4.5.2 Impact of financial constraints on the nonlinear relationship between trade credit and firm profitability...36 4.6.. 20 Table 3: Descriptive Statistics of Variables...28 Table 4: C
INTRODUCTION
Reasons for conducting the thesis topic
Commercial credit is essential for the survival of enterprises, serving as a necessary short-term funding source across various sectors It allows borrowers to defer payments when purchasing goods and services, thereby alleviating short-term financial stress This financial tool is particularly beneficial for growing companies that can negotiate favorable terms with suppliers Additionally, commercial credit provides access to extra working capital when traditional financial institutional credit is limited or unavailable.
2022) Because of the frequent business dealings between organizations, commercial credit is a type of short-term loan (Kehinde, 2022) According to Al-Hadi and Al-Abri
In 2022, most companies utilized trade credit, borrowing from suppliers and lending to consumers, making commercial credit the primary source of short-term external financing This spontaneous funding option is less formal than bank loans and can serve as a strategic tool to enhance sales revenue By embracing higher risks associated with commercial credit, businesses can potentially achieve greater profitability and a stronger competitive advantage.
Numerous studies have explored the relationship between a business's operational efficiency, profitability, and commercial credit (Kestens et al., 2012; Abuhommous, 2017; Dary & James, 2019; Thao & Ha, 2020), highlighting its critical role as a credit channel A key inquiry in existing literature is whether commercial credit enhances or detracts from a company's operating performance While some research suggests a positive impact of commercial credit on profits, empirical findings vary significantly due to differing methodologies Additionally, substantial investments in commercial credit can introduce high risks and costs, potentially reducing earnings Consequently, the nature of the relationship between commercial credit and business performance remains ambiguous This study aims to examine the connection between commercial credit and business performance specifically within the Vietnamese market to address this gap.
Research objectives and questions
This research aims to analyze the connection between commercial credit and the performance of non-financial companies listed on the Ho Chi Minh City Stock Exchange (HOSE) during the period from 2017 to 2022.
To clarify the above objective, the research raises the following questions:
(1) What is the relationship between commercial credit and the performance of companies listed on the Ho Chi Minh City Stock Exchange?
(2) Is there a nonlinear correlation between the success of the Ho Chi Minh City Stock Exchange-listed enterprises and commercial credit?
Research object
The relationship between commercial credit and the performance of companies listed on the Ho Chi Minh Stock Exchange.
Research scope
The data gathered focuses on companies listed on the Ho Chi Minh City Stock Exchange, specifically excluding those in the finance, banking, insurance, and investment fund sectors, alongside Vietnam's economic growth rate.
Research methodology
This research analyzes panel data from 172 companies over six years (2017-2022) to explore the relationship between business performance and commercial credit It employs panel data regression techniques, including Pooled Ordinary Least Squares, Fixed Effects, and Random Effects models, to identify the optimal method for analysis Additionally, the study utilizes Feasible Generalized Least Squares and Generalized Method of Moments to estimate model coefficients while addressing issues of autocorrelation, heteroscedasticity, and endogeneity To enhance the reliability of the findings, a Robustness Test is also conducted.
Research results
The study reveals that commercial financing significantly enhances the performance of businesses listed on the HCM City Stock Exchange Additionally, it identifies an inverted U-shaped relationship between business performance and commercial credit, indicating that the effects of financing vary at different levels.
Research structure
The structure of the research is as follows:
Chapter 1: General introduction of the topic including research rationale, research objectives, research objects, research methods, research data, summary of the meaning of the topic Chapter 2: Present the main theoretical frameworks and results from previous studies Chapter 3: Introduce research hypotheses, collected data and research methods, analyze collected data Chapter 4: Present detailed results and analysis of empirical results, compare with expected assumptions and results from previous studies Chapter 5: Summarize the main research results, draw conclusions and point out limitations and applications of the topic.
THEORETICAL BASIS AND RESEARCH MODEL
Firm performance is a critical measure of a company's success in meeting its financial and strategic goals, encompassing key metrics such as profitability, revenue growth, market share, return on investment, cost management, and shareholder value It serves as an indicator of a business's overall financial health and operational efficiency, showcasing how effectively a company creates value and drives growth by optimizing resources and maximizing outcomes.
Trade credit can significantly enhance a firm's performance by providing a competitive edge over rivals with weaker financial positions By offering trade credit financing, firms can engage in more aggressive sales strategies aimed at increasing market share This approach fosters stronger relationships with customers who rely on such financing, leading to increased loyalty and a lower likelihood of switching suppliers Ultimately, strategically leveraging trade credit yields various performance benefits, including improved competitive positioning, demand generation, operational efficiency, effective risk management, and enhanced external credibility, all of which contribute to the firm's growth and financial health.
The concept of financial constraints lacks a unified definition, as it cannot be directly observed and its expression remains ambiguous Various studies have attempted to define financial constraints, highlighting the complexities involved in understanding this phenomenon.
Research by Fazzari et al (1988) highlights that external capital cannot fully substitute internal capital, as noted by Myers (1984), leading to significant differences in their costs Fazzari posits that the cost of investment is influenced by the availability of internal capital, indicating that firms face financial constraints when there is a substantial gap between internal and external capital costs.
