The Effect of Firm Type on the Relationship between Accounting Quality and Trade Credit in Iraqi listed firms
Nael Jaafar Ali1 luay abdulwahid shihab 2 Zinah abdulsttar Abdullah3 mohammed hamid falih4
1 Instructor of basic science college of nursing – university of Basrah
2Ass.prof. department of basic science – college of nursing – university of Basrah
Luay.abdulwahid@uobasrah.edu.iq
3Ass. instructor of basic science college of nursing – university of Basrah
Zinah.abdulsttar@uobasrah.edu.iq
4Ass. instructor of basic science college of nursing – university of Basrah
HNSJ, 2024, 5(9); https://doi.org/10.53796/hnsj59/17
Published at 01/09/2024 Accepted at 20/08/2024
Citation Methods
Abstract
the main aim of this study is to evaluate The Effect of Firm Type on the Relationship between Accounting Quality and Trade Credit in Iraqi listed firms. In this research, the dependent variable is trade credit, which is accounts payable divided by total assets (Petersen& Rajan, 1997; Giannetti et al., 2011; Ke, 2015). The independent variable is accounting quality, which is calculated by three indicators, including profit sustainability, profit predictability as well as real earnings management, for which we used Kormendi& Lipe (1987), Francies et al. (2004) and Roychowdhury (2006) models respectively. The data is based on a sample of 35 firms from 2011 to 2016. During this period, between 2011 and 2013 we have financial crisis – ISIS entered Iraq – and between 2014 and 2016 we have the period after the crisis. Our hypotheses are run by panel data on STATA14 software. The results show that the effect of firm type on the relationship between firm sustainability and trade credit is not significant; the moderating effect of firm type on the relationship between profit predictability and trade credit is not significant; and the moderating effect of firm type on the relationship between real earnings management and trade credit is significant-positive.
Key Words: Accounting Quality, Trade Credit, Profit Sustainability, Profit Predictability, Earnings Management, Firm Type
1. Introduction
Today, one of the factors affecting economic decisions of people working in the capital markets is access to reliable information that is relevant to the field of decision. The lack of information or low quality of information increases the ambiguity of the decision making process. Financial reports released by companies provide a source of information required to make a decision. In the process of making financial and economic decisions by the venture capitalists, creditors, and other users of the management and financial information, not only is the availability of information of paramount importance, but the quality of the information provided matters as well (Saghafi and Sadidi, 1986). For example, cash is an element of financial statements that has long been of particular interest to financial statement holders. The importance of cash is entwined with the survival of a company, so that a company will not be able to continue its operations in the absence of sufficient cash. Given the importance of this issue, companies have always had strong incentives to retain cash. The policy adopted by a business executive for the management of its current assets has an impact on the level of its cash reserve. Deciding about how to use the internal cash flow is a major decision that may become the source of conflicts between shareholders and managers. During the economic growth of a company, with the increase of cash reserves, the managers need to decide whether the cash needs to be distributed among the shareholders, spent on in-house expenditures, used for external purchases of the company, or remain untouched. In this context, an abstruse issue for profit-seeking managers is to choose between the use or maintenance of cash reserves (Resayian et al., 2010).
In any economy, achieving long-term and sustained growth is not possible without optimal allocation of financial resources at the national level. Lack of access to sufficient financial resources is a major obstacle to the growth and development of small and medium-sized companies. However, such companies, which have great potentials for economic growth, play an important role in the economies of developing and developed countries and they can reach high levels of productivity if find access to sufficient financial resources (Khazaei, 2016). One way of financing these companies is through financial markets, which include money market, capital and insurance. The money market, as a place for short-term financial claims, is further composed of banking and non-bank financial institutions (financial and credit institutions). These institutes act as intermediaries in the financial markets and their impact on the economy goes beyond their number and size. Experience has shown that credit institutions can significantly contribute to the growth of small and medium-sized enterprises, but in developing countries, the importance of financial and credit institutions is largely neglected and the quality or the extent and quality of service provided by these services is limited .The result is that the market’s reaction to unconfirmed negative news is faster and more dramatic than its reaction to confirmed positive news ( NAEL,2022).Image recognition problems are usually difficult to solve using unprocessed data. To improve the differentiation process, there is often ( Manhal Mohammad , 2022).
