Impact of performance commitment in mergers and acquisitions on trade credit policy: evidence from China

ABSTRACT

This study examines the incentive effect of performance commitments in mergers and acquisitions and discovers that performance commitments increase the supply of trade credit and that seeking profitability is the impact mechanism. According to our cross-sectional analyses, the positive impact of performance commitment on trade credit is more pronounced for firms located in low-marketization regions with fierce market competition, high corporate risk, low internal control quality, low audit quality, or state-owned enterprises. Furthermore, our study reveals that bad debt losses and earnings management rise in tandem with trade credit. This research contributes to the literature on Chinese-specific earnouts.

KEYWORDS:

• Mergers and acquisitions
• performance commitment
• trade credit
• corporate governance
• bad debt

1. Introduction

Mergers and acquisitions (M&A) are critical for listed companies seeking to upgrade and optimize their organizational structure. To facilitate M&A, many firms use performance commitments as contractual arrangements for post-adjustment of transaction pricing. According to Williamson’s (1979) theoretical analysis, contractual commitments can reduce costs incurred due to information asymmetry for both parties. According to Kohers and Ang (2000) and Srikant (2001), earnout, a form of commitment, helps hedge risk and reduce acquisition costs when there is greater information asymmetry about target firms. Similarly, performance commitments in M&A reduce information asymmetry, mitigate adverse selection risks, and lower agency costs (Changjiang and Han 2014; Pan, Qiu, and Yang 2017). Furthermore, performance commitments serve as an incentive for management to work hard and meet the promised target. However, according to the new institutional economics theory, the institutional environment influences commitment reliability. As the marketization of M&A grows, the limitations of performance commitment become apparent. Non-fulfilled commitments, as well as adjusting performance commitments to avoid compensation, are common and increase risks for both parties in M&A.

Performance commitments convert asset sales from one-time to intertemporal transactions, resulting in unpassed and intertemporal risks (Dou and Zhai 2020). Regardless of whether they are achieved, performance commitments increase financial risk (Zhang et al. 2019). Customers, as key stakeholders, are exposed to a cross-period risk of performance commitment, which inevitably affects their relationships. Furthermore, performance commitments trigger earnings management (Wang and Fan 2017), which affects customers’ perceptions about the firm’s prospects and even harms customer – supplier relationships (Raman and Shahrur 2008). Good supplier – customer relationships foster mutual trust and promote mutual information sharing and resource complementarity (Fei and Xuejun 2009), which are critical for meeting performance commitments. Signing a performance commitment implies accepting compensation responsibility and motivates management to improve supplier – customer relationships. According to the signaling function and competition theory, the supply of trade credit can imply good product quality and financial status (Long, Malitz, and Abraham Ravid 1993; Ng, Kiholm Smith, and Smith 1999) and be used as a competitive tool to attract new customers, consequently promoting product sales.

This leads us to the following questions: Will firms send positive signals after signing performance commitments by increasing trade credit supply, thereby maintaining stable customer relationships? Will trade credit be used to attract new customers from competitors to maximize performance goals? Using a dataset of all Chinese A-share listed companies that have signed performance commitments as the acquirer, we discover that performance commitments lead to significant increases in trade credit supply and that companies with low profitability extend substantially more trade credit. According to our cross-sectional analyses, the positive impact of performance commitment on trade credit is more pronounced for firms located in low-marketization regions, with fierce market competition, high corporate risk, low internal control quality, low audit quality, or state-owned enterprises. We investigate the potential consequences of increasing trade credit and discover that bad debt losses and earnings management rise in tandem with trade credit. Furthermore, we address potential endogenous problems in the study by conducting a series of robustness tests.

This study contributes to the existing literature in several ways. First, as the first study to investigate the impact of performance commitment on trade credit, this study advances research on performance commitment. Unlike existing studies on the economic consequences of performance commitments that have primarily focused on M&A efficiency, market reaction, executive behavior, and the risk of small and medium investors, we studying the economic consequences by focusing on internal operational activities and general stakeholders. Second, our study makes a significant contribution to the literature on trade credit theory by utilizing the unique economic environment of performance commitment to verify the competition theory and signal function of trade credit. Third, our findings on the profitability mechanism contribute to the literature on the incentive effect of performance commitment in emerging markets.

The remainder of this paper is organized as follows: Section 2 provides a background on the performance commitment policy, reviews related literature, and develops the hypotheses. Section 3 introduces the sample, variables, and empirical methodology used in the study. Sections 4 and 5 present the empirical results and further analyses (including robustness tests), respectively. Section 6 presents the conclusions of the study.

2. Theoretical background and hypotheses development

The China Securities Regulatory Commission (CSRC) issued the ‘Administrative Measures for the Major Asset Restructuring of Listed Companies’ (CSRC Order No. 53) in April 2008 to regulate the major asset restructuring of listed companies. According to CSRC Order No. 53, if valuation methods based on expected future earnings are used to purchase assets, the counterparty must sign a compensation contract. A clear and feasible method of compensation is provided in the contract for cases where actual performance does not meet the performance goals. In general, the target firm’s shareholders must compensate the listed company in cash or shares for the difference between actual and target performance. If share compensation is adopted, failure to meet performance commitments means that the shares of the listed company held by the target firm must be returned. As a result, the target firm reduces the number of shares, which has an impact on the shareholding status of the listed company. Even if the target firm’s future performance meets expectations, it will be unable to reap the benefits of the listed company.

The performance commitment agreement has gradually become an important contractual arrangement to promote M&A since the promulgation of CSRC Order No. 53. It has also played an important role in preventing malicious manipulation of valuation and pricing, ensuring transaction fairness, and protecting small and medium investors. Since its introduction, CSRC Order No. 53 has been revised several times to meet the capital market’s development needs. In addition to limiting performance commitment adjustments, regulatory agencies have gradually strengthened the supervision of performance commitment. If the counterparty fails to perform or violates its performance commitment, the CSRC may order corrections and take regulatory measures such as regulatory talks, issuing warning letters, and ordering public explanations, as well as include the situation in the integrity file.

