# Empirical Analysis of Supplier Inefficient Investment at Customer Risks Based on Supply Chain Data

Authors: Qun Bao; Ya-Nan Mao; Rui Xie; Li-Jun Xu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

As for whether the customer risk leads to the inefficient investment of suppliers, 776 suppliers and customers of Shanghai and Shenzhen A-shares from 2007 to 2017 are chosen as the supply chain data of listed companies, the relationship between customer risk and supplier enterprise investment efficiency is studied by manual sorting, and the moderating effect of the nature of supply chain relationship on customer risk and enterprise investment efficiency is taken into account. According to the research, the customer risk will reduce the investment efficiency of supplier enterprises, which is manifested as underinvestment. Based on further research, the relationship is more significant in supplier enterprises with a higher customer concentration and state-owned enterprises that are customers and suppliers. The research of this paper enriches the relevant literature on investment efficiency and the relationship between customer and supplier, provides a new perspective for studying investment efficiency and the empirical evidence for risk prevention behavior among enterprises, and has important practical significance for supplier enterprises to choose core customers and manage customer relations.


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## Body

## 1. Introduction

Pursuant to the report of the 19th National Congress of the Communist Party of China, China’s current economy has transitioned from the stage of rapid growth to the stage of high-quality development, and Chinese enterprises should pay more attention to innovation and efficiency of capital allocation at the stage. However, now the efficiency of capital allocation of Chinese listed companies was low and there were many inefficient investment behaviors, such as overinvestment and underinvestment [1]. Based on the need to create value, the enterprise should carry out investment activity effectively, but the investment efficiency often deviates from the optimal one due to the influence of principal-agent relation and information asymmetry in the investment activity. The inefficient investment behavior of Chinese enterprises may cause the rupture of the enterprise capital chain and the decline of share price and even plunge the enterprise into bankruptcy finally [2].As the downstream enterprise in the supply chain, the customer is the source of supplier enterprise cash flow and enterprise value. The relationship of the community of shared interests of “people of the same kind or in the same place subject to consistent gains and losses” was formed between customer and supplier enterprise due to the business contractual relationship established between an enterprise and its customers in the daily trading activity based on trade contacts (namely, customer relations) [3]. As one of the important external stakeholders of an enterprise, the customer should also bear the corresponding risk when investing in specific capital. For the purpose of own benefits, the customer usually chooses a certain way to restrict the company’s business activities, investment activities, etc. The selection of suppliers by the customer also plays a role in signal transmission, and the customer quality not only indirectly affirms the product quality of the supplier enterprise but also reflects the strength of the supplier enterprise. One of the main determinants of customer quality was that the customers were able to timely pay the payment for goods and perform obligations. However, when the customer encountered financial difficulties, it might reallocate the resources purely for its own benefit, causing the customer risk to be transmitted to the supplier enterprise and the “possible mismatching of its resources” [4]. The spillover effect of customer risk through the supply chain will further reduce the efficiency of enterprise resource allocation. It will cause its investment efficiency to deviate from the optimal level and lead to inefficient investment behavior.However, the empirical study on whether the customer risk will aggravate supplier enterprises’ inefficient investment behavior by the supply chain is not sufficient in the current literature. On this basis, this paper studies the relationship between customer risk and supplier enterprise investment efficiency based on the supply chain data regarding 776 pairs of suppliers and customers of Shanghai and Shenzhen A-shares from 2007 to 2017 being listed companies and further analyzes the difference in the influence among suppliers with different customer concentrations and natures of property right.According to the study of this paper, (1) in the upstream and downstream relationship of the supply chain, when the customer risk is high, the supplier will face more serious financing restrictions and may be forced to give up the investments with large potential returns, resulting in underinvestment; (2) It is harder for the supplier enterprises with a higher customer concentration to have a narrow escape in the face of customer risk, that is to say, the positive influence of customer risk on underinvestment is more significant in supplier enterprises with a higher customer concentration; (3) the supply chain relationship between state-owned enterprises is more stable and the stable relationship may make it impossible for one party to avoid risk by discontinuing the relationship when the other party is in trouble. When the supplier and customer enterprises are state-owned enterprises, the effect of customer risk on the underinvestment of supplier enterprises will be more significant.The possible research contributions of this paper are as follows: (1) from the perspective of customers as the external stakeholders, the influence of customer characteristics on the investment efficiency of supplier enterprises is studied in this paper, which enriches the references related to the influencing factors of enterprises’ inefficient investments; (2) in this paper, more interpretations from various perspectives are provided for the study of the relationships between the suppliers and customers, contributing to developing the exploration of the exposure of enterprise risks through the supply chains, and providing enlightenment and references for the enterprises to recognize the transmission paths of supply chain risks, reasonably plan investment strategies, and promote the sustainable development of enterprises.Section2 of this paper covers the literature review and research hypothesis, Section 3 of it is the research design, Section 4 of it includes empirical results and analysis, and Section 5 of it pertains to research conclusions and policy suggestions.


## 2. Literature Review and Research Hypotheses

### 2.1. Literature Review

#### 2.1.1. Literature on Enterprises’ Inefficient Investment

In the perfect capital markets mentioned by Modigliani and Miller, the enterprises can achieve the goal of maximizing the enterprise value through appropriate investment behaviors. However, a series of studies had shown that the investment efficiency of Chinese listed companies was not high, and there were many inefficient investment behaviors in recent years [5].On one hand, the management layer may take such measures as expanding the scale of the company, wanton acquisition of assets, and diversified operations to trigger the tendency to overinvest due to the need to expand the rights when the enterprises have too many free amounts of cash and fewer opportunities for growth according to “Free Cash Flow Hypothesis”. On the other hand, information asymmetry exists between the internal management layer of the enterprise and external investors, so the external financing cost is generally higher than the cost of internal finance. In this case, the manager may lower the proportion of external financing to reduce the financing costs, but the enterprise may be forced to give up the investment projects with positive potential values, resulting in underinvestment when the internal capital is insufficient [6].In most literature, its influence on enterprise investment efficiency is discussed from the perspective of internal control, accounting conservatism, accounting information quality, environmental uncertainty, and irrational psychology. Li Wanfu et al. (2011) believed that there was a negative correlation between the quality of internal control and the degree of enterprise inefficient investment, that is to say, where the quality of internal control was higher, the inefficient investment behavior of enterprises could be curbed better [7]. In terms of accounting conservatism, upon research by Liu Hongxia and Suo Lingling (2011), it is found that accounting conservatism could curb the enterprises’ overinvestment, while it would exacerbate the underinvestment of enterprises [8]. In terms of accounting information quality, Li Qingyuan (2009) said that the higher the accounting information quality was, the higher the enterprises’ investment efficiency would be because the high-quality accounting information could effectively lower the ethical risk and adverse selection in the investment decision-making [9]. In terms of environmental uncertainty, Shen Huihui et al. (2012) carried out research from the perspective of financing restriction, and the result showed that the enterprises’ inefficient investment would be incurred by the environmental uncertainty [10]. In terms of the irrational mind, the irrational mind of the management layer would trigger the enterprise investment efficiency deviating from the optimal [11], and there was a significant positive correlation between investor sentiment and enterprise overinvestment [12].


#### 2.1.2. Literature on Customer Relations

The customer is one of the important external stakeholders of enterprises, and the effect of customer relations on enterprise behavior and economic consequences gets more and more attention in the practice field and academic world.On the one hand, the close customer relations will help to promote information sharing and improve supply chain management efficiency to produce the supply chain integration effect [13]; on the other hand, both parties may fail to have a narrow escape when facing difficulties, and the other party may be affected once one party encounters financial distress [14]. Dou et al. (2013) discovered that there was a certain relationship between the stock return rate of major customers and the future share price of supplier enterprises, and the future share price of suppliers would rise if the stock return rate of supplier’s main customers was good and vice versa [15]. However, when customer enterprises were caught in financial distress or even bankruptcy, the investors would choose to transfer some of the financial distress costs of customer enterprises to suppliers to make suppliers pay for customers’ risks; as a result, the share price of supplier enterprises fell sharply [16].On the other hand, enterprises’ financial and nonfinancial information is conveyed to third parties (such as investors, creditors, analysts, and banks) by the close relationship between customers and suppliers to influence their decisions pursuant to current research studies. Kim et al. (2015) researched the influence of customers’ public earning information on the nonprice clauses of credit contracts between banks and supplier enterprises [17]. By event study, Wang Xiongyuan and Gao Xi (2017) found that the announcement regarding the earnings of customers had an effect on the fluctuations of their share price and the change in the supplier enterprise’s share price, the share price reaction of the two showed a positive correlation, and the closer and the more dependent the customer relations were, the stronger the contagion effect would be [18]. Yin Feng and Jia Jingyue (2017) carried out an empirical study of the relationship between big customer earnings management and supplier enterprise’s inefficient investment, and it is found that the earnings management behavior of big customers would promote inefficient investment of enterprises, the positive earnings management of big customers would induce suppliers’ overinvestment behavior, and the negative earnings management would lead to the underinvestment of suppliers upon research [19]. Wang Yong (2019) investigated the influence of customer’s public debt level information on supplier enterprise credit contract price clauses, and the research results indicated that the debt level of customers would influence the credit financing cost of supplier enterprises and there was a significant positive correlation between them [20].


### 2.2. Research Hypothesis

#### 2.2.1. Influence of Customer Risk on Supplier Enterprise Investment Efficiency

The customer was one of the important external stakeholders of an enterprise, and the relationship of the community of shared interests of “people of the same kind or in the same place subject to consistent gains and losses” was formed by the customer and the enterprise by trade contacts [21, 22]. The other party failed to have a narrow escape when one party faced difficulties due to the close relationship. The customer was a key link in the supply chain. When the customer enterprises were caught in financial distress or even bankruptcy, the investors would choose to transfer some of the financial distress costs of customer enterprises to suppliers to make suppliers pay for customers’ risks; as a result, the share price of supplier enterprises fell sharply [23].Firstly, the customer risk will result in insufficient internal capital for supplier enterprises. Cho et al. (2019) pointed out that the threat of breach of contract by the customer would be also high when customers faced financial distress, that is to say, when the customer risk was relatively high, and the potential loss of the supplier enterprise’s future cash flow might be incurred once the customer had to discontinue the transaction [24]. If the customer fails to repay the payment for goods on schedule or is unable to repay the payment for goods, the supplier enterprises’ business term and risk of bad debts will be increased and the capital turnover efficiency of supplier enterprises will be lowered, incurring the loss of future cash flow to supplier enterprises ultimately [25].Secondly, the supplier enterprises will undergo external financing restrictions on account of customer risks. The customer risks might be contaminated to supplier enterprises by the supply chain, resulting in a strong impact on their operating and financial conditions as well as capital structure, and the leverage ratio of supplier enterprises might be lowered [26]. The banks would also require higher interest margins, lower maturities, or stricter contract restrictions when providing loans to suppliers [27]. The supplier enterprise’s ability to obtain new financing by securitization of accounts receivable was also impaired with the financing restriction further increased [28]. To sum up, if the supplier enterprises have insufficient internal finances when undergoing the external financing restriction, they will be forced to give up the investment projects with positive potential values, resulting in underinvestment.Thirdly, the customer risks are transmitted to the enterprises through the supply chains, thereby aggravating the enterprise risks. When there is larger volatility with the customer operation, on the one hand, the uncertainty of product requirements is increased, compelling the enterprises to adopt more conservative strategies to prevent the risks. On the other hand, due to the increase in customer risks, the resources will be reallocated by the customers from their own interests, and the customer risks will be transmitted to the supplier enterprises through the close supply chain relationship, thus exacerbating the mismatching of resources. Uncertain requirements and inappropriate resource allocation can have negative impacts on the future strategic planning of the enterprises, thereby limiting the investment scale and causing insufficient investments. Thus, hypothesis 1 is put forward.H1: the customer risk will lead to inefficient investment by supplier enterprise, and the inefficient investment is manifested as underinvestment.


