The Impact of Sustainable Supply-Chain Partnership on Bank Loans: Evidence from Chinese-Listed Firms

Abstract

With the rapid development of economic globalization, keeping the global supply chains sustainable is becoming increasingly important in improving supply chain performance and firm value. To fully understand the role of the supply chain relationship, it is necessary to comprehensively assess different characteristics of supply chain partnership in achieving sustainability goals. This study explores the implication of concentrated supply-chain partnership on loan features. Using a sample of Chinese-listed firms, we find that concentrated customer or supplier bases positively influence loan features, including loan size, loan maturity, and loan cost. We propose that concentrated customer and supplier bases influence the loan features through different mediators. Through mediating analysis, concentrated customer bases affect loan features through better corporate governance and lower operational risk, and concentrated supplier bases through lower operational cost and longer accounts payable cycle. The benefit of a concentrated chain partnership is more pronounced when customers or suppliers have a greater certification role, less uncertainty, and stronger geographical advantage. The results are robust to instrumental variable analysis, propensity-matched analysis, and alternative measures of chain partnership concentration. Findings in this study have important implications for understanding the sustainable supply-chain partnership management and loan decisions of banks in an emerging market.

1. Introduction

In the era of supply chain competition, supply chain partnership becomes one of the most powerful business concepts for organizations to gain a sustainable competitive advantage. With increasing numbers of nodes in supply network relationships, understanding partnership management is important for sustainable supply chain management [1,2]. The supply-chain partnership is the business relationship between the hosting firm and its upper-stream suppliers and downstream customers. It unveils the input and output channels on which the firm depends and facilitates business activities between trading parties [3]. Successful collaboration with partner firms could generate benefits for firms’ performance and sustainable development, and it acts as a signal to stakeholders in the capital market [4,5,6,7,8]. A concentrated chain partnership indicates that the firm assigns a great portion of procurements or sales to a few major suppliers or customers.

Meanwhile, a bank’s loan review committee might pay close attention to the supply-chain partnership since it has an essential implication on solvency. Previous studies find that the factors that influence accessing bank loans include both performance and non-performance elements, such as political background [9,10,11,12,13], misconduct [14,15,16], social capital [17,18], and macroeconomic factors [19]. Critically, another characteristic that banks might be highly concerned about, which continues to receive little attention, is a firm’s supply-chain partnership. Banks in China tend to think highly of firms with major chain partners for at least three reasons. First, securing well-known business partners contributes to the reputation of the hosting firm [5,6]. China’s manufacturing enterprises have long clustered on medium- to low-value-added production and located at low-end positions of the global supply chain, lacking brand recognition. The credit evaluation system of commercial banks in China is still far from mature [20]. Therefore, doing business with well-known chain partners is a positive signal for the quality and credibility of the hosting firm and indicates reliable operational performance. Second, chain collaboration creates collaborative synergy [21]. Due to the low profit margins, keeping down costs is a key factor for Chinese companies to maintain their competitive advantage over other rivals. Chain concentration not only promotes operating efficiencies [22] but also reduces operating expenses [23,24]. Interfirm mutual aid based on a committed relationship provides additional support in financial distress [25] and shares risk among partners [26,27]. Third, major chain partners set up clear information channels, which simplify loan assessment [25,28,29]. The financial condition of the hosting firm can be assessed by observing if the firm is willing to accept delayed payment or offer trade credit [6]. The performance of major chain partners is also an indicator of a firm’s default risk [30]. Although it might be safe to conjecture a positive relationship between chain partnership concentration and bank loans in China, some other evidence calls attention to the dark side of relying on concentrated chain partners [31,32]. Therefore, hosting major chain partnerships might be a double-edged weapon, and the relationship between supply-chain concentration and bank loans becomes an interesting empirical question.

This study seeks to understand the effect of concentrated supplier and customer bases on the volume, maturity, and cost of bank loans of Chinese public firms. We manually collect the firm characteristics of the major chain partners. To reveal the mechanism by which concentrated chain partnership is related to bank loans, we consider the mediating effect of several factors, including corporate governance, operational risk, operational cost, and the accounts payable cycle. Furthermore, we conduct several cross-sectional tests on the characteristics of the chain partners to investigate heterogeneous settings where the strength of the influence of chain concentration varies.

Using a sample of 14,691 firm-year observations, we find that concentrated chain membership is associated with a higher bank loan volume, longer maturity, and lower cost. Ceteris paribus, a one-standard-deviation increase in customer (supplier) concentration is associated with an additional 3.8% (2.5%) loan size, 7.5% (6.9%) more long-term loans, and a 62 (54) basis-point lower interest rate. Through mediating analysis, we propose that corporate governance, operational risk, operational cost, and the accounts payable cycle mediate the impact of concentrated chain partnerships on bank loans. To dig deeper into the relationship between concentrated chain partnership and loan size, we then provide additional cross-sectional tests. First, if the supplier or customer is a long-standing company, its certification role tends to be stronger than that of young companies [8,33,34]. Correspondingly, we find that banks are more likely to lend to firms with older chain partners. Second, the supply of input material and payment for goods is steadier and more predictable for chain members with less uncertainty. We find that the positive relationship between chain concentration and bank loans grows stronger when the customer or supplier is not in the high-tech industry, which is generally regarded as a high-risk profession. Third, it is easier for the hosting firm and its chain partner to create collaborative synergy when they are in neighboring areas. Banks can learn more about the hosting firm from its chain partners due to less information asymmetry between nearby firms. We find that firms with neighboring major chain partners can obtain more loans than firms with distant partners.

We contribute to the existing literature in the following aspects. First, we manually collected the data of supply-chain membership based on annual reports of listed companies and conduct several cross-sectional tests on the characteristics of the chain partners to investigate heterogeneous settings where the strength of the influence of chain concentration varies. Second, we reveal a new positive influence of having concentrated chain partnership bases. Much of the current literature on chain concentration pays particular attention to its potential risk. For example, firms with concentrated customer bases are more likely to become involved in tax avoidance [32], suffer a high cost of equity capital [31], and be granted strict loan contracts [7]. By contrast, a growing body of literature explores the advantages of having concentrated chain bases. These advantages include but are not limited to improving financial performance [22,35], boosting communication and operational efficiency [24,36], and reducing transaction costs [37,38,39]. This study shows the positive impact of a concentrated chain partnership on loan features in the context of an emerging market, suggesting that the implication of chain concentration is an issue of cost–benefit assessment and may perform differently in different scenarios. Third, we provide new insight into the determinants of obtaining bank loans in the emerging market. Unlike previous studies, which primarily focus on a firm’s internal characteristics [10,17,18,40], this study examines the impact of a significant external stakeholder: The supply-chain partner. Our results suggest that long-standing, low-risk, and neighboring major suppliers or customers play a positive role in accessing loans.

The rest of the paper is organized as follows. Section 2 develops testable hypotheses. In Section 3, we introduce data and main variables. Section 4 presents our main findings on the relationship between concentrated supply-chain partnership and loan features. We address endogeneity concerns in Section 5. Section 6 documents the channels through which concentrated chain partnerships influence loans. Section 7 shows several factors that moderate the effect of chain concentration. Section 8 presents robustness checks. Section 9 concludes.

2. Literature Review and Hypotheses Development

2.1. Literature Review

Obtaining bank loans is difficult in China because the financial system is largely monopolized by state-owned banks and requires strict risk–return assessment. The chain partnership reveals with whom the firm runs a business and to what extent the firm depends on its partners. Chain partners, which have a fundamental influence on the hosting firm’s performance and solvency, play an important role in loan evaluation [7,25,41]. In China, in order to prevent enterprises from making unnecessary investments in loan funds and ensure that enterprises use the loan in accordance with the loan agreement, banks will set up a closed-loop loan operation process. The enterprise repays based on the cash flow generated by the trade events with upstream and downstream enterprises. Through these trade behaviors, the information between banks and enterprises is more transparent. Therefore, this supply-chain-based bank credit business can be operated repeatedly, and the financing difficulty of enterprises has been partially alleviated. According to the 2019 China Supply Chain Finance Research Report, more than 85% of the supply chain financial service providers’ own capital sources come from bank loans.