2.1 Literature review

2.1.1 Performance commitment in M&A

The structure of performance commitment contracts is similar to that of earnout contracts, which specify deferred variable payments to the target. Prior research on earnouts shows that when there is greater information asymmetry about target firms, earnouts help hedge risk and lower acquisition costs (Kohers and Ang 2000; Srikant 2001; Roberto and Reuer 2009). Earnouts are designed to minimize the costs of valuation uncertainty and moral hazard in acquisition negotiations (Cain, Denis, and Denis 2011). According to Cadman, Carrizosa, and Faurel (2014), the economic determinants of earnout provisions include bridging valuation gaps and retaining target firm managers. In performance commitment contracts, acquirers pay the targets in full, and the targets pay back the pre-specified amount subject to conditions similar to those in earnout contracts. While Cadman, Carrizosa, and Faurel (2014) also argue that earnouts pose risks, such as management manipulating earnings to meet performance goals. After signing performance commitments, the target firms tend to adjust net profit during the commitment period to avoid paying any compensation or reduce the compensation amount, triggering earnings management (Wang and Fan 2017).

Previous studies have observed performance commitment to increase the risks of acquirers and exacerbate the risks of small and medium investors, which is mostly attributed to China’s poor investor protection. According to Zhai, Jiahui, and Zhen (2019), performance commitment raises asset valuations, but the target firm does not realize the expected future returns, posing a risk to small and medium investors. Zhang et al. (2019) demonstrate that failing to meet performance commitments has an impact on debt-paying ability, while meeting performance commitments creates potential financial risks. According to Dou and Zhai (2020), performance commitment causes small investors to suffer greater losses.

2.1.2 Trade credit

There are two mainstream views on trade credit: the alternative financing theory and the competition theory. According to the alternative financing theory, when firms are unable to obtain bank loans or credit rationing, they turn to suppliers for assistance with trade credit (Petersen and Rajan 1997; Nilsen 2002; Fisman and Love 2003; Burkart and Ellingsen 2004). Trade credit transfers bank credit from financially stronger firms to weaker firms, complementing the bank credit system (Inessa, Preve, and Sarria-Allende 2007). Firms that were hit hard during the global financial crisis turned to trade credit from suppliers as a supplement to external financing (Garcia-Appendini and Montoriol-Garriga 2013). Carbo‐valverde, Rodriguez‐fernandez, and Udell (2016) demonstrate that credit-constrained SMEs depend on trade credit rather than bank loans. Furthermore, the availability of supplier credit is crucial for facilitating access to institutional funding (Agostino and Trivieri 2014). Conversely, according to the competition theory, trade credit can be used as a competitive tool to retain customers (Fisman and Raturi 2004; Van Horen 2005; Giannetti, Burkart, and Ellingsen 2011). Hyndman and Serio (2010) discover an inversely U-shaped relationship between trade credit and supplier market competition. According to Daniela and Klapper (2016), suppliers with weak bargaining power with their customers are more likely to extend trade credit, have a higher proportion of goods sold on credit, and offer a longer payment period before imposing penalties. According to Chen (2017), the higher the customer concentration, the more competitive pressure firms face and the greater the need to provide trade credit. Biais and Gollier (1997) contend that firms without bank relationships rely more on trade credit, and sellers with greater ability to generate cash flows provide more trade credit. According to Zhengfei and Yang (2011), trade credit conforms to the competition theory when the monetary policy is loose and is primarily explained by the alternative financing theory when the monetary policy is tight.

Furthermore, trade credit conveys information about product quality (Lee and Stowe 1993). According to Long, Malitz, and Abraham Ravid (1993), trade credit, as an alternative means of establishing reputation, can provide product quality guarantees, enhance marketability, and serve to distinguish high-quality goods from low-quality goods (and producers). Ng, Kiholm Smith, and Smith (1999) contend that trade credit serves as a contractual solution to information problems related to product quality and buyer creditworthiness. According to Box et al. (2018), aggressive trade credit policies provide a unique channel for improving product market performance.

In summary, although previous studies have demonstrated the impact of performance commitments on M&A efficiency, market reaction, executive behavior, and the risk of small and medium investors, internal operational activities have not been examined. While previous studies have evidenced the importance of trade credit in a firm’s working capital management, the influence of performance commitments on trade credit policy remains unclear. Therefore, further theoretical and empirical research on the impact of performance commitments on trade credit policies is necessary.

2.2 Hypotheses development

2.2.1 Performance commitment and trade credit policy

Quality guarantee motivation. As discussed earlier, signing performance commitments converts asset sales from one-time to intertemporal transactions, thereby increasing the uncertainty regarding a firm’s future cashflows and resulting in unpassed transaction and intertemporal risks (Dou and Zhai 2020). Failure to meet performance commitments reduces operating performance and debt solvency; and success in meeting performance commitments may lead to management blindness and excessive optimism, increasing irrational debt financing, and inducing potential financial risks (Zhang et al. 2019). Firms seek to maintain a stable cooperative relationship with their customers by increasing the supply of trade credit to guarantee their product quality as well as signal a sound financial status (Lee and Stowe 1993; Long, Malitz, and Abraham Ravid 1993; Ng, Kiholm Smith, and Smith 1999). The more trade credit a firm provides, the more eager it is to build good relationships with customers (Wilson and Summers 2002). Moreover, even if the firm’s financial situation is temporarily precarious, it can maintain its current level of sales by expanding credit sales to signal an improved financial situation in the future (Petersen and Rajan 1997). Therefore, after signing performance commitments, firms increase their trade credit supply to send positive signals, which in turn maintains good customer cooperation and eventually meets performance commitments.

Sales expansion motivation. Performance commitment has an incentive effect, which motivates the target firm’s management to participate more diligently in the post-acquisition running-in process, reduce running-in costs and post-merger agency costs (Changjiang and Han 2014), and ultimately improve M&A performance. Given that signing performance commitments implies accepting compensation responsibility, management will fully mobilize subjective initiative, double down on efforts, and rationally allocate limited resources to relevant activities that can achieve performance goals (Pan, Qiu, and Yang 2017). According to competition theory, trade credit not only retains original customers and prevents customer loss, but also attracts customers from competitors and opens up new markets. Consequently, the firm’s operating performance improves, which in turn leads to the firm meeting its performance commitments. Considering the aforementioned arguments, we propose the following hypothesis:

H1:

Firms increase the supply of trade credit during the performance commitment period after signing performance commitments in M&A.

