THE EFFECT OF ASSET STRUCTURE, SIZE, GROWTH AND PROFITABILITY OF THE COMPANY ON THE CAPITAL STRUCTURE OF MANUFACTURING COMPANIES

The purpose of this study is to analyze empirical factors that affect capital structure in manufacturing companies in Indonesia. In this study the independent variables are asset structure, company size, growth and profitability. The dependent variable is the capital structure defined as the ratio of debt to assets. The population in this study is manufacturing companies listed on the Indonesia Stock Exchange during 2007-2016. There were 181 manufacturing companies, using purposive sampling method, 119 manufacturing companies used as a sample of selected population companies that had never been delisted from the Indonesia Stock Exchange during 2007-2016. The sample type is panel