2. Theoretical Foundations
Quality of accounting information describes the precision of financial reporting in stating accounting information and firm performance, especially the expected cash flows that is presented to investors. Accounting information is an important and useful source of decision making for the contracting parties as well as a key source of assessing the job of advising managers (Arab MazarBadi& Taliban, 2008). The financial reporting supplies information on the expected cash flows. Therefore, accounting quality is concerned with the accuracy and soundness of information. The high quality of information reporting enables a company to retain a lower amount of cash and reduce non- cash generating assets in the balance sheet. These findings contribute to the literature on the role of accounting quality in reducing information asymmetry, which thwarts rigorous corporate capital policies and gives valuable insights to managers, investors, creditors and researchers. Moreover, improving the quality of corporate accounting may improve cash management, reduce cash flow in the balance sheet and thus increase the return on investment capital. Hence, its economic implications are important to corporate executives.Creditors and investors may consider the quality of accounting information as a key factor in determining discount rates and debt contracts. Also, the high quality of accounting information boosts creditor’s incentive to grant facilities to companies.Trade credit is an agreement between a buyer and a seller by virtue of which the seller allows the buyer to postpone the payment for the purchased goods (Maine & Smith, 1992). Suppliers, as the providers of short-term financial, consider several factors including the profit margin of sales on credit, the ability of the customer to fulfill his business obligations in a timely manner and the long-term financial status of the customer when they contemplate granting credits to their customers. Business credit plays a central role in the activities of any company as it reflects the level of trust that creditors and suppliers placed on that company. Companies with good business credit can purchase goods, and services from customers without ant cash payment. Banks and other creditors look for information that can be used to assess the status of companies before granting any facilities. Accounting is one of the most important sources of information that can be of particular value to creditors. Given the importance of accounting quality and its effect on trade credit, along with the type of company and its effect on the said relationship, the results of a survey conducted by Graham, Harvey, and Rajagopalan (2005) show that service managers prefer to provide suppliers with smooth earnings to assure them about the sustainability of their business (one aspect of accounting quality). Earnings smoothness is not only considered to be less risky, but can also reduce information asymmetry between suppliers and customer companies. In this context, product features can also influence the relationship between accounting quality and the use of trade credit. If a company is on the verge of bankruptcy, its suppliers are entitled to confiscate the products they have sold to the company. Suppliers can sell those goods to other buyers in the market and therefore gain (at least partially) the settlement value of their goods. On the other hand, service providers are unable to retrieve the services already offered to these customers. Consequently, it exacerbates the consequences of information asymmetry and service providers are at greater risk for raising trade credit. The results of this research can be useful to different groups such as creditors, stockholders in financial markets, stock exchange officials, financial analysts and scholars in order to provide transparent information, optimize portfolio design, help with op timum investment decisions in the Stock Exchange and provide new avenues of research for favorable analysis of other financial variables.