Bank loans have long been discussed in the literature. They are a key driver of long-term firm value and have become a crucial factor that affects short-term growth and long-term development. Although a number of studies show that a firm’s financial efficiency is influenced by many factors, such as ideology [9], ownership structure [10], political background [11,12,13], corporate fraud [14,15], tax avoidance behavior [16], social capital [17], CEO characteristics [18], and macroeconomic factors [19], few studies have explicitly examined the impact of concentrated chain partnership on loan features in the context of an emerging market.

Existing research has shown that chain members, as an important governance mechanism, influence firm decision making and outcomes. Researchers have primarily examined the performance implications of major chain partners [22,24] and how they affect the financial environment [7,31]. Firms with concentrated customer bases are more likely to have collaborative advantages along the supply chain [21] and less likely to find themselves in financial distress [22]. In addition, concentrated chain partnership has a positive effect on accounting conservatism [23]. In the capital market, concentrated chain partnership could be a good signal to stakeholders, as investors and creditors give more financial support and flexibility in terms of duration and scale [24,25]. Firms clearly benefit from concentrated chain partnership by boosting communication [24], operational efficiency [36], and reducing transaction costs [37,38,39]. However, opportunistic behaviors of supply chain members may exist: Members of the supply chain are also independent business entities that need to survive and make profits, which leads to operational risk and information asymmetry [7,31,32]. Concentrated chain partnership therefore makes sense for firms to perform these channels effectively.

Chain members can be a double-edged sword. Although the benefits and costs have been mentioned in prior work, the impact of concentrated chain partnership on loan features in the context of an emerging market has not been sufficiently examined. Meanwhile, the different channels upstream and downstream in supply chain management are not clear. Hence, it is helpful for firms to recognize the importance of the relationship between suppliers and customers, improve the financing environment, optimize the financing structure, and improve the financing efficiency according to their own and supply chain member enterprises.

2.2. Hypotheses Development

The existing literature suggests several possible channels through which concentrated chain partnerships may have a positive impact on bank loans. First, cooperating with well-known business partners builds a reputation for the hosting firm and suggests its overall quality. A distinctive feature of Chinese companies is located on the low end of the global supply-chain hierarchy and running a business in labor-intensive and resource-based industries. Due to the low technical threshold, the market is full of homogeneous manufacturers with similar products. Only reputable companies with superior products and management can be successful in competing for business and high-quality raw materials. Therefore, winning major chain partners proves the competitiveness of the borrowing company [5,6]. For example, Kim et al. [25] suggest that the performance of major customers favors their suppliers’ loan contracts, especially when their suppliers have no prior loan relationship with the bank. Moreover, Johnson et al. [5] show that the certification role of a major chain partner is significant when the hosting firm has no alternative certification mechanism, which is the case in China. China’s social credit system was first introduced in 2003 and is still far from mature. China’s banks have a limited way of evaluating the credibility of a firm. Major suppliers or customers have a strong desire to avoid unreliable chain partners and closely monitor their financial health condition, providing convincing evidence for loan credibility. Additionally, Chinese companies are well known to utilize their social capital, such as reputation and networks, to obtain loans [9,10,11,12,17,42]. The supply-chain relationship represents the trust and reputation rooted in commercial intercourse, serving as the firm’s social capital and favoring bank loans.

Second, a concentrated chain partnership creates operational synergy for the hosting company. A direct implication of operational synergy is cost savings. Repeatedly cooperating with familiar partners simplifies the business pattern. Long-term relationships improve operating efficiency through vertical integration and strategic alliance [22]. Firms maintaining stable chain partners also save time in looking for new partners. Moreover, producers are more likely to undertake mass purchasing from major suppliers or mass production for major customers, which increases asset utilization and economies of scale to reduce marginal costs [23]. Having major chain partners is associated with more efficient inventory utilization [43], a better rate of return and lower operating cost [24], better innovation efficiency [44], and lower auditing cost and higher auditing quality [45]. The value of operational synergy is also reflected in providing collaborative support for firms connected by the supply-chain. After long-term cooperation, the business and strategy of the supplier and the customer become intertwined. For example, Dyer and Singh [46] suggest that relation-specific assets, knowledge-sharing routines, and complementary capabilities provide an interfirm advantage. Bensaou and Anderson [47] find that relationship-specific investment bonds supplier and customer together. Therefore, when the hosting firm experiences financial distress, its chain partners have incentives to protect the partnership by offering resources [26,27] and flexibility [48]. Cao and Zhang [21] point out that chain collaboration improves the collaborative advantage with a bottom-line influence on firm performance. A stable stakeholder relationship also improves a firm’s investment efficiency [49].

Third, a concentrated chain partnership reduces information asymmetry and benefits earnings forecasts. Information asymmetry is the main obstacle banks face in loan assessment, especially when the borrower has a complicated business and is reluctant to disclose sensitive operational information. Interlinked processes among chain partners require rich information sharing and market knowledge creation [50,51,52]. Banks can assess a firm’s financial condition by observing its willingness to offer trade credit or accept delayed payment [6]. Some major customers or suppliers are well-known public firms that regularly disclose their financial statements and have dense media coverage. Therefore, a significant amount of sensitive and private information (such as market share or operational dynamics), which the borrower might not want to share with banks, becomes more informative. Krishnan et al. [45] find that concentrated chain partnership is associated with higher audit quality. Additionally, running a business with several major customers or suppliers simplifies the borrower’s business pattern, improving the predictability of loan risk. As an emerging market with rapid growth speed, the Chinese market is uncertain and dynamic. Companies struggle for development against policy uncertainty [53], political uncertainty [54], and sales uncertainty [55]. Long-term purchasing contracts and interfirm governance enable chain partners to resist risk in a highly uncertain business environment [56]. Banks in China have incentives to prefer firms with concentrated chain partnerships and impose fewer restrictions on loans of these firms for three reasons: Reputation building, operational synergy, and performance predictability.

We conjecture that banks would grant more favorable loan agreements to companies with concentrated chain partnerships based on the above three reasons. We further propose the following three testable hypotheses:

H1a. 

Concentrated chain partnership is associated with higher loan volume.

H2a. 

Concentrated chain partnership is associated with longer loan maturity.

H3a. 

Concentrated chain partnership is associated with a lower loan cost.

In contrast to the above hypotheses, literature about the dark side of concentrated chain partnerships provides alternative predictions. Relying on major chain partners is risky. Once the major chain members switch to other business partners or experience financial distress, the hosting firm might experience large losses in revenue or a shortage of input material [7,31,57]. The relationship-specific investment requested by the major customer makes the supplier more conservative in financial policy, such as paying fewer dividends [58] and holding more cash [32,59]. Dhaliwal et al. [31] document that customer concentration is related to higher systematic and idiosyncratic risk. These adverse effects lead to alternative hypotheses:

H1b. 

Concentrated chain partnership is associated with lower loan volume.

H2b. 

Concentrated chain partnership is associated with shorter loan maturity.

H3b. 

Concentrated chain partnership is associated with a higher loan cost.

Although both sets of predictions seem to make sense, we conjecture that the advantage of depending on major chain members might be stronger, due to China’s unique institutional background and market characteristics, as stated above. To defend our conjecture and rule out the alternative hypotheses, we conduct a series of empirical tests in the following sections. The economic channels or research conceptual model is shown in Figure 1.

