on fulfilling capital needs. Due to their limited investment funds, enterprises lack enthusiasm for innovation or high-risk projects when investing in financial assets at this time, and the “crowded” effect plays a dominant role, with industry funds and risky project investment funds occupied. Not only that, in this case, the enterprise’s financial behavior, in the configuration of its financial assets, is motivated more by capital arbitrage, which weakens its ability to grow, is triggered by excessive debt, leads to troubled financial situations, exacerbates financial risk, and enhances commercial credit financing’s restrictive effect on corporate risk-taking.

On the contrary, when an enterprise’s level of commercial credit financing is higher, the enterprise, at the front end of the supply chain, is in a dominant position. Its advantage in a buyer’s market allows it to obtain more commercial credit financing; due to business expansion, market development, research and development for new products, high returns, and other expansion needs, the enterprise then increases its investment in high-risk projects with a net present value greater than zero. At this time, in investing in financial assets, the “reservoir” effect plays a leading role in preserving liquidity for the enterprise’s risky investment. In addition, in this case, the enterprise is motivated to hold financial assets with the aim of fund management, and this financial behavior enhances its ability to resist future uncertainty, improves the efficiency of its investment in risky projects, and enhances the role of commercial credit financing in promoting corporate risk-taking.

2.3.2. Moderating Effects of Debt Default

Debt default risk, which is the possibility that an enterprise will not be able to pay its debts when due, has a serious impact on enterprise sustainability and is an issue that must be taken into account in the enterprise’s investment decisions. It is decisive whether and to what extent an enterprise will make venture capital investments. A firm’s ability to repay its debt is affected by both its internal financing and its investment decisions. When a firm’s level of commercial credit financing is low, under the pressure of maturing debt, this exacerbates the negative impact of its limited commercial credit financing capacity on its venture capital investments and inhibits its risk-taking. In contrast, when the level of commercial credit financing is high, impending mature debt not only improves the efficiency of risk investments by restraining inefficient behavior on the part of stakeholders but also efficiently controls the enterprise’s free cash flow by restraining management, which, in turn, exerts a positive governing influence and provides an incentive for enterprises [34]. As a result, debt default risk reinforces the role of commercial credit financing in promoting corporate risk-taking.

2.3.3. Moderating Effects of Property Rights Attributes

The impact of commercial credit financing on the level of enterprise risk-taking may vary according to ownership attributes. State-owned enterprises are owned by and have their capital controlled by the government, and most of them are leading enterprises in their industries, with many financing channels and a strong financing ability. They can obtain abundant returns and income due to their mature technology and product production capacities, greatly reducing their motivation to seek higher income through high-risk projects. Indeed, insufficient incentives for risky investments have led to commercial credit financing having a limited effect on corporate risk-taking, weakening both its negative and positive repercussions. Based on the above analysis, we propose Hypothesis H2, as follows:

H2: 

Corporate financialization and debt default strengthen the U-shaped impact of commercial credit financing on corporate risk-taking; state-owned property attributes weaken the U-shaped impact of commercial credit financing on corporate risk-taking.

3. Research Methods

3.1. Sample Selection and Data Source

We selected Chinese A-share listed companies from the period 2007–2021 as the initial sample and applied the following steps: (1) we deleted listed companies from the financial industry; (2) we deleted Special Treatment (ST and *ST) listed companies; (3) we deleted listed companies who had important data missing; and (4) we shrunk the tails of the main continuous variables at the 1% and 99% levels to eliminate the effects of extreme values in the sample data. Ultimately, 30,445 sample observations were obtained with the data obtained from the China Stock Market and Accounting Research database.

3.2. Variable Measurement and Indicator Selection

3.2.1. Explained Variables

Corporate risk-taking (Risk): The literature previously used indicators such as earnings volatility, stock volatility, debt ratios, and capital expenditure to measure corporate risk-taking. However, for reasons such as the high volatility of the stock market, the uncertainty of a firm’s future earnings is largely reflected in the volatility of its operating performance [35], and a greater number of scholars have used corporate earnings volatility to measure the level of corporate risk-taking [36]. Therefore, we combine the problem under study with its important impact on the level of sustainable enterprise development, referring to the studies by Li et al. [2] and Deng et al. [31]. We also adopt corporate earnings volatility to measure the level of corporate risk-taking, and the higher the index, the higher the level of corporate risk-taking.

The specific approach to constructing this indicator is as follows: First, the ratio of corporate EBITDA to total assets (Roa) is selected as a performance measure, and in order to mitigate the impact of different industry types and cyclical fluctuations on corporate performance, Formula (1) is used, and the corporate Roa is subtracted from the average annual industry Roa to obtain Adj_Roa; second, every five years (from year t − 2 to year t + 2) is taken as the period of observation. Equation (2) is used to calculate the standard deviation of the industry-adjusted Roa (Adj_Roa) multiplied by 100 as the degree of corporate surplus volatility on a rolling basis to measure corporate risk-taking (Risk); finally, Equation (3) utilizes the five-year period extreme variance in the industry-mean-adjusted Roa (Adj_Roa) multiplied by 100 as a proxy for corporate risk-taking (RT) to conduct a robustness test. Here, X represents the total number of firms in an industry; for industry classification, manufacturing firms are subdivided into two-digit codes, and other industries are divided into broad categories; T takes the value of 5, which represents the observation time period from year t − 2 to year t + 2; and i, t, and k stand for the firms, year, and the kth firm in an industry, respectively.