Financially constrained firms are unable to access internal capital, forcing them to rely on external funding, which results in higher capital costs Conversely, unconstrained firms can leverage various funding sources at lower costs.
Financial constraints, as defined by Booth and colleagues (2015), occur when a company cannot secure the necessary capital at a reasonable cost, limiting its ability to invest in projects and achieve growth.
In conclusion, a consensus exists that financial constraints arise when companies face challenges in accessing external financing due to high capital costs As a result, these financially constrained firms must increasingly depend on internal funds, utilizing their cash reserves to support investment decisions.
2.2.1 Financing advantage theories of trade credit
The concept of trade credit, first introduced by Le Goff in 1957, has roots dating back to the Middle Ages, where sellers allowed buyers to receive goods without immediate payment This practice has evolved significantly over time, continuing to attract scholarly attention Ferris (1981) describes trade credit as a unique form of short-term loan, intricately linked to the exchange of goods In this arrangement, sellers provide credit to buyers, enabling them to postpone payment until the loan term concludes.
Trade credit occurs when buyers receive goods or services and are allowed to defer payment to a later date For sellers, offering trade credit is an investment in accounts receivable, anticipating future recovery of funds Conversely, buyers benefit from trade credit as it serves as a crucial short-term financing option, which is reflected as debt on their balance sheets.
Trade credit is an agreement that allows buyers and sellers to postpone payment for a specified period after a transaction, serving as essential short-term funding that enhances working capital flexibility It commonly appears in forms such as cash on delivery terms, payment schedules, and installment plans for purchased goods By offering flexibility in payment timing, trade credit promotes commerce, improves cash flow lending, and strengthens financial capacity.
Suppliers have a unique ability to monitor and enforce trade credit repayment effectively, giving them an edge over traditional lenders in assessing clients' creditworthiness This advantage allows suppliers to offer buyers credit at a lower cost compared to banks, stemming from multiple sources of cost efficiency.
Information Advantage Theory posits that suppliers possess superior access to information regarding the creditworthiness and financial stability of their customers compared to financial institutions Due to established business relationships, suppliers can closely monitor buyers' payment behaviors and patterns over time This information advantage allows suppliers to make better-informed decisions about trade credit, including the determination of suitable credit limits and payment terms Additionally, when buyers fail to utilize early payment discounts, it may signal to suppliers a decline in the buyers' creditworthiness While financial institutions can also gather similar information, suppliers typically acquire it more efficiently and at a lower cost.
In a less competitive market, buyers rely heavily on suppliers due to the limited availability of substitutes, which allows suppliers to exert control by threatening to halt goods provision if buyers take steps that could jeopardize repayment This threat is particularly pronounced when a buyer contributes only a small fraction of a supplier's revenue Conversely, financial institutions often have less influence, as a borrower's operations may not be as directly affected by potential funding withdrawal Additionally, bankruptcy regulations may restrict a financial institution's ability to withdraw funds effectively.
Suppliers have a significant advantage in controlling buyers by using their goods as collateral against trade credit defaults Durable supplies that maintain long-term value enable suppliers to offer higher credit limits with reduced risk Unlike financial institutions, suppliers face lower costs when reclaiming and redistributing inventory if buyers fail to pay Their familiarity with products and established distribution channels allow for efficient resale, minimizing losses Additionally, the less altered the goods are by buyers before repossession, the easier it is for suppliers to rechannel them, allowing for more flexible trade credit terms.
By utilizing goods as in-kind collateral and accessing distributor networks, suppliers can significantly reduce their risk of defaults and enjoy greater trade credit flexibility compared to traditional lenders who do not offer these advantages.
2.2.2 Price discrimination through trade credit
RESEARCH DESIGN
In emerging countries, limited access to external financial sources means there is still little data and attention on trade credit, including in Vietnam Only Hoang et al
A 2019 study explored the relationship between trade credit and the operating performance of small and medium enterprises in East Asia and the Pacific, including countries like China, Vietnam, and Japan The research highlighted that this relationship has not been extensively investigated By focusing on listed companies on the HOSE, this study aims to provide further insights into the topic within the context of Vietnam, enhancing our understanding of the issue.
This study focuses on the data period from 2017 to 2022, as it encompasses the significant impact of Covid-19 on company operations and their subsequent recovery Utilizing data from 2022 ensures that the findings are current and relevant Additionally, this timeframe offers a wealth of published company data, facilitating more comprehensive research.
The data utilized in this analysis was sourced from the FiinPro Platform and required complete synchronization across all variables for each observed year Initially, a list of 402 companies listed on HOSE was compiled, which was subsequently refined to ensure the sample data met specific criteria.
+ Eliminate companies listed after 2017 Got 174 companies.
+ Eliminate Hoa Sen Group Joint Stock Company because its 2021 sales revenue was 0 and Truong Thanh Furniture Corporation because its 2019 and 2020 equity was negative, getting 172 companies.
The study gathered data from 172 enterprises across eight industries, including Information Technology, Industry, Oil and Gas, Consumer Services, Pharmaceuticals, Consumer Goods, Materials, Utilities, and Telecommunications, in accordance with ICB standards Over a six-year period, a total of 1,032 observations were compiled and presented in a panel data format.
This study develops a model based on the research by Detthamrong and Chansanam (2023), which examines the connection between trade credit and the operational performance of Thai agricultural companies, alongside the findings of Hoang et al (2019), who explored the relationship between commercial credit and operational outcomes in the context of financial constraints.