2.2.2 Performance commitment, profitability, and trade credit policy

Performance commitments are mostly manifested as the target firm’s future earnings, and if the agreed performance conditions are not achieved, the target firm pays a certain amount of cash or shares as compensation to the acquirer (see Appendix A). Firms with lower profitability must work harder to achieve their promised goals, and a sound customer relationship is especially important. As discussed earlier, firms seek to maintain sound customer relationships and increase sales using trade credit, thereby increasing the likelihood of meeting their performance commitment and avoiding compensation liability. Firms with high profitability face relatively less pressure to meet performance goals. Given that trade credit provision consumes daily working capital and increases operating costs, high-profitability firms prefer to meet performance commitment goals through normal operations rather than significantly increasing trade credit. Therefore, we propose the following hypothesis:

H2:

The relationship between performance commitment and trade credit is more pronounced for firms with lower profitability.

3. Data and methodology

3.1 Sample selection

Target firms in M&A are typically unlisted, and their data cannot be obtained directly from public databases. According to accounting standards, after the listed company completes an acquisition, the target company should be included in the consolidated financial statements, which will reflect the target firm’s accounting information. Thus, accounting data from acquiring firms can be used as a replacement variable for target firms. Following Liu, Sun, and Yuan (2018) and Liu, Tingting, and Jun (2021), we begin constructing our sample by using the China Securities Market and Accounting Research (CSMAR) database to identify all Chinese A-share listed companies that have signed performance commitments as the acquirer from 2008 to 2020. The following are then excluded from the sample: (1) debt restructuring, share repurchase, and asset divestiture M&A; (2) financial industry firms; (3) unsuccessful transactions; (4) special treated (ST, ST*), and others in an abnormal trading status. Finally, we have a total of 12,515 firm-year observations. Furthermore, we winsorize the continuous variables at the 1% and 99% levels to mitigate the effect of outliers.

3.2 Model specification

To investigate the impact of performance commitment on trade credit policy, we estimate the following model:

AR = β₀ + β₁ VAM + β₂ Controls+ind+year+ε

The dependent variable, AR, is the firm’s trade credit policy, calculated as the total trade credit provided divided by total assets: AR = (net notes receivable + net accounts receivable + net prepayments)/total assets. The primary independent variable, performance commitment (VAM), equals one for samples within the performance commitment period and zero otherwise.

The control variables (Controls) are potential factors that have been shown to affect trade credit in extant literature. We include ten firm-level variables, including the ownership type (SOE), leverage ratio (lev), firm size (lnsize), return on assets (ROA), cash reinvestment ratio (cash_inv), growth rate (growth), bank loans (bank), firm age (age), market share (MP), and sales expense ratio (sale). Our regressions also include industry and year fixed effects (ind and year), and we cluster the standard errors at the firm level. Table 1 presents the definitions of all the aforementioned variables.

Table 1. Variable definitions.

Variable	Definition	Measurement
AR	trade credit	(Net notes receivable + net accounts receivable + net prepayments)/total assets
LRL	operating profit rate	operating profit/operating revenue
VAM	performance commitment	value of the year during the performance commitment period is 1, otherwise 0
VAM_time	duration of performance commitment	natural logarithm of the number of days a performance commitment lasts
SOE	ownership type	State-owned enterprises take the value 1, otherwise 0
lev	leverage ratio	total liabilities/total assets
lnsize	firm size	natural logarithm of total assets
ROA	return on assets	EBIT/average assets
cash_inv	cash reinvestment ratio	net cash flows from operating activities/(net fixed assets + net held-to-maturity investments + net long-term debt investments + net long-term equity investments + total current assets – total current liabilities)
growth	growth rate	growth rate of operating revenue
bank	bank loans	(short-term loans + long-term loans)/total assets
age	firm age	natural logarithm of days since firm established
MP	market share	firm i‘s annual sales as a percentage of the industry’s total sales
sale	sales expense ratio	sales expenses/operating revenue
direct	independent director ratio	ratio of the number of independent directors to the total number of directors
hfd5	ownership concentration	sum of the squares of the shareholding ratios of the top five shareholders
year	year	year dummy variables
ind	industry	industry dummy variables

Note: This table provides variable definitions and measurements.

4. Results

4.1 Descriptive statistics

Table 2 presents the descriptive statistics for the main variables used in the empirical analyses. The sample maximum of trade credit policy (AR) is 0.55, with a standard deviation of 0.12, indicating that trade credit policy varies significantly. The sample mean of performance commitment (VAM) is 0.34, indicating that 34% of the sample is within the performance commitment period. The mean SOE is 0.31, indicating that 31% of the firms are state-owned enterprises. All other control variables are also within a reasonable range.

Table 2. Descriptive statistics.

Variables	N	Mean	SD	Min	Median	Max
AR	12515	0.19	0.12	0.00	0.17	0.55
LRL	12500	0.06	0.23	−1.28	0.08	0.51
VAM	12515	0.34	0.48	0.00	0.00	1.00
SOE	11749	0.31	0.46	0.00	0.00	1.00
lnsize	12515	8.02	1.16	5.40	7.92	11.06
cash_inv	12515	0.10	0.26	−0.95	0.09	1.28
lev	12515	0.42	0.21	0.05	0.41	0.97
bank	12515	0.14	0.13	0.00	0.10	0.54
ROA	11755	0.05	0.08	−0.31	0.05	0.27
MP	12515	0.02	0.05	0.00	0.00	0.31
age	12515	8.63	0.42	7.17	8.70	9.36
growth	11740	0.26	0.75	−0.62	0.12	5.83
sale	12500	0.07	0.08	0.00	0.04	0.44

Note: This table presents the descriptive statistics for the main variables used in this paper. The dependent variable is trade credit (AR), and the main independent variable is performance commitment in mergers and acquisitions (VAM). All other variables are reported in Table 1.