## 2.1. Literature Review

### 2.1.1. Literature on Enterprises’ Inefficient Investment

In the perfect capital markets mentioned by Modigliani and Miller, the enterprises can achieve the goal of maximizing the enterprise value through appropriate investment behaviors. However, a series of studies had shown that the investment efficiency of Chinese listed companies was not high, and there were many inefficient investment behaviors in recent years [5].On one hand, the management layer may take such measures as expanding the scale of the company, wanton acquisition of assets, and diversified operations to trigger the tendency to overinvest due to the need to expand the rights when the enterprises have too many free amounts of cash and fewer opportunities for growth according to “Free Cash Flow Hypothesis”. On the other hand, information asymmetry exists between the internal management layer of the enterprise and external investors, so the external financing cost is generally higher than the cost of internal finance. In this case, the manager may lower the proportion of external financing to reduce the financing costs, but the enterprise may be forced to give up the investment projects with positive potential values, resulting in underinvestment when the internal capital is insufficient [6].In most literature, its influence on enterprise investment efficiency is discussed from the perspective of internal control, accounting conservatism, accounting information quality, environmental uncertainty, and irrational psychology. Li Wanfu et al. (2011) believed that there was a negative correlation between the quality of internal control and the degree of enterprise inefficient investment, that is to say, where the quality of internal control was higher, the inefficient investment behavior of enterprises could be curbed better [7]. In terms of accounting conservatism, upon research by Liu Hongxia and Suo Lingling (2011), it is found that accounting conservatism could curb the enterprises’ overinvestment, while it would exacerbate the underinvestment of enterprises [8]. In terms of accounting information quality, Li Qingyuan (2009) said that the higher the accounting information quality was, the higher the enterprises’ investment efficiency would be because the high-quality accounting information could effectively lower the ethical risk and adverse selection in the investment decision-making [9]. In terms of environmental uncertainty, Shen Huihui et al. (2012) carried out research from the perspective of financing restriction, and the result showed that the enterprises’ inefficient investment would be incurred by the environmental uncertainty [10]. In terms of the irrational mind, the irrational mind of the management layer would trigger the enterprise investment efficiency deviating from the optimal [11], and there was a significant positive correlation between investor sentiment and enterprise overinvestment [12].


### 2.1.2. Literature on Customer Relations

The customer is one of the important external stakeholders of enterprises, and the effect of customer relations on enterprise behavior and economic consequences gets more and more attention in the practice field and academic world.On the one hand, the close customer relations will help to promote information sharing and improve supply chain management efficiency to produce the supply chain integration effect [13]; on the other hand, both parties may fail to have a narrow escape when facing difficulties, and the other party may be affected once one party encounters financial distress [14]. Dou et al. (2013) discovered that there was a certain relationship between the stock return rate of major customers and the future share price of supplier enterprises, and the future share price of suppliers would rise if the stock return rate of supplier’s main customers was good and vice versa [15]. However, when customer enterprises were caught in financial distress or even bankruptcy, the investors would choose to transfer some of the financial distress costs of customer enterprises to suppliers to make suppliers pay for customers’ risks; as a result, the share price of supplier enterprises fell sharply [16].On the other hand, enterprises’ financial and nonfinancial information is conveyed to third parties (such as investors, creditors, analysts, and banks) by the close relationship between customers and suppliers to influence their decisions pursuant to current research studies. Kim et al. (2015) researched the influence of customers’ public earning information on the nonprice clauses of credit contracts between banks and supplier enterprises [17]. By event study, Wang Xiongyuan and Gao Xi (2017) found that the announcement regarding the earnings of customers had an effect on the fluctuations of their share price and the change in the supplier enterprise’s share price, the share price reaction of the two showed a positive correlation, and the closer and the more dependent the customer relations were, the stronger the contagion effect would be [18]. Yin Feng and Jia Jingyue (2017) carried out an empirical study of the relationship between big customer earnings management and supplier enterprise’s inefficient investment, and it is found that the earnings management behavior of big customers would promote inefficient investment of enterprises, the positive earnings management of big customers would induce suppliers’ overinvestment behavior, and the negative earnings management would lead to the underinvestment of suppliers upon research [19]. Wang Yong (2019) investigated the influence of customer’s public debt level information on supplier enterprise credit contract price clauses, and the research results indicated that the debt level of customers would influence the credit financing cost of supplier enterprises and there was a significant positive correlation between them [20].


## 2.1.1. Literature on Enterprises’ Inefficient Investment

In the perfect capital markets mentioned by Modigliani and Miller, the enterprises can achieve the goal of maximizing the enterprise value through appropriate investment behaviors. However, a series of studies had shown that the investment efficiency of Chinese listed companies was not high, and there were many inefficient investment behaviors in recent years [5].On one hand, the management layer may take such measures as expanding the scale of the company, wanton acquisition of assets, and diversified operations to trigger the tendency to overinvest due to the need to expand the rights when the enterprises have too many free amounts of cash and fewer opportunities for growth according to “Free Cash Flow Hypothesis”. On the other hand, information asymmetry exists between the internal management layer of the enterprise and external investors, so the external financing cost is generally higher than the cost of internal finance. In this case, the manager may lower the proportion of external financing to reduce the financing costs, but the enterprise may be forced to give up the investment projects with positive potential values, resulting in underinvestment when the internal capital is insufficient [6].In most literature, its influence on enterprise investment efficiency is discussed from the perspective of internal control, accounting conservatism, accounting information quality, environmental uncertainty, and irrational psychology. Li Wanfu et al. (2011) believed that there was a negative correlation between the quality of internal control and the degree of enterprise inefficient investment, that is to say, where the quality of internal control was higher, the inefficient investment behavior of enterprises could be curbed better [7]. In terms of accounting conservatism, upon research by Liu Hongxia and Suo Lingling (2011), it is found that accounting conservatism could curb the enterprises’ overinvestment, while it would exacerbate the underinvestment of enterprises [8]. In terms of accounting information quality, Li Qingyuan (2009) said that the higher the accounting information quality was, the higher the enterprises’ investment efficiency would be because the high-quality accounting information could effectively lower the ethical risk and adverse selection in the investment decision-making [9]. In terms of environmental uncertainty, Shen Huihui et al. (2012) carried out research from the perspective of financing restriction, and the result showed that the enterprises’ inefficient investment would be incurred by the environmental uncertainty [10]. In terms of the irrational mind, the irrational mind of the management layer would trigger the enterprise investment efficiency deviating from the optimal [11], and there was a significant positive correlation between investor sentiment and enterprise overinvestment [12].


## 2.1.2. Literature on Customer Relations

The customer is one of the important external stakeholders of enterprises, and the effect of customer relations on enterprise behavior and economic consequences gets more and more attention in the practice field and academic world.On the one hand, the close customer relations will help to promote information sharing and improve supply chain management efficiency to produce the supply chain integration effect [13]; on the other hand, both parties may fail to have a narrow escape when facing difficulties, and the other party may be affected once one party encounters financial distress [14]. Dou et al. (2013) discovered that there was a certain relationship between the stock return rate of major customers and the future share price of supplier enterprises, and the future share price of suppliers would rise if the stock return rate of supplier’s main customers was good and vice versa [15]. However, when customer enterprises were caught in financial distress or even bankruptcy, the investors would choose to transfer some of the financial distress costs of customer enterprises to suppliers to make suppliers pay for customers’ risks; as a result, the share price of supplier enterprises fell sharply [16].On the other hand, enterprises’ financial and nonfinancial information is conveyed to third parties (such as investors, creditors, analysts, and banks) by the close relationship between customers and suppliers to influence their decisions pursuant to current research studies. Kim et al. (2015) researched the influence of customers’ public earning information on the nonprice clauses of credit contracts between banks and supplier enterprises [17]. By event study, Wang Xiongyuan and Gao Xi (2017) found that the announcement regarding the earnings of customers had an effect on the fluctuations of their share price and the change in the supplier enterprise’s share price, the share price reaction of the two showed a positive correlation, and the closer and the more dependent the customer relations were, the stronger the contagion effect would be [18]. Yin Feng and Jia Jingyue (2017) carried out an empirical study of the relationship between big customer earnings management and supplier enterprise’s inefficient investment, and it is found that the earnings management behavior of big customers would promote inefficient investment of enterprises, the positive earnings management of big customers would induce suppliers’ overinvestment behavior, and the negative earnings management would lead to the underinvestment of suppliers upon research [19]. Wang Yong (2019) investigated the influence of customer’s public debt level information on supplier enterprise credit contract price clauses, and the research results indicated that the debt level of customers would influence the credit financing cost of supplier enterprises and there was a significant positive correlation between them [20].


## 2.2. Research Hypothesis

### 2.2.1. Influence of Customer Risk on Supplier Enterprise Investment Efficiency

The customer was one of the important external stakeholders of an enterprise, and the relationship of the community of shared interests of “people of the same kind or in the same place subject to consistent gains and losses” was formed by the customer and the enterprise by trade contacts [21, 22]. The other party failed to have a narrow escape when one party faced difficulties due to the close relationship. The customer was a key link in the supply chain. When the customer enterprises were caught in financial distress or even bankruptcy, the investors would choose to transfer some of the financial distress costs of customer enterprises to suppliers to make suppliers pay for customers’ risks; as a result, the share price of supplier enterprises fell sharply [23].Firstly, the customer risk will result in insufficient internal capital for supplier enterprises. Cho et al. (2019) pointed out that the threat of breach of contract by the customer would be also high when customers faced financial distress, that is to say, when the customer risk was relatively high, and the potential loss of the supplier enterprise’s future cash flow might be incurred once the customer had to discontinue the transaction [24]. If the customer fails to repay the payment for goods on schedule or is unable to repay the payment for goods, the supplier enterprises’ business term and risk of bad debts will be increased and the capital turnover efficiency of supplier enterprises will be lowered, incurring the loss of future cash flow to supplier enterprises ultimately [25].Secondly, the supplier enterprises will undergo external financing restrictions on account of customer risks. The customer risks might be contaminated to supplier enterprises by the supply chain, resulting in a strong impact on their operating and financial conditions as well as capital structure, and the leverage ratio of supplier enterprises might be lowered [26]. The banks would also require higher interest margins, lower maturities, or stricter contract restrictions when providing loans to suppliers [27]. The supplier enterprise’s ability to obtain new financing by securitization of accounts receivable was also impaired with the financing restriction further increased [28]. To sum up, if the supplier enterprises have insufficient internal finances when undergoing the external financing restriction, they will be forced to give up the investment projects with positive potential values, resulting in underinvestment.Thirdly, the customer risks are transmitted to the enterprises through the supply chains, thereby aggravating the enterprise risks. When there is larger volatility with the customer operation, on the one hand, the uncertainty of product requirements is increased, compelling the enterprises to adopt more conservative strategies to prevent the risks. On the other hand, due to the increase in customer risks, the resources will be reallocated by the customers from their own interests, and the customer risks will be transmitted to the supplier enterprises through the close supply chain relationship, thus exacerbating the mismatching of resources. Uncertain requirements and inappropriate resource allocation can have negative impacts on the future strategic planning of the enterprises, thereby limiting the investment scale and causing insufficient investments. Thus, hypothesis 1 is put forward.H1: the customer risk will lead to inefficient investment by supplier enterprise, and the inefficient investment is manifested as underinvestment.


## 2.2.1. Influence of Customer Risk on Supplier Enterprise Investment Efficiency

The customer was one of the important external stakeholders of an enterprise, and the relationship of the community of shared interests of “people of the same kind or in the same place subject to consistent gains and losses” was formed by the customer and the enterprise by trade contacts [21, 22]. The other party failed to have a narrow escape when one party faced difficulties due to the close relationship. The customer was a key link in the supply chain. When the customer enterprises were caught in financial distress or even bankruptcy, the investors would choose to transfer some of the financial distress costs of customer enterprises to suppliers to make suppliers pay for customers’ risks; as a result, the share price of supplier enterprises fell sharply [23].Firstly, the customer risk will result in insufficient internal capital for supplier enterprises. Cho et al. (2019) pointed out that the threat of breach of contract by the customer would be also high when customers faced financial distress, that is to say, when the customer risk was relatively high, and the potential loss of the supplier enterprise’s future cash flow might be incurred once the customer had to discontinue the transaction [24]. If the customer fails to repay the payment for goods on schedule or is unable to repay the payment for goods, the supplier enterprises’ business term and risk of bad debts will be increased and the capital turnover efficiency of supplier enterprises will be lowered, incurring the loss of future cash flow to supplier enterprises ultimately [25].Secondly, the supplier enterprises will undergo external financing restrictions on account of customer risks. The customer risks might be contaminated to supplier enterprises by the supply chain, resulting in a strong impact on their operating and financial conditions as well as capital structure, and the leverage ratio of supplier enterprises might be lowered [26]. The banks would also require higher interest margins, lower maturities, or stricter contract restrictions when providing loans to suppliers [27]. The supplier enterprise’s ability to obtain new financing by securitization of accounts receivable was also impaired with the financing restriction further increased [28]. To sum up, if the supplier enterprises have insufficient internal finances when undergoing the external financing restriction, they will be forced to give up the investment projects with positive potential values, resulting in underinvestment.Thirdly, the customer risks are transmitted to the enterprises through the supply chains, thereby aggravating the enterprise risks. When there is larger volatility with the customer operation, on the one hand, the uncertainty of product requirements is increased, compelling the enterprises to adopt more conservative strategies to prevent the risks. On the other hand, due to the increase in customer risks, the resources will be reallocated by the customers from their own interests, and the customer risks will be transmitted to the supplier enterprises through the close supply chain relationship, thus exacerbating the mismatching of resources. Uncertain requirements and inappropriate resource allocation can have negative impacts on the future strategic planning of the enterprises, thereby limiting the investment scale and causing insufficient investments. Thus, hypothesis 1 is put forward.H1: the customer risk will lead to inefficient investment by supplier enterprise, and the inefficient investment is manifested as underinvestment.