3. Research Background
Patterson and Rajan (1997) illustrated that if companies face difficulty in bank financing, trade credit can be an attractive alternative for financing, even if it is costly. Wilson and Summers (2002) demonstrated that trade credit has diverse applications in various industries. Danielson and Scott (2004) found that since smaller companies have to deal with more barriers to obtain credit from banks, they display a greater tendency towards trade credit compared to large companies. In addition, Bogasias, Matteo, and Meizen (2009) revealed that a decline in bank lending would reinforce trade credit. Bharath, Sunder, and Sunder (2008) suggested that an increase in the quality of accounting information would lead to easier financing (including financing through trade credit). The high quality of accounting information reduces information asymmetry, and facilitates the purchase on credit (Bhattacharya, Ecker, Olsen, & Shipper, 2011). Giannettiet al. (2011) exhibited that companies prefer to fund new investments using in-house financial resources through short-term debt (including trade credit), interest-bearing debt, and new stock issuance. Companies struggling with liquidity problems can take advantage of trade credit and sustain their operations, thereby preventing any interruptions in their activities (Ferrando& Muller, 2013). Altunac (2012) argues that about 40 to 60 percent of companies pay their debts to suppliers with delay. This late payment is not limited to small firms, and large corporations may be delayed in settlement for their business obligations. Murfinand Njoroge(2013) showed that late payment of big customers restricts the ability of suppliers to invest in property and equipment and reduce capital expenditures. Therefore, when granting trade credit to customers, suppliers should consider the possibility of late payment and in some cases default. Hui, Klasa, and Yeung(2012) and Dai and Yang (2015) found that suppliers grant more trade credit to companies that present more conservative financial reports. Yan (2013) found that with improvement of earnings management, which leads to escalated information asymmetry, the level of business credibility drops. The study of Martinezet al. (2014) revealed that a lower level of earnings fluctuations increases the possibility of predictability, and a higher quality of reporting boosts trade credit. Dhieux, Severin, and Vigneron(2015) also asserted that there is a positive relationship between accounting information quality and trade credit, so that creditors tend to grant less credits to customers whose financial reporting quality is low. Hyun(2017) in his study on the commercial credit of small and medium-sized enterprises of Korea during the financial crisis of 1993 stated that firms affected by the financial crisis were more likely to rely on their trade credit than other firms did.
In Iran, scant attention has been paid to the topic of trade credit and its associated factors. For example, Izadinia and Taheri (2015) showed that earnings smoothness and conservatism are not significantly related to the trade credit, but they reported a negative and significant relationship between earnings management and trade credit. EbrahimKordler and Taheri (2015) showed that there is a positive and significant connection between accruals quality and trade credit, but earnings fluctuation is not significantly related to trade credit. Kamyabi and Georgian (2016) found that accounting conservatism enhances trade credit, but the implementation of monetary policies in the country weakens the effect of accounting conservatism on trade credit.
4. Sample Selection, Variable Measurement and Research Design
In this study, the data is gathered by documentary research for literature review. For this purpose, we used articles, journals along with other sources available at universities.
4.1. Sample Selection
The financial data are collected from the firms’ annual reports. Our sample stands on all available data of Iranian listed firms on the Tehran Stock Exchange market during the examination period and became ready for analysis by using Microsoft Excel software. The data is based on a sample of 35 firms from 2011 to 2016. During this period, between 2011 and 2013 we have financial crisis and between 2014 and 2016 we have the period after the crisis we used Stata14 along with other statistical software for our analysis. Firms that joined the market during the examination period, or the ones that are delisted are eliminated from the examination sample; investment, bank, and insurance industries due to their particular nature; firms that adjusted their fiscal year during the examination period; firms which their fiscal year – according to the solar calendar – do not end in March. Altogether, this results in 35 firms.
4.2. Variable Measurement
The dependent variable is trade credit which is derived from accounts payable divided by total assets (Petersen &Rajan, 1997; Giannetti et al., 2011; Ke, 2015).
The independent variable is accounting quality which is displayed from three indicators, encompassing profit sustainability, profit predictability and real earnings management, for which we used Kormendi&Lipe (1987), Francies et al. (2004) and Roychowdhury (2006) models in turn.
Profit sustainability by the model of Kormendi&Lipe (1987) is calculated as below:
In this model, EARN it equals operational profit of firm i in year t and Total Assest equals total assets.
If the explanatory variable coefficient of profit sustainability model (δ1) is near one or above it, it shows that profit is sustainable, zero shows otherwise.
Profit predictability by the model of Francies (2004) is calculated as below:
The small amount shows higher profit predictability, thereby profit quality is on a high level, and vise versa.
Real earnings management by the model of Roychowdhury (2006) is calculated as below:
In this model, CFO is net operating cash flow; S is sales, and TA is total assets.