The non-linear relationship between commercial credit and company operational efficiency:
ROAi.t = ao 4- PiTCRi.t + p2TCR2i,t + P^FSIZEm 4- p4LEVi,t + p5LQDu 4- PóSGRi.t + p/GDPi.i 4- Pi 4- ội 4- q.t
ROEu = ao + P i TCRm 4- p2TCR2iA + psFSIZEu + p4LEVi,t 4- PìLQDu + p6SGRu + p7GDPi,t + Pi + ội +q,t
ROAit = (Xo + piTCR.t 4- p2TCP2i,t + p3FSIZEi,t + p4LEVu 4- P5LQDU + PôSGRia + P?GDPj,t 4- Pi 4- ộ I 4- q.t
ROEia = ao 4- PiTCPu + p2TCP2u 4- p3FSIZEi.t 4- p4LEVi,t + psLQDi.t + PôSGRm + P?GDPi.t 4- Pi 4- ội 4- €i,(
The impact of financial constraints on the optimal threshold of trade credit
ROAi.t = ao 4- (pi+ aixFC)TCRi.t 4- (p2 4- a2xFC)TCR2i,t 4- p3FSIZEi.t 4- p4LEVi,t + PsLQDi.t 4- PóSGRi.t 4- p?GDPi,t 4- Pi + ội + €i,t
ROEi.t = ao + (pi+ aixFC)TCRi,t + (p2 + a2xFC)TCR2i,t + p3FSIZEi.t + p4LEVi,t + PsLQDi.i 4- PóSGRi.t 4- P?GDPi.t 4- Pi 4- ội 4- €i,t
ROAi.t = ao 4- (P1+ aixFC)TCPi,t + (p2 4- a2xFC)TCP2i.t 4- p3FSIZEu + p4LEVi4 + p5LQDi,t 4- póSGRụ 4- p-GDPi.t 4- Pi 4- ội 4- €i,t
ROEij = ao + (01+ aixFC)TCPi.( + (p2 + a2xFC)TCP2i,t + PsFSIZEi.t + p4LEVM + p5LQDi,t + póSGRi.t + pyGDPi.t + Pi + ội + €i,t
Pl, P2, p3, p4, p5, p6, p?: regression coefficients reflecting the change in the dependent variable when one of the independent variables changes.
To optimize operational efficiency, companies set the derivative to zero, resulting in the optimal value of receivables/payables for financially unconstrained firms at -pi.
-7— and tor 232 financially constrained companies as -pl+ul
Return on Assets (ROA), also known as Return on Total Assets or Profitability Ratio, is a key financial metric that assesses a company's ability to generate profit relative to its total assets Commonly abbreviated as ROA, this ratio is calculated by dividing earnings before interest and taxes (EBIT) by total assets Research indicates that ROA is a reliable indicator of a company's operational efficiency and profit-generating potential (Viet and Phuc, 2020; Detthamrong and Chansanam, 2023).
Return on Equity (ROE) indicates a company's profitability relative to its equity, calculated by dividing earnings before interest and taxes (EBIT) by equity Detthamrong and Chansanam (2023) highlight ROE as a key metric for assessing a company's operational efficiency and its ability to generate profits.
_ The book value of total assets - The book value of equity+The market value of equity
' The book value of total assets
The Tobin's Q ratio, introduced by James Tobin in 1969, serves as an indicator of a company's future growth potential Research by Chung & Pruitt (1994) shows a positive correlation between a company's market value of equity and its Tobin's Q, suggesting that a high Q value reflects investor expectations regarding operational efficiency and can lead to increased valuations Consequently, Tobin's Q is a commonly utilized measure in investment behavior studies (El Mehdi, 2007; Perrini et al., 2008; Jackling & Johl, 2009; Veprauskaite & Adams, 2013; Nguyen et al., 2014), with various methods available for its calculation depending on the study's objectives According to Brainard & Tobin (1968), Tobin's Q is calculated as the ratio of the total replacement cost of equity and debt to the total replacement cost of productive capacities However, due to the limitations of financial reporting in the Vietnamese market, the authors propose a calculation method that takes the replacement cost of total assets, subtracts the replacement cost of equity, adds the replacement cost of equity, and divides the result by the replacement cost of total assets (O'Connell, 2010; Garcia-Ramos & Garcia-Olalla, 2011; Eluyela et al., 2018; Govindan et al., 2023).
Trade Credit Accounts Receivable (TCR)
Trade Accounts Receivable (TCR), commonly referred to as trade credit offered to customers, is a key aspect of a business's credit policy It can be quantified using the ratio of accounts receivable to total assets, as highlighted in various studies (Deloof & Jegers, 1999; Kestens et al., 2012; Martinez-Sola et al., 2014; Dary & James, 2019).
The Square of the Trade Credit Accounts Receivable (TCR2)
The square of the trade credit accounts receivable (TCR²) is incorporated in the model to examine the nonlinear relationship between trade accounts receivable and a business's profit-generating capability, as supported by various studies (Deloof, 2003; Garcia-Teruel & Martinez-Solano, 2007; Banos-Caballero et al., 2012; Martinez-Sola et al., 2014; Hoang et al., 2019; Baker et al., 2022) It is anticipated that trade accounts payable will also exhibit a nonlinear relationship with profitability This indicates that a company's credit policy does not always lead to profit; when implemented within a reasonable range or below the optimal threshold, the benefits can outweigh the associated costs Conversely, exceeding this optimal threshold can result in a negative correlation between the credit policy and the company's profit-generating capability, particularly at elevated levels of accounts receivable.