4.2 Correlation analysis

Table 3 presents the correlation matrix for the main variables, with the Pearson and Spearman correlation coefficients shown in the lower left and upper right corners, respectively. The Pearson and Spearman correlations between trade credit (AR) and performance commitment (VAM) are both positive and significant at the 1% level, indicating that the trade credit policy is more aggressive during the performance commitment period, which is consistent with H1. Conversely, the Pearson and Spearman correlations between trade credit (AR) and operating profit rate (LRL) are both negative and significant at the 1% level, indicating that the trade credit policy of low-profitability firms is more aggressive, consistent with our H2.

Table 3. Correlation matrix.

	AR	VAM	SOE	lnsize	cash_inv	lev	bank	ROA	MP	age	growth	sale	LRL
AR	1	0.08***	−0.14***	−0.09***	−0.19***	−0.01	−0.06***	0.03***	−0.07***	−0.15***	0.12***	0.05***	−0.11***
VAM	0.08***	1	−0.16***	0.21***	0.04***	−0.05***	−0.03***	0.10***	−0.01	0.14***	0.23***	0.06***	0.14***
SOE	−0.12***	−0.16***	1	0.26***	0.02**	0.31***	0.18***	−0.12***	0.22***	0.15***	−0.10***	−0.21***	−0.15***
lnsize	−0.07***	0.19***	0.28***	1	0.13***	0.40***	0.31***	0.05***	0.50***	0.28***	0.07***	−0.22***	0.05***
cash_inv	−0.15***	0.03***	0.02**	0.10***	1	0.05***	−0.01	0.26***	0.19***	0.07***	0.03***	0.00	0.14***
lev	0.02**	−0.06***	0.31***	0.37***	0.03***	1	0.69***	−0.20***	0.25***	0.18***	−0.04***	−0.30***	−0.40***
bank	−0.09***	−0.08***	0.20***	0.26***	−0.02**	0.68***	1	−0.18***	0.22***	0.07***	−0.03***	−0.26***	−0.32***
ROA	0.00	0.06***	−0.06***	0.08***	0.14***	−0.21***	−0.17***	1	0.16***	−0.06***	0.38***	0.03***	0.75***
MP	−0.06***	−0.01	0.08***	0.24***	0.11***	0.09***	0.07***	0.10***	1	0.04***	0.09***	−0.14***	−0.03***
age	−0.12***	0.16***	0.16***	0.26***	0.05***	0.19***	0.07***	−0.06***	−0.00	1	−0.10***	−0.08***	−0.02**
growth	0.03***	0.19***	−0.03***	0.07***	0.02**	0.04***	−0.01	0.24***	0.05***	0.02	1	−0.02**	0.29***
sale	−0.04***	0.03***	−0.14***	−0.16***	0.03***	−0.23***	−0.21***	−0.00	−0.04***	−0.02**	−0.03***	1	0.10***
LRL	−0.04***	0.08***	−0.04***	0.12***	0.09***	−0.30***	−0.20***	0.74***	0.05***	−0.05***	0.15***	−0.03***	1

Note: This table presents the Pearson and Spearman correlation coefficients of the main variables in the lower left and upper right corners, respectively. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

4.3 Regression analysis

Table 4 presents the estimates for H1 and H2. In column (1), when other factors are not controlled, the VAM coefficient is significantly positive at the 5% level. In column (2), the VAM coefficient remains significantly positive at the 5% level after including the firm-level control variables. These findings support H1 that performance commitment leads to a significant increase in trade credit supply. Among the control variables, the coefficients of firm size (lnsize), cash reinvestment ratio (cash_inv), bank loans (bank), firm age (age), and sales expense ratio (sale) are significantly negative, whereas the coefficients of leverage ratio (lev) and return on assets (ROA) are significantly positive, consistent with the findings of previous studies.

Table 4. Determinants of trade credit policy.

	(1)	(2)	(3)
Variable	AR	AR	AR
VAM	0.006**	0.008**	0.012***
	(2.18)	(2.39)	(3.53)
VAM*LRL			−0.059***
			(−4.20)
LRL			0.013
			(0.93)
SOE		−0.006	−0.006
		(−0.98)	(−0.92)
lnsize		−0.010***	−0.011***
		(−3.34)	(−3.42)
cash_inv		−0.069***	−0.069***
		(−9.79)	(−9.84)
lev		0.183***	0.184***
		(8.83)	(8.87)
bank		−0.173***	−0.172***
		(−6.08)	(−6.08)
ROA		0.113***	0.135***
		(4.51)	(3.31)
MP		0.030	0.026
		(0.46)	(0.40)
age		−0.043***	−0.043***
		(−5.64)	(−5.62)
growth		0.002	0.002
		(1.31)	(1.32)
sale		−0.115***	−0.114***
		(−3.48)	(−3.44)
constant	0.055***	0.462***	0.461***
	(4.96)	(6.93)	(6.92)
Year FE	Y	Y	Y
Industry FE	Y	Y	Y
R^2	0.128	0.210	0.212
Adj. R^2	0.126	0.207	0.209
N	12515	10965	10965

Note: This table presents the estimates for H1 and H2. The estimated results for H1 are shown in columns (1) and (2). The dependent variable is trade credit (AR), and the independent variable is performance commitment in mergers and acquisitions (VAM). The estimated result for H2 is shown in column (3). The dependent variable is trade credit (AR). The key independent variable is the interaction between operating profit rate (LRL) and performance commitment (VAM). T-values are presented in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

To examine the impact of profitability on the relationship between performance commitment and trade credit (H2), we include firm profitability (LRL) as well as the interaction between profitability and performance commitment in the analysis. As shown in column (3) of Table 4, the interaction coefficient between profitability and performance commitment is negative and significant at the 1% level. The control variable coefficients are similar to those in column (2). These results support H2, indicating that the positive relationship between performance commitment and trade credit is stronger for firms with lower profitability.

5. Further analyses and robustness tests

Based on our hypotheses tests, which reveal that the supply of trade credit increases significantly during the performance commitment period, we further examine the cross-sectional factors and economic consequences of an aggressive trade credit policy.