5.Research Design
5.1. First Hypothesis
APit = β0 + β1PSit + β2SERVICEit + β3PS*SERVICEit + β4SIZEit + β5SALEGROWTHi,t + β6AGEi,t + β7AGE2i,t + β8ROAi,t + β9ARi,t + β10TOBINSQi,t + β11CASHi,t + β12DEBTi,t + β13MARKETSHAREi,t + εi,t
In this hypothesis, AP equals trade credit which is accounts payable divided by total assets; PS equals profit sustainability; SERVICE is firm type which is equal one if a firm is a service company, zero otherwise.; SIZEit which is firm size derived from natural logarithm of total assets; SALEGROWTH it equals current sales minus previous year sales divided by previous year sales; AGE it equals natural logarithm of the number of years which a firm is doing business; AGE2it is the sum of squares of the age of a company; ROA itis the return on assets; ARitequals total accounts receivable divided by total assets; TOBINSQitequals the market value of equity plus the book value of debts divided by the book value of total assets; CASH it equals total cash and its equivalents divided by total assets; DEBT ittotal debts divided by total assets;MARKETSHAREitequals total salesdivided by total exports in a fiscal year.
5.2. Second Hypothesis
APit = β0 + β1Pit + β2SERVICEit + β3P*SERVICEit + β4SIZEit + β5SALEGROWTHi,t + β6AGEi,t + β7AGE2i,t + β8ROAi,t + β9ARi,t + β10TOBINSQi,t + β11CASHi,t + β12DEBTi,t + β13MARKETSHAREi,t + εi,t
5.3. Third Hypothesis
APit = β0 + β1EMit + β2SERVICEit + β3EM*SERVICEit + β4SIZEit + β5SALEGROWTHi,t + β6AGEi,t + β7AGE2i,t + β8ROAi,t + β9ARi,t + β10TOBINSQi,t + β11CASHi,t + β12DEBTi,t + β13MARKETSHAREi,t + εi,t
6. Tests Results
Descriptive statistics of the current study is as the table below:
Table 1-1 Descriptive Statistics of Quantitative Variables
| Normality | Kurtosis | Skewness | Min. | Max. | Std. Dev | Median | Mean | Symbol | Variable |
| 0.000 | 2.964 | 0.930 | 0.010 | 0.920 | 0.261 | 0.235 | 0.298 | AP | Trade Credit |
| 0.000 | 4.259 | 0.966 | -0.200 | 0.380 | 0.121 | -0.010 | 0.005 | PS | Profit sustainability |
| 0.000 | 5.330 | 1.645 | 0.010 | 0.550 | 0.133 | 0.09 | 0.134 | P | Profit Predictability |
| 0.000 | 4.295 | 0.646 | -0.240 | 0.390 | 0.130 | 0.010 | 0.008 | EM | Earnings Management |
| 0.000 | 3.692 | 0.366 | 8.370 | 11.290 | 0.567 | 9.752 | 0.703 | SIZE | Firm Size |
| 0.000 | 9.217 | 2.476 | -0.980 | 4.03 | 1.097 | 0.010 | 0.259 | SG | Sales Growth |
| 0.000 | 3.493 | 1.254 | 10 | 70 | 13.303 | 23 | 31.042 | AGE | Firm Age |
| 0.000 | 2.960 | 0.544 | 2.30 | 4.25 | 0.382 | 3.26 | 3.358 | LNAGE | Natural Logarithm of
Age |
| 0.000 | 2.923 | 0.808 | 5.30 | 18.05 | 2.657 | 10.62 | 11.419 | AGE2 | Sum Square of Age |
| 0.000 | 6.140 | -1.637 | -0.67 | 0.29 | 0.190 | 0.02 | -0.012 | ROA | Return on Asset |
| 0.000 | 3.312 | 1.003 | 0.01 | 0.95 | 0.238 | 0.20 | 0.263 | AR | Accounts Receivable Ratio |
| 0.000 | 5.014 | 1.751 | 0.36 | 16.88 | 4.534 | 2.215 | 4.153 | TQ | Tobin’s Q Ratio |
| 0.000 | 3.806 | 1.277 | 0.01 | 0.68 | 0.169 | 0.11 | 0.163 | CASH | Cash Ratio |
| 0.000 | 7.260 | 2.082 | 0.02 | 1.97 | 0.453 | 0.25 | 0.395 | DEBT | Debt Ratio |