The authors anticipate a positive correlation between Trade Credit Ratio (TCR) and Return on Assets (ROA) until an optimal threshold of accounts receivable is reached Conversely, the square of TCR is expected to exhibit a negative correlation, indicating an inverse relationship between trade accounts receivable and the company's ability to generate profit These findings are supported by research from Martinez-Sola et al (2014), Asimakopoulos et al (2016), Hoang et al (2019), and Baker et al (2022).
Trade Credit Accounts Payable to Suppliers (TCP)
Recent research by Hoang et al (2019) and Baker et al (2022) has employed the accounts payable to total assets ratio to assess supplier financing policies in the East Asia-Pacific region and India Building on this methodology, the authors apply the same formula to evaluate trade credit policy (TCP) in Vietnam.
The Square of the Trade Credit Accounts Payable (TCP2)
The square term of trade credit accounts payable (TCP2) is incorporated into the regression model to assess the nonlinear effects of supplier financing on firm profitability The authors suggest that firms can enhance their profits by increasing reliance on supplier financing up to an optimal level However, exceeding this threshold can lead to a decline in profitability as supplier debt rises.
The authors anticipate a positive relationship between TCP and ROA, while expecting a negative relationship for TCP2, suggesting that supplier financing negatively impacts firm profitability when firms surpass the optimal threshold These expectations are consistent with prior empirical findings (Hoang et al., 2019; Baker et al., 2022).
The Size of Firm (FSIZE)
The natural logarithm of total assets is used to calculate the value of the FSIZE variable, as per research by Tongurai and Vithessonthi (2022) and Dary and James
(2019) The expectation of this study is that firm performance (ROA) and firm size will positively correlate The following formula is how the authors measures:
RESEARCH RESULTS
The study begins by performing descriptive statistics to provide a comprehensive overview of the research data As shown in Table 3, the results encompass all variables involved in the study, including dependent, independent, and control variables.
Table 3: Descriptive Statistics of Variables
Variable Obs Mean Std dev Min Max
Source: From the analysis results of Stata/MP 17.0
The research sample, consisting of 1,032 observations from 172 companies, reveals key statistical figures The mean Trade Credit Receivables (TCR) stands at 0.1477, surpassing the mean Trade Credit Payables (TCP) of 0.0923, indicating that Vietnamese companies are more likely to extend trade credit to customers than to receive it from suppliers Additionally, the average return on assets is about 8.04%, while the average return on equity (ROE) is approximately 14.84%.
The Q ratio stands at approximately 1.27, indicating a favorable investment environment Most businesses utilize trade credit, with companies allocating about 14.77% of their total assets to trade credit receivables and 9.23% to trade credit payables Additionally, the average financial leverage is notably high at 43.79%, while the revenue growth rate reflects a robust 13.16%.
Table 4 below shows the correlation levels between variables in the research model.
Table 4 presents the Pearson correlation matrix for the variables studied, revealing generally low correlation coefficients While most coefficients fall below 0.20, some exceed this threshold, prompting the need for multicollinearity tests Notably, ROA and ROE demonstrate a strong correlation (r = 0.8507), and both ROA and TQ, as well as ROE and TQ, exhibit significant correlations (r = 0.6169 and r = 0.4976, respectively), underscoring their close relationship as indicators of company operational efficiency.
ROA ROE TQ TCR TCR2 TCP TCP2 FSIZE LEV LỌD SGR GDP
Note: Symbols * ** and *** represent statistical significance at the 10% 5% and 1% levels, respectively
Source: b rom the analysis results of Slata/MP 17.0
Table 5: Results of multicollinearity test using Variance Inflation Factor (VIF)
Variable VIF VIF VIF VIF VIF V1F
Source: From the analysis results of Stata/MP 17.0
The Variance Inflation Factor (VIF) for the variables in both linear and nonlinear regression models is below 10, with an average VIF value of less than 3 This indicates that the research sample does not demonstrate the presence of multicollinearity, as noted by Hair et al (1995).
After conducting the tests, tables 6 and 7 present the selected suitable models.
Table 6: Results of selecting the appropriate model (Dependent variable: ROA)
Suitable model REM FEM REM FEM
Columns (I) and (3) evaluate regression methods for analyzing the nonlinear relationship between trade credit receivables and firm operational efficiency, while Columns (2) and (4) assess regression methods for the nonlinear relationship between trade credit payables and firm operational efficiency.
Source: From the analysis results of Stata/MP /7.0
The study identifies the Random Effects Model (REM) as the most suitable method for analyzing the nonlinear relationship between accounts receivable, accounts payable credit, and operating performance, using Return on Assets (ROA) as the dependent variable Conversely, the Fixed Effects Model (FEM) is deemed appropriate for examining how financial constraints influence this nonlinear relationship in the context of accounts receivable and accounts payable credit, as well as operating performance.