3. Data

3.1. Sample Selection

Our sample consists of all firms listed on the Shanghai and Shenzhen stock exchanges during 2008–2016. We excluded companies (i) in the financial industry, (ii) flagged with ST (special treatment), (iii) with negative ROA, and (iv) with a missing value for variables employed in this study. Eventually, we collected 14,691 firm-year observations for 2670 unique firms. All continuous variables were winsorized at their 1st and 99th percentiles to give less weight to extreme cases and outliers. We gathered the names and the percentage of sales/procurements represented by the top five customers/suppliers from the CSMAR database. To improve accuracy, we rechecked the chain partners’ information based on the firms’ annual reports.

3.2. Main Variables

The paper examines three bank loan features, including loan size, loan term, and loan cost. First, Loan Size is defined as total loans scaled by total assets. Second, following prior literature [60,61,62], Loan Maturity is defined as the ratio of long-term loans to total loans. Third, Loan Cost is the ratio of interest expenses to total loans. We also used short-term loans, industry-average maturity, and industry-average cost as alternative dependent variables in robustness tests. We used the Herfindahl–Hirschman Index (HHI) of the top five customers/suppliers to measure customer concentration (Customer) and supplier concentration (Supplier). In the later robustness tests, we used the share of sales (purchase) represented by the top five customers (suppliers) as an alternative proxy of chain concentration.

Following prior literature [7,18,63,64], we used the following regression models to examine the impact of chain partnership concentration on loan features:

$$\begin{gathered} Loan Features {\left(Loan Size, Loan Maturity, Loan Cost\right)}_{i,t} \\ =\alpha +{\beta }_{1}Customer{\left(Supplier\right)}_{i,t-1}+{\beta }_{2}Siz{e}_{i,t-1}+{\beta }_{3}RO{A}_{i,t-1} \\ +{\beta }_{4}SaleGrowt{h}_{i,t-1}+{\beta }_{5}Tangibilit{y}_{i,t-1}+{\beta }_{6}FirmAg{e}_{i,t-1} \\ +{\beta }_{7}Top{1}_{i,t-1}+{\beta }_{8}SO{E}_{i,t-1}+{\beta }_{9}Boardsiz{e}_{i,t-1} \\ +{\beta }_{10}Independenc{e}_{i,t-1}+{\beta }_{11}Duilit{y}_{i,t-1}+IndustryFE+YearFE+{\epsilon }_{i,t} \end{gathered}$$

(1)

We controlled a series of time-varying firm characteristics. Firm size (Size) is proxied by the natural logarithm of total assets. As the proxy of profitability, ROA is income before extraordinary items scaled by total assets. Sale Growth is the rate of annual sales growth. Tangibility is the sum of fixed assets scaled by total assets. Firm Age is the number of years since the firm was incorporated. SOE is a dummy variable, which equals one if the firm is a state-owned enterprise (SOE), and zero otherwise. Top1 is the shareholding ratio of the largest shareholder. We added board characteristics and CEO characteristics as control variables. Board Size is the number of board directors. Independence is the ratio of independent directors. Duality is a dummy variable, which equals one if the chairman is also the CEO, and zero otherwise. We also controlled for industry (According to Industry Classification 2012 by China Securities Supervision Commission.) and year fixed effects. In the addition, STATA 14 was used for sorting out statistical data. Depending on the software, the author analyzed data and obtained the necessary results.

Table 1. Variable definitions.

Variable	Definition
Customer	Herfindahl-Hirschman Index of the sales represented by the top five customers.
Supplier	Herfindahl-Hirschman Index of the procurement held by the top five suppliers.
Customer_dummy	A dummy variable, which equals one if the firm has at least one major customer representing 10% or more of sales, and zero otherwise.
Supplier_dummy	A dummy variable, which equals one if the firm has at least one major supplier representing 10% or more of the procurement, and zero otherwise.
Loan Size	The ratio of total loan to total assets.
Loan Maturity	The ratio of long-term loan to total loan.
Loan Cost	The ratio of interest expense to total loan.
Size	The natural logarithm of total assets.
ROA	The ratio of income before extraordinary items to total assets.
Sale Growth	The annual growth rate of sales.
Tangibility	The ratio of fixed assets to total assets.
Firm Age	The number of years since a firm was founded.
Top1	The portion of shares held by the largest shareholder.
Board Size	The number of board directors.
Independence	The proportion of independent directors on the board.
Duality	A dummy variable, which equals one if the CEO is also the chairman, and zero otherwise.
SOE	A dummy variable, which equals one if the firm is a state-owned enterprise, and zero otherwise.
Governance	The first principal component of executive incentive (executive compensation and executive shareholding ratio), supervision (the proportion of independent directors, the size of the board, institutional shareholding ratio, and equity balance status), and CEO power.
Risk	The volatility of a firm’s quarterly return on assets over the previous three years.
Operational Cost	The ratio of operating cost to operating income.
Accounts payable cycle	The ratio of 365 days to accounts payable turnover.

shows the definitions of the main variables in the paper.

3.3. Descriptive Statistics

Table 2. Summary statistics. This table reports summary statistics of the main variables. The sample consists of 14,691 firm-year observations of 2670 companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange over the period of 2008 to 2016. Definitions of all the variables are presented in Table 1.

Variable	Mean	Std Dev	Min	Median	Max	N
Customer	0.041	0.089	0.000	0.015	0.998	14,691
Supplier	0.046	0.068	0.000	0.030	0.998	14,691
Loan Size	0.151	0.141	0.000	0.123	0.566	14,691
Loan Maturity	0.049	0.087	0.000	0.002	0.717	14,691
Loan Cost	0.019	0.042	0.000	0.007	0.313	14,691
Size	21.903	1.258	19.377	21.733	25.705	14,691
ROA	0.051	0.040	0.001	0.042	0.196	14,691
Sale Growth	0.480	1.514	−0.679	0.116	11.289	14,691
Tangibility	0.225	0.168	0.002	0.190	0.746	14,691
Firm Age	14.867	5.873	1.000	14.000	72.000	14,691
SOE	0.433	0.496	0.000	0.000	1.000	14,691
Top1	0.365	0.155	0.003	0.346	0.894	14,691
Board Size	2.163	0.199	1.609	2.197	2.708	14,691
Independence	0.370	0.053	0.250	0.333	0.571	14,691
Duality	0.236	0.425	0.000	0.000	1.000	14,691

reports the summary statistics of the main variables in the paper. The table shows that the mean (median) value of Loan Size is 0.151 (0.123), indicating that bank loans on average represent 15.1% of firm assets. Loan Maturity shows a mean value of 0.049 and a median value of 0.002, indicating that only a minority of Chinese public firms can obtain a long-term bank loan. The cost of a loan is, on average, 1.9%. As the main explanatory variables, the mean (median) value of customer concentration Customer and supplier concentration Supplier are 0.041 (0.015) and 0.046 (0.030), respectively.

In terms of control variables, the mean value of the natural log of firm size is 21.093. Income before extraordinary items is approximately 5.1% of total assets. The average sales growth rate and the tangibility ratio are 48% and 22.5%, respectively. The mean number of years since incorporation is 14.867. Meanwhile, approximately 43.3% of sample firms are SOEs. The average ratio of independent directors is 36.5%. Furthermore, 23.6% of CEOs also serve as chairs.

4. Concentrated Chain Partnership and Loan Features

In this section, we employ empirical methodology to show the impact of chain concentration on loans. First, we analyze univariate differences in bank loans for the subsamples with high and low chain concentrations. Second, we examine the magnitude of the impact of concentrated chain partnership on bank loans using multivariate regression.

4.1. Univariate Test

Table 3. Univariate test. This table reports the results of the univariate analysis. In Panel A, we divide the sample into low- and high-customer-concentration groups. The last column shows the difference between the two groups in loan size, loan maturity, and loan cost, respectively. Similarly, Panel B shows the univariate results based on the subsamples of low and high supplier concentration. ***, **, and * denote significance at the 1%, 5%, and 10% levels of T-test, respectively.