$${\mathrm{Adj}\text{\_}\mathrm{Roa}}_{\mathrm{i},\mathrm{t}}{=\mathrm{Roa}}_{\mathrm{i},\mathrm{t}}-{\displaystyle \frac{1}{\mathrm{X}}}{\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{X}}{\mathrm{Roa}}_{\mathrm{i},\mathrm{t}}}$$

(1)

$${\mathrm{Risk}}_{\mathrm{i},\mathrm{t}}=100\times \sqrt{{\displaystyle \frac{1}{\mathrm{T}-1}}{\displaystyle \sum _{\mathrm{t}=1}^{\mathrm{T}}{(\mathrm{Adj}\text{\_}\mathrm{Roa}}_{\mathrm{i},\mathrm{t}}-\frac{1}{\mathrm{T}}{\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{X}}{\mathrm{Adj}\text{\_}\mathrm{Roa}}_{\mathrm{i},\mathrm{t}}}{)}^2}},\mathrm{T}=5$$

(2)

$${\mathrm{RT}}_{\mathrm{i},\mathrm{t}}{=\mathrm{Max}(\mathrm{Adj}\text{\_}\mathrm{Roa}}_{\mathrm{i},\mathrm{t}}{)-\mathrm{Min}(\mathrm{Adj}\text{\_}\mathrm{Roa}}_{\mathrm{i},\mathrm{t}})$$

(3)

3.2.2. Explanatory Variables

Commercial credit financing (Credit): Enterprises obtain commercial credit from upstream suppliers, which is recorded as accounts payable and notes payable. At the same time, enterprises may also pay part of the purchase price to suppliers in advance in the form of prepayments. Therefore, drawing on Huang et al. [37], in order to measure commercial credit financing more reasonably, we use the sum of accounts payable and notes payable minus prepayment and then combine the total assets into a relative indicator to measure enterprise levels of commercial credit financing. We use the specific definition of commercial credit financing: (Credit) = (accounts payable + notes payable − prepayments)/total assets. In addition, when both upstream suppliers and downstream customers in the supply chain are considered, the commercial credit financing of an enterprise includes both accounts payable and notes payable from upstream suppliers and prepayments from customers downstream of the enterprise. Therefore, drawing on the study by Lu and Yang [16], in the robustness test section, the sum of accounts payable, notes payable, and advance receipts normalized by total assets is used to measure the level of firms’ commercial credit financing. It is defined as commercial credit financing (TC) = (accounts payable + notes payable + net receipts in advance)/total assets.

3.2.3. Main Moderating Variables

Enterprise financialization level (Fin): According to the new classification of financial assets under the three new accounting standards for financial instruments issued by China’s Ministry of Finance, we adopt a relative indicator of the proportion of the sum of the value of an enterprise’s investment in eight types of financial assets. These include trading financial assets, derivative financial assets, available-for-sale financial assets, held-to-maturity investments, investment real estate, long-term equity investments, other debt investments, and other investments in equity instruments and collectively measure the level of financialization of an enterprise (Fin). The larger the value of Fin, the more the enterprise invests in financial assets and the higher its level of financialization.

Debt default risk (Zscore): The debt default risk is the likelihood that a firm will not be able to pay its debts when they are due. The stronger a firm’s ability to repay its debts when they are owed, the lower its debt default risk, and vice versa. Drawing on existing research [38,39,40], the Zscore model is used to measure the size of an enterprise’s debt default risk: the larger the Zscore value, the stronger the enterprise’s ability to repay debts, and the lower the risk of debt default. Its calculation formula is as follows:

$$\begin{array}{c}\mathrm{Zscore}=1.2\times ({\displaystyle \frac{\mathrm{Working}\mathrm{capital}}{\mathrm{total}\mathrm{assets}}})+1.4\times ({\displaystyle \frac{\mathrm{retained}\mathrm{earnings}}{\mathrm{total}\mathrm{assets}}})+3.3\times ({\displaystyle \frac{\mathrm{EBITDA}}{\mathrm{total}\mathrm{assets}}})\\ \hspace{1em}\hspace{1em}\hspace{1em}+0.6\times ({\displaystyle \frac{\mathrm{total}\mathrm{market}\mathrm{value}}{\mathrm{total}\mathrm{liabilities}}})+0.999\times ({\displaystyle \frac{\mathrm{operating}\mathrm{income}}{\mathrm{total}\mathrm{assets}}})\end{array}$$

(4)

3.2.4. Control Variables

Drawing on the existing literature on the level of corporate risk-taking, we select the following control variables: firm size (Size), gearing ratio (Lev), Tobin’s Q (TBQ), cash flow ratio (Cash), growth rate (Grow), age of the firm (Age), the proportion of shares held by the first largest shareholder (Top1), the proportion of shares held by the supervisory layer (Hold), the number of board directors (Board), the proportion of independent directors (IndDir), whether the chairman and general manager are the same person (Dual), and ownership attributes (Soe). In addition, the year effect (Year_d) and the industry effect (Ind_d) are included to control for the effects of different industry types and years on the research questions. All the variable names and calculations are shown in

Table 1. Definition of variables.