Table 7: Results of selecting the appropriate model (Dependent variable: ROE)
Suitable model OLS REM OLS OLS
The analysis utilizes columns (1) and (3) to determine the suitable regression method for assessing the nonlinear relationship between trade credit receivables and firm operational efficiency, while columns (2) and (4) focus on identifying the appropriate regression method for the nonlinear relationship between trade credit payables and operational efficiency Statistical significance levels are indicated by *, **, and ***, representing 10%, 5%, and 1% respectively.
Source: From the analysis results of Stata/MP 17.0
In evaluating the dependent variable Return on Equity (ROE), Ordinary Least Squares (OLS) is found to be appropriate for analyzing the nonlinear relationships between accounts receivable credit, accounts payable credit, and operating performance Furthermore, the Random Effects Model (REM) effectively captures the influence of financial constraints on the nonlinear relationship between accounts receivable credit and operating performance, while OLS remains suitable for examining the impact of financial constraints on the nonlinear relationship of accounts payable credit and operating performance.
4.5.1 Nonlinear Relationship (n) between Trade Credit and Firm Profitability
The regression results are presented in Table 8 below.
Table 8: Nonlinear Relationship (A) between Trade Credit and Firm Profitability
for 10%, 5%, and 1%, respectively.
Source: From the analysis results of Stata/M p /7.0
Table 8 reveals the regression analysis of trade credit receivables (TCR) and trade credit payables (TCP) in relation to the operational efficiency of listed companies, assessed through return on assets (ROA) and return on equity (ROE) The findings indicate a consistent positive coefficient for TCR and a negative coefficient for TCR2, suggesting a nonlinear, inverted U-shaped relationship between trade credit receivables and firm profitability This supports the study's hypothesis of an optimal trade credit receivables threshold that enables companies to optimize operational efficiency by balancing benefits and costs Specifically, the regression coefficients show TCR at 0.0367 and TCR2 at -0.0542 with ROA, indicating an optimal TCR level of approximately 0.34, where investments below this threshold enhance profits through increased sales, while exceeding it diminishes profitability When evaluating ROE, the optimal level is identified at around 0.24.
Research findings suggest that TCP demonstrates an inverted U-shaped relationship with firm profitability, evidenced by regression coefficients of 0.1056 for TCP and -0.1001 for TCP2 The optimal TCP level for operational efficiency, measured by ROA, is approximately 0.529, while the optimal level using ROE is around 0.361.
The p-values from both the Modified Wald test and the Wooldridge test indicate statistically significant results at the 1% level, revealing the existence of heteroscedasticity and autocorrelation within the regression model Consequently, it is necessary to apply the Feasible Generalized Least Squares (FGLS) method for correction The regression outcomes obtained through FGLS are detailed in the following table.
Table 9: Nonlinear Relationship (A) between Trade Credit and Firm Profitability
The regression results in Columns (I) and (3) analyze the nonlinear relationship between trade credit receivables and firm operational efficiency, while Columns (2) and (4) focus on trade credit payables and its impact on operational efficiency Each regression model incorporates time dummy variables and industry sector dummy variables The table presents p-values for the tests conducted, with standard errors indicated in parentheses Statistical significance levels are denoted by *, **, and ***, representing significance at 10%, 5%, and 1%, respectively.
Source: From the analysis results of Stata/MP 17.0
The FGLS regression results in Table 9 reaffirm the signs of the independent variables consistent with those in Table 8, achieving significance at the 1% or 5% level and supporting the arguments presented in Chapter 2 However, the optimal thresholds for trade credit receivables and trade credit payables exhibit minor variations, attributed to changes in the regression coefficients of TCR, TCR2, TCP, and TCP2.
CONCLUSION
This research investigates the link between commercial credit and firm efficiency, highlighting its significance in corporate strategy and liquidity management Analyzing annual data from listed companies on the Ho Chi Minh City Stock Exchange (HOSE) from 2017 to 2022, the study employs various econometric methods, including Pooled OLS, Fixed Effects Model (FEM), Random Effects Model (REM), Feasible Generalized Least Squares (FGLS), and Generalized Method of Moments (GMM) The findings reveal that commercial credit investment enhances the efficiency of HOSE-listed companies; however, efficiency declines when commercial credit investment exceeds optimal levels, illustrating an inverse U-shaped relationship Additionally, factors such as company size, financial leverage, liquidity, revenue growth rate, and economic growth rate yield inconsistent results across different regression models.
Research indicates that companies with elevated levels of commercial credit often achieve higher Return on Assets (ROA) and Return on Equity (ROE) ratios, implying that commercial credit may serve as a predictor of operational efficiency To sustain profitability, businesses should aim to keep their commercial credit investments at an optimal level However, increasing credit can lead to a rise in outstanding receivables, necessitating vigilant monitoring of customers to ensure timely payments Companies that extend commercial credit also face the risk of non-repayment, which can adversely affect cash flow and require them to actively pursue outstanding payments, highlighting the importance of managing potential losses effectively.
Financially stable companies typically maintain higher optimal levels of accounts receivable and accounts payable compared to their financially constrained counterparts, primarily due to the latter's increased credit risk These constrained companies often struggle with insufficient capital, necessitating lower levels of accounts receivable and payable to prevent cash shortages and manage risks effectively Consequently, reducing the thresholds for these accounts serves as a strategy to minimize risks and protect financial stability.