| 0.000 | 9.618 | 2.673 | 0.001 | 0.91 | 0.209 | 0.02 | 0.113 | MS | Market Share |
According to Table 1-1, the mean of Trade Credit and ProfitSustainability are 0.298 and 0.005 respectively. These results show that Iraqi listed firms are consistently profitable, but in a low amount. Moreover, ProfitPredictability and RealEarningsManagement have the mean of 0.134 and 0.008 in turn. The mean of FirmSize, SalesGrowth and FirmAge are 9.703, 0.259 and 31.042 respectively. Also, NaturalLogarithmofAge, SumSquareofAge, Return on Asset, Accounts Receivable Ratio, Tobin’s Q Ratio, Cash Ratio, Debt Ratio and MarketShare have the mean of 3.358, 11.419, -0.012, 0.263, 4.153, 0.163, 0.395 and 0.113 in turn. The results of normality test showed that quantitative variables have no normal distribution. But, because there is not a considerable difference between mean and median, and that there are 210 observations, we can conclude that the distribution of variables is normal (Greene, 2011). The coefficient correlation between two variables measures the linear dependence between them. The coefficient correlation of the main research variables are listed as table below:
Table 2-1 Pearson coefficient correlation among the main research variables
| Variable | AP | PS | P | EM | SIZE | SG | AGE | AGE2 | ROA | AR | TQ | CASH | DEBT | MS |
| AP | 1 | |||||||||||||
| PS | 0.08 | 1 | ||||||||||||
| P | *0.37 | 0.05 | 1 | |||||||||||
| EM | 0.04 | *0.15 | 0.01 | 1 | ||||||||||
| SIZE | *0.15 | -0.04 | *-0.23 | -0.09 | 1 | |||||||||
| SG | -0.07 | *0.28 | -0.03 | -0.08 | 0.04 | 1 | ||||||||
| AGE | *0.19 | -0.13 | *0.19 | -0.09 | 0.02 | 0.02 | 1 | |||||||
| AGE2 | *0.18 | *-0.14 | *0.20 | -0.10 | 0.01 | 0.03 | *0.99 | 1 | ||||||
| ROA | *-0.42 | *0.40 | *-0.25 | 0.15 | 0.02 | *0.16 | *-0.42 | *-0.42 | 1 | |||||
| AR | *0.42 | 0.03 | 0.09 | -0.01 | *0.26 | -0.07 | *-0.16 | *-0.17 | -0.06 | 1 | ||||
| TQ | *0.22 | 0.13 | *0.31 | 0.04 | *-0.26 | 0.02 | -0.13 | *-0.14 | 0.01 | *0.17 | 1 | |||
| CASH | *-0.16 | *0.18 | -0.10 | 0.12 | -0.12 | 0.03 | *-0.24 | *-0.24 | *0.29 | *0.24 | -0.12 | 1 | ||
| DEBT | *0.86 | 0.04 | *0.39 | 0.01 | 0.10 | -0.04 | *0.29 | *0.29 | *-0.59 | *0.30 | 0.10 | *-0.14 | 1 | |
| MS | 0.00 | *0.17 | -0.14 | -0.04 | *0.65 | 0.09 | *-0.23 | *-0.24 | *0.26 | 0.05 | -0.04 | *0.17 | 1 |
Table 3-1 Test Results of H1
| Variable | Coefficient | Std | valueZ | Z Probability | |
| C | -1.757 | 0.732 | -2.40 | **0.016 | |
| PS | 0.041 | 0.070 | 0.59 | 0.558 | |
| SERVICE | -0.018 | 0.027 | -0.68 | 0.500 | |
| PS* SERVICE | -0.007 | 0.101 | -0.08 | 0.938 | |
| SIZE | 0.101 | 0.027 | 3.63 | *0.000 | |
| SALEGROWTH | -0.005 | 0.004 | -1.14 | 0.255 | |
| AGE | 0.509 | 0.393 | 1.29 | 0.196 | |
| AGE2 | -0.073 | 0.055 | -1.32 | 0.187 | |
| ROA | 0.114 | 0.067 | 1.68 | ***0.092 | |
| AR | 0.057 | 0.043 | 1.31 | 0.189 | |
| TOBINSQ | 0.006 | 0.001 | 3.33 | *0.001 | |
| CASH | -0.013 | 0.037 | -0.35 | 0.726 | |
| DEBT | 0.443 | 0.034 | 12.88 | *0.000 | |
| MARKET SHARE | -0.116 | 0.104 | -1.12 | 0.264 | |
| R2 | 0.836 | ||||
| Wald Test | 454.75 | ||||
| Wald Test Sig. | *0.000 | ||||
*Significant at the 0.01 level.