Variable Name	Variable Symbol	Calculation Method
Corporate risk-taking	Risk	Standard deviation of industry year-adjusted Roa from period t − 2 to t + 2 multiplied by 100
	RT	Grade difference in industry year-adjusted Roa for periods t − 2 to t + 2 multiplied by 100
Commercial credit financing	Credit	(Accounts payable + notes payable − prepayments)/total assets
	TC	(Accounts payable + notes payable + net receipts in advance)/total assets
Enterprise financialization level	Fin	Carrying value of financial assets held by the enterprise/total assets
Debt default risk	Zscore	See the specific variable definitions
Firm size	Size	Natural logarithm of total assets
Gearing ratio	Lev	Total liabilities/total assets
Tobin’s Q	TBQ	Enterprise market capitalization/total assets
Cash flow ratio	Cash	Net cash flow from operating activities/total assets
Growth rate	Grow	Revenue growth rate
Age of the firm	Age	Ln (year of observation − year of establishment + 1)
Proportion of shares held by the first largest shareholder	Top1	Shareholding ratio of the company’s largest shareholder
Proportion of shares held by the supervisory layer	Hold	Number of shares held by directors, supervisors, and senior management/total shares
Number of board directors	Board	Total number of directors
Proportion of independent directors	IndDir	Proportion of independent directors to the total number of directors
Whether the chairman and general manager are the same person	Dual	If the chairman and general manager are the same person, this takes a value of 1; otherwise, it takes a value of 0
Ownership attributes	Soe	State-owned enterprises take a value of 1; non-state-owned enterprises take a value of 0

.

4. Research Design and Empirical Results

4.1. Statistical Analysis of the Variables

Table 2. Basic descriptive statistics.

Variable	Mean	SD	min	p25	Median	p75	max	N
Risk	3.155	3.478	0	0.985	1.906	3.847	26.608	30,445
RT	0.141	0.105	0	0.059	0.110	0.201	0.526	30,445
Credit	0.104	0.101	−0.700	0.033	0.082	0.153	0.662	30,445
TC	0.175	0.148	0	0.081	0.141	0.234	0.497	30,445
Fin	0.074	0.104	0	0.005	0.030	0.089	0.972	30,445
Zscore	7.301	10.137	−54.950	2.202	4.022	8.023	99.848	30,445
Size	21.992	1.278	19.529	21.067	21.814	22.711	25.983	30,445
Lev	0.413	0.204	0.053	0.249	0.405	0.565	0.884	30,445
TBQ	2.071	1.323	0.871	1.274	1.650	2.345	8.909	30,445
Cash	0.051	0.070	−0.164	0.012	0.051	0.092	0.251	30,445
Grow	0.195	0.425	−0.531	−0.001	0.124	0.288	2.708	30,445
Age	2.804	0.389	0	2.565	2.833	3.091	4.159	30,445
Top1	0.354	0.149	0.088	0.237	0.334	0.453	0.750	30,445
Hold	0.140	0.204	0	0	0.005	0.262	0.994	30,445
Board	9.699	2.816	2	8	9	11	30	30,445
IndDir	0.392	0.089	0	0.333	0.375	0.444	1	30,445
Dual	0.219	0.414	0	0	0	0	1	30,445
Soe	0.375	0.484	0	0	0	1	1	30,445

Notes: This table presents summary statistics for the variables. Variables are defined in Table 1. Mean, SD, min, p25, median, p75, and max represent the average, the standard deviation, the minimum value, the 25th percentile, the median, the 75th percentile, and the maximum value for each variable, respectively; N is the number of sample observations.

reports the statistical results for the variables. The mean value of corporate risk-taking (Risk) is 3.155, the standard deviation is 3.478, and the median is 1.906, indicating that the level of risk-taking of the listed firms varies widely across the firms, with the majority of firms having a low level of risk-taking. The mean value of commercial credit financing (Credit) is 0.104, the standard deviation is 0.101, and the median is 0.082, indicating that the level of commercial credit financing of the listed companies varies greatly among the firms, with most of the firms having a low level of commercial credit financing. The mean value of corporate financialization (Fin) is 0.074, and the standard deviation is 0.104, indicating that investment in financial assets is more common among the listed companies, and their financial asset holdings vary widely. The minimum value for debt default risk (Zscore) is −54.950, the median is 4.022, and the maximum value is 99.848, indicating that some of the listed companies face more severe debt default risks, and there is a large difference in the solvency among the sample companies.

4.2. Empirical Results

To test Hypotheses H1, H1a, and H1b, the following model (5) is constructed. In the model, Risk represents corporate risk-taking, Credit represents commercial credit financing, Credit² represents a quadratic term for commercial credit financing, Controls represents all the control variables mentioned above, Year_d represents an annual dummy variable, Ind_d represents an industry dummy variable, λ₀ is a constant term, λ₁ is the regression coefficient for commercial credit financing (Credit), λ₂ is the regression coefficient of the quadratic term for commercial credit financing (Credit²), ε is the error term, i represents the firms, and t represents the year.

$${\mathrm{Risk}}_{\mathrm{i},\mathrm{t}}{=\mathsf{\lambda }}_0{+\mathsf{\lambda }}_1{\mathrm{Credit}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\lambda }}_2{\mathrm{Credit}}_{\mathrm{i},\mathrm{t}}{ }^2+{\displaystyle \sum {\mathrm{Controls}}_{\mathrm{i},\mathrm{t}}}+\mathrm{Year}\text{\_}\mathrm{d}+\mathrm{Ind}\text{\_}\mathrm{d}+\mathsf{\epsilon }$$

(5)

The results in

Table 3. Commercial credit financing and corporate risk-taking.