5.1 Cross-sectional analyses

5.1.1 Marketization level

Lower marketization reduces the firm’s bargaining power with major customers and raises the costs of selecting alternative trading partners, further reducing the firm’s bargaining power. As a result, firms must offer trade credit to retain high-quality customers and optimize their relationship with such customers, thereby expanding their market scale. Therefore, the positive impact of performance commitment on trade credit is expected to be more pronounced for firms located in less marketized regions.

To measure marketization, we use Wang et al.’s (2019) marketization index, where a higher value indicates a higher level of marketization. Since the index for 2017–2020 is unavailable, the 2016 index is used instead. MK is a dummy variable that equals one if the marketization index of a firm’s location is above the 70th percentile of all sample locations and zero otherwise. Furthermore, to examine the impact of marketization, we include marketization level (MK) as well as the interaction between MK and performance commitment (VAM) in the regression. Column (1) of Table 5 presents these findings. The coefficient of the interaction between marketization level and performance commitment is negative and significant at the 5% level. All control variable coefficients are similar to those reported in Table 4. This suggests that the positive relationship between performance commitment and trade credit is stronger for firms located in low-marketization regions, which is consistent with our expectations. In other words, given the fewer market participants in low-marketization regions, a firm’s bargaining power with major customers is relatively weak. Therefore, to increase sales and meet its performance commitments, it is necessary for a firm to increase its trade credit supply.

Table 5. Cross-sectional analyses.

	(1)	(2)	(3)	(4)	(5)	(6)
Variable	AR	AR	AR	AR	AR	AR
VAM	0.011***	0.000	0.003	0.066***	0.005	0.009***
	(3.10)	(0.01)	(0.89)	(4.32)	(1.23)	(2.74)
VAM*MK	−0.017**
	(−2.39)
MK	−0.006
	(−0.99)
VAM*HHI		0.011*
		(1.78)
HHI		0.025***
		(4.27)
VAM*risk			0.013***
			(2.63)
risk			−0.002
			(−0.61)
VAM*IC				−0.009***
				(−3.81)
IC				0.013***
				(8.39)
VAM*BIG4						−0.029*
						(−1.93)
BIG4						0.017
						(0.86)
VAM*SOE					0.013*
					(1.83)
SOE	−0.007	−0.008	−0.006	−0.005	−0.010	−0.007
	(−1.10)	(−1.18)	(−0.95)	(−0.79)	(−1.49)	(−1.00)
lnsize	−0.010***	−0.011***	−0.010***	−0.012***	−0.010***	−0.011***
	(−3.27)	(−3.42)	(−3.23)	(−3.84)	(−3.33)	(−3.43)
cash_inv	−0.068***	−0.067***	−0.068***	−0.072***	−0.068***	−0.068***
	(−9.79)	(−9.62)	(−9.77)	(−9.76)	(−9.79)	(−9.75)
lev	0.182***	0.182***	0.183***	0.201***	0.183***	0.181***
	(8.79)	(8.87)	(8.84)	(9.71)	(8.85)	(8.78)
bank	−0.171***	−0.170***	−0.173***	−0.185***	−0.173***	−0.168***
	(−6.05)	(−6.01)	(−6.10)	(−6.45)	(−6.07)	(−5.97)
ROA	0.114***	0.107***	0.115***	0.088***	0.112***	0.115***
	(4.55)	(4.31)	(4.59)	(3.03)	(4.48)	(4.62)
MP	0.031	0.103	0.029	0.013	0.028	0.022
	(0.47)	(1.60)	(0.45)	(0.18)	(0.44)	(0.35)
age	−0.043***	−0.041***	−0.043***	−0.041***	−0.043***	−0.043***
	(−5.67)	(−5.46)	(−5.65)	(−5.28)	(−5.62)	(−5.61)
growth	0.002	0.002	0.002	0.003	0.002	0.002
	(1.20)	(1.21)	(1.21)	(1.42)	(1.31)	(0.88)
sale	−0.119***	−0.125***	−0.114***	−0.109***	−0.115***	−0.115***
	(−3.56)	(−3.87)	(−3.45)	(−2.99)	(−3.48)	(−3.46)
constant	0.463***	0.440***	0.461***	0.377***	0.463***	0.464***
	(6.95)	(6.66)	(6.90)	(5.36)	(6.94)	(6.96)
Year FE	Y	Y	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y	Y	Y
R^2	0.212	0.220	0.211	0.228	0.211	0.210
Adj. R^2	0.209	0.217	0.208	0.224	0.208	0.207
N	10965	10965	10965	9138	10965	10916

Note: This table presents the regression results on the effects of cross-sectional factors on trade credit (AR). In columns (1)-(6), the cross-sectional factors are marketization level (MK), market competition (HHI), enterprise risk (risk), internal governance (IC), ownership type (SOE) and audit quality (BIG4), respectively. T-values are presented in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

5.1.2 Market competition

Given the market structure of upstream and downstream firms, the more competitive the market is, the more firms tend to provide trade credit to attract and consolidate customers. When a firm signs performance commitment contracts, its future is uncertain, and customers may turn to competitors for business stability. We predict that firms in a competitive market will provide more trade credit to attract more customers than their competitors and meet their performance commitments.

To measure market competition, we use the Herfindahl-Hirschman Index (HHI) of operating revenue in the same industry; the lower the index, the greater the competition. HHI is a dummy variable that equals one if a firm’s industry Herfindahl-Hirschman index is lower than the 65th percentile of all samples and zero otherwise. Furthermore, we include market competition (HHI) as well as the interaction between HHI and performance commitment (VAM) in the regression, the results of which are presented in column (2) of Table 5. The interaction coefficient between HHI and VAM is positive and significant at the 10% level, confirming our aforementioned prediction. In other words, as performance commitments increase uncertainty and operating risk, firms increase their trade credit supply significantly to gain customer support in a competitive market.

5.1.3 Enterprise risk

Performance commitments result in a series of unpassed risks (Dou and Zhai 2020). Regardless of whether the performance commitments are met, they increase the potential financial risk (Zhang et al. 2019). However, increasing the supply of trade credit can guarantee that their product quality and financial status are both satisfactory (Long, Malitz, and Abraham Ravid 1993; Ng, Kiholm Smith, and Smith 1999). Therefore, we posit that after signing performance commitments, high-risk firms have a stronger incentive to provide trade credit in order to maintain a positive customer perception.