## 3. Research Design

### 3.1. Sample Selection and Data Source

This paper chooses 15808 listed companies of Shanghai and Shenzhen A-shares disclosing the name information of the top five big customers from 2007 to 2017, of which 1531 pairs of customers and suppliers are listed companies of Shanghai and Shenzhen A-shares. Considering the effect of lag for one phase, this paper eliminates ST and∗ ST, financial industry, and listed companies without financial data and finally obtains 776 groups of customers and suppliers as effective samples of listed companies. This paper carries out ±1% Winsorize processing of all continuous variables to eliminate the influence of extreme values on regression results. The data concerning the top five customers are manually collected and other data types come from the CSMAR database.


### 3.2. Model Setup

To test the research hypothesis of this paper, the main effect regression models (1) and (2) were constructed to verify hypothesis 1, and grouping regression was performed as per customer concentration and nature of the property right to verify hypotheses 2 and 3.(1)InvestEffi,t+1=β0+β1khRiski,t+β2Sizei,t+β3Levi,t+β4Cashi,t+β5Agei,t+β6Ocfi,t+β7Taci,t+β8Sharei,t+β9ROAi,t+β10khSizei,t+β11khLevi,t+β12khAgei,t+β13khOcfi,t+ε,(2)OverInvestUnderInvesti,t+1=β0+β1khRiski,t+β2Sizei,t+β3Levi,t+β4Cashi,t+β5Agei,t+β6Ocfi,t+β7Taci,t+β8Sharei,t+β9ROAi,t+β10khSizei,t+β11khLevi,t+β12khAgei,t+β13khOcfi,t+ε.In the above two formulas, the variable-InvestEff represents the degree of the enterprise’s inefficient investment. The bigger it is, the lower the investment efficiency will be; OverInvest and UnderInvest show the degree of overinvestment and underinvestment of the enterprise. The higher they are, the higher the overinvestment or underinvestment will be; KhRisk indicates the customer risk. The bigger it is, the higher the customer risk will be; refer to Table1 for the definition and explanation of the remaining control variables. Moreover, the industry and year are controlled.Table 1
Variable definition.
VariableVariable definitionInvestEffInvestment efficiency = residual of Richardson regressionUnderInvestUnderinvestment = residual with Richardson regression being less than 0OverInvestOverinvestment = residual with Richardson regression being more than 0khRiskCustomer risk = customer ROA volatilitySizeSupplier enterprise scale = natural logarithm of total assetsLevSupplier asset-liability ratio = liabilities/total assetsCashHolding amount of supplier’s monetary capital = monetary capitals/total assetsAgeNatural logarithm of supplier’s listing yearsOcfCash flow from supplier’s operating activities = net cash flow from operating activities/total assetsTacSupplier’s total asset turnover ratio = operating revenues/total assetsShareSupplier equity balance degree = natural log of the sum of squares of shareholding ratios of the second to the tenth largest shareholdersROAReturn rate of supplier’s total assets = net profits/total assetsCusConcenSupplier customer concentration, and proportion of sales revenue of the largest customer in the total sales revenueGysnstataNature of property right of the supplier. The assignment will be 1 in case of being a state-owned enterprise or will be 0 if notkhSizeCustomer enterprise scale = natural logarithm of total assetskhLevAsset-liability ratio of customer enterprise = liabilities/total assetskhAgeNatural logarithm of customer enterprise’s listing yearskhOcfCash flow from customer’s operating activities = net cash flow from operating activities/total assetskhnstataNature of property right of the customer. The assignment will be 1 in case of being a state-owned enterprise or will be 0 if not


### 3.3. Variable Description

#### 3.3.1. Inefficient Investment

The investment efficiency prediction model of Richardson (2006) [29] was used to estimate the enterprises’ investment efficiency. The model is as follows:(3)Investt=α0+α1To bingqt−1+α2Levt−1+α3Casht−1+α4Aget−1+α5Sizet−1+α6Returnt−1+α7Investt−1+ε.The newly increased investment of the enterprises is measured by Invest, and the calculation method is as follows: Invest = (capital expenditure-income from the sale of long-term assets)/total assets at the beginning of the period. The capital expenditure is the item of “expenditures for purchasing and constructing fixed assets, intangible assets, and other long-term assets” in the cash flow statement (direct method). Refer to Table2 for the definition of other variables.Table 2
Definition of investment efficiency regression model variables of Richardson.
VariablesVariable definitionTobinqTobin’s Q = (price per share∗ quantity of circulating shares + net asset per share ∗ quantity of noncirculation shares + book value of liabilities)/book value of total assetsLevAsset-liability ratio = liabilities/total assetsCashHolding amount of monetary capital = monetary capital/total assetsAgeListing years = observation year- establishment yearSizeEnterprise scale = log(total assets)ReturnConsidering the annual return rate of stocks reinvested by cash dividendsThe OLS regression is carried out after industry and year control against model 3. The residual estimated by OLS indicates the unanticipated capital investment of an enterprise. Generally, it means overinvestment when the residual exceeds 0; it means underinvestment when the residual is less than 0. The residual is represented by the variable-OverInvest and UnderInvest after the absolute value is obtained. The absolute values of all residuals are obtained and the variable-InvestEff is used for expression with the degree of the enterprise’s inefficient investment represented [30].


#### 3.3.2. Customer Risk

In the literature, indicators such as earnings volatility and stock return volatility are commonly used to measure corporate risk. Due to the high volatility of China’s stock market, the risk-taking level of Chinese enterprises is widely measured by earnings volatility. Based on the research by Boubakri et al. (2013) [31], this paper adopts the ROA volatility of customer enterprises within three years adjusted by the annual industry average to measure the customer risk with for expression. The greater the earnings volatility, the higher the risk-taking level of the customer enterprise. ∂khROAi, namely,(4)khRiskTi=1N−1∑n=1NADJ_khROAin−1N∑n=1NADJ_khROAin2,N=3,wherein ADJ_khROAin=khROAin−1/X∑k=1XkhROAkn.


#### 3.3.3. Definition of Other Variables

Refer to Table1 for a definition and detailed explanation of other variables.


## 3.1. Sample Selection and Data Source

This paper chooses 15808 listed companies of Shanghai and Shenzhen A-shares disclosing the name information of the top five big customers from 2007 to 2017, of which 1531 pairs of customers and suppliers are listed companies of Shanghai and Shenzhen A-shares. Considering the effect of lag for one phase, this paper eliminates ST and∗ ST, financial industry, and listed companies without financial data and finally obtains 776 groups of customers and suppliers as effective samples of listed companies. This paper carries out ±1% Winsorize processing of all continuous variables to eliminate the influence of extreme values on regression results. The data concerning the top five customers are manually collected and other data types come from the CSMAR database.


## 3.2. Model Setup

To test the research hypothesis of this paper, the main effect regression models (1) and (2) were constructed to verify hypothesis 1, and grouping regression was performed as per customer concentration and nature of the property right to verify hypotheses 2 and 3.(1)InvestEffi,t+1=β0+β1khRiski,t+β2Sizei,t+β3Levi,t+β4Cashi,t+β5Agei,t+β6Ocfi,t+β7Taci,t+β8Sharei,t+β9ROAi,t+β10khSizei,t+β11khLevi,t+β12khAgei,t+β13khOcfi,t+ε,(2)OverInvestUnderInvesti,t+1=β0+β1khRiski,t+β2Sizei,t+β3Levi,t+β4Cashi,t+β5Agei,t+β6Ocfi,t+β7Taci,t+β8Sharei,t+β9ROAi,t+β10khSizei,t+β11khLevi,t+β12khAgei,t+β13khOcfi,t+ε.In the above two formulas, the variable-InvestEff represents the degree of the enterprise’s inefficient investment. The bigger it is, the lower the investment efficiency will be; OverInvest and UnderInvest show the degree of overinvestment and underinvestment of the enterprise. The higher they are, the higher the overinvestment or underinvestment will be; KhRisk indicates the customer risk. The bigger it is, the higher the customer risk will be; refer to Table1 for the definition and explanation of the remaining control variables. Moreover, the industry and year are controlled.Table 1
Variable definition.
VariableVariable definitionInvestEffInvestment efficiency = residual of Richardson regressionUnderInvestUnderinvestment = residual with Richardson regression being less than 0OverInvestOverinvestment = residual with Richardson regression being more than 0khRiskCustomer risk = customer ROA volatilitySizeSupplier enterprise scale = natural logarithm of total assetsLevSupplier asset-liability ratio = liabilities/total assetsCashHolding amount of supplier’s monetary capital = monetary capitals/total assetsAgeNatural logarithm of supplier’s listing yearsOcfCash flow from supplier’s operating activities = net cash flow from operating activities/total assetsTacSupplier’s total asset turnover ratio = operating revenues/total assetsShareSupplier equity balance degree = natural log of the sum of squares of shareholding ratios of the second to the tenth largest shareholdersROAReturn rate of supplier’s total assets = net profits/total assetsCusConcenSupplier customer concentration, and proportion of sales revenue of the largest customer in the total sales revenueGysnstataNature of property right of the supplier. The assignment will be 1 in case of being a state-owned enterprise or will be 0 if notkhSizeCustomer enterprise scale = natural logarithm of total assetskhLevAsset-liability ratio of customer enterprise = liabilities/total assetskhAgeNatural logarithm of customer enterprise’s listing yearskhOcfCash flow from customer’s operating activities = net cash flow from operating activities/total assetskhnstataNature of property right of the customer. The assignment will be 1 in case of being a state-owned enterprise or will be 0 if not


## 3.3. Variable Description

### 3.3.1. Inefficient Investment

The investment efficiency prediction model of Richardson (2006) [29] was used to estimate the enterprises’ investment efficiency. The model is as follows:(3)Investt=α0+α1To bingqt−1+α2Levt−1+α3Casht−1+α4Aget−1+α5Sizet−1+α6Returnt−1+α7Investt−1+ε.The newly increased investment of the enterprises is measured by Invest, and the calculation method is as follows: Invest = (capital expenditure-income from the sale of long-term assets)/total assets at the beginning of the period. The capital expenditure is the item of “expenditures for purchasing and constructing fixed assets, intangible assets, and other long-term assets” in the cash flow statement (direct method). Refer to Table2 for the definition of other variables.Table 2
Definition of investment efficiency regression model variables of Richardson.
VariablesVariable definitionTobinqTobin’s Q = (price per share∗ quantity of circulating shares + net asset per share ∗ quantity of noncirculation shares + book value of liabilities)/book value of total assetsLevAsset-liability ratio = liabilities/total assetsCashHolding amount of monetary capital = monetary capital/total assetsAgeListing years = observation year- establishment yearSizeEnterprise scale = log(total assets)ReturnConsidering the annual return rate of stocks reinvested by cash dividendsThe OLS regression is carried out after industry and year control against model 3. The residual estimated by OLS indicates the unanticipated capital investment of an enterprise. Generally, it means overinvestment when the residual exceeds 0; it means underinvestment when the residual is less than 0. The residual is represented by the variable-OverInvest and UnderInvest after the absolute value is obtained. The absolute values of all residuals are obtained and the variable-InvestEff is used for expression with the degree of the enterprise’s inefficient investment represented [30].


### 3.3.2. Customer Risk

In the literature, indicators such as earnings volatility and stock return volatility are commonly used to measure corporate risk. Due to the high volatility of China’s stock market, the risk-taking level of Chinese enterprises is widely measured by earnings volatility. Based on the research by Boubakri et al. (2013) [31], this paper adopts the ROA volatility of customer enterprises within three years adjusted by the annual industry average to measure the customer risk with for expression. The greater the earnings volatility, the higher the risk-taking level of the customer enterprise. ∂khROAi, namely,(4)khRiskTi=1N−1∑n=1NADJ_khROAin−1N∑n=1NADJ_khROAin2,N=3,wherein ADJ_khROAin=khROAin−1/X∑k=1XkhROAkn.


### 3.3.3. Definition of Other Variables

Refer to Table1 for a definition and detailed explanation of other variables.