Variable	(1)	(2)	(3)
	Risk	Risk	Risk
Credit	−5.296 ***	−4.554 ***	−5.115 ***
	(−12.35)	(−9.91)	(−10.40)
Credit2	7.729 ***	2.630 **	4.064 ***
	(6.63)	(2.16)	(3.28)
Size		−0.347 ***	−0.389 ***
		(−18.31)	(−19.06)
Lev		2.683 ***	3.359 ***
		(18.89)	(22.47)
TBQ		0.284 ***	0.293 ***
		(16.12)	(15.36)
Cash		−2.084 ***	−2.324 ***
		(−6.28)	(−6.86)
Grow		−0.200 ***	−0.188 ***
		(−3.28)	(−3.05)
Age		0.345 ***	0.196 ***
		(7.40)	(3.59)
Top1		−1.610 ***	−1.571 ***
		(−12.37)	(−12.15)
Hold		−0.789 ***	−0.913 ***
		(−7.46)	(−8.61)
Board		0.054 ***	0.045 ***
		(6.84)	(5.71)
IndDir		−1.470 ***	−0.413 *
		(−7.21)	(−1.93)
Dual		0.025	−0.014
		(0.54)	(−0.31)
Soe		−0.722 ***	−0.595 ***
		(−14.97)	(−12.20)
_cons	3.545 ***	9.622 ***	11.673 ***
	(108.50)	(24.45)	(24.06)
Year Effect	No	No	Yes
Industry Effect	No	No	Yes
N	30,445	30,445	30,445
A-R2	0.009	0.078	0.116

Notes: Variables are defined in Table 1; *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively; t-values for coefficients are in parentheses; _cons is a constant term; the following table is identical.

demonstrate the effect of commercial credit financing (Credit) on corporate risk-taking (Risk). The regression in column (1) with only commercial credit financing and its quadratic term shows a significant U-shaped relationship between commercial credit financing and corporate risk-taking; column (2) also includes control variables, and the regression results are consistent with the findings in column (1); in column (3), further controlling for year and industry fixed effects, the regression results show a coefficient of −5.115 for commercial credit financing, and the coefficient of the quadratic term for commercial credit financing is 4.064. Both pass the test at the 1% significance level, which indicates that there is a U-shaped relationship between commercial credit financing and enterprise risk-taking; that is, a lower level of commercial credit financing inhibits corporate risk-taking, and a higher level of commercial credit financing facilitates corporate risk-taking. Thus, Hypotheses H1, H1a, and H1b are verified.

5. Endogeneity and Robustness Tests

5.1. The Endogeneity Problem

There may be reverse causality between the core explanatory variable in this paper, commercial credit financing, and corporate risk-taking. That is, the higher the level of corporate risk-taking, the more actively a firm invests in high-risk, high-return projects and expands and diversifies its financing channels, thus obtaining more commercial credit financing to a certain extent. Therefore, we have to test whether the benchmark model (5) has endogeneity problems, such as omitted variables or reverse causality, leading to biased or non-consistent estimated coefficients. Thus, we select the mean value of commercial credit financing for other enterprises in the same province in the same year (Credit_pro) and the mean value of commercial credit financing for other enterprises in the same city in the same year (Credit_city) as instrumental variables of commercial credit financing and re-validate the original model using the two-stage least squares (2SLS) method.

Table 4. Two-stage least squares method (2SLS).

Variable	First Stage	Second Stage
	(1)	(2)
	Credit	Risk
Credit_pro	0.201 ***
	(0.033)
Credit_city	0.570 ***
	(0.016)
Credit		−7.833 ***
		(−3.53)
Credit2		6.979 ***
		(6.56)
_cons	−0.009	11.780 ***
	(−0.71)	(23.86)
Control Variables	Yes	Yes
Year/industry effect	Yes	Yes
LM statistic		72.362
Wald F test		164.269
Hansen’s J statistic		8.817
N	30,445	30,445
A-R2	0.373	0.113

Notes: Credit_pro and Credit_city are instrumental variables; LM statistic, Wald F test, and Hansen’s J statistic are statistics to test for under-identification, weak instrumental variables, and over-identification, respectively; *** denotes significance at the 1% level.

reports the results of the two-stage regression. The estimated coefficients in the first-stage regression results verify that commercial credit financing (Credit) is highly correlated with the two instrumental variables; the second-stage regression results show that the direction of the coefficients of commercial credit financing and its quadratic term are consistent with the results of the baseline regression test, and both are significant at the 1% level. More importantly, the LM statistic of 72.362 passes the under-identification test; the Wald F test result of 164.269 is greater than 10, which passes the weak instrumental variable test; and the Hansen’s J statistic of 8.817 passes the over-identification test. The above results indicate that the above hypotheses still hold after considering endogeneity.