Panel A. Univariate test of customer concentration
Variable	Customer = low	Customer = high	Diff. (high-low)
Loan Size	0.143	0.154	0.011 ***
Loan Maturity	0.043	0.052	0.009 ***
Loan Cost	0.017	0.013	−0.004 ***
Panel B. Univariate test of supplier concentration
Variable	Supplier = low	Supplier = high	Diff. (high-low)
Loan Size	0.144	0.154	0.010 ***
Loan Maturity	0.045	0.052	0.007 ***
Loan Cost	0.015	0.013	−0.002 ***

shows the mean values of loan features for two subsamples of firms, partitioned by high- and low-concentration chain partnerships. For every year in the sample period, we sort all firms according to their customer/supplier concentration and assign a firm to the low (high) concentration group if its concentration value is below (above) the median of the industry values.

In Panel A, the difference in loan size between the low- and high-customer-concentration groups is 0.011 and significant at the 1% level. Similarly, the mean values of loan maturity are 0.043 and 0.052 for low- and high-customer-concentration groups, significantly different at the 1% level. In addition, the low-customer-concentration group has a higher loan cost than the high-concentration group. Panel B tells a similar story. The value of loan size and loan maturity in low-supplier-concentration groups is significantly lower than in high-concentration groups. The loan cost in the low group is 0.2% greater than its counterpart in the high group. The univariate tests provide preliminary evidence for the positive impact of concentrated chain partnership on loan financing. To control for other potential explanatory factors, we further perform multivariate analysis.

4.2. Multivariate Regression

Table 4. Concentrated chain partnership and loan features. This table reports the results of OLS regressions relating the loan features to customer/supplier concentration and other control variables. The dependent variables are loan size (Columns 1–3), loan maturity (Columns 4–6), and loan cost (Columns 7–9). The main explanatory variables are customer concentration (Customer) and supplier concentration (Supplier). Control variables are defined in the Table 1. The t-statistics reported in parentheses are based on standard errors clustered at the firm level. ***, **, and * denote significance at the 1%, 5%, and 10% levels.

	Loan Size			Loan Maturity			Loan Cost
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Customer	0.069 ***		0.065 ***	0.036 ***		0.042 ***	−0.008 **		−0.007 *
	(7.443)		(6.905)	(6.138)		(3.995)	(−2.054)		(−1.691)
Supplier		0.064 ***	0.055 ***		0.035 **	0.050 *		−0.014 **	−0.008 ***
		(5.449)	(4.688)		(2.307)	(1.792)		(−2.364)	(−3.063)
Size	0.002 **	0.002 **	0.002 **	0.012 ***	0.010 ***	0.019 ***	−0.001 ***	−0.001 ***	−0.001 ***
	(2.183)	(2.123)	(2.418)	(23.296)	(21.052)	(20.484)	(−10.095)	(−4.346)	(−10.241)
ROA	−0.480 ***	−0.475 ***	−0.479 ***	−0.124 ***	−0.099 ***	−0.216 ***	0.020 ***	0.040 ***	0.020 ***
	(−22.386)	(−22.135)	(−22.339)	(−9.240)	(−7.418)	(−8.953)	(5.391)	(4.482)	(5.372)
Sale Growth	−0.003 ***	−0.003 ***	−0.003 ***	−0.000	−0.000	−0.000	0.000	0.000	0.000
	(−5.073)	(−4.897)	(−5.085)	(−0.026)	(−0.114)	(−0.117)	(0.052)	(1.264)	(0.063)
Tangibility	0.132 ***	0.133 ***	0.132 ***	0.072 ***	0.075 ***	0.135 ***	−0.005 ***	−0.008 ***	−0.005 ***
	(23.527)	(23.657)	(23.494)	(20.701)	(21.352)	(21.428)	(−5.337)	(−3.573)	(−5.291)
Firm Age	−0.000	−0.000	−0.000 *	−0.000 **	−0.000 ***	−0.000	−0.000	−0.000	−0.000
	(−1.626)	(−1.561)	(−1.721)	(−2.000)	(−5.259)	(−0.511)	(−1.499)	(−1.470)	(−1.427)
Top1	−0.015 ***	−0.014 **	−0.015 ***	−0.009 ***	−0.009 **	−0.016 ***	0.003 ***	0.004 *	0.003 ***
	(−2.732)	(−2.552)	(−2.761)	(−2.753)	(−2.549)	(−2.701)	(3.405)	(1.770)	(3.432)
Duality	0.004 **	0.004 *	0.004 **	−0.001	−0.001	−0.004 *	−0.000	−0.001	−0.000
	(2.107)	(1.958)	(2.080)	(−0.566)	(−1.054)	(−1.841)	(−0.782)	(−1.173)	(−0.771)
Board Size	0.000	0.000	0.000	−0.004	−0.002	−0.008	0.003 ***	0.006 ***	0.003 ***
	(0.006)	(0.000)	(0.030)	(−1.518)	(−0.532)	(−1.451)	(3.238)	(3.153)	(3.225)
Independence	0.002	0.004	0.002	0.016	0.017	0.043 **	0.005 *	0.012 *	0.005 *
	(0.094)	(0.225)	(0.119)	(1.573)	(1.628)	(2.315)	(1.822)	(1.761)	(1.807)
SOE	0.027 ***	0.027 ***	0.027 ***	0.005 ***	0.003 ***	0.011 ***	−0.001 *	−0.002 **	−0.001 *
	(14.422)	(14.400)	(14.442)	(4.582)	(2.589)	(5.139)	(−1.709)	(−2.169)	(−1.682)
Cons	0.019	0.018	0.013	−0.254 ***	−0.232 ***	−0.385 ***	0.059 ***	0.081 ***	0.059 ***
	(0.964)	(0.889)	(0.665)	(−20.577)	(−18.890)	(−17.244)	(16.820)	(9.884)	(16.987)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	14,691	14,691	14,691	14,691	14,691	14,691	14,691	14,691	14,691
Adjusted R2	0.573	0.572	0.574	0.436	0.429	0.160	0.254	0.194	0.254
F-test	503.958	502.420	492.612	290.657	344.683	69.740	127.918	90.416	125.026

presents the results of ordinary least squares (OLS) regressions relating the loan features to customer/supplier concentration and other control variables. The dependent variable in Columns 1–3 is loan size, in Columns 4–6 is loan maturity, and in Columns 7–9 is loan cost. We first test the influence of customer or supplier concentration separately for each loan feature and then examine their joint impact.

Columns 1–3 present that both customer and supplier concentration are positively associated with loan size and statistically significant at the 1% level. In terms of economic magnitude, a one-standard-deviation increase in Customer (Supplier) increases Loan Size by 0.57% (0.37%), based on the coefficients estimated in column 3. Given that the sample’s mean loan size is 15.1%, this finding translates into a 3.8% (2.5%) bigger loan size. Therefore, the results in Columns 1–3 support H1a, indicating a positive association between concentrated supply-chain members and bank loans.

The results in Columns 4–6 show positive relationships between loan maturity and customer/supplier concentration, which are significant at the 10% level or better. The coefficients estimated in Column 6 imply that a one-standard-deviation increase in Customer (Supplier) results in a 0.37% (0.34%) higher portion of long-term loans. Given that the sample mean of loan maturity is 4.9%, this increment equals a 7.5% (6.9%) greater long-term loan. This result suggests that relative to firms with dispersive chain partnerships, firms with a concentrated customer or supplier base tend to have longer loan maturity, which is in line with H2a.

The regression results obtained from Columns 7–9 reveal the influence of customer and supplier concentration on the cost of loan financing. The joint test of customer and supplier concentration in Column 9 is statistically significant. A one-standard-deviation increase in Customer (Supplier) is associated with a 62 (54) basis points lower interest rate. Overall, the results in Table 3 appear most aligned with the hypothesis that concentrated chain partnership benefits companies in obtaining bigger loans with longer maturity and at a lower cost.