Following Zhang and Huang (2009), we use stock return volatility as a proxy for firm risk, as measured by the annual standard deviation of weekly stock returns. Risk is a dummy variable that equals one if the firm’s risk is above the 65th percentile of all sample firms and zero otherwise. Firm risk (risk) and the interaction between risk and performance commitment (VAM) are included to test the impact of firm risk on the relationship between performance commitment and trade credit. Column (3) of Table 5 summarizes the regression results. The interaction coefficient between risk and VAM is positive and significant at the 1% level, supporting our contention that high-risk firms have a stronger incentive to provide trade credit.

5.1.4 Internal governance

High-quality internal control can effectively mitigate information asymmetry and interest conflicts in the M&A process, reduce decision bias, improve M&A efficiency, and eventually reduce performance commitment risks through check-and-balance decision-making mechanisms, strict control and supervision activities, and unimpeded communication channels. Furthermore, high-quality internal control improves the reliability of a firm’s accounting information and the trust of trading partners (Zheng, Lin, and Lin 2013). Therefore, we expect the impact of performance commitments on trade credit to be mitigated for firms with better internal control.

The DIB-China Listed Companies Internal Control Index is used to assess internal control quality; a higher value indicates better internal control. Internal control (IC) and the interaction between IC and performance commitment (VAM) are included in the regression to test our aforementioned expectation. The regression results presented in column (4) of Table 5 reveal that the estimated interaction coefficient between IC and VAM is significantly positive at the 10% level, which is in accordance with our anticipation. In other words, high-quality internal control not only ensures more scientific and prudent M&A decisions, but also increases mutual trust with customers, resulting in a weakened positive association between performance commitment and trade credit.

5.1.5 Ownership type

Given that the government has the final decision-making power over the appointment and dismissal of SOE heads, M&A of state-owned enterprises is significantly influenced by the government’s political goals or policies (Pan and Minggui 2014). SOEs also have stronger incentives to increase trade credit supply to consolidate and attract customers, ensuring performance commitments are met and M&A efficiency is improved. Furthermore, SOEs have stronger financing capabilities and can increase trade credit supply in a timely manner based on business needs. Therefore, we posit that SOEs increase the supply of trade credit significantly after signing performance commitments.

To examine the impact of ownership type on the relationship between performance commitment and trade credit, we include SOE, an indicator variable for SOEs, and the interaction between SOE and VAM in the regression. According to the results presented in column (5) of Table 5, the estimated interaction coefficient between SOE and VAM is significantly positive at the 10% level, which supports the aforementioned conjecture. This demonstrates that, following the signing of performance commitments, SOEs provide more trade credit to expand sales, improve operating performance, and enhance M&A performance to meet the evaluation requirements of higher-level regulatory agencies.

5.1.6 Audit quality

Asset sales become intertemporal transactions after signing performance commitments, increasing the uncertainty of future cash flows (Dou and Zhai 2020) and information asymmetry with customers. According to Wang and Fan (2017), performance commitments improve a firm’s earnings management. A high-quality audit can decrease a firm’s earnings management (Gaver and Paterson 2001), reduce information asymmetry, and increase customer trust, all of which can influence trade credit. We believe that the relationship between performance commitments and trade credit is stronger for firms with lower audit quality.

Following Francis and Wang (2008), we use BIG4 as a proxy for audit quality, which equals one for firms audited by the international big-four audit firms and zero otherwise. To test the aforementioned contention, we include audit quality (BIG4), and the interaction between BIG4 and performance commitment (VAM) in the regression. According to the regression results presented in column (6) of Table 5, the estimated interaction coefficient between BIG4 and VAM is significantly negative at the 10% level, which is consistent with our expectation. In other words, firms with high-quality audits have a weaker positive relationship between performance commitment and trade credit.

5.2 Economic consequences

5.2.1 Losses from bad debts

Aggressive trade credit policies are frequently accompanied by malicious defaults, resulting in financial losses. Malicious delinquencies and defaults eventually convert a large number of late payments into bad debt losses (Liu 2012), increasing operating risk and resource allocation costs. Therefore, we normalize bad debt losses by total assets to test the economic consequences of an aggressive trade credit supply, the results of which are presented in column (1) of Table 6. After controlling for firm-specific variables, the VAM coefficient is significantly positive at the 1% level, indicating that bad debt losses increase significantly during the performance commitment period. Therefore, to avoid such a financial risk, aggressive trade credit policies should be accompanied by an optimized collection policy, improving the firm’s debt collection ability, and preventing malicious defaults.

Table 6. Consequences of increasing trade credit supply.

	(1)	(2)
Variable	LOSS	DA
VAM	0.001***	0.023***
	(3.28)	(4.19)
SOE	−0.001***	−0.012***
	(−3.56)	(−2.61)
lnsize	−0.001***	−0.009***
	(−5.49)	(−4.01)
cash_inv	−0.002***
	(−6.11)
lev	0.005***	0.072***
	(6.94)	(6.00)
bank	−0.004***
	(−4.31)
ROA	−0.016***	0.002
	(−9.48)	(0.04)
MP	−0.001	−0.099***
	(−0.33)	(−3.07)
age	−0.001***	0.011**
	(−3.09)	(2.19)
growth	0.001
	(0.35)
sale	0.003**
	(2.41)
direct		0.057
		(1.43)
hfd5		−0.062***
		(−4.46)
constant	0.013***	0.019
	(6.34)	(0.40)
Year FE	Y	Y
Industry FE	Y	Y
R^2	0.190	0.032
Adj. R^2	0.187	0.028
N	10965	8222

Note: This table presents the estimates for the economic consequences of an aggressive trade credit policy. In columns (1) and (2), the dependent variables are bad debt losses (LOSS) and earnings management (DA), respectively. The independent variable is performance commitment in mergers and acquisitions (VAM). T-values are presented in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

5.2.2 Earnings management

Firms tend to adjust net profits during the commitment period to avoid compensation obligations or reduce compensation amounts, triggering earnings management (Wang and Fan 2017). Furthermore, management resorts to earnings management to influence suppliers’ and customers’ perceptions of the firm’s reputation for meeting implicit claims (Bowen, Rajgopal, and Venkatachalam 2008). Therefore, when a firm’s management increases trade credit under pressure to meet performance commitments, they also tend to manage their earnings. We estimate the level of discretionary accruals with the modified Jones (1991) model proposed by Dechow, Sloan, and Sweeney (1995), and further proxy earnings management by the absolute value of discretionary accruals (DA). Column (2) of Table 6 reveals that the estimated VAM coefficient is significantly positive at the 1% level, supporting the aforementioned conjecture. This demonstrates that firms engage in more earnings management after signing performance commitments.