## 3.3.1. Inefficient Investment

The investment efficiency prediction model of Richardson (2006) [29] was used to estimate the enterprises’ investment efficiency. The model is as follows:(3)Investt=α0+α1To bingqt−1+α2Levt−1+α3Casht−1+α4Aget−1+α5Sizet−1+α6Returnt−1+α7Investt−1+ε.The newly increased investment of the enterprises is measured by Invest, and the calculation method is as follows: Invest = (capital expenditure-income from the sale of long-term assets)/total assets at the beginning of the period. The capital expenditure is the item of “expenditures for purchasing and constructing fixed assets, intangible assets, and other long-term assets” in the cash flow statement (direct method). Refer to Table2 for the definition of other variables.Table 2
Definition of investment efficiency regression model variables of Richardson.
VariablesVariable definitionTobinqTobin’s Q = (price per share∗ quantity of circulating shares + net asset per share ∗ quantity of noncirculation shares + book value of liabilities)/book value of total assetsLevAsset-liability ratio = liabilities/total assetsCashHolding amount of monetary capital = monetary capital/total assetsAgeListing years = observation year- establishment yearSizeEnterprise scale = log(total assets)ReturnConsidering the annual return rate of stocks reinvested by cash dividendsThe OLS regression is carried out after industry and year control against model 3. The residual estimated by OLS indicates the unanticipated capital investment of an enterprise. Generally, it means overinvestment when the residual exceeds 0; it means underinvestment when the residual is less than 0. The residual is represented by the variable-OverInvest and UnderInvest after the absolute value is obtained. The absolute values of all residuals are obtained and the variable-InvestEff is used for expression with the degree of the enterprise’s inefficient investment represented [30].


## 3.3.2. Customer Risk

In the literature, indicators such as earnings volatility and stock return volatility are commonly used to measure corporate risk. Due to the high volatility of China’s stock market, the risk-taking level of Chinese enterprises is widely measured by earnings volatility. Based on the research by Boubakri et al. (2013) [31], this paper adopts the ROA volatility of customer enterprises within three years adjusted by the annual industry average to measure the customer risk with for expression. The greater the earnings volatility, the higher the risk-taking level of the customer enterprise. ∂khROAi, namely,(4)khRiskTi=1N−1∑n=1NADJ_khROAin−1N∑n=1NADJ_khROAin2,N=3,wherein ADJ_khROAin=khROAin−1/X∑k=1XkhROAkn.


## 3.3.3. Definition of Other Variables

Refer to Table1 for a definition and detailed explanation of other variables.


## 4. Empirical Test

### 4.1. Descriptive Statistics

The software is used for descriptive statistics first. Refer to Table3 for the various index values of the main variables.Table 3
Descriptive statistics.
VariableNMinMaxMeanP50SdInvestEfft + 17760.00040.24940.04480.03150.0447OverInvestt + 1444≤0.0010.27520.04840.03490.0456UnderInvestt + 13320.00010.34840.04150.02830.0516khRiskt7760.00120.54230.04540.01610.0898Sizet77619.59125.332322.147221.99941.3194Levt7760.03981.01680.44670.45540.2188Cash7760.01340.76560.18460.14690.1361Aget7761.09863.29582.57662.63910.4205Ocft776-0.19430.2170.04220.04140.068Tact7760.10212.38650.64780.56390.4004Sharet7760.00010.10520.01890.00960.024ROAt776-0.21630.21370.04190.03940.0538CusConcent7761.0969.3914.00799.43512.625gysnstatat773≤0.00110.517510.5khSizet77620.53228.508723.957823.78181.7522khLevt7760.10850.94860.57480.58970.1635khAget7761.09863.46572.62632.70810.4118khOCFt776-0.14170.21130.05740.05620.0641khnstatat745≤0.00110.785210.4109The data in Table3 shows the following:(1)
During thet + 1 period, the average overinvestment index (OverInvest) is 0.0484 and the maximum value reaches 0.2752, showing that overinvestment occurs in the listed companies in China. During the t + 1 period, the average underinvestment index (UnderInvest) is 0.0415 and the maximum value reaches 0.3484, showing that the underinvestment of listed companies in China is relatively serious.(2)
The total number of samples reaches 776, including 444 overinvestment samples and 332 underinvestment samples. These data preliminarily show that the low efficiency of capital allocation is common in listed companies in China.


### 4.2. Regression Analysis

Next, based on the historical data of the main variables collected, the basic regression result of supplier enterprise investment efficiency at customer risks can be obtained by the regression analysis. Refer to Table4 for details.Table 4
Basic regression result.
Regression modelInvestEfft + 1UnderInvestt + 1OverInvestt + 1(1)(2)(3)khRiskt0.046∗0.062∗∗0.026(1.948)(1.970)(0.724)Sizet−0.008∗∗∗−0.021∗∗∗−0.004∗(−3.729)(−5.105)(−1.825)Levt0.014≥−0.0010.010(1.085)(−0.025)(0.561)Casht0.0200.058∗∗≤0.001(1.247)(2.462)(0.006)Aget0.0060.0070.001(1.135)(1.147)(0.095)Ocft0.015−0.0390.026(0.581)(−1.184)(0.670)Tact−0.007−0.003−0.008(−1.286)(−0.411)(−1.085)Sharet0.064−0.0750.098(0.982)(−0.748)(1.190)ROAt0.077∗∗0.0470.069(2.054)(0.983)(1.084)khSizet≥−0.001−0.0010.001(−0.044)(−0.806)(0.483)khLevt−0.0010.010−0.006(−0.046)(0.570)(−0.369)khAget0.002−0.0030.002(0.375)(−0.648)(0.229)khOcft−0.025−0.035−0.037(−0.997)(−1.007)(−0.934)Cons0.291∗∗∗0.370∗∗∗0.300∗∗∗(3.476)(4.938)(5.114)YearControlControlControlIndustryControlControlControlN776332444R-squared0.0950.2500.087Note. The symbols “∗∗∗”, “∗∗”, and “∗” indicate 1%, 5%, and 10% of the significance level. The numerical value in the bracket is the T statistic of the corresponding coefficient.In Table4, Column (1) represents the relationship between customer risks and supplier enterprise’s inefficient investments. The regression coefficient is 0.046 and significant at the level of 10%. The positive correlation between customer risks and supplier enterprise’s inefficient investments can be preliminarily judged, that is to say, the customer risks will lower the supplier enterprise’s investment efficiency. The underinvestment group and overinvestment group are distinguished by Column (2) and Column (3) with the effect of customer risk on investment efficiency reported, respectively. In the overinvestment group, the regression coefficient is 0.026 but is not significant. In the underinvestment group, the regression coefficient is 0.062 and significant at the level of 5%, showing that there is a significant positive correlation between customer risk and underinvestment of supplier enterprises.The above results show that the customer risk will lower the investment efficiency of supplier enterprises and aggravate the underinvestment of supplier enterprises, verifying that hypothesis 1 is true.


### 4.3. Influence Path of Customer Risk on Supplier Enterprise Investment Efficiency

Pursuant to the preamble, the customer risk may influence the investment efficiency of supplier enterprises by increasing their financing restrictions or risks, resulting in the underinvestment of supplier enterprises. As per the mediation effect testing procedure from Wen Zhonglin et al. [32], the above-mentioned two paths are inspected, respectively. The specific inspection steps are as follows.


#### 4.3.1. Financing Restriction Path

According to the method of Kaplan and Zingales (1997) [33], the KZ index is constructed based on the listed companies of A-shares in China as samples to measure the financing restriction of enterprises. The specific practice is as follows: the financing restriction index is constructed in line with financial indexes (such as net operating cash flow of the company (Cash), share dividend (Div), cash holding (Ocf), asset-liability ratio, and Tobin’s Q (TBQ)), the score of financing restriction of each company in each year is obtained to be used as the dependent variable, the sequential logic regression model is used for regression of Casht/Sizet-1, Ocft/Sizet-1, Div t/Sizet-1, TBQ, and Lev, and the regression coefficient of a respective variable is estimated. Finally, the financing restriction index of each company (KZ) is estimated as per the above regression estimation result. When the KZ value is high, it means the financing restriction of such listed companies is serious.In accordance with the current research [34], the KZ index is used as the supplier enterprise’s financing restriction for an empirical test of intermediary channels of financing restriction by three models: one is (1); the other two are as follows:(5)KZi,t=α0+α1khRiski,t+α2Sizei,t+α3Levi,t+α4Cashi,t+α5Agei,t+α6Ocfi,t+α7Taci,t+α8Sharei,t+α9ROAi,t+α10khSizei,t+α11khLevi,t+α12khAgei,t+α13khOcfi,t+ε,InvestEffi,t+1=β0+β1khRiski,t+β2KZi,t+β3Sizei,t+β4Levi,t+β5Cashi,t+β6Agei,t+β7Ocfi,t+β8Taci,t+β9Sharei,t+β10ROAi,t+β11khSizei,t+β12khLevi,t+β13khAgei,t+β14khOcfi,t+ε.Refer to Table5 for the inspection result of the financing restriction path. Column (1) shows that the regression coefficient is positive significantly at the level of 1%, that is to say, the customer risk will obviously lead to the supplier enterprise’s inefficient investment without intervening variables taken into account. Column (2) shows that the regression coefficient is 1.671 which is positive significantly at the level of 1%, indicating that the influence of the independent variable-customer risk on the intervening variable-financing restriction is significant, that is to say, the higher the customer risk is, the more serious of the financing restriction of supplier enterprises will be. Column (3) indicates that the regression coefficient of financing restriction is positive significantly at the level of 1%, the regression coefficient of customer risk is positive significantly at the level of 5%, and the regression coefficient is less than that in Column (1), showing that part of mediation effects is true. It means that the financing restriction is the influence path of customer risk leading to supplier enterprise’s inefficient investment, that is to say, the customer risk will lead to supplier enterprise’s inefficient investment by increasing the financing restriction of supplier enterprise.Table 5
Test results of mediating effect of financing restriction.
Regression model(1)(2)(3)InvestEffKZInvestEffkhRisk0.054∗∗∗1.671∗∗∗0.047∗∗(2.94)(4.24)(2.52)KZ——0.004∗∗∗——(2.60)Size−0.005∗∗∗−0.337∗∗∗−0.003∗∗(−3.15)(−10.49)(−2.03)Lev0.0175.119∗∗∗−0.005(1.63)(22.38)(−0.37)Cash0.006−6.439∗∗∗0.035∗∗(0.47)(−21.7)(1.98)Age−0.0010.507∗∗∗−0.004(−0.36)(5.95)(−0.90)OCF0.041−10.916∗∗∗0.089∗∗∗(1.52)(−18.96)(2.73)Tac−0.011∗∗0.098−0.011∗∗∗(−2.48)(1.05)(−2.58)Share0.048−1.6480.055(0.72)(−1.14)(0.83)ROA0.082∗∗−3.487∗∗∗0.097∗∗(2.15)(−4.25)(2.53)khSize≤0.0010.046∗∗≤0.001(0.35)(1.98)(0.17)khLev0.0070.1860.006(0.65)(0.78)(0.58)khAge−0.002−0.098−0.002(−0.55)(−1.14)(−0.45)khOCF−0.0090.475−0.011(−0.33)(0.82)(−0.41)_cons0.1362∗∗∗5.866∗∗∗0.110∗∗∗(3.64)(7.33)(2.86)YearControlControlControlIndustryControlControlControlN771771771R-squared0.04620.8330.037Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