**Significant at the 0.05 level.
***Significant at the 0.10 level.
According to Table 3-1, R2 shows that independent and control variables account for approximately 84 percent of the dependent variable. Wald Test Significance – 454.75 – shows the total significance of H4 model. The results demonstrated that Firm Type has no significant effect on Trade Credit. In addition, PS*CRISIS has a no impact on the relationship between Profit Sustainability and Trade Credit. Therefore, our H4 is rejected. In other words, Firm Type does not affect the relationship between Profit Sustainability and Trade Credit. The results illustrated that there is significant-positive relationship between Trade Credit and Size, ROA, Tobin’s Q Ration and Debt Ratio. However, there is no significant relationship between Trade Credit and other control variables.
Table 4-1 Test Results of H2
| Variable | Coefficient | Std | valueZ | Z Probability | |
| C | -2.108 | 0.574 | -3.67 | *0.000 | |
| P | 0.171 | 0.087 | 1.97 | **0.049 | |
| SERVICE | -0.022 | 0.035 | -0.63 | 0.526 | |
| P* SERVICE | 0.119 | 0.182 | 0.66 | 0.512 | |
| SIZE | 0.122 | 0.027 | 4.39 | *0.000 | |
| SALEGROWTH | -0.003 | 0.004 | -0.73 | 0.467 | |
| AGE | 0.602 | 0.297 | 2.02 | **0.043 | |
| AGE2 | -0.089 | 0.042 | -2.10 | **0.036 | |
| ROA | 0.130 | 0.062 | 2.10 | **0.036 | |
| AR | 0.043 | 0.044 | 0.97 | 0.332 | |
| TOBINSQ | 0.006 | 0.002 | 2.92 | *0.004 | |
| CASH | -0.006 | 0.035 | -0.20 | 0.844 | |
| DEBT | 0.431 | 0.035 | 12.16 | *0.000 | |
| MARKET SHARE | -0.112 | 0.100 | -1.11 | 0.266 | |
| R2 | 0.843 | ||||
| Wald Test | 451.99 | ||||
| Wald Test Sig. | *0.000 | ||||
*Significant at the 0.01 level.
**Significant at the 0.05 level.
***Significant at the 0.10 level.
According to Table 4-1, R2 shows that independent and control variables account for 84 percent of the dependent variable. Wald Test Significance – 451.99 – shows the total significance of H5 model. The results demonstrated that Firm Type has no significant effect on Trade Credit. Moreover, P*CRISIS has a no impact on the relationship between Profit Predictability and Trade Credit. Therefore, our H5 is rejected. In other words, Firm Type does not affect the relationship between Profit Predictability and Trade Credit. The results illustrated that there is significant-positive relationship between Trade Credit and Size, ROA, Tobin’s Q Ration and Debt Ratio, but there is significant-negative relationship between Trade Credit and Sum Square of Age. However, there is no significant relationship between Trade Credit and other control variables. According to Table 5-1, R2 shows that independent and control variables account for approximately 84 percent of the dependent variable. Wald Test Significance – 459.87 – shows the total significance of H6 model. The results showed that Firm Type has no significant effect on Trade Credit. Moreover, EM*CRISIS has a significant-positive impact on the relationship between Real Earnings Management and Trade Credit. Therefore, the H6 is accepted. In other words, Firm Type affects the relationship between Real Earnings Management and Trade Credit in a significant-positive way. The results illustrated that there is significant-positive relationship between Trade Credit and Size, ROA, Tobin’s Q Ration and Debt Ratio. However, there is no significant relationship between Trade Credit and other control variables.