5.3 Robustness tests

5.3.1 PSM test

We use the propensity score matching method (PSM) to reduce sample selection bias. Furthermore, we use the nearest neighbor matching method to match control groups for firms, that sign performance commitments, at a ratio of 1:1 based on the ownership type, firm size, cash reinvestment ratio, leverage ratio, bank loans, return on assets, market share, firm age, growth rate, and sales expense ratio. Finally, we obtain a total of 7,254 (3627 pairs) matching samples.

Table 7 presents the PSM balance test results. After matching, the difference between treated and control groups has been reduced for all matched variables, and the absolute value of the standard deviation is no greater than 5%. The t statistic is not significant based on the mean T test results, indicating that the difference between the sample groups is not significant after matching. The matched samples conform to randomness to some extent, which improves the reliability of the empirical results. Column (1) of Table 8 presents the regression results of PSM samples. The performance commitment coefficient (VAM) is significantly positive, and our primary findings remain robust.

Table 7. PSM balance test.

		Mean		Reduct (%)		t-test
Variable	Unmatched Matched	Treated	Control	Bias (%)	|bias|	t	p > |t|
SOE	U	0.228	0.449	−48.1	96.6	−26.57	0.000
	M	0.228	0.220	1.6		0.81	0.420
lnsize	U	8.370	8.305	5.4	62.0	2.90	0.004
	M	8.370	8.395	−2.1		−0.91	0.360
cash_inv	U	0.113	0.115	−0.7	−82.8	−0.42	0.673
	M	0.113	0.116	−1.3		−0.60	0.546
lev	U	0.409	0.446	−18.3	97.4	−10.19	0.000
	M	0.409	0.408	0.5		0.22	0.829
bank	U	0.126	0.151	−19.4	94.8	−10.50	0.000
	M	0.126	0.128	−1.0		−0.48	0.631
ROA	U	0.061	0.059	3.1	84.2	1.88	0.060
	M	0.061	0.061	0.5		0.22	0.828
MP	U	0.020	0.027	−13.5	98.4	−7.26	0.000
	M	0.020	0.020	−0.2		−0.12	0.906
age	U	8.725	8.672	14.7	66.7	8.16	0.000
	M	8.725	8.742	−4.9		−2.27	0.023
growth	U	0.392	0.152	43.0	88.6	30.68	0.000
	M	0.392	0.364	4.9		1.73	0.083
sale	U	0.074	0.070	4.8	90.8	2.72	0.007
	M	0.074	0.074	−0.4		−0.19	0.850

Note: This table presents the PSM balance test results. All variables are reported in Table 1.

Table 8. Robustness tests.

	(1)	(2)	(3)	(4)
Variable	AR	AR	AR_	AR
VAM	0.024***	0.010**	0.015***
	(6.19)	(2.55)	(5.23)
VAM_time				0.001**
				(2.36)
SOE	−0.006	−0.006	−0.012**	−0.006
	(−1.16)	(−0.91)	(−2.22)	(−0.98)
lnsize	−0.020***	−0.008**	−0.013***	−0.010***
	(−8.66)	(−2.55)	(−4.98)	(−3.34)
cash_inv	−0.091***	−0.071***	−0.051***	−0.069***
	(−12.14)	(−9.36)	(−8.93)	(−9.79)
lev	0.237***	0.183***	0.139***	0.183***
	(12.87)	(8.38)	(8.40)	(8.83)
bank	−0.169***	−0.182***	−0.127***	−0.173***
	(−6.64)	(−6.21)	(−5.39)	(−6.08)
ROA	0.129***	0.154***	0.077***	0.113***
	(4.70)	(5.37)	(3.62)	(4.51)
MP	0.026	0.028	0.031	0.030
	(0.52)	(0.41)	(0.57)	(0.46)
age	−0.025***	−0.044***	−0.037***	−0.043***
	(−3.61)	(−5.70)	(−5.71)	(−5.64)
growth	0.000	0.002	−0.000	0.002
	(0.18)	(0.94)	(−0.20)	(1.31)
sale	−0.104***	−0.113***	−0.066**	−0.115***
	(−4.43)	(−3.12)	(−2.57)	(−3.48)
constant	0.372***	0.459***	0.407***	0.462***
	(6.08)	(6.77)	(7.17)	(6.93)
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
R^2	0.257	0.212	0.236	0.210
Adj. R^2	0.253	0.209	0.233	0.207
N	7254	9300	10965	10965

Note: This table presents the robustness tests. Column (1) presents the regression results of PSM samples. Column (2) presents the regression results for the sample excluding the 2019 and 2020 samples. Columns (3) and (4) present alternative measures of trade credit (AR_) and performance commitment (VAM_time). T-values are presented in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

5.3.2 Sample subinterval test

In financial statements beginning in 2019, a new account named ‘receivables financing’ represents receivables sold to banks or other financial institutions with recourse. Therefore, the calculation of trade credit must be adjusted. Column (2) of Table 8 presents the regression results for the samples excluding 2019 and 2020. The performance commitment coefficient (VAM) remains significantly positive, which is consistent with previous findings.

5.3.3 Alternative measures of trade credit and performance commitment

To further test the robustness of our findings, we recalculate trade credit using the net accounts receivable to total assets ratio (AR_). Column (3) of Table 8 presents the regression results, where the performance commitment coefficient (VAM) is significantly positive at the 1% level. Furthermore, we use the natural logarithm of the number of days performance commitments last to calculate the length of performance commitment (VAM_time) and use this variable instead of VAM in the regression. Column (4) of Table 8 presents the regression results, where the coefficient of VAM_time is significantly positive, thereby validating the robustness of our primary findings.