The estimated coefficients of the control variables exhibit the expected signs. For example, in Columns 1–3, loan financing size is positively related to firm size, ratios of tangibility assets, and CEO duality, and is negatively associated with ROA, sales growth, and the shareholding ratio of the largest shareholder. In addition, larger firm size and ratios of tangibility assets assist firms in bargaining for a low interest rate.

5. Endogeneity Concern

The above empirical results imply that more concentrated customer or supplier bases in China are associated with more relaxed loan contracts, including larger loan sizes, longer loan terms, and lower loan costs. However, another possible explanation is the endogeneity issue. First, some omitted or unobservable variables might be associated with supply-chain member concentration and loan financing simultaneously, such as the reputation and business pattern of the company. Second, reverse causality could also threaten the credibility of our results, since firms with a strong loan advantage could attract large customers and suppliers. We thus employ various methods to mitigate endogeneity concerns.

5.1. Instrumental Variable Test

In this subsection, we use a two-stage instrumental variable (IV) approach to address potential endogeneity. First, following Dhaliwal et al. [31] and Krolikowski and Yuan [65], we employ one- and two-year lagged industry means of customer concentration (supplier concentration) as instrumental variables of Customer (Supplier). In the second-stage regression, we then regress loan features (Loan Size, Loan Maturity, and Loan Cost) on the predicted value of the Customer and Supplier.

Table 5. Endogeneity: IV. This table presents the estimation results of the IV-2SLS approach. Columns 1 and 2 show the results of the first-stage result, where chain concentration measures, Customer and Supplier, are instrumented with the lagged one- and two-year industry average customer concentration and supplier concentration. Columns 3–8 show the results of the second stage. All models include year- and industry-fixed effects. The t-statistics reported in parentheses are based on standard errors clustered at the firm level. Definitions of all variables are in the Table 1. ***, ** and * denote significance at the 1%, 5%, and 10% levels, respectively.

	First-Stage Regression		Second-Stage Regression
	Customer	Supplier	Loan Size		Loan Maturity		Loan Cost
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Customer (fitted)			0.168 ***		0.151 ***		−0.106 ***
			(4.347)		(5.552)		(−8.547)
Supplier (fitted)				0.300 **		0.264 ***		−0.185 ***
				(2.500)		(4.581)		(−7.147)
Industry Customer t−1	0.495 ***
	(23.397)
Industry Customer t−2	0.322 ***
	(14.196)
Industry Supplier t−1		0.239 ***
		(11.845)
Industry Supplier t−2		0.049 **
		(2.406)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	8535	8337	8339	8339	8339	8339	8339	8339
Adjusted R2	0.303	0.124	0.547	0.546	0.439	0.435	0.086	0.083
Sargan-C (p-value)	0.51	0.39
Anerson-Rubin (p-value)	0.00	0.00
Cragg-Donald Wald F	84.71 ***	32.58 ***

displays the two-stage model estimation results. Columns 1–2 present the first-stage regression outcome and show that instrumental variables are significantly and positively associated with the endogeneity variables (Customer and Supplier). The Cragg–Donald Wald F statistic can easily pass the weak instruments test. The Sargan test rules out the overidentification concern. Columns 3–8 present the outcome of the second-stage regression. In Columns 3 and 4, we find that the predicted Customer and Supplier are both positively related to Loan Size, significant at the 5% level or better, which is consistent with the results of our baseline regression. Additionally, we use the same approach to confirm the robust impact of the Customer (Supplier) on Loan Maturity and Loan Cost, suggesting that the relationship between chain concentration and loan feature is not driven by endogeneity in our setting.

5.2. Propensity Score Matching Approach

We adopt the propensity score matching (PSM) approach to control self-selection bias caused by non-random factors between firms with major chain partners and firms without major chain partners. Following Dhaliwal et al. [31] and Campello and Gao [7], we define major customers (suppliers) as those representing 10% or more of the firms’ annual revenue (manufacturing cost). Based on the logit model, we then regress the indicator variable of having a major customer (Customer_dummy) or major supplier (Supplier_dummy) on a series of firm characteristics, including all our baseline regression control variables. Using the estimated coefficient of the first-stage regression, we calculate the propensity score for each observation. The propensity score represents the probability that a firm has a major chain partner. Then, for each treated firm belonging to the group with a major chain partner, we match it with another firm with no major chain partner by the closest propensity score to generate the matched control group.

Table 6. Endogeneity: PSM. This table reports the results by applying a propensity score matching (PSM) procedure. Panel A presents the results of the first-stage regression. The dependent variables are Customer_dummy and Supplier_dummy. Customer_dummy is a dummy variable, which equals one if the firm has a major customer representing 10% or more of the annual revenue, and zero otherwise. Supplier_dummy is a dummy variable, which equals one if the firm has major suppliers representing 10% or more of the manufacturing cost, and zero otherwise. Panel B presents the difference in the means across the treatment (the sample with at least one corporate major customer/supplier) and the propensity-score-matched sample. Panel C shows the second-stage regression results using the jointed treatment and matched sample. The dependent variables are Loan Size (Columns 1 and 2), Loan Maturity (Columns 3 and 4), and Loan Cost (Columns 5 and 6). All models include year- and industry-fixed effects. The t-statistics reported in parentheses are based on standard errors clustered at the firm level. Definitions of all variables are in the Table 1. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel A: First stage of propensity score
	Customer_dummy			Supplier_dummy
	(1)			(2)
Size	−0.264 ***			−0.205 ***
	(−12.00)			(−8.00)
ROA	−0.395			−1.105 *
	(−0.72)			(−1.67)
Sale Growth	0.014 **			0.006
	(2.03)			(0.38)
Tangibility	−0.108			0.813 ***
	(−0.73)			(4.70)
Firm Age	0.004			0.022 ***
	(1.00)			(5.22)
Top1	0.332 **			0.414 **
	(2.41)			(2.57)
Duality	−0.073			0.050
	(−1.52)			(0.90)
Board Size	−0.019			−0.306 **
	(−0.15)			(−2.07)
Independence	−0.490			−1.450 ***
	(−1.13)			(−2.81)
SOE	−0.065			−0.235 ***
	(−1.34)			(−4.07)
Cons	3.041 ***			1.628 ***
	(5.66)			(4.24)
Year &Industry	Yes			Yes
N	14,678			14,690
Pseudo R2	0.084			0.412
Panel B: Descriptive statistics for propensity-score matched subsamples
	Customer_dummy = 1	Customer_dummy = 0		Supplier_dummy = 1	Supplier_dummy = 0
	Mean	Mean	t-stat	Mean	Mean	t-stat
Size	21.696	21.678	(0.68)	21.694	21.702	(−0.28)
ROA	0.051	0.051	(0.45)	0.050	0.050	(−0.10)
Sale Growth	0.507	0.504	(0.08)	0.541	0.519	(0.39)
Tangibility	0.224	0.221	(0.85)	0.226	0.224	(0.34)
Firm Age	14.736	14.726	(0.07)	15.578	15.428	(0.80)
Top1	0.364	0.362	(0.50)	0.359	0.360	(−0.15)
Duality	0.258	0.262	(−0.39)	0.298	0.294	(0.19)
Board Size	2.151	2.150	(0.27)	2.135	2.138	(−0.47)
Independence	0.370	0.370	(−0.33)	0.369	0.370	(−0.23)
SOE	0.381	0.378	(0.028)	0.302	0.305	(−0.18)
Panel C: Sample matched by propensity score: second stage
	Loan Size		Loan Maturity		Loan Cost
	(1)	(2)	(3)	(4)	(5)	(6)
Customer_dummy	0.011 ***		0.006 ***		−0.002 **
	(5.222)		(3.942)		(−2.221)
Supplier_dummy		0.010 ***		0.005 ***		−0.001 **
		(3.799)		(2.831)		(−2.171)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
N	6737	8673	6737	8673	6737	8673
Adjusted R2	0.610	0.579	0.456	0.429	0.061	0.033
F-test	283.633	312.971	151.595	209.673	22.980	9.658

reports the results by applying a propensity score matching (PSM) approach. Panel A presents the results of our first-stage regression. We find that firms with smaller sizes, lower leverage, higher sales growth, or a higher shareholding ratio of the largest shareholder are more likely to have a major corporate customer. Meanwhile, firms that have a smaller size, higher intangible assets, and a lower ratio of independent directors are more likely to have a major corporate supplier. Panel B presents the difference in the means across the treatment and the propensity-score-matched samples. The treatment group refers to the sample with at least one corporate major customer/supplier, and the results indicate that these two samples are well matched. Panel C shows the regression of the propensity-score-matched sample. Consistent with our baseline regression, firms with major chain partners tend to have more loans (Columns 1–2), longer maturity (Columns 3–4), and lower loan costs (Columns 5–6).