#### 4.3.2. Enterprise Risk Path

Similarly, the volatility of the supplier enterprise’s ROA within three years adjusted by the annual industry average is used to measure the supplier enterprise risk with gysRisk used for expression.Pursuant to the current research, the gysRisk is used as the measurement index of supplier enterprise risks for an empirical test of intermediary channels of enterprise risks by three models: one is (1); the other two are as follows:(6)gysRiski,t=α0+α1khRiski,t+α2Sizei,t+α3Levi,t+α4Cashi,t+α5Agei,t+α6Ocfi,t+α7Taci,t+α8Sharei,t+α9ROAi,t+α10khSizei,t+α11khLevi,t+α12khAgei,t+α13khOcfi,t+ε,InvestEffi,t+1=β0+β1khRiski,t+β2gysRiski,t+β3Sizei,t+β4Levi,t+β5Cashi,t+β6Agei,t+β7Ocfi,t+β8Taci,t+β9Sharei,t+β10ROAi,t+β11khSizei,t+β12khLevi,t+β13khAgei,t+β14khOcfi,t+ε.Refer to Table6 for the inspection result of enterprise risk paths. Column (1) shows that the regression coefficient is positive significantly at the level of 1%, that is to say, the customer risk will obviously lead to the supplier enterprise’s inefficient investment without the intervening variable taken into account. Column (2) shows the regression coefficient is 0.199 which is positive significantly at the level of 1%, indicating that the influence of the independent variable-customer risk on the intervening variable-risk of supplier enterprise is significant, that is to say, the higher the customer risk is, the higher the risk of supplier enterprise will be. Column (3) shows that the regression coefficient of customer risk is still positive significantly at the level of 10%, but the regression coefficient of supplier enterprise risk is nonsignificant. Whether the supplier enterprise risk has a mediating effect cannot be judged directly; thus, it is necessary to further conduct Sobel intermediary factor inspection. The inspection result of Sobel intermediary factor is not of significant statistical significance, showing that the enterprise risk is not the influence path of customer risk leading to supplier enterprise’s inefficient investment, that is to say, the customer risk will not result in supplier enterprise’s inefficient investment by increasing the supplier enterprise risk.Table 6
Test results of mediating effect of enterprise risk.
Regression model(1)(2)(3)InvestEffGysriskInvestEffkhRisk0.046∗0.199∗∗∗0.046∗(1.948)(3.32)(1.918)gysRisk——0.002——(0.08)Size−0.008∗∗∗−0.015∗∗∗−0.008∗∗∗(−3.729)(−3.514)(−3.671)Lev0.0140.076∗∗0.014(1.085)(2.532)(1.078)Cash0.0200.062∗∗0.020(1.247)(2.472)(1.235)Age0.0060.019∗∗∗0.006(1.135)(2.970)(1.126)OCF0.015−0.0930.015(0.581)(−1.377)(0.578)Tac−0.0070.046∗∗∗−0.007(−1.286)(3.815)(−1.269)Share0.064−0.1140.064(0.982)(−1.181)(0.981)ROA0.077∗∗−0.1680.077∗∗(2.054)(−1.195)(2.051)khSize−0.0010.002−0.001(−0.044)(1.200)(−0.048)khLev−0.001−0.018−0.001(−0.046)(−0.915)(−0.042)khAge0.002−0.0020.002(0.375)(−0.253)(0.374)khOCF−0.0250.030−0.025(−0.997)(0.704)(−0.997)YearControlControlControlIndustryControlControlControlN776776776R-squared0.1570.2750.157Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


### 4.4. Further Research: Influence of Relationship Nature on Customer Risk Contagion Effect on the Supply Chain

#### 4.4.1. Customer Concentration, Customer Risk, and Underinvestment of Supplier Enterprises

The current literature has shown that the closeness of customer relations will influence the financial behavior of supplier enterprises. For example, customer concentration will affect many respects of an enterprise (such as cash holding, investment efficiency, dividend distribution, and behavior of tax avoidance) [35–37]. The higher the customer concentration was, the more dependent the supplier would become on the customer, the lower the capital recovery speed would be [38], and the more likely the supplier would get into financial trouble [14]. If the major customers chose to break a contract or discontinue trading, the accounts receivable of problematic customers would encounter the risk of failure in the recovery of funds, the probability of bad debts in supplier enterprises would be greatly increased, the operating profit and cash flow would also be significantly reduced [39], and the internal capital available to supplier enterprises would be further decreased. In addition to this, the investors and creditors would be also aware of the risks incurred by customer concentration and set more restrictions and claim higher compensation for risk or risk premium when funding, resulting in the external financing of supplier enterprises being more constrained; thus, the following hypothesis was put forward.H2: compared to the supplier enterprises with a low customer concentration, the positive effect of customer risk on the underinvestment of supplier enterprises with a high customer concentration is more significant.


#### 4.4.2. Nature of Property Right, Customer Risk, and Underinvestment of Supplier Enterprises

On the one hand, the state-owned enterprises have the natural resource advantage with an implicit government guarantee behind it, the banks also prefer state-owned enterprises when serving a loan, and the state-owned enterprises can also choose the bank loan to make up for the financial deficit even if being negatively impacted by the customer risk [40]. However, on the other hand, the state-owned enterprises, supported by government resources, were also subject to many interventions, the transaction between state-owned enterprises might be a government action, and the state-owned customers and state-owned supplier enterprises might be controlled by the same government [41]. The supply chain relationship between state-owned enterprises is more stable, but such stable relationship will also cause the suppliers subject to the nature of state-owned property rights to fail to avoid risks by suspending the relationship when the customers subject to the nature of the state-owned property right face the financial distress; as a result, they are inevitably exposed to customer risk and the following hypothesis is put forward [42].H3: When the supplier and the enterprise are subject to the nature of state-owned property rights, the positive impact of customer risk on the underinvestment of supplier enterprises is more significant.The high and low groups are divided in line with the customer concentration median of the supplier enterprise. Column (1) and Column (2) results in Table7 show that the significant positive correlation between customer risk (khRiskt) and underinvestment of supplier enterprises (UnderInvestt + 1) only exists in the group with a high customer concentration, and the regression coefficient is 0.068 and significant at the level of 10%. It means that the increase in customer concentration will make the external financing of supplier enterprises more constrained, that is to say, the customer risk will have a stronger effect on the underinvestment of supplier enterprises when the importance and dependence of customers are relatively high. Thus, that hypothesis 2 is true is verified.Table 7
Difference test of grouped samples.
Regression modelUnderInvestt + 1(1)(2)(3)(4)khRiskt0.068∗0.0890.061∗∗∗0.049(1.87)(1.52)(2.71)(0.94)Sizet−0.024∗∗∗−0.020∗∗∗−0.018∗∗∗−0.028∗∗∗(−3.92)(−7.25)(−4.80)(−6.54)Levt0.0330.013−0.0120.024(1.00)(0.68)(−0.59)(1.13)Casht0.114∗∗∗0.032∗0.072∗∗0.038(3.15)(1.73)(2.43)(1.57)Aget0.008≤0.0010.0170.008(0.82)(−0.04)(1.40)(0.90)Ocft−0.08−0.020.004−0.065∗(−1.48)(−0.64)(0.14)(−1.94)Tact−0.008−0.006−0.008−0.002(−0.41)(−1.31)(−1.53)(−0.24)Sharet−0.123−0.0370.086−0.194(−0.77)(−0.30)(0.72)(−1.59)ROAt0.101−0.0020.099∗∗0.041(1.52)(−0.05)(2.27)(0.86)khSizet−0.002≤0.0010.001−0.002(−0.89)(−0.06)(1.18)(−0.90)khLevt0.0440.027−0.0010.027(1.03)(1.19)(−0.03)(1.06)khAget0.0030.0020.0020.007(0.29)(0.52)(0.30)(1.16)khOcft−0.01−0.0320.041−0.019(−0.15)(−0.76)(1.27)(−0.46)Cons0.443∗∗∗0.425∗∗∗0.320∗∗∗0.620∗∗∗(3.52)(7.35)(3.56)(5.47)YearControlControlControlControlIndustryControlControlControlControlN164168157173R-squared0.41020.48430.48180.5180Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.The group of state-owned enterprises versus state-owned enterprises and other groups are divided according to the nature of enterprise property rights. The Column (3) and Column (4) results in Table3 reveal that the significant positive correlation between customer risk (khRiskt) and underinvestment of supplier enterprises (UnderInvestt + 1) only exists in the group of state-owned enterprises versus state-owned enterprises, and the regression coefficient is 0.061 and significant at the level of 1%, showing the relationship between state-owned enterprises is more stable, and it is difficult for state-owned supplier enterprises to have a narrow escape when facing the risk of state-owned customers, that is to say, the customer risk will have a stronger impact on the underinvestment of supplier enterprises when the supplier and the customer enterprise are state-owned enterprises. Thus, that hypothesis 1 is true is verified.


### 4.5. Robustness Test

Customer risk is the key studied in this paper, and the test for its robustness is an indispensable link. To verify the reliability of conclusions, we draw on the method of Boubakri et al. (2011) to measure customer risk, namely, volatility of three-year EBIT of total assets (VOL_EBIT) and volatility of five-year ROA) to measure the customer risk (khRisk).


#### 4.5.1. Volatility of Three-Year EBIT of Total Assets (VOL_EBIT)

Pursuant to the method of Boubakri et al. (2011), this paper uses the volatility of EBIT of total assets (VOL_EBIT) of an enterprise within three years to measure the enterprise risk. When calculated, the EBIT of total assets is adjusted by the annual industry average. The bigger the VOL_EBIT is, the higher the enterprise risk will be. Column (1) and Column (2) in Table8 show the following: the regression coefficient of customer risk and supplier enterprise’s inefficient investment is 0.045 and significant at the level of 10%, and the regression coefficient of customer risk and underinvestment of supplier enterprises is 0.074 and significant at the level of 5%, that is to say, the customer risk will lead to inefficient investment behavior of enterprises and be further manifested as underinvestment.Table 8
Robustness test.
Regression modelVOL_EBITVolatility of five-year ROAInvestEfft + 1UnderInvestt + 1InvestEfft + 1UnderInvestt + 1(1)(2)(3)(4)khRiskt0.045∗0.074∗∗0.042∗0.073∗∗(1.729)(2.184)(1.657)(2.040)Sizet−0.010∗∗∗−0.021∗∗∗-0.008∗∗∗−0.021∗∗∗(−5.231)(−6.932)(−3.821)(−5.358)Levt0.0110.0040.014≥-0.001(0.949)(0.273)(1.155)(−0.007)Casht0.029∗∗0.053∗∗∗0.0180.054∗∗(2.177)(3.309)(1.127)(2.345)Aget0.0060.0040.0050.005(1.236)(0.794)(1.031)(0.972)Ocft0.018−0.0370.020−0.023(0.749)(−1.311)(0.782)(−0.699)Tact−0.009∗−0.003−0.007−0.002(−1.950)(−0.485)(−1.273)(-0.366)Sharet0.090−0.0770.067−0.059(1.408)(−0.818)(1.065)(-0.604)ROAt0.073∗∗0.073∗0.074∗∗0.044(2.299)(1.869)(2.001)(0.944)khSizet0.002∗−0.001≥−0.001−0.001(1.716)(−0.489)(−0.032)(−0.567)khLevt0.0090.020−0.0010.011(0.765)(1.127)(−0.090)(0.632)khAget−0.001−0.0010.003−0.002(−0.202)(−0.158)(0.624)(−0.428)khOcft−0.014−0.014−0.023−0.031(−0.599)(−0.488)(−0.970)(−0.961)YearControlControlControlControlIndustryControlControlControlControlN901419796345R-squared0.1410.3050.0950.242Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


#### 4.5.2. Volatility of Five-Year ROA

According to the research by Sheng Mingquan et al. (2018) [43], this paper sets the observation session as five years, that is to say, the standard deviation of ROA adjusted for the industry of supplier enterprises within five years is used to measure the customer risk. Column (3) and Column (4) in Table 8 show that the regression coefficient of customer risk and supplier enterprise’s inefficient investment is 0.042 and significant at the level of 10%, and the regression coefficient of customer risk and underinvestment of supplier enterprises is 0.073 and significant at the level of 5%, that is to say, the customer risk will lead to the underinvestment of enterprises.To sum up, after the above robustness treatment, the main conclusion of this research is still significant, that is to say, the customer risk will result in the inefficient investment behavior of supplier enterprises and underinvestment of supplier enterprises.


## 4.1. Descriptive Statistics

The software is used for descriptive statistics first. Refer to Table3 for the various index values of the main variables.Table 3
Descriptive statistics.
VariableNMinMaxMeanP50SdInvestEfft + 17760.00040.24940.04480.03150.0447OverInvestt + 1444≤0.0010.27520.04840.03490.0456UnderInvestt + 13320.00010.34840.04150.02830.0516khRiskt7760.00120.54230.04540.01610.0898Sizet77619.59125.332322.147221.99941.3194Levt7760.03981.01680.44670.45540.2188Cash7760.01340.76560.18460.14690.1361Aget7761.09863.29582.57662.63910.4205Ocft776-0.19430.2170.04220.04140.068Tact7760.10212.38650.64780.56390.4004Sharet7760.00010.10520.01890.00960.024ROAt776-0.21630.21370.04190.03940.0538CusConcent7761.0969.3914.00799.43512.625gysnstatat773≤0.00110.517510.5khSizet77620.53228.508723.957823.78181.7522khLevt7760.10850.94860.57480.58970.1635khAget7761.09863.46572.62632.70810.4118khOCFt776-0.14170.21130.05740.05620.0641khnstatat745≤0.00110.785210.4109The data in Table3 shows the following:(1)
During thet + 1 period, the average overinvestment index (OverInvest) is 0.0484 and the maximum value reaches 0.2752, showing that overinvestment occurs in the listed companies in China. During the t + 1 period, the average underinvestment index (UnderInvest) is 0.0415 and the maximum value reaches 0.3484, showing that the underinvestment of listed companies in China is relatively serious.(2)
The total number of samples reaches 776, including 444 overinvestment samples and 332 underinvestment samples. These data preliminarily show that the low efficiency of capital allocation is common in listed companies in China.