5.2 Limitations of the Study and Future Research Directions
This study has several limitations, primarily relying on data from the Ho Chi Minh City Stock Exchange (HOSE), which excludes many listed and unlisted companies Furthermore, the research encompasses all non-financial sectors without focusing on specific industries, potentially overlooking variations in how commercial credit affects firm efficiency across different sectors A more detailed analysis that considers industry-specific effects would improve the study's comprehensiveness.
Future research in the Vietnamese market should concentrate on specialized industries to enable companies to create tailored credit granting plans Additionally, utilizing larger datasets, including firms listed on the Hanoi Stock Exchange and the Upcom market, will provide a more comprehensive analysis Furthermore, examining the cost of capital could reveal its influence on the relationship between commercial credit and firm efficiency, as investment decisions related to commercial credit may be affected by capital costs.
Abuhommous, A A A (2017) The impact of offering trade credit on firms’ profitability Journal of Corporate Accounting and Finance, 28(6), 29-40 doi: 10.1002/jcaf.22298
Al-Hadi A., & Al-Abri, A (2022) Firm-level trade credit responses to COVID-19- induced monetary and fiscal policies: International evidence International Business and Finance, 60, 101568 doi: https://doi.Org/10.1016/j.ribaf.2021.101568
Asimakopulos, A., Fernandes, F., & Karavias, Y (2016) Trade credit and firm performance [Abstract] A paper presented at 9th International Conference of the ERCIM WG on Computational and Methodological Statistics, University of Seville Spain.
Dary, s K., & James, H s Jr (2019) Does investment in trade credit matter for profitability? Evidence from publicly listed agro-food firms International Business and Finance, 47, 237-250 doi: https://doi.Org/10.1016/j.ribaf.2018.07.012
Detthamrong, u., & Chansanam, w (2023) Do the trade credit influence firm performance in agro-industry? Heliyon doi: https://doi.Org/10.10I6/j.heliyon.2023.el456I
Emery, G.w (1984) A pure financial explanation for trade credit The Journal of Financial and Quantitative Analysis, 19(3), 271-285 doi: https://doi.org/! 0.2307/2331090
Ferris, J s (1981) A Transactions Theory of Trade Credit Use Quarterly Journal of Economics, 94, 243-270 doi: https://doi.org/10.2307/18 82390
Hair J F., Black, w C., Babin, B J (1995) Multivariate Data Analysis Macmillan Publishing Company, doi: 10.4236/ijaa.2011.14023
Hoang, H c., Xiao, Q., Akbar, s (2019) Trade credit, firm profitability, and financial constraints: evidence from listed SMEs in East Asia and the Pacific International Journal of Islamic and Middle Eastern Finance and Management, 15(5), 744-770 doi: https://doi.org/10.1108/IJMF-09-2018-0258
Kehinde (2022) explores the relationship between access to trade credit and the utilization of EU-approved pesticides among smallholder cocoa farmers in Ondo State, Nigeria The study highlights how financial support can influence agricultural practices and enhance compliance with safety standards By examining the impact of trade credit on pesticide usage, the research underscores its significance in improving crop yield and promoting sustainable farming within the cocoa sector The findings contribute to understanding the economic factors that affect agricultural productivity and environmental compliance among smallholder farmers.
Kestens, K., Van Cauwenberge, p., Bauwhede, H V (2012) Trade credit and company performance during the 2008 financial crisis Accounting & Finance, 52, 1125-1151 doi: https://doi.org/IO.! 11 l/j.!467-629X.2011.00452.x.
In their 2020 study published in the Journal of Asian Finance, Lc Khương Ninh and Phan Anh TÚ examine the relationship between bank credit, trade credit, and the growth of listed agricultural firms in Vietnam The research highlights the significant impact of financial resources on the expansion of these firms, suggesting that both bank and trade credit play crucial roles in enhancing their growth potential The findings underscore the importance of access to diverse financing options for agricultural enterprises in Vietnam, contributing to a better understanding of the financial dynamics within this sector.
Mahmud, A A., Miah, M s., Bhuiyan, M R u (2022) Does trade credit financing affect firm performance? Evidence from an emerging market Journal of Finance, 85 doi: https://doi.org/10.3390/ijfs 10040085.
Brick, I E & Fung, w K (1984) The effect of taxes on the trade credit decision Financial Management, 13(2), 24-30.
Martinez-Sola, c., Garcia-Teruel, p J., Martinez-Solano, p (2014) Trade credit and firm value Accounting & Finance, doi: 10.111 l/j.l467-629X.2012.00488.x
Meltzer, A H (1960) Mercantile Credit, Monetary Policy, and Size of Firms Review of Economics and Statistics, 42, 429-437 doi: https://doi.org/l0.2307/1925692
Mian, s., & Smith, c w (1992) Accounts Receivable Management Policy: Theory and Evidence Journal of Finance, 47, 169-200 doi: https://doi.org/10.2307/2329094
Petersen, M A & Rajan, R G (1997) Trade credit: theories and evidence The Review of Financial Studies, 10(3), 661-691 doi: 10.1093/rfs/10.3.661
Phạm Dương Phương Tháo and Huỳnh Thị cầm Hà (2020) The impact of trade credit investment on manufacturing firms' profitability: evidence from Vietnam Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 68, 775-796 doi: https://doi.org/10J 1118/actaun202068040775
Phạm Quỏc Việt and Phạm Trân Quang Phúc (2020) Does Trade Credit Spur Firm Performance? A Case Study in Vietnam International Journal of Economics & Business Administration, 8(3), 215-227 doi: 10.35808/ijeba/497
Schwartz, R A., & Whitcomb, D (1979) The Trade Credit Decision Handbook of Financial Economics, doi: https://doi.org/10.1002/mde 1049
Tongurai, J., & Vithessonthi, c (2022) Learning, foreign operations and operating performance Global Finance Journal, 52, 100721 doi: https://doi.Org/l 0.1016/j.gfj.2022.100721
Cunat, V (2006), “Trade credit: suppliers as debt collectors and insurance providers*', The Review of Financial Studies, Vol 20 No 2, pp 491-527.