5.2. Robustness Tests

5.2.1. Replacing the Explanatory Variable “Commercial Credit Financing”

Considering upstream and downstream enterprises in the supply chain related to the purchasing and sales of an enterprise, we adopt the proxy indicator of commercial credit financing (TC) constructed in the previous section to test the robustness of the baseline model. The regression results upon replacing the “commercial credit financing” measure in column (1) of

Table 5. Robustness test results.

Variable	Replace Commercial Credit Financing	Replace Corporate Risk-Taking	Propensity Score Matching (PSM)	One-Period Lag
	(1)	(2)	(3)	(4)
	Risk	RT	Risk	Risk
Credit		−0.207 ***	−5.595 ***
		(−16.11)	(−10.54)
Credit2		0.214 ***	5.258 ***
		(6.63)	(3.96)
TC	−4.219 ***
	(−15.54)
TC2	1.462 ***
	(8.10)
L.Credit				−4.958 ***
				(−10.88)
L.Credit2				4.522 ***
				(3.94)
_cons	10.147 ***	0.305 ***	11.748 ***	13.154 ***
	(18.73)	(21.03)	(20.83)	(25.69)
Control	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes
N	30,445	30,445	22,909	26,434
A-R2	0.111	0.143	0.114	0.115

Notes: TC is a substitution variable for commercial credit financing (Credit), and RT is a substitution variable for corporate risk-taking (Risk). Control, Year, and Industry represent control variables, year effects, and industry effects, respectively; *** denotes significance at the 1% level.

show that the direction of the coefficients of commercial credit financing (TC) and its quadratic term (TC²) are consistent with the results of the baseline regression test, and both of them are significant at the 1% level. This confirms the robustness of the previous conclusions.

5.2.2. Replacing the Explanatory Variable “Corporate Risk-Taking”

The five-year period rolling polarity of Roa (Adj_Roa) adjusted by the industry mean is used as a measure of corporate risk-taking (RT) in the robustness test. The regression results upon replacing the “corporate risk-taking” measure in column (2) of Table 5 show that the direction of the coefficients of commercial credit financing (Credit) and its quadratic term (Credit²) are consistent with the results of the baseline regression test, and both are significant at the 1 percent level. This confirms the robustness of the previous conclusions.

5.2.3. Propensity Score Matching (PSM)

In order to test the impact of systematic differences on the research question, column (3) of Table 5 utilizes the PSM method to match the sample data to test whether there are systematic differences in the sample data at the level of commercial credit financing. This is carried out by first dividing the sample into experimental and control groups based on whether a business’s credit financing is greater than the industry annual median (Tredt); secondly, we select firm size, gearing ratio, Tobin’s Q, growth rate, the age of the firm, the proportion of shares held by the first largest shareholder, the proportion of shares held by the supervisory level, the number of board directors, the proportion of independent directors, whether the chairman and general manager are the same person, and the nature of property rights as the matching variables using 1:1 propensity score matching with no repeated sampling of nearest neighbors and a caliper range of 0.05; finally, the regression is re-tested on the matched samples. The test results are consistent with the benchmark regression, proving the robustness of the previous findings.

5.2.4. Lagging by One Period

To further test the robustness of the baseline regression results, column (4) of Table 5 is regressed using one lagged period of commercial credit financing (L. Credit) and its quadratic term (L. Credit²), and the results of the test prove the robustness of the previous conclusions.

6. Further Research

6.1. Tests of Moderating Factors

In order to test Hypothesis H2 and further explore the effects of corporate financialization, debt default risk, and ownership attributes on the U-shaped relationship between commercial credit financing and corporate risk-taking levels, we construct the following moderating effect test model (6).

$$\begin{array}{c}{\mathrm{Risk}}_{\mathrm{i},\mathrm{t}}{=\mathsf{\gamma }}_0{+\mathsf{\gamma }}_1{\mathrm{Credit}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\gamma }}_2{\mathrm{Credit}}_{\mathrm{i},\mathrm{t}}{ }^2{+\mathsf{\gamma }}_3{\mathrm{Credit}}_{\mathrm{i},\mathrm{t}}\times {\mathrm{Moder}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\gamma }}_4{\mathrm{Credit}}_{\mathrm{i},\mathrm{t}}{ }^2\times {\mathrm{Moder}}_{\mathrm{i},\mathrm{t}}{+\mathsf{\gamma }}_5{\mathrm{Moder}}_{\mathrm{i},\mathrm{t}}\\ \hspace{1em}\hspace{1em}\hspace{1em}+{\displaystyle \sum {\mathrm{Controls}}_{\mathrm{i},\mathrm{t}}}+\mathrm{Year}\text{\_}\mathrm{d}+\mathrm{Ind}\text{\_}\mathrm{d}+\mathsf{\epsilon }\end{array}$$

(6)

In model (6), Moder represents Fin (enterprise financialization), Zscore (debt default risk), and Soe (ownership attributes), respectively. In testing the moderating effect of the U-shaped relationship, we mainly seek to determine the sign and significance of the coefficient γ₄ of the quadratic term for commercial credit financing and the cross-multiplier term of the moderating variable (Credit² × Moder); if γ₄ is significantly greater than 0, this means that the moderating variable enhances the role of the main effect, and in the opposite case, it weakens the role of the main effect.