5.4 Exclusionary explanation

5.4.1 Short-term solvency

According to the operating motivation, firms provide trade credit as an assurance for product quality and build good customer relationships to promote sales growth (Fisman and Raturi 2004). To investigate whether operating motivation is influenced by short-term solvency, we use the current ratio as a proxy for short-term solvency and divide the sample into two groups based on the annual-industry median of current ratio. Columns (1) and (2) of Table 9 present the empirical results for high and low current ratio subgroups, respectively. According to the chi-square test, the coefficients of both groups are significantly positive and have no significant difference. Therefore, we can conclude that short-term solvency has no impact on the trade credit policy during the performance commitment period.

Table 9. Exclusionary explanation.

	(1)	(2)	(3)	(4)
	high current ratio	low current ratio	high financing constraint	low financing constraint
Variable	AR	AR	AR	AR
VAM	0.007**	0.009***	0.011***	0.007*
	(2.11)	(2.59)	(2.96)	(1.78)
SOE	−0.003	−0.007**	−0.009***	−0.001
	(−0.92)	(−2.10)	(−2.63)	(−0.19)
lnsize	−0.020***	−0.004***	−0.006***	−0.015***
	(−10.57)	(−2.74)	(−2.99)	(−8.49)
cash_inv	−0.165***	−0.040***	−0.052***	−0.089***
	(−16.26)	(−8.86)	(−9.54)	(−14.39)
lev	0.371***	0.167***	0.177***	0.188***
	(25.89)	(14.70)	(16.20)	(16.42)
bank	−0.191***	−0.153***	−0.203***	−0.134***
	(−8.83)	(−11.19)	(−12.98)	(−7.85)
ROA	0.158***	0.073***	0.093***	0.124***
	(6.99)	(3.47)	(4.18)	(5.73)
MP	0.105**	−0.036	0.070*	−0.019
	(2.53)	(−1.01)	(1.80)	(−0.49)
age	−0.035***	−0.046***	−0.026***	−0.039***
	(−8.46)	(−9.78)	(−3.24)	(−6.99)
growth	0.000	0.002	0.005***	−0.003
	(0.12)	(0.81)	(2.73)	(−1.04)
sale	−0.062***	−0.186***	−0.068***	−0.183***
	(−3.63)	(−8.52)	(−3.68)	(−8.71)
constant	0.413***	0.438***	0.292***	0.446***
	(10.49)	(10.21)	(4.08)	(8.78)
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
R^2	0.293	0.220	0.186	0.244
Adj. R^2	0.287	0.215	0.181	0.238
N	5224	5741	5640	5325
chi-sq test p-value	0.728		0.519

Note: This table presents the regression results on the effects of the current ratio and financing constraints on trade credit (AR). T-values are presented in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

5.4.2 Financing constraints

Performance commitments can result in intertemporal (Dou and Zhai 2020) and financial risks (Zhang et al. 2019). To test whether financing constraints have an impact on the trade credit policy, we follow Hadlock and Pierce (2010) and construct the SA index¹; a higher SA index indicating severe financing constraints. We divide the sample into two groups based on the annual-industry median of SA index. Columns (3) and (4) in Table 9 present the empirical results for subgroups with strong or weak financing constraints, respectively. According to the chi-square test, the VAM coefficients in both groups are significantly positive and have no significant difference. In other words, financial constraints have no impact on the supply of trade credit during the performance commitment period.

6. Conclusions

This study, being the first to explore the impact of signing performance commitments on trade credit policies, makes a significant contribution to the extant literature. Using M&As with performance commitment contracts for the period 2008–2020, we find that performance commitments lead to significant increases in trade credit supply, and companies with low profitability extend substantially more trade credit. Overall, our results support the incentive effect of performance commitments in mergers and acquisitions.

Although a firm’s management may use trade credit to maintain customer relationships and attract new customers to expand sales after signing a performance commitment, the aggressive trade credit policy should be complemented by strong trade credit management and an optimized collection policy to avoid additional financial risks arising from bad debt losses. Furthermore, strengthening the supervision and penalties for unfulfilled commitments without compensation will increase the incentive effect of performance commitments, which is beneficial to capital market and economic development.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study is supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (grant number 20XND011)

Notes

1. The SA index is calculated as −0.737 × lnsize+0.043 × lnsize ^ 2–0.040 × age.

Appendix A

Table

	Acquirer	Target	Transaction Year	Transaction Size (in million RMB)	PerformanceCommitment	Compensation mode
1	Deluxe Family Co., Ltd	Beijing Graphene Holding Group Co. LTD	2015	750	Total net profit is no less than 7.41 million RMB from 2015 to 2017	Cash; the chairman and secretary were criticized in a circulated notice.
2	Jiuzhitang Co., Ltd	YouBo Pharmaceutical Co., Ltd	2015	6,517.81	After deducting non-recurring gains and losses, net profit attributable to owners of parent company is no less than 463, 518 and 588 million RMB for the 2015, 2016 and 2017, respectively	Fulfillment (by 101.29%, 100.69%,101.66%, respectively)
3	Pengxin International Mining Co., Ltd,	Ningbo Tianhong Yihua Trading Co., LTD	2018	1,908.82	After deducting non-recurring gains and losses, net profit attributable to owners of parent company is no less than 1943.86 million RMB from 2018 to 2024.	Stock and Cash
4	Shenzhen GuoHua Network Security Technology Co., Ltd	Beijing ZhiYou Network Security Technology Co., Ltd	2019	1,281	After deducting non-recurring gains and losses, net profit attributable to owners of parent company is no less than 90, 117 and 152.1 million RMB for the first, second and third years following the M&A, respectively.	Stock
5	Beijing SPC Environment Protection Tech Co., Ltd.	Shenzhen ShenShui water Resources Consulting Co. LTD	2021	440	Total net profit is no less than 240 million RMB from 2021 to 2023	Fulfillment