Table 5-1 Test Results of H3
| Variable | Coefficient | Std | valueZ | Z Probability | |
| C | -1.742 | 0.767 | -2.27 | **0.023 | |
| EM | 0.025 | 0.046 | 0.55 | 0.583 | |
| SERVICE | -0.017 | 0.025 | -0.70 | 0.485 | |
| EM* SERVICE | 0.132 | 0.065 | 2.02 | **0.043 | |
| SIZE | 0.098 | 0.027 | 3.63 | *0.000 | |
| SALEGROWTH | -0.003 | 0.004 | -0.81 | 0.416 | |
| AGE | 0.514 | 0.406 | 1.26 | 0.206 | |
| AGE2 | -0.073 | 0.057 | -1.28 | 0.200 | |
| ROA | 0.131 | 0.060 | 2.17 | **0.030 | |
| AR | 0.056 | 0.045 | 1.26 | 0.207 | |
| TOBINSQ | 0.006 | 0.001 | 3.40 | *0.001 | |
| CASH | -0.023 | 0.039 | -0.59 | 0.556 | |
| DEBT | 0.447 | 0.033 | 13.42 | *0.000 | |
| MARKET SHARE | -0.108 | 0.104 | -1.04 | 0.300 | |
| R2 | 0.839 | ||||
| Wald Test | 459.87 | ||||
| Wald Test Sig. | *0.000 | ||||
*Significant at the 0.01 level.
**Significant at the 0.05 level.
***Significant at the 0.10 level.
7. Conclusion
1. Firm Type has no significant effect on Trade Credit. Also, the H1 is rejected because PS*SERVICE does not have an effect the relationship between Profit Sustainability and Trade Credit. The results also showed that there is significant-positive relationship between Trade Credit and Size, ROA, Tobin’s Q Ration and Debt Ratio. However, there is no significant relationship between Trade Credit and other control variables. Compared to the other studies results, we can refer to Dai & Yang (2015), Anthony et al. (2015), Garcia et al. (2014), Kamyabi&Mehlabani (2017) and IzadiNia&Taheri (2016) studies, which are not in line with our findings.
2. Firm Type has no significant effect on Trade Credit. Also, the H2 is rejected because P*SERVICE does not have an effect the relationship between Profit Predictability and Trade Credit. The results also showed that there is significant-positive relationship between Trade Credit and Size, ROA, Tobin’s Q Ration and Debt Ratio, but there is significant-negative relationship between Trade Credit and Sum Square of Age. However, there is no significant relationship between Trade Credit and other control variables. Compared to the other studies results, we can refer to Dai & Yang (2015), Anthony et al. (2015), Garcia et al. (2014), Kamyabi&Mehlabani (2017) and IzadiNia&Taheri (2016) studies, which are in line with our findings.
3. Firm Type has no significant effect on Trade Credit. Also, the H3 is accepted because EM*SERVICE has a significant-positive effect the relationship between Real Earnings Management and Trade Credit. The results also showed that there is significant-positive relationship between Trade Credit and Size, ROA, Tobin’s Q Ration and Debt Ratio. However, there is no significant relationship between Trade Credit and other control variables. Compared to the other studies results, we can refer to Dai & Yang (2015), Anthony et al. (2015), Garcia et al. (2014), Kamyabi&Mehlabani (2017) and IzadiNia&Taheri (2016) studies, which are not in line with our findings.
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