6.1.1. Enterprise Financialization

Enterprise entities primarily allocate financial assets according to their capital management needs and obtain financial investment income for two purposes: reserving liquidity under the “reservoir” effect and squeezing out industrial funds under the “squeeze” effect. These two opposite economic effects on the relationship between commercial credit financing and enterprise risk-taking are also very different. The test results in columns (1)–(2) of

Table 6. Robustness test results.

(1)	(2)	(3)	(4)	(5)	(6)
Variable	Risk	Variable	Risk	Variable	Risk
Credit	−4.460 ***	Credit	−3.106 ***	Credit	−5.622 ***
	(−7.95)		(−5.12)		(−8.86)
Credit2	2.402 *	Credit2	5.836 ***	Credit2	5.807 ***
	(1.71)		(4.12)		(3.24)
Credit × Fin	−9.152 *	Credit × Zscore	−0.235 ***	Credit × Soe	1.684 *
	(−1.91)		(−5.84)		(1.90)
Credit2 × Fin	30.715 **	Credit2 × Zscore	−1.415 ***	Credit2 × Soe	−4.875 **
	(1.98)		(−4.41)		(−2.06)
Fin	1.555 ***	Zscore	0.003 *	Soe	−0.668 ***
	(5.58)		(1.69)		(−8.84)
_cons	11.801 ***	_cons	11.981 ***	_cons	11.747 ***
	(24.24)		(24.69)		(24.04)
Control	Yes	Control	Yes	Control	Yes
Year	Yes	Year	Yes	Year	Yes
Industry	Yes	Industry	Yes	Industry	Yes
N	30,445	N	30,445	N	30,445
A-R2	0.115	A-R2	0.118	A-R2	0.114

Notes: Control, Year, and Industry represent control variables, year effects, and industry effects, respectively; *, **, and *** denote significance at the 10%, 5%, and 1% levels.

show that the coefficient of the interaction term between the quadratic term for commercial credit financing and enterprise financialization (Credit² × Fin) is significantly positive, indicating that enterprise financialization strengthens the U-shaped impact of commercial credit financing on corporate risk-taking and steepens the curves either side of the inflection point of the main effect. This may be because when enterprises allocate their financial assets to profiting from financial investments, the “crowding-out” effect enhances commercial credit financing’s restriction of corporate risk-taking; on the contrary, when enterprises allocate financial assets to reserving liquidity, the “reservoir” effect enhances commercial credit financing’s facilitation of corporate risk-taking.

6.1.2. Debt Default Risk

The larger the Zscore value, which is a measure of debt default risk, the lower the risk of corporate debt defaults. The test results in columns (3)–(4) of Table 6 show that the coefficient of the interaction term between the quadratic term for commercial credit financing and the debt default risk (Credit² × Zscore) is significantly negative, indicating that debt default risk strengthens the U-shaped impact of commercial credit financing on corporate risk-taking, steepening the curves either side of the inflection point of the main effect. This reinforces both that low levels of commercial credit financing inhibit corporate risk-taking and that high levels of commercial credit financing facilitate corporate risk-taking.

6.1.3. Ownership Attributes

The test results in columns (5)–(6) of Table 6 show that the coefficient of the interaction term between the quadratic term for commercial credit financing and ownership attributes (Credit² × Soe) is significantly negative, indicating that state-owned ownership attributes weaken the U-shaped impact of commercial credit financing on corporate risk-taking, which flattens the curves on either side of the inflection point of the main effect. This weakens both low commercial credit financing’s inhibitory effect on corporate risk-taking and high commercial credit financing’s facilitation of corporate risk-taking.

6.2. Heterogeneity Analysis

In order to further discuss the heterogeneity of the impact of commercial credit financing on corporate risk-taking, the benchmark model is regressed in groups according to corporate internal characteristics such as corporate cash flow position, the degree of financing constraints, and the degree of commercial credit supply, respectively.

6.2.1. Heterogeneity of Firms’ Cash Flow Positions

Cash flow position is another influential factor in enterprises making high-risk investments. Referring to Pornupatham et al.’s approach [41], we define enterprise cash flow position (FC) = (money funds + trading financial assets)/total assets. The higher this indicator, the greater the cash flow of the enterprise, and the stronger its ability to cope with the uncertainty of risky investments. The sample firms are divided into two groups to regress the benchmark model, with the annual industry median of this indicator as the boundary.

The test results in columns (1)–(2) of

Table 7. Heterogeneity test results.