## 4.2. Regression Analysis

Next, based on the historical data of the main variables collected, the basic regression result of supplier enterprise investment efficiency at customer risks can be obtained by the regression analysis. Refer to Table4 for details.Table 4
Basic regression result.
Regression modelInvestEfft + 1UnderInvestt + 1OverInvestt + 1(1)(2)(3)khRiskt0.046∗0.062∗∗0.026(1.948)(1.970)(0.724)Sizet−0.008∗∗∗−0.021∗∗∗−0.004∗(−3.729)(−5.105)(−1.825)Levt0.014≥−0.0010.010(1.085)(−0.025)(0.561)Casht0.0200.058∗∗≤0.001(1.247)(2.462)(0.006)Aget0.0060.0070.001(1.135)(1.147)(0.095)Ocft0.015−0.0390.026(0.581)(−1.184)(0.670)Tact−0.007−0.003−0.008(−1.286)(−0.411)(−1.085)Sharet0.064−0.0750.098(0.982)(−0.748)(1.190)ROAt0.077∗∗0.0470.069(2.054)(0.983)(1.084)khSizet≥−0.001−0.0010.001(−0.044)(−0.806)(0.483)khLevt−0.0010.010−0.006(−0.046)(0.570)(−0.369)khAget0.002−0.0030.002(0.375)(−0.648)(0.229)khOcft−0.025−0.035−0.037(−0.997)(−1.007)(−0.934)Cons0.291∗∗∗0.370∗∗∗0.300∗∗∗(3.476)(4.938)(5.114)YearControlControlControlIndustryControlControlControlN776332444R-squared0.0950.2500.087Note. The symbols “∗∗∗”, “∗∗”, and “∗” indicate 1%, 5%, and 10% of the significance level. The numerical value in the bracket is the T statistic of the corresponding coefficient.In Table4, Column (1) represents the relationship between customer risks and supplier enterprise’s inefficient investments. The regression coefficient is 0.046 and significant at the level of 10%. The positive correlation between customer risks and supplier enterprise’s inefficient investments can be preliminarily judged, that is to say, the customer risks will lower the supplier enterprise’s investment efficiency. The underinvestment group and overinvestment group are distinguished by Column (2) and Column (3) with the effect of customer risk on investment efficiency reported, respectively. In the overinvestment group, the regression coefficient is 0.026 but is not significant. In the underinvestment group, the regression coefficient is 0.062 and significant at the level of 5%, showing that there is a significant positive correlation between customer risk and underinvestment of supplier enterprises.The above results show that the customer risk will lower the investment efficiency of supplier enterprises and aggravate the underinvestment of supplier enterprises, verifying that hypothesis 1 is true.


## 4.3. Influence Path of Customer Risk on Supplier Enterprise Investment Efficiency

Pursuant to the preamble, the customer risk may influence the investment efficiency of supplier enterprises by increasing their financing restrictions or risks, resulting in the underinvestment of supplier enterprises. As per the mediation effect testing procedure from Wen Zhonglin et al. [32], the above-mentioned two paths are inspected, respectively. The specific inspection steps are as follows.


### 4.3.1. Financing Restriction Path

According to the method of Kaplan and Zingales (1997) [33], the KZ index is constructed based on the listed companies of A-shares in China as samples to measure the financing restriction of enterprises. The specific practice is as follows: the financing restriction index is constructed in line with financial indexes (such as net operating cash flow of the company (Cash), share dividend (Div), cash holding (Ocf), asset-liability ratio, and Tobin’s Q (TBQ)), the score of financing restriction of each company in each year is obtained to be used as the dependent variable, the sequential logic regression model is used for regression of Casht/Sizet-1, Ocft/Sizet-1, Div t/Sizet-1, TBQ, and Lev, and the regression coefficient of a respective variable is estimated. Finally, the financing restriction index of each company (KZ) is estimated as per the above regression estimation result. When the KZ value is high, it means the financing restriction of such listed companies is serious.In accordance with the current research [34], the KZ index is used as the supplier enterprise’s financing restriction for an empirical test of intermediary channels of financing restriction by three models: one is (1); the other two are as follows:(5)KZi,t=α0+α1khRiski,t+α2Sizei,t+α3Levi,t+α4Cashi,t+α5Agei,t+α6Ocfi,t+α7Taci,t+α8Sharei,t+α9ROAi,t+α10khSizei,t+α11khLevi,t+α12khAgei,t+α13khOcfi,t+ε,InvestEffi,t+1=β0+β1khRiski,t+β2KZi,t+β3Sizei,t+β4Levi,t+β5Cashi,t+β6Agei,t+β7Ocfi,t+β8Taci,t+β9Sharei,t+β10ROAi,t+β11khSizei,t+β12khLevi,t+β13khAgei,t+β14khOcfi,t+ε.Refer to Table5 for the inspection result of the financing restriction path. Column (1) shows that the regression coefficient is positive significantly at the level of 1%, that is to say, the customer risk will obviously lead to the supplier enterprise’s inefficient investment without intervening variables taken into account. Column (2) shows that the regression coefficient is 1.671 which is positive significantly at the level of 1%, indicating that the influence of the independent variable-customer risk on the intervening variable-financing restriction is significant, that is to say, the higher the customer risk is, the more serious of the financing restriction of supplier enterprises will be. Column (3) indicates that the regression coefficient of financing restriction is positive significantly at the level of 1%, the regression coefficient of customer risk is positive significantly at the level of 5%, and the regression coefficient is less than that in Column (1), showing that part of mediation effects is true. It means that the financing restriction is the influence path of customer risk leading to supplier enterprise’s inefficient investment, that is to say, the customer risk will lead to supplier enterprise’s inefficient investment by increasing the financing restriction of supplier enterprise.Table 5
Test results of mediating effect of financing restriction.
Regression model(1)(2)(3)InvestEffKZInvestEffkhRisk0.054∗∗∗1.671∗∗∗0.047∗∗(2.94)(4.24)(2.52)KZ——0.004∗∗∗——(2.60)Size−0.005∗∗∗−0.337∗∗∗−0.003∗∗(−3.15)(−10.49)(−2.03)Lev0.0175.119∗∗∗−0.005(1.63)(22.38)(−0.37)Cash0.006−6.439∗∗∗0.035∗∗(0.47)(−21.7)(1.98)Age−0.0010.507∗∗∗−0.004(−0.36)(5.95)(−0.90)OCF0.041−10.916∗∗∗0.089∗∗∗(1.52)(−18.96)(2.73)Tac−0.011∗∗0.098−0.011∗∗∗(−2.48)(1.05)(−2.58)Share0.048−1.6480.055(0.72)(−1.14)(0.83)ROA0.082∗∗−3.487∗∗∗0.097∗∗(2.15)(−4.25)(2.53)khSize≤0.0010.046∗∗≤0.001(0.35)(1.98)(0.17)khLev0.0070.1860.006(0.65)(0.78)(0.58)khAge−0.002−0.098−0.002(−0.55)(−1.14)(−0.45)khOCF−0.0090.475−0.011(−0.33)(0.82)(−0.41)_cons0.1362∗∗∗5.866∗∗∗0.110∗∗∗(3.64)(7.33)(2.86)YearControlControlControlIndustryControlControlControlN771771771R-squared0.04620.8330.037Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


### 4.3.2. Enterprise Risk Path

Similarly, the volatility of the supplier enterprise’s ROA within three years adjusted by the annual industry average is used to measure the supplier enterprise risk with gysRisk used for expression.Pursuant to the current research, the gysRisk is used as the measurement index of supplier enterprise risks for an empirical test of intermediary channels of enterprise risks by three models: one is (1); the other two are as follows:(6)gysRiski,t=α0+α1khRiski,t+α2Sizei,t+α3Levi,t+α4Cashi,t+α5Agei,t+α6Ocfi,t+α7Taci,t+α8Sharei,t+α9ROAi,t+α10khSizei,t+α11khLevi,t+α12khAgei,t+α13khOcfi,t+ε,InvestEffi,t+1=β0+β1khRiski,t+β2gysRiski,t+β3Sizei,t+β4Levi,t+β5Cashi,t+β6Agei,t+β7Ocfi,t+β8Taci,t+β9Sharei,t+β10ROAi,t+β11khSizei,t+β12khLevi,t+β13khAgei,t+β14khOcfi,t+ε.Refer to Table6 for the inspection result of enterprise risk paths. Column (1) shows that the regression coefficient is positive significantly at the level of 1%, that is to say, the customer risk will obviously lead to the supplier enterprise’s inefficient investment without the intervening variable taken into account. Column (2) shows the regression coefficient is 0.199 which is positive significantly at the level of 1%, indicating that the influence of the independent variable-customer risk on the intervening variable-risk of supplier enterprise is significant, that is to say, the higher the customer risk is, the higher the risk of supplier enterprise will be. Column (3) shows that the regression coefficient of customer risk is still positive significantly at the level of 10%, but the regression coefficient of supplier enterprise risk is nonsignificant. Whether the supplier enterprise risk has a mediating effect cannot be judged directly; thus, it is necessary to further conduct Sobel intermediary factor inspection. The inspection result of Sobel intermediary factor is not of significant statistical significance, showing that the enterprise risk is not the influence path of customer risk leading to supplier enterprise’s inefficient investment, that is to say, the customer risk will not result in supplier enterprise’s inefficient investment by increasing the supplier enterprise risk.Table 6
Test results of mediating effect of enterprise risk.
Regression model(1)(2)(3)InvestEffGysriskInvestEffkhRisk0.046∗0.199∗∗∗0.046∗(1.948)(3.32)(1.918)gysRisk——0.002——(0.08)Size−0.008∗∗∗−0.015∗∗∗−0.008∗∗∗(−3.729)(−3.514)(−3.671)Lev0.0140.076∗∗0.014(1.085)(2.532)(1.078)Cash0.0200.062∗∗0.020(1.247)(2.472)(1.235)Age0.0060.019∗∗∗0.006(1.135)(2.970)(1.126)OCF0.015−0.0930.015(0.581)(−1.377)(0.578)Tac−0.0070.046∗∗∗−0.007(−1.286)(3.815)(−1.269)Share0.064−0.1140.064(0.982)(−1.181)(0.981)ROA0.077∗∗−0.1680.077∗∗(2.054)(−1.195)(2.051)khSize−0.0010.002−0.001(−0.044)(1.200)(−0.048)khLev−0.001−0.018−0.001(−0.046)(−0.915)(−0.042)khAge0.002−0.0020.002(0.375)(−0.253)(0.374)khOCF−0.0250.030−0.025(−0.997)(0.704)(−0.997)YearControlControlControlIndustryControlControlControlN776776776R-squared0.1570.2750.157Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