Garcia-Teruel, P.J and Martinez-Solano, p (2007), “Effects of working capital management on SME profitability", International Journal of Managerial Finance, Vol
Appendix 1: Breusch-Pagan test of the nonlinear relationship between accounts receivable trade credit and firm operational efficiency (dependent variable ROA).
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity Assumption: i.i.d error terms
Variable: Fitted values of ROA H0: Constant variance
Appendix 2: Fixed Effects Model (FEM) regression of the nonlinear relationship between accounts receivable trade credit and firm operational efficiency
xtreg ROA TCR TCR2 FSIZE LEV LQD SGR GDP i.YEAR, fe note: 2022.YEAR omitted because of collinearity.
Fixed-effects (within) regression Group variable: ID
Number of obs = Number of groups =
ROA Coefficient std err t p>|t| [95% conf interval]
_cons -.3563878 2109015 -1.69 0.091 -.7703372 0575617 sigma_u 06439182 sigma_e 04089559 rho 71257612 (fraction of variance due to u_i)
Appendix 3: Hausman test of the nonlinear relationship between accounts receivable trade credit and firm operational efficiency (dependent variable ROA).
The rank of the differenced variance matrix (6) does not match the number of coefficients being tested (11), which may indicate potential issues with the computation of the test It is advisable to review the output of your estimators for any unexpected results and consider scaling your variables to ensure that the coefficients are on a standard scale.
Difference sqrt(ciac(v.b-v_8ằ std err. tot 8229157 tram -.9146525 8 3 25686
2821 -.894887? -.9W8811 -.9239266 8111583 b - Consilient undr* IM1 >rd Ha; obtained free xtr^e
II - Incontinent wdrr IM, efficient under Mb; obtained free xtrvK fest of H8 Difference in coefficients rot systematic ƠU2|z| (95% conf, interval]
CORP Cống nghệ thòng tin -.2280425 4.584164 -0.85 0.968 -9.056842 8.599957
Dịch vụ tiêu dung 3814669 1.587802 0.24 0.818 -2.728999 3.491933 nang tiêu dùng -1.133829 2.484801 -0.47 0.637 -5.845583 3.577926
_cons -2.391391 1.728198 -1.39 0.164 -5.762917 9801358 warning: Uncorrected two-step standard errors are unreliable.
Instruments for first differences equation
GOT-type (missing-0, separate instruncnls for each period unless collapsed)
L(2/S).(L3.TCR L2.FSIZE L2.LQD 2017bL.YEAR 2018L.YEAR 2019L.YEAR
2820L.YEAR 2021L.YEAR IbL.CORP 2L.CORP 3L.CORP 41.CORP SI.CORP 61.CORP
GM-type (missing-0, separate instruments for each period unless collapsed)
OL.(l3.TCR L2.FSIZE L2.LQO 2017bL.YEAR 20181.YEAR 2019L.VEAR 20201.YEAR
20211.YEAR IbL.CORP 2L.CORP 3L.CORP 41.CORP SL.CORP 6L.C0RP 7L.CORP
Arellano-8ond test for AR(1) in first differences: z - -1.81 Pr > z - 0.070
Arellano-Bond test for AR(2) in first differences: z ’ -1.8S Pr > z - 0.065
Sargan test of overid restrictions: chi2(23) - 68.11 Prob > chi2 - 8.008
(Not robust, but not weakened by many instruments.)
Hansen test of overid restrictions: chi2(23) - 27.97 Prob > chi2 - 8.217
(Robust, but weakened by many instruments.)
Appendix 5: Breusch-Pagan test of the financial constraints influencing the nonlinear relationship between accounts receivable trade credit and firm operational efficiency (dependent variable ROA).
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity Assumption: i.i.d error terms
Variable: Fitted values of ROA H0: Constant variance
Appendix 6: Fixed Effects Model (FEM) regression of the financial constraints influencing the nonlinear relationship between accounts receivable trade credit and firm operational efficiency (dependent variable ROA).
The analysis employed a fixed effects regression model to evaluate the impact of various financial indicators on Return on Assets (ROA), including Total Capital Ratio (TCR), Total Capital Ratio squared (TCR2), Firm Size (FSIZE), Leverage (LEV), Liquidity (LQD), Sales Growth Rate (SGR), and Gross Domestic Product (GDP) It is important to note that the year 2022 and several corporate identifiers were omitted from the model due to issues of collinearity, which could affect the reliability of the results.