Variable	Cash Flow Position		Financing Constraints		Commercial Credit Supply
	Worse	Better	Lower	Higher	Low Degree	High Degree
	(1)	(2)	(3)	(4)	(5)	(6)
	Risk	Risk	Risk	Risk	Risk	Risk
Credit	−3.087 ***	−6.682 ***	−5.002 ***	−4.301 ***	−4.458 ***	−5.261 ***
	(−3.83)	(−11.48)	(−8.33)	(−6.41)	(−6.18)	(−7.58)
Credit2	3.045	6.660 ***	5.580 ***	2.006	6.161 ***	2.517
	(1.42)	(4.76)	(3.74)	(1.25)	(2.69)	(1.62)
_cons	13.288 ***	10.560 ***	4.192 ***	15.675 ***	12.372 ***	9.734 ***
	(16.38)	(17.01)	(6.32)	(23.26)	(19.62)	(11.44)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes
N	10,124	20,321	13,738	16,707	15,153	15,292
A-R2	0.132	0.120	0.082	0.161	0.140	0.110

Notes: Control, Year, and Industry represent control variables, year effects, and industry effects, respectively; *** denotes significance at the 1% level.

show that the U-shaped relationship between commercial credit financing and corporate risk-taking is significant in the group with an optimal cash flow, while in the group with a worse cash flow, commercial credit financing only negatively impacts corporate risk-taking. This may be because a better cash flow position is often indicative of an enterprise having abundant internal cash flow and optimal operating conditions. It is infrequently the case in this scenario that commercial credit financing is used to alleviate the pressure of production and operation costs without time to consider profitable and risky investment projects; on the contrary, greater commercial credit financing can supplement an enterprise’s internal financing and formal external channel financing sources, enhance the liquidity of its funds, provide financial support for risky investment projects, and improve its level of risk-taking. On the other hand, a poor cash flow position is often accompanied by insufficient internal funds, poor operating conditions, and a lack of sufficient funds for risky investments. In this scenario, enterprises seek out commercial credit financing only to try to maintain the existing production and operation of their business, precluding high-risk investment projects.

6.2.2. Heterogeneity in the Degree of Financing Constraints

Financing constraints are a financing problem commonly encountered by enterprises in the process of operation and development. They seriously affect the operation and performance of enterprises, restrict their growth and sustainable development, and negatively impact risky investment decisions. We adopt the KZ index constructed by Kaplan and Zingales [42], which is widely used in academia, to measure the degree of enterprises’ financing constraints. The sample firms are divided into two groups to regress the benchmark model, using the annual industry median of this index as the boundary.

The results of the test in columns (3)–(4) of Table 7 show that the U-shaped relationship between commercial credit financing and enterprise risk-taking is significant for the group with less severe financing constraints, while in the group with more severe financing constraints, commercial credit financing solely impedes enterprise risk-taking. This may be because fewer financing constraints indicate an enterprise with diversified financing channels and better financing ability. Alternatively, the effect of a lower commercial credit financing ability is weaker, and this lower level of commercial credit financing is only used to maintain an enterprise’s business activities and meet the capital demand for production and operation; it cannot handle investment projects with high profitability, high risk, and high capital demands. However, greater commercial credit financing reduces the proportion of interest-bearing liabilities, which, in turn, improves an enterprise’s investment efficiency and risk-bearing level by reducing its investment costs. On the contrary, when the degree of financing constraints is high and it is difficult for enterprises to obtain credit support from banks and other financial institutions, commercial credit financing becomes an important channel through which they can alleviate these financing constraints. When barely maintaining production and operation capital demands and coping with industrial capital pressure, enterprises will not use commercial credit financing to support high-risk projects with longer return periods.

6.2.3. Heterogeneity in the Degree of Commercial Credit Supply

Commercial credit supply refers to the short-term credit funds provided by enterprises to downstream customers in the process of credit transactions [43], which mainly manifest as accounts receivable and notes receivable in the process of selling goods on credit. Commercial credit supply and commercial credit financing together constitute the business connections between upstream and downstream enterprises in the supply chain, realizing the flow of funds within the industrial supply chain. The amount of commercial credit financing an enterprise has access to will directly affect its willingness to offer commercial credit in turn and the degree of its supply of commercial credit to downstream customers. Drawing on research by Wu et al. [44], this paper defines commercial credit supply (Cresup) = (net accounts receivable + net notes receivable − advance receipts)/total assets. The sample firms are divided into two groups to regress the benchmark model, using the annual industry median of this indicator as a boundary.

The test results in columns (5)–(6) of Table 7 show that the U-shaped relationship between commercial credit financing and corporate risk-taking is significant in the group with a low commercial credit supply, while in the group with a high commercial credit supply, commercial credit financing solely limits corporate risk-taking. This may be due to the fact that in offering a supply of commercial credit, non-financial enterprises essentially act as credit intermediaries, allocating commercial credit secondarily, which is essentially a shadow banking activity. The provision of a moderate commercial credit supply to downstream customers not only increases a business’s sales volume, reduces the cost of its commodity inventory, and maintains customer stability, but also improves the speed of product flow and liquidity. Therefore, the U-shaped relationship between commercial credit financing and risk-taking is not affected by an enterprise’s normal sales, which is the only way for it to supply commercial credit to downstream customers. On the contrary, if the enterprise is located in a seller’s market, competition is fierce, and its downstream customers find themselves in a strong buyer’s market. The enterprise is forced to sell goods on credit and provide a high supply of commercial credit. This abundance of receivables necessitates the difficult recovery of high-risk assets. Not only does this significantly reduce the enterprise’s financial liquidity, but it also exacerbates the risk of contagion between upstream and downstream enterprises in the supply chain, which is seriously detrimental to its interests. The difficulty of recovering these funds and a lack of liquidity exacerbate commercial credit financing’s discouragement of risky business investments.