## 4.3.1. Financing Restriction Path

According to the method of Kaplan and Zingales (1997) [33], the KZ index is constructed based on the listed companies of A-shares in China as samples to measure the financing restriction of enterprises. The specific practice is as follows: the financing restriction index is constructed in line with financial indexes (such as net operating cash flow of the company (Cash), share dividend (Div), cash holding (Ocf), asset-liability ratio, and Tobin’s Q (TBQ)), the score of financing restriction of each company in each year is obtained to be used as the dependent variable, the sequential logic regression model is used for regression of Casht/Sizet-1, Ocft/Sizet-1, Div t/Sizet-1, TBQ, and Lev, and the regression coefficient of a respective variable is estimated. Finally, the financing restriction index of each company (KZ) is estimated as per the above regression estimation result. When the KZ value is high, it means the financing restriction of such listed companies is serious.In accordance with the current research [34], the KZ index is used as the supplier enterprise’s financing restriction for an empirical test of intermediary channels of financing restriction by three models: one is (1); the other two are as follows:(5)KZi,t=α0+α1khRiski,t+α2Sizei,t+α3Levi,t+α4Cashi,t+α5Agei,t+α6Ocfi,t+α7Taci,t+α8Sharei,t+α9ROAi,t+α10khSizei,t+α11khLevi,t+α12khAgei,t+α13khOcfi,t+ε,InvestEffi,t+1=β0+β1khRiski,t+β2KZi,t+β3Sizei,t+β4Levi,t+β5Cashi,t+β6Agei,t+β7Ocfi,t+β8Taci,t+β9Sharei,t+β10ROAi,t+β11khSizei,t+β12khLevi,t+β13khAgei,t+β14khOcfi,t+ε.Refer to Table5 for the inspection result of the financing restriction path. Column (1) shows that the regression coefficient is positive significantly at the level of 1%, that is to say, the customer risk will obviously lead to the supplier enterprise’s inefficient investment without intervening variables taken into account. Column (2) shows that the regression coefficient is 1.671 which is positive significantly at the level of 1%, indicating that the influence of the independent variable-customer risk on the intervening variable-financing restriction is significant, that is to say, the higher the customer risk is, the more serious of the financing restriction of supplier enterprises will be. Column (3) indicates that the regression coefficient of financing restriction is positive significantly at the level of 1%, the regression coefficient of customer risk is positive significantly at the level of 5%, and the regression coefficient is less than that in Column (1), showing that part of mediation effects is true. It means that the financing restriction is the influence path of customer risk leading to supplier enterprise’s inefficient investment, that is to say, the customer risk will lead to supplier enterprise’s inefficient investment by increasing the financing restriction of supplier enterprise.Table 5
Test results of mediating effect of financing restriction.
Regression model(1)(2)(3)InvestEffKZInvestEffkhRisk0.054∗∗∗1.671∗∗∗0.047∗∗(2.94)(4.24)(2.52)KZ——0.004∗∗∗——(2.60)Size−0.005∗∗∗−0.337∗∗∗−0.003∗∗(−3.15)(−10.49)(−2.03)Lev0.0175.119∗∗∗−0.005(1.63)(22.38)(−0.37)Cash0.006−6.439∗∗∗0.035∗∗(0.47)(−21.7)(1.98)Age−0.0010.507∗∗∗−0.004(−0.36)(5.95)(−0.90)OCF0.041−10.916∗∗∗0.089∗∗∗(1.52)(−18.96)(2.73)Tac−0.011∗∗0.098−0.011∗∗∗(−2.48)(1.05)(−2.58)Share0.048−1.6480.055(0.72)(−1.14)(0.83)ROA0.082∗∗−3.487∗∗∗0.097∗∗(2.15)(−4.25)(2.53)khSize≤0.0010.046∗∗≤0.001(0.35)(1.98)(0.17)khLev0.0070.1860.006(0.65)(0.78)(0.58)khAge−0.002−0.098−0.002(−0.55)(−1.14)(−0.45)khOCF−0.0090.475−0.011(−0.33)(0.82)(−0.41)_cons0.1362∗∗∗5.866∗∗∗0.110∗∗∗(3.64)(7.33)(2.86)YearControlControlControlIndustryControlControlControlN771771771R-squared0.04620.8330.037Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


## 4.3.2. Enterprise Risk Path

Similarly, the volatility of the supplier enterprise’s ROA within three years adjusted by the annual industry average is used to measure the supplier enterprise risk with gysRisk used for expression.Pursuant to the current research, the gysRisk is used as the measurement index of supplier enterprise risks for an empirical test of intermediary channels of enterprise risks by three models: one is (1); the other two are as follows:(6)gysRiski,t=α0+α1khRiski,t+α2Sizei,t+α3Levi,t+α4Cashi,t+α5Agei,t+α6Ocfi,t+α7Taci,t+α8Sharei,t+α9ROAi,t+α10khSizei,t+α11khLevi,t+α12khAgei,t+α13khOcfi,t+ε,InvestEffi,t+1=β0+β1khRiski,t+β2gysRiski,t+β3Sizei,t+β4Levi,t+β5Cashi,t+β6Agei,t+β7Ocfi,t+β8Taci,t+β9Sharei,t+β10ROAi,t+β11khSizei,t+β12khLevi,t+β13khAgei,t+β14khOcfi,t+ε.Refer to Table6 for the inspection result of enterprise risk paths. Column (1) shows that the regression coefficient is positive significantly at the level of 1%, that is to say, the customer risk will obviously lead to the supplier enterprise’s inefficient investment without the intervening variable taken into account. Column (2) shows the regression coefficient is 0.199 which is positive significantly at the level of 1%, indicating that the influence of the independent variable-customer risk on the intervening variable-risk of supplier enterprise is significant, that is to say, the higher the customer risk is, the higher the risk of supplier enterprise will be. Column (3) shows that the regression coefficient of customer risk is still positive significantly at the level of 10%, but the regression coefficient of supplier enterprise risk is nonsignificant. Whether the supplier enterprise risk has a mediating effect cannot be judged directly; thus, it is necessary to further conduct Sobel intermediary factor inspection. The inspection result of Sobel intermediary factor is not of significant statistical significance, showing that the enterprise risk is not the influence path of customer risk leading to supplier enterprise’s inefficient investment, that is to say, the customer risk will not result in supplier enterprise’s inefficient investment by increasing the supplier enterprise risk.Table 6
Test results of mediating effect of enterprise risk.
Regression model(1)(2)(3)InvestEffGysriskInvestEffkhRisk0.046∗0.199∗∗∗0.046∗(1.948)(3.32)(1.918)gysRisk——0.002——(0.08)Size−0.008∗∗∗−0.015∗∗∗−0.008∗∗∗(−3.729)(−3.514)(−3.671)Lev0.0140.076∗∗0.014(1.085)(2.532)(1.078)Cash0.0200.062∗∗0.020(1.247)(2.472)(1.235)Age0.0060.019∗∗∗0.006(1.135)(2.970)(1.126)OCF0.015−0.0930.015(0.581)(−1.377)(0.578)Tac−0.0070.046∗∗∗−0.007(−1.286)(3.815)(−1.269)Share0.064−0.1140.064(0.982)(−1.181)(0.981)ROA0.077∗∗−0.1680.077∗∗(2.054)(−1.195)(2.051)khSize−0.0010.002−0.001(−0.044)(1.200)(−0.048)khLev−0.001−0.018−0.001(−0.046)(−0.915)(−0.042)khAge0.002−0.0020.002(0.375)(−0.253)(0.374)khOCF−0.0250.030−0.025(−0.997)(0.704)(−0.997)YearControlControlControlIndustryControlControlControlN776776776R-squared0.1570.2750.157Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


## 4.4. Further Research: Influence of Relationship Nature on Customer Risk Contagion Effect on the Supply Chain

### 4.4.1. Customer Concentration, Customer Risk, and Underinvestment of Supplier Enterprises

The current literature has shown that the closeness of customer relations will influence the financial behavior of supplier enterprises. For example, customer concentration will affect many respects of an enterprise (such as cash holding, investment efficiency, dividend distribution, and behavior of tax avoidance) [35–37]. The higher the customer concentration was, the more dependent the supplier would become on the customer, the lower the capital recovery speed would be [38], and the more likely the supplier would get into financial trouble [14]. If the major customers chose to break a contract or discontinue trading, the accounts receivable of problematic customers would encounter the risk of failure in the recovery of funds, the probability of bad debts in supplier enterprises would be greatly increased, the operating profit and cash flow would also be significantly reduced [39], and the internal capital available to supplier enterprises would be further decreased. In addition to this, the investors and creditors would be also aware of the risks incurred by customer concentration and set more restrictions and claim higher compensation for risk or risk premium when funding, resulting in the external financing of supplier enterprises being more constrained; thus, the following hypothesis was put forward.H2: compared to the supplier enterprises with a low customer concentration, the positive effect of customer risk on the underinvestment of supplier enterprises with a high customer concentration is more significant.


### 4.4.2. Nature of Property Right, Customer Risk, and Underinvestment of Supplier Enterprises

On the one hand, the state-owned enterprises have the natural resource advantage with an implicit government guarantee behind it, the banks also prefer state-owned enterprises when serving a loan, and the state-owned enterprises can also choose the bank loan to make up for the financial deficit even if being negatively impacted by the customer risk [40]. However, on the other hand, the state-owned enterprises, supported by government resources, were also subject to many interventions, the transaction between state-owned enterprises might be a government action, and the state-owned customers and state-owned supplier enterprises might be controlled by the same government [41]. The supply chain relationship between state-owned enterprises is more stable, but such stable relationship will also cause the suppliers subject to the nature of state-owned property rights to fail to avoid risks by suspending the relationship when the customers subject to the nature of the state-owned property right face the financial distress; as a result, they are inevitably exposed to customer risk and the following hypothesis is put forward [42].H3: When the supplier and the enterprise are subject to the nature of state-owned property rights, the positive impact of customer risk on the underinvestment of supplier enterprises is more significant.The high and low groups are divided in line with the customer concentration median of the supplier enterprise. Column (1) and Column (2) results in Table7 show that the significant positive correlation between customer risk (khRiskt) and underinvestment of supplier enterprises (UnderInvestt + 1) only exists in the group with a high customer concentration, and the regression coefficient is 0.068 and significant at the level of 10%. It means that the increase in customer concentration will make the external financing of supplier enterprises more constrained, that is to say, the customer risk will have a stronger effect on the underinvestment of supplier enterprises when the importance and dependence of customers are relatively high. Thus, that hypothesis 2 is true is verified.Table 7
Difference test of grouped samples.
Regression modelUnderInvestt + 1(1)(2)(3)(4)khRiskt0.068∗0.0890.061∗∗∗0.049(1.87)(1.52)(2.71)(0.94)Sizet−0.024∗∗∗−0.020∗∗∗−0.018∗∗∗−0.028∗∗∗(−3.92)(−7.25)(−4.80)(−6.54)Levt0.0330.013−0.0120.024(1.00)(0.68)(−0.59)(1.13)Casht0.114∗∗∗0.032∗0.072∗∗0.038(3.15)(1.73)(2.43)(1.57)Aget0.008≤0.0010.0170.008(0.82)(−0.04)(1.40)(0.90)Ocft−0.08−0.020.004−0.065∗(−1.48)(−0.64)(0.14)(−1.94)Tact−0.008−0.006−0.008−0.002(−0.41)(−1.31)(−1.53)(−0.24)Sharet−0.123−0.0370.086−0.194(−0.77)(−0.30)(0.72)(−1.59)ROAt0.101−0.0020.099∗∗0.041(1.52)(−0.05)(2.27)(0.86)khSizet−0.002≤0.0010.001−0.002(−0.89)(−0.06)(1.18)(−0.90)khLevt0.0440.027−0.0010.027(1.03)(1.19)(−0.03)(1.06)khAget0.0030.0020.0020.007(0.29)(0.52)(0.30)(1.16)khOcft−0.01−0.0320.041−0.019(−0.15)(−0.76)(1.27)(−0.46)Cons0.443∗∗∗0.425∗∗∗0.320∗∗∗0.620∗∗∗(3.52)(7.35)(3.56)(5.47)YearControlControlControlControlIndustryControlControlControlControlN164168157173R-squared0.41020.48430.48180.5180Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.The group of state-owned enterprises versus state-owned enterprises and other groups are divided according to the nature of enterprise property rights. The Column (3) and Column (4) results in Table3 reveal that the significant positive correlation between customer risk (khRiskt) and underinvestment of supplier enterprises (UnderInvestt + 1) only exists in the group of state-owned enterprises versus state-owned enterprises, and the regression coefficient is 0.061 and significant at the level of 1%, showing the relationship between state-owned enterprises is more stable, and it is difficult for state-owned supplier enterprises to have a narrow escape when facing the risk of state-owned customers, that is to say, the customer risk will have a stronger impact on the underinvestment of supplier enterprises when the supplier and the customer enterprise are state-owned enterprises. Thus, that hypothesis 1 is true is verified.


## 4.4.1. Customer Concentration, Customer Risk, and Underinvestment of Supplier Enterprises

The current literature has shown that the closeness of customer relations will influence the financial behavior of supplier enterprises. For example, customer concentration will affect many respects of an enterprise (such as cash holding, investment efficiency, dividend distribution, and behavior of tax avoidance) [35–37]. The higher the customer concentration was, the more dependent the supplier would become on the customer, the lower the capital recovery speed would be [38], and the more likely the supplier would get into financial trouble [14]. If the major customers chose to break a contract or discontinue trading, the accounts receivable of problematic customers would encounter the risk of failure in the recovery of funds, the probability of bad debts in supplier enterprises would be greatly increased, the operating profit and cash flow would also be significantly reduced [39], and the internal capital available to supplier enterprises would be further decreased. In addition to this, the investors and creditors would be also aware of the risks incurred by customer concentration and set more restrictions and claim higher compensation for risk or risk premium when funding, resulting in the external financing of supplier enterprises being more constrained; thus, the following hypothesis was put forward.H2: compared to the supplier enterprises with a low customer concentration, the positive effect of customer risk on the underinvestment of supplier enterprises with a high customer concentration is more significant.