6. Mediating Analysis in the Relation between Chain Concentration and Bank Loan

Although we have provided robust evidence to support the positive influence of concentrated customer/supplier bases on loan financing, it is still unclear how concentrated chain membership benefits firms’ loan financing. Therefore, we explore the sources of the benefit of chain concentration from three potential channels: Corporate governance improvement, risk reduction, and cost saving. For each channel, we specify the following joint models of mediating analysis:

$$Loan Siz{e}_{i,t}=\alpha +{\beta }_{1}Customer{\left(Supplier\right)}_{i,t-1}+\lambda Control{s}_{i,t-1}+IndustryFE+YearFE+{\epsilon }_{i,t}$$

(2)

$$Mediatio{n}_{i,t}=\alpha +{\beta }_{1}Customer{\left(Supplier\right)}_{i,t-1}+\lambda Control{s}_{i,t-1}+IndustryFE+YearFE+{\epsilon }_{i,t}$$

(3)

$$\begin{gathered} Loan Siz{e}_{i,t}=\alpha +{\beta }_{1}Customer{\left(Supplier\right)}_{i,t-1}+{\beta }_{2}Mediatio{n}_{i,t-1}+\lambda Control{s}_{i,t-1}+IndustryFE \\ +YearFE+{\epsilon }_{i,t} \end{gathered}$$

(4)

The model consists of three equations. We use Mediation as the proxy of mediating variables. Control includes other control variables that have been used in the baseline regression. If Mediation plays a mediating role in the relationship between chain partnership concentration and loan size, the significance of the Customer (Supplier) declines after including the mediating variable in Equation (4). We employ the Sobel test [66] to examine the indirect effect of supply-chain partnership concentration on loan financing through mediating variables. The research model is shown in Figure 2.

6.1. Mediators of Customer Concentration

6.1.1. Corporate Governance

As important stakeholders, chain partners are banded together by common interests. Major customers have a strong incentive to monitor their partners’ corporate governance to ensure the continued existence of the joint benefit [6,67]. Whenever a firm-specific decision is to be made, chain partners will value its impact on the partnership and take steps against potential misconduct. Stakeholders are often granted a voice regarding operational, managerial, and strategic issues [68]. Cao and Zhang [21] suggest that supply-chain collaboration has a significant positive effect on firm performance. Therefore, we conjecture that customer concentration has a positive impact on corporate governance.

Moreover, the consequential benefits of improved governance are valued by banks. Ashbaugh et al. [69] argue that strong corporate governance is effective in mitigating the risk caused by information asymmetry. Chen et al. [70] suggest that corporate governance has significant effects on reducing firm financing costs and improving financing efficiency. Given the potential consequences of corporate governance for economic performance, we conjecture that corporate governance is a mediating factor in the relationship between customer concentration and loan volume.

Following Dai et al. [71], we conduct principal component analysis (PCA) to measure the overall quality of corporate governance. We extract the principal component from a series of indicators of corporate governance, including executive incentive, supervision, and CEO power. The incentive element is represented by the executive compensation and executive shareholding ratio. Supervision is represented by the proportion of independent directors, the size of the board, the institutional shareholding ratio, and equity balance status. Equity balance status is measured by the ratio of shares held by the second- to the fifth-largest shareholders to the shares represented by the largest shareholder. CEO duality is used to indicate the decision-making power of managers. We extract the first principal component from the above measures as the proxy of corporate governance (Governance). We then apply Equations (2) and (3) to provide empirical evidence.

As shown in Panel A of

Table 7. Mediating analysis of the effect of concentrated chain partnership. This table reports the mediating role in the relationship between chain concentration and loan size. Panel A presents the mediating effects of corporate governance and risk. Using principal component analysis, we construct Governance by extracting the first principal component among executive incentive (executive compensation and executive shareholding ratio), supervision (the proportion of independent directors, the size of the board, institutional shareholding ratio, and equity balance status), and CEO power. The mediating variable, Operational risk, is defined as the volatility of a firm’s quarterly return on assets over the prior three years. Panel B presents the mediating effect of operational cost, inventory turnover, and accounts payable cycle. Operational Cost is defined as the proportion of operating cost to operating income. Inventory turnover is defined as the proportion of cost of goods sold to inventory. Accounts payable cycle is defined as the proportion of 365 days to accounts payable turnover. All models include year- and industry-fixed effects. The t-statistics reported in parentheses are based on standard errors clustered at the firm level. Definitions of all variables are in the Table 1. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel A. Mediating effect of corporate governance and risk
	Loan Size	Governance	Loan Size	Operational Risk	Loan Size
	(1)	(2)	(3)	(4)	(5)
Customer	0.069 ***	0.119 **	0.055 ***	−0.284 **	0.058 ***
	(7.443)	(2.010)	(6.260)	(−2.417)	(5.057)
Governance			0.006 ***
			(4.622)
Operational Risk					−0.006 ***
					(−7.072)
Control	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
N	14,691	Yes	Yes	Yes	Yes
Adjusted R2	0.573	Yes	Yes	Yes	Yes
F	503.958	14,691	14,691	14,691	14,691
Panel B. Mediating effect of operational cost and accounts payable cycle
	Loan Size	Operational Cost	Loan Size	Accounts Payable Cycle	Loan Size
	(1)	(2)	(3)	(4)	(5)
Supplier	0.064 ***	−0.115 ***	0.051 ***	29.477 ***	0.037 ***
	(5.497)	(−10.694)	(4.657)	(17.839)	(3.110)
Operational Cost			−0.066 ***
			(−7.805)
Accounts Payable Cycle					0.001 ***
					(16.063)
Control	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
N	14,691	14,691	14,691	14,691	14,691
Adjusted R2	0.572	0.310	0.575	0.093	0.580
F	500.710	168.535	495.348	38.433	504.259

, we find that the coefficient of Customer is 0.069 in Column 1, significant at the 1% level, indicating the positive influence of customer concentration on loan financing volume. Next, Column 2 reports that customer concentration has significant and positive effects on corporate governance. In the last step, we include the mediating variable (Governance) and the main explanatory variable together in Column 3, and then find that the coefficient of Customer is reduced from 0.069 (in Column 1) to 0.055 (in Column 3). The results support our conjecture that corporate governance is a mediating factor in the relationship between concentrated supply-chain partnership and loan size.

6.1.2. Operational Risk

Concentrated chain partnership can reduce firms’ operational risk for two reasons. First, the impact of inter-organizational trust on buyer and supplier performance is particularly significant when they encounter unforeseen market changes. Facing a highly uncertain business environment in China [53,54,55], chain partners have the incentive to form strategic alliances to share the risk [25,26,27]. Since the more customers buy/spend, the more important they are [72,73], a firm’s priority is to maintain the relationship with major customers when the market is depressed. The same thing happens to major suppliers in times of crisis. To safeguard business credibility, customers with long-term purchasing contracts are unlikely to cancel major supplier orders causally. Close upstream and downstream integrations reduce supply and demand uncertainty, making the hosting firm more likely to survive market turmoil. Second, the simple business pattern and interfirm information sharing lead to higher transparency. Firms tend to be required by their large supply-chain partners to share their operating and governance information [50,51,52]. The process of information sharing improves the predictability of loan risk and mitigates the potential adverse selection issue. Therefore, we conjecture that a concentrated chain partnership reduces operational risk, which in turn increases a firm’s favorableness in taking out bank loans.