Number of obs = Number of groups =
ROA Coefficient std err t p>|t| [95% conf interval]
Công nghệ thõng tin 0 (omitted)
Dịch vụ tièu dùng 0 (omitted)
_cons -.5420748 2009015 -2.70 0.007 -.936398 -.1477517 sigma_u 06392477 sigma_e 03877545 rho 731027 (fraction of variance due to u_i)
Appendix 7: Hausman test of the financial constraints influencing the nonlinear relationship between accounts receivable trade credit and firm operational efficiency (dependent variable ROA).
The rank of the differenced series does not match the number of coefficients being tested, which may indicate potential issues with the test It is essential to review the output of your estimation for any unexpected results and consider rescaling your variables to ensure that the coefficients are on a similar scale.
Difference sqrt(dlag(VJ>-V6ằ std ôrr.
TCR2KFC 6712512 7198899 •.8477187 8483933 b • Consistent under H6 ord He; obtained frcr xtreg.
8 - Inconsistent wider Ka, efficient under Hd; obtained free rtreg.
Test of HO: difference in coefficients rot systemic
Appendix 8: GMM regression of the financial constraints influencing the nonlinear relationship between accounts receivable trade credit and firm operational efficiency (dependent variable ROA).
The article discusses various financial instruments and metrics, including RCA, TCR, and FSI7F, while emphasizing the importance of data analysis in understanding market trends It highlights the need to monitor key performance indicators and suggests using specific datasets to make informed decisions Additionally, it encourages users to engage with interactive tools for better insights into financial data.
2017b.YEAR dropped due to collinearity
2019.YEAR dropped due to collinearity
2021.YUM dropped due to collinearity
Ib.ow dropped due to collinearity
Naming: Two-Step estimated covariance ratrix of wents Is singular.
Using a generalised inverse to calculate optical weighting ratrix for two-step estimation.
0lff^rcrkl [95X conf interval]
Instruments for first differences aquatic*!
GW-type (iri$slngx6, separate lost relents for each period unless collapsed)
L.U.TCR UKR2 I.FSI7F I.IFV L.IQO 2817U.YFAR 28181 YEAR 29191 YEAR
2O2OL YEAR 23211.YEAR Ibl ow 21 aw 31.CORP 41 CORP SI CORP 61 CORP
7L.GW BL CORP L.TCRxFC L.TCR2xFC)
GW-type (irlsslng^j separate instruments for each period unless collapsed)
D.Ũ.TCR L.KR2 L.FSlh I.LEV L.lQD 78l7bl YEAR 28181 YFAR 28191 YEAR
26201.YEAR 282U.YW IbL.CCW 2L.OW 31.CORP 4L.C0RP SL.CORP 61.CORP
7L.CCRP BL.CORP L.TCRxFC L.TCR2XFC)
Arellano-Bond test for AR(1) in first differences: X - -1.91 Pr > X - 8.088
Arellano-Good test for AR(2) in first differences: X X -6.66 Pr > X X 8.567
Sargan test of over id restrictions: chU(W - 148.15 Prob > Chl2 - 8.688
(Mot robust, but not weakened by nany Instruments.)
Hansen test of over id restrictions: chi2(78) - 61.62 Prob > chi? - 8.752
(Robust, out weakened by nany instruments.)
Difference-in-Hansen tests of exogeneity of instrument subsets: ivd.SGR L.GCP}
Hansen test excluding group: Chl2(69) * 59.82 Prob > CW2 - 8.777
Difference (null H - exogenous): chU(l) - 1.79 Prob > Ch12 - 8.181
Appendix 9: Breusch-Pagan test of the nonlinear relationship between accounts payable trade credit and firm operational efficiency (dependent variable ROA).
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity Assumption: i.i.d error terms
Variable: Fitted values of ROA H0: Constant variance
Appendix 10: Fixed Effects Model (FEM) regression of the nonlinear relationship between accounts payable trade credit and firm operational efficiency (dependent variable ROA).
xtreg ROA TCP TCP2 FSIZE LEV LQO SGR GDP i.YEAR, fe note: 2022.YEAR omitted because of collinearity.
Fixed-effects (within) regression Group variable: ID
Number of obs = Number of groups =
ROA Coefficient std err t p>|t| [95% conf interval]
_cons -.3795684 2095445 -1.81 0.070 -.7968545 8317176 sigma_u 06447937 sigma_e 04078622 rho 71422678 (fraction of variance due to u_i)
Appendix 11: Hausman test of the nonlinear relationship between accounts payable trade credit and firm operational efficiency (dependent variable ROA).
When conducting a statistical test, ensure that the degrees of freedom for the differenced variance matrix do not equal the number of coefficients being tested This discrepancy may indicate potential issues with the computation of the test It is crucial to examine the output of your estimators for any unexpected results and consider standardizing your variables to ensure that the coefficients are on a similar scale.
2021 -.0876912 -.C6J4646 -.6242266 8111258 b • Consistent under HO and Ha; obtained fren xtreg
B - Inconsistent under Ha, efficient under HB; obtained fro® Kt reg
Test of HO: Difference in coefficients rot systematic
Prcb > ch!2 - 0.2719 ôb-v_8 is rot positive definite)
Appendix 12: GMM regression of the nonlinear relationship between accounts payable trade credit and firm operational efficiency (dependent variable ROA).