7. Discussion

7.1. Research Findings

Our findings are in three main areas.

First, commercial credit financing has a U-shaped relationship with enterprise risk-taking. That is, when there is a low level of commercial credit financing, corporate risk-taking is inhibited; when there is a high level of commercial credit financing, corporate risk-taking is facilitated. The main reasons behind this conclusion are commercial credit financing’s role as an alternative financing role and its role in a competitive market. This conclusion remains robust after accounting for endogeneity issues, substituting different metrics, and taking into account the impact of systemic differences on the research question.

Second, further exploration of the mediation of corporate financialization, debt default risk, and ownership attributes in the relationship between commercial credit financing and corporate risk-taking reveals that corporate financialization and debt default risk strengthen the U-shaped impact of commercial credit financing on corporate risk-taking, while state ownership attributes have the opposite effect.

Third, the results of the heterogeneity analysis show that the effect of commercial credit financing on corporate risk-taking depends on internal characteristics such as cash flow position, financing constraints, and commercial credit supply. In other words, in enterprises with a better cash flow position, lower financing constraints, and a lower commercial credit supply, commercial credit financing has a significant U-shaped effect on corporate risk-taking; meanwhile, in enterprises with a poor cash flow position, higher financing constraints, and a higher commercial credit supply, commercial credit financing significantly inhibits corporate risk-taking. The findings of this study not only substantiate “alternative financing” and “buyer’s market” theories on commercial credit financing with further evidence but also have profound practical significance for improving enterprise risk-taking levels and promoting their high-quality development.

7.2. Practical Significance

The practical significance of this study mainly pertains to the following two areas:

First, this study provides a basis for enterprises to prevent commercial credit financing from hindering risk-taking and instead make full use of its ability to facilitate risk-taking. Equally, the results of this study offer a point of reference for a more scientific understanding and evaluation of the role of commercial credit financing in the sustainable operation of enterprises and their project investment, as well as for rational allocation of commercial credit financing, informed risk capital decision-making, and the promotion of sustainable and high-quality enterprise development.

Second, enterprise characteristics such as their cash flow status, degree of financing constraint, and commercial credit supply evidently influence the research questions differently. This provides new empirical evidence on how enterprises with different characteristics can effectively utilize upstream and downstream supply chain relationships to support “common governance” over enterprises participating in the supply chain and also guides the sustainable development of the whole supply chain.

7.3. Policy Recommendations

In order to optimize the influential relationship identified, we put forward the following policy recommendations:

At the level of the industrial supply chain, the first suggestion is to formulate reasonable and effective industrial supply chain policies to ensure its basic stability, promote information sharing and mutual trust among upstream and downstream enterprises in the supply chain, endeavor to improve the ability of enterprises and upstream suppliers to negotiate and encourage selecting supplier enterprises that can provide greater commercial credit. The second suggestion is to help enterprises in the supply chain attract more investors outside the chain through supply chain industrial integration so as to provide various kinds of financial support for the investment decisions of enterprises in the chain. This would be conducive to guiding the optimal allocation of resources across society.

At the level of the enterprise, enterprises should first correctly recognize that commercial credit financing is a double-edged sword when deciding to invest in high-risk and high-yield projects and reasonably make use of this distinction in its effect at different stages. When the operating capacity of an enterprise is weak, although commercial credit financing is not conducive to improving its risk-bearing level, commercial credit can enhance the performance of the main business and its market competitiveness by expanding the sales and market shares of its products. In the purchasing process, the enterprise gradually forms and grows its position in a buyer’s market. This will ultimately improve its ability to obtain all kinds of resources and the financial support required for high-yield investment projects, augmenting its risk-taking levels, its ability to withstand risks and its sustainable development.

Second, enterprises should innovate their forms of credit management, make smart use of commercial credit financing, consider the structural proportions of commercial credit financing and short-term debt financing, and maximize the positive effects of these two financing modes on enterprise governance.

Third, enterprises should weigh the structural ratio of commercial credit financing against short-term debt financing and fully utilize the positive impacts these two financing methods can have on enterprise oversight and behavior. In addition, enterprises should endeavor to improve their cash liquidity, strive for multi-channel financing, reduce corporate financing constraints, prudently provide commercial credit to downstream enterprises, and seek to improve their risk-taking level.

7.4. Study Limitations and Future Research

Although this research implemented careful logical arguments and rigorous empirical tests, it still has the following shortcomings and room for future improvements:

Firstly, we opted to analyze the two effects of commercial credit financing through the “substitute financing theory” and “buyer’s market theory”, followed by constructing a quadratic term model and empirically testing the U-shaped effect of commercial credit financing on corporate risk-taking; thus, we need to further explore whether there are other types of models between the two.

Secondly, our thesis is based on sample information from Chinese A-share listed companies; further research and empirical tests are needed to ascertain whether the research conclusions apply to unlisted companies.

Finally, the research objects in this paper were Chinese microenterprises, so it did not consider the impact of the global economic environment and technological changes on the research findings. In the future, we will expand the scope of this study to explore related research on the impact of sustainable supply chains on risk-taking in an international macro-trade environment.