We use the volatility of the quarterly return on assets over the prior three years as the proxy of firms’ operational risk. Panel A of Table 7 reports that the coefficient of Customer (in Column 4) is negative and significant at the 5% level, indicating that a concentrated customer base leads to a decrease in the level of firm risk. In Column 5, we find that adding firm risk as an additional independent variable reduces the coefficient of customer concentration on Loan Size, supporting the mediating role of firm risk. The results present that the risk buffer effect of chain concentration serves as a mediating factor in the indirect association between supplier concentration and loan size.

6.2. Mediators of Supplier Concentration

6.2.1. Operational Cost

One of the advantages of running a business with concentrated chain partners is operational synergy. Due to operational synergy, the hosting firm can save marginal costs by mass production and effective asset utilization, reduce communication costs by vertical integration, and control other unexpected expenditures by strategic alliance [22,24,43]. Gosman and Kohlbeck [74] find that firms identified by suppliers as important customers have higher operating profitability and profitability persistence, due to the advantages of supply chain arrangements. Another advantage is to ask the supplier to customize the product and make a relationship investment. The joint problem-solving between customers and suppliers reduces the expenditure of customers. Since banks take a firm’s operating efficiency into account in loan assessment [75], we suggest that operational cost is a potential mechanism through which concentrated customers affect loan size. We use the ratio of operating cost to operating income as the proxy of operational synergy.

Panel B of Table 7 provides evidence of the mediating role of operational cost. In Column 3, we find that the coefficients of Supplier decrease after including the mediating variable: Operational cost (Operational Cost). The Sobel test is significant at the 1% level, indicating a pronounced indirect association between supplier concentration on loan size through operational cost.

6.2.2. Accounts Payable Cycle

Keeping a major relationship with a supplier is associated with increased bargaining power over purchase prices. For example, Kalwani and Narayandas [43] find that the profit of suppliers can be bargained away by their customers through lower prices over time. Customers’ large expenditures also give them privileges to obtain favorable payment terms from their suppliers [32]. Klapper et al. [76] find that the largest and most creditworthy buyers receive contracts with the longest maturities. Murfin and Njoroge [77] suggest that large customers tend to use trade credit frequently and delay payment to their suppliers. Using Chinese data, Fabbri and Klapper [78] point out that suppliers with weak bargaining power tend to extend trade credit and have a large share of goods sold on credit.

In our sample, all hosting firms are listed companies. Given the fact that listed firms have the largest size and the most superior status in the Chinese market, they have a great chance to use their priority of being important buyers to extend trade credit. We use the accounts payable cycle to measure how long it takes a company to pay off its suppliers. The results are presented in Table 7 Panel B.

In column (4), Supplier has a significant negative coefficient with Accounts Payable Cycle, indicating that firms with a higher supplier concentration enjoy a longer time before repaying trade credit. Then we add Accounts Payable Cycle as the additional explanatory variable in column (5). The coefficient of Supplier shrinks to 0.037, a 42% decrease compared to its original dimension, suggesting that Accounts Payable Cycle functions as a mediator in the relationship between supplier concentration and loan size. From the above results, the path analysis results are shown in Figure 3.

7. Heterogeneity

To dig deeper into the relationship between concentrated chain partnership and loan size, we provide additional cross-sectional tests focusing on chain partners’ certification effect, bankruptcy risk, and geographical advantage.

Table 8. Heterogeneity. This table reports the result of heterogeneity analysis based on characteristics of customers or suppliers. In all panels, the dependent variable is Loan Size, and the main independent variable of interest is customer concentration (Customer) or supplier concentration (Supplier). In Panel A, we divide the sample into two groups based on the median value of firm age of customers or suppliers. In Panel B, we divide the sample into two groups based on whether at least one customer or supplier is in the high-tech industry. In Panel C, we divide the sample into two groups based on whether at least one customer or supplier is located in the same province as the hosting firm. All models include year- and industry-fixed effects. The t-statistics reported in parentheses are based on standard errors clustered at the firm level. Definitions of all variables are in the Table 1. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel A. Certification effect
	Long firm age		Short firm age
	(1)	(2)	(3)	(4)
Customer	0.051 *		0.001
	(1.930)		(0.018)
Supplier		0.061 **		−0.037
		(2.050)		(−1.007)
Control	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	1135	778	959	973
Adjusted R2	0.659	0.617	0.592	0.391
Panel B. Firm risk
	Non-high-tech industry		High-tech industry
	(1)	(2)	(3)	(4)
Customer	0.046 **		−0.010
	(1.990)		(−0.245)
Supplier		0.048 *		0.003
		(1.736)		(0.060)
Control	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	1233	908	863	842
Adjusted R2	0.627	0.590	0.622	0.483
Panel C. Geographical advantage
	In same province		In different province
	(1)	(2)	(3)	(4)
Customer	0.040 *		−0.094
	(1.723)		(−1.294)
Supplier		0.104 **		−0.012
		(2.231)		(−0.472)
Control	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	939	330	565	1838
Adjusted R2	0.629	0.582	0.134	0.632

presents the empirical results (Because there is no database providing the financial data of customers and suppliers directly, we manually collect customer data for 1926 firm-year observations and supplier data for 2403 firm-year observations from Sina Finance, China Institution Database, and Tianyancha. Compared to the baseline regression sample, the sample in heterogeneity tests shrinks in size due to limited data availability.).

7.1. Firm Age

Customers or suppliers with a long history tend to signal more credibility because long-standing companies have a well-known brand image and stronger incentives to maintain their reputation [8,33,34]. Established firms face less bankruptcy risk than firms at the early stages of the life cycle [79]. Mature firms also pay attention to their image in the external environment and invest significantly more in social responsibility activities [80].

Therefore, we divide the observations into two subsamples based on whether the firm age of customers is below or above the sample mean. Similarly, we then construct the other two subsamples based on the firm age of suppliers. In Panel A of Table 8, we find that the chain concentration has a significant and positive impact on loan size for firms with older customers or suppliers, while this impact is not significant for firms with young chain partners.

7.2. High-Tech Firm

Compared with non-high-tech firms, high-tech firms face larger income uncertainties, higher information asymmetry [81], and higher stock return volatility [82]. Banks tend to impose serious loan constraints on innovative firms [83]. Therefore, we conjecture that banks are less likely to issue loans to firms with high-tech customers or suppliers due to risk concerns.

In Panel B of Table 8, we partition the sample into two groups based on whether the firm has customers (Columns 1 and 3) or suppliers (Columns 2 and 4) in high-tech industries (as defined by Loughran and Ritter [84]). The results suggest that customer and supplier concentrations have a significant and positive influence on loan size only for firms with non-high-tech chain partners, which is consistent with our expectations.

7.3. Geographical Advantage

A firm can benefit from operating with customers or suppliers located nearby. First, nearby chain partners can save transportation and information costs in terms of time and money [85]. Second, geographic proximity can strengthen external monitoring effectiveness and mitigate the cultural distance disadvantage [86]. Therefore, firms with nearby chain partners have a geographical advantage to perform better, which might help them access bank loans.

We investigate how the geographical advantage of chain partners affects the influence of customer (supplier) concentration on loan size in Panel C of Table 8. We partition the sample into two groups according to whether the hosting firm and its chain partners (at least one of the top five customers/suppliers) are in the same province. In the subsample of firms with adjacent major chain partners, we find that the chain concentration is significantly associated with more bank loans. The relationship between chain concentration and loan volume is negative in the subsample of firms with chain partners in different provinces, although this relationship is not significant.