## 4.4.2. Nature of Property Right, Customer Risk, and Underinvestment of Supplier Enterprises

On the one hand, the state-owned enterprises have the natural resource advantage with an implicit government guarantee behind it, the banks also prefer state-owned enterprises when serving a loan, and the state-owned enterprises can also choose the bank loan to make up for the financial deficit even if being negatively impacted by the customer risk [40]. However, on the other hand, the state-owned enterprises, supported by government resources, were also subject to many interventions, the transaction between state-owned enterprises might be a government action, and the state-owned customers and state-owned supplier enterprises might be controlled by the same government [41]. The supply chain relationship between state-owned enterprises is more stable, but such stable relationship will also cause the suppliers subject to the nature of state-owned property rights to fail to avoid risks by suspending the relationship when the customers subject to the nature of the state-owned property right face the financial distress; as a result, they are inevitably exposed to customer risk and the following hypothesis is put forward [42].H3: When the supplier and the enterprise are subject to the nature of state-owned property rights, the positive impact of customer risk on the underinvestment of supplier enterprises is more significant.The high and low groups are divided in line with the customer concentration median of the supplier enterprise. Column (1) and Column (2) results in Table7 show that the significant positive correlation between customer risk (khRiskt) and underinvestment of supplier enterprises (UnderInvestt + 1) only exists in the group with a high customer concentration, and the regression coefficient is 0.068 and significant at the level of 10%. It means that the increase in customer concentration will make the external financing of supplier enterprises more constrained, that is to say, the customer risk will have a stronger effect on the underinvestment of supplier enterprises when the importance and dependence of customers are relatively high. Thus, that hypothesis 2 is true is verified.Table 7
Difference test of grouped samples.
Regression modelUnderInvestt + 1(1)(2)(3)(4)khRiskt0.068∗0.0890.061∗∗∗0.049(1.87)(1.52)(2.71)(0.94)Sizet−0.024∗∗∗−0.020∗∗∗−0.018∗∗∗−0.028∗∗∗(−3.92)(−7.25)(−4.80)(−6.54)Levt0.0330.013−0.0120.024(1.00)(0.68)(−0.59)(1.13)Casht0.114∗∗∗0.032∗0.072∗∗0.038(3.15)(1.73)(2.43)(1.57)Aget0.008≤0.0010.0170.008(0.82)(−0.04)(1.40)(0.90)Ocft−0.08−0.020.004−0.065∗(−1.48)(−0.64)(0.14)(−1.94)Tact−0.008−0.006−0.008−0.002(−0.41)(−1.31)(−1.53)(−0.24)Sharet−0.123−0.0370.086−0.194(−0.77)(−0.30)(0.72)(−1.59)ROAt0.101−0.0020.099∗∗0.041(1.52)(−0.05)(2.27)(0.86)khSizet−0.002≤0.0010.001−0.002(−0.89)(−0.06)(1.18)(−0.90)khLevt0.0440.027−0.0010.027(1.03)(1.19)(−0.03)(1.06)khAget0.0030.0020.0020.007(0.29)(0.52)(0.30)(1.16)khOcft−0.01−0.0320.041−0.019(−0.15)(−0.76)(1.27)(−0.46)Cons0.443∗∗∗0.425∗∗∗0.320∗∗∗0.620∗∗∗(3.52)(7.35)(3.56)(5.47)YearControlControlControlControlIndustryControlControlControlControlN164168157173R-squared0.41020.48430.48180.5180Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.The group of state-owned enterprises versus state-owned enterprises and other groups are divided according to the nature of enterprise property rights. The Column (3) and Column (4) results in Table3 reveal that the significant positive correlation between customer risk (khRiskt) and underinvestment of supplier enterprises (UnderInvestt + 1) only exists in the group of state-owned enterprises versus state-owned enterprises, and the regression coefficient is 0.061 and significant at the level of 1%, showing the relationship between state-owned enterprises is more stable, and it is difficult for state-owned supplier enterprises to have a narrow escape when facing the risk of state-owned customers, that is to say, the customer risk will have a stronger impact on the underinvestment of supplier enterprises when the supplier and the customer enterprise are state-owned enterprises. Thus, that hypothesis 1 is true is verified.


## 4.5. Robustness Test

Customer risk is the key studied in this paper, and the test for its robustness is an indispensable link. To verify the reliability of conclusions, we draw on the method of Boubakri et al. (2011) to measure customer risk, namely, volatility of three-year EBIT of total assets (VOL_EBIT) and volatility of five-year ROA) to measure the customer risk (khRisk).


### 4.5.1. Volatility of Three-Year EBIT of Total Assets (VOL_EBIT)

Pursuant to the method of Boubakri et al. (2011), this paper uses the volatility of EBIT of total assets (VOL_EBIT) of an enterprise within three years to measure the enterprise risk. When calculated, the EBIT of total assets is adjusted by the annual industry average. The bigger the VOL_EBIT is, the higher the enterprise risk will be. Column (1) and Column (2) in Table8 show the following: the regression coefficient of customer risk and supplier enterprise’s inefficient investment is 0.045 and significant at the level of 10%, and the regression coefficient of customer risk and underinvestment of supplier enterprises is 0.074 and significant at the level of 5%, that is to say, the customer risk will lead to inefficient investment behavior of enterprises and be further manifested as underinvestment.Table 8
Robustness test.
Regression modelVOL_EBITVolatility of five-year ROAInvestEfft + 1UnderInvestt + 1InvestEfft + 1UnderInvestt + 1(1)(2)(3)(4)khRiskt0.045∗0.074∗∗0.042∗0.073∗∗(1.729)(2.184)(1.657)(2.040)Sizet−0.010∗∗∗−0.021∗∗∗-0.008∗∗∗−0.021∗∗∗(−5.231)(−6.932)(−3.821)(−5.358)Levt0.0110.0040.014≥-0.001(0.949)(0.273)(1.155)(−0.007)Casht0.029∗∗0.053∗∗∗0.0180.054∗∗(2.177)(3.309)(1.127)(2.345)Aget0.0060.0040.0050.005(1.236)(0.794)(1.031)(0.972)Ocft0.018−0.0370.020−0.023(0.749)(−1.311)(0.782)(−0.699)Tact−0.009∗−0.003−0.007−0.002(−1.950)(−0.485)(−1.273)(-0.366)Sharet0.090−0.0770.067−0.059(1.408)(−0.818)(1.065)(-0.604)ROAt0.073∗∗0.073∗0.074∗∗0.044(2.299)(1.869)(2.001)(0.944)khSizet0.002∗−0.001≥−0.001−0.001(1.716)(−0.489)(−0.032)(−0.567)khLevt0.0090.020−0.0010.011(0.765)(1.127)(−0.090)(0.632)khAget−0.001−0.0010.003−0.002(−0.202)(−0.158)(0.624)(−0.428)khOcft−0.014−0.014−0.023−0.031(−0.599)(−0.488)(−0.970)(−0.961)YearControlControlControlControlIndustryControlControlControlControlN901419796345R-squared0.1410.3050.0950.242Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


### 4.5.2. Volatility of Five-Year ROA

According to the research by Sheng Mingquan et al. (2018) [43], this paper sets the observation session as five years, that is to say, the standard deviation of ROA adjusted for the industry of supplier enterprises within five years is used to measure the customer risk. Column (3) and Column (4) in Table 8 show that the regression coefficient of customer risk and supplier enterprise’s inefficient investment is 0.042 and significant at the level of 10%, and the regression coefficient of customer risk and underinvestment of supplier enterprises is 0.073 and significant at the level of 5%, that is to say, the customer risk will lead to the underinvestment of enterprises.To sum up, after the above robustness treatment, the main conclusion of this research is still significant, that is to say, the customer risk will result in the inefficient investment behavior of supplier enterprises and underinvestment of supplier enterprises.


## 4.5.1. Volatility of Three-Year EBIT of Total Assets (VOL_EBIT)

Pursuant to the method of Boubakri et al. (2011), this paper uses the volatility of EBIT of total assets (VOL_EBIT) of an enterprise within three years to measure the enterprise risk. When calculated, the EBIT of total assets is adjusted by the annual industry average. The bigger the VOL_EBIT is, the higher the enterprise risk will be. Column (1) and Column (2) in Table8 show the following: the regression coefficient of customer risk and supplier enterprise’s inefficient investment is 0.045 and significant at the level of 10%, and the regression coefficient of customer risk and underinvestment of supplier enterprises is 0.074 and significant at the level of 5%, that is to say, the customer risk will lead to inefficient investment behavior of enterprises and be further manifested as underinvestment.Table 8
Robustness test.
Regression modelVOL_EBITVolatility of five-year ROAInvestEfft + 1UnderInvestt + 1InvestEfft + 1UnderInvestt + 1(1)(2)(3)(4)khRiskt0.045∗0.074∗∗0.042∗0.073∗∗(1.729)(2.184)(1.657)(2.040)Sizet−0.010∗∗∗−0.021∗∗∗-0.008∗∗∗−0.021∗∗∗(−5.231)(−6.932)(−3.821)(−5.358)Levt0.0110.0040.014≥-0.001(0.949)(0.273)(1.155)(−0.007)Casht0.029∗∗0.053∗∗∗0.0180.054∗∗(2.177)(3.309)(1.127)(2.345)Aget0.0060.0040.0050.005(1.236)(0.794)(1.031)(0.972)Ocft0.018−0.0370.020−0.023(0.749)(−1.311)(0.782)(−0.699)Tact−0.009∗−0.003−0.007−0.002(−1.950)(−0.485)(−1.273)(-0.366)Sharet0.090−0.0770.067−0.059(1.408)(−0.818)(1.065)(-0.604)ROAt0.073∗∗0.073∗0.074∗∗0.044(2.299)(1.869)(2.001)(0.944)khSizet0.002∗−0.001≥−0.001−0.001(1.716)(−0.489)(−0.032)(−0.567)khLevt0.0090.020−0.0010.011(0.765)(1.127)(−0.090)(0.632)khAget−0.001−0.0010.003−0.002(−0.202)(−0.158)(0.624)(−0.428)khOcft−0.014−0.014−0.023−0.031(−0.599)(−0.488)(−0.970)(−0.961)YearControlControlControlControlIndustryControlControlControlControlN901419796345R-squared0.1410.3050.0950.242Symbols∗∗∗, ∗∗, and ∗ represent significance levels of 1%, 5%, and 10%, respectively.


## 4.5.2. Volatility of Five-Year ROA

According to the research by Sheng Mingquan et al. (2018) [43], this paper sets the observation session as five years, that is to say, the standard deviation of ROA adjusted for the industry of supplier enterprises within five years is used to measure the customer risk. Column (3) and Column (4) in Table 8 show that the regression coefficient of customer risk and supplier enterprise’s inefficient investment is 0.042 and significant at the level of 10%, and the regression coefficient of customer risk and underinvestment of supplier enterprises is 0.073 and significant at the level of 5%, that is to say, the customer risk will lead to the underinvestment of enterprises.To sum up, after the above robustness treatment, the main conclusion of this research is still significant, that is to say, the customer risk will result in the inefficient investment behavior of supplier enterprises and underinvestment of supplier enterprises.


## 5. Research Conclusions and Policy Suggestions

By studying the relationship between customer risk and supplier enterprise’s investment efficiency, this paper draws the following main conclusion: (1) In the upstream and downstream relationship of the supply chain, when the customer risk is high, the supplier will face more serious financing restrictions when the internal capital is insufficient and may be forced to give up the investments with large potential returns, resulting in underinvestment. (2) In the supplier enterprise with a higher customer concentration, the pressure of financing restriction will be larger. In the supplier enterprise with a high customer concentration, the positive effect of customer risk on underinvestment is more significant. (3) The supply chain relationship between state-owned enterprises is more stable and the stable relationship may make it impossible for one party to avoid risk by discontinuing the relationship when the other party is in trouble. When the supplier and customer enterprises are state-owned enterprises, the effect of customer risk on the underinvestment of supplier enterprises will be more significant.This research has important practical significance for CSRC and supplier enterprises. Firstly, the supplier enterprises must pay attention to customer risk signals and respond to them early and choose a good customer with a good financial position when choosing the customer to strengthen the risk defense ability between enterprises. Secondly, for the supplier enterprise, the risk of core customers has a stronger impact on investment efficiency, so the supplier enterprises need to reduce their dependence on core customers and strengthen their risk resistance. Thirdly, there may be a government transaction between state-owned enterprises; the supplier enterprise subject to the nature of state-owned property rights can select the high-quality customers not controlled by the state to avoid transmission of risks to some extent when choosing the customers. Fourthly, the public financial information of the customer is of certain information value to creditors (such as supplier enterprises and banks), and CSRC should further encourage the listed company to disclose the financial and nonfinancial information of customers and give full play to the decision-making role of supply chain information in stakeholders [44].This research is also with certain limitations. Because it is hard to get the specific characteristic data of nonlisted companies, we can only choose the customers and suppliers as samples of listed companies. There may be a certain bias in the sample selection in this paper which is an inherent defect in the research design of such literature. Therefore, how to collect specific characteristic information of nonlisted customers is the research direction that can be further expanded in the future.



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*Source: 6275511-2022-04-07.xml*