8. Robustness Test

In Panel A of

Table 9. Robustness checks. This table reports the result of our robustness checks. In Panel A, we use alternative dependent variables. In Columns 1 and 2, the dependent variable is Short-term Loan, which is defined as the ratio of short-term loans to total assets. In Columns 3 and 4, the dependent variable is Industry Loan Maturity, which is defined as industry-adjusted loan maturity. In Columns 5 and 6, the dependent variable is Industry Loan Cost, which is defined as industry-adjusted loan cost. In Panel B, we use the portion of sales or purchases represented by firms’ top five customers (Customer_portion) or suppliers (Supplier_portion) as an alternative measure of chain concentration. In Panel C, the dependent variables ΔLoan Size, Δloan Maturity, and Δloan Cost are the annual change in Loan Size, Loan Maturity, and Loan Cost. The independent variable ΔCustomer (ΔSupplier) is the annual change in customer-base concentration (supplier concentration). All models include year- and industry-fixed effects. The t-statistics reported in parentheses are based on standard errors clustered at the firm level. Definitions of all variables are in the Table 1. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel A. Alternative methods of identifying dependent variables
	Short-term Loan		Industry-adj. Loan Maturity		Industry-adj. Loan Cost
	(1)	(2)	(3)	(4)	(5)	(6)
Customer	0.027 ***		0.044 ***		−0.008 **
	(3.307)		(4.214)		(−2.054)
Supplier		0.053 ***		0.062 **		−0.009 ***
		(5.193)		(2.236)		(−3.278)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
N	14,691	14,691	14,691	14,691	14,691	14,691
Adjusted R2	0.427	0.428	0.160	0.159	0.254	0.254
F	280.359	281.076	71.436	71.047	127.918	128.142
Panel B. Alternative methods of identifying independent variables
	Loan Size		Loan Maturity		Loan Cost
	(1)	(2)	(3)	(4)	(5)	(6)
Customer_Portion	0.042 ***		0.022 ***		−0.003 **
	(6.624)		(5.197)		(−2.258)
Supplier_Portion		0.066 ***		0.010 *		−0.010 ***
		(7.769)		(1.746)		(−2.608)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
N	14,691	14,691	14,691	14,691	14,691	14,691
Adjusted R2	0.573	0.573	0.432	0.431	0.254	0.194
F	503.269	504.254	285.150	284.072	127.948	90.455
Panel C. Analysis of changes in concentrated chain partnership
	ΔLoan Size		ΔLoan Maturity		ΔLoan Cost
	(1)	(2)	(3)	(4)	(5)	(6)
ΔCustomer	0.018 *		0.011 *		−0.001
	(1.795)		(1.882)		(−0.201)
ΔSupplier		0.019 *		0.016 **		−0.012 **
		(1.879)		(2.375)		(−2.047)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
N	11,175	11,175	11,175	11,175	11,175	11,175
Adjusted R2	0.120	0.067	0.285	0.051	0.075	0.075
F	43.336	22.826	126.678	16.994	25.853	25.981

, we use three alternative dependent variables to check the robustness of our results. We use the ratio of short-term loans to total assets (Short-term Loan), industry-adjusted loan maturity (Industry Loan Maturity), and industry-adjusted loan cost (Industry Loan Cost) as the alternative dependent variables. We obtain consistent results in all model specifications: The chain concentration measures are significantly associated with a higher loan volume, longer loan maturity, and lower loan costs.

Panel B of Table 9 presents the results relating alternative measures of chain partnership concentration to loan features. We use the portion of sales or purchases represented by firms’ top five customers (Customer_portion) and suppliers (Supplier_portion) instead of HHI as the explanatory variables. In Columns 1–2, we find that the share of top-five customers and suppliers is positively related to loan size, significantly at the 1% level. Similarly, in Columns 3–4, the proportion of major chain partners is significantly and positively associated with industry-adjusted loan maturity, statistically significant at the 10% level or better. Columns 5–6 indicate the negative relationship between chain concentration and industry-adjusted loan cost. Overall, the findings in Table 8 provide robust evidence that firms with concentrated chain partnerships have more favorable loan features.

In Panel C of Table 9, we examine the intertemporal association between changes in concentrated chain partnership and changes in firms’ loan features, following Cao et al. [87]. We use the annual change in Loan Size ΔLoan Size, Loan Maturity ΔLoan Maturity, and Loan Cost ΔLoan Cost as the dependent variables, and we use the annual change in customer-base concentration (supplier concentration) ΔCustomer (ΔSupplier) as the independent variable. The results in Panel C show that ΔCustomer (ΔSupplier) is positively and significantly associated with the change in Loan Size ΔLoan Size and Loan Maturity ΔLoan Maturity and negatively and associated with the change in Loan Cost ΔLoan Cost, indicating that the hosting firm will increase loan volume, longer loan maturity, and reduce their loan cost when the chain concentration increases.

9. Conclusions and Implications

In developing countries, enterprises attribute one-third of their sales to a small set of “large customers”, and concentrated chain partnerships could significantly influence their operating behaviors. The maintenance of a sustainable supply chain relationship not only effectively maximizes the performance of the supply chain but also helps enterprises to improve the uncertainty risk of the external financing environment. Since the difficulty of obtaining bank loans has long hindered the development of firms in the emerging market, identifying the elements that affect banks’ loan decision has important practical significance. Previous researchers have shown that large customers bear negative consequences for a firm’s relations with its creditors, investors, and stakeholders, revealing limits to integration along the supply chain [7,31,32]. While the benefit of running a business with major chain partners is salient, the risk of relying on limited input and output channels is not negligible.

The objectives of this study were to examine the role played by the concentrated chain partnership in achieving the sustainability goals of firms, and how concentrated partnership influences loan features from the supplier and customer perspectives. This study additionally investigated the mediating role of corporate governance, operational risk, operational cost, and the accounts payable cycle on the hypothesized relationships.

Consistent with the benefits of chain concentration, we find firms with major chain partners have more favorable loan features, including larger loan size, longer loan maturity, and lower loan costs. To explore potential sources of the benefit, we further show a series of mediating factors. We find that the positive impact of chain concentration on loan features is moderated by improved corporate governance, less operational risk, and lower cost. We also show that when major chain partners are long-standing, non-high-tech, and geographically adjacent firms, the chain concentration is even more advantageous. Our study contributes to the literature on inter-supply-chain partnerships by shedding light on the importance of chain partners as a firm’s intangible resource.

This study has significant and potential implications for practicing managers. For financial institutions, it is beneficial for banks and other institutions to formulate more reasonable credit strategies for enterprises. Banks and other credit institutions pay attention to the potential benefits and risks of enterprise supply chain development, and make reasonable judgments based on the specific conditions of supply chain relation-based transactions, which undoubtedly has practical significance for improving the loan quality of financial institutions and the level of supply chain financial services. Therefore, enterprises should fully recognize the different roles and mechanisms of suppliers and customers in transmitting information to banks and make reasonable use of supply chain management and financial tools to further foster win–win cooperation with upstream and downstream enterprises in the supply chain and ultimately effectively improve the sustainable performance of the supply chain.

Our research has some limitations that might form the basis for future research. First, our study is based on a set of Chinese-listed firms. It may raise the question of whether the results are generalizable to other types including small firms or private firms, and we encourage future studies to confirm our results in other contexts. Second, our study only focuses on the role played by the concentrated chain partnership in achieving bank loans, but we were unable to examine how chain partnership affects other sustainability goals. Future research should extend the role of chain partnership in corporate decision making. Third, our study has taken classical and empirical models of prior studies in this area, and future studies could extend the analysis by adopting theoretical models [88,89] and other multilevel analysis techniques, which may help to provide a more complete picture of firms’ supply chain management in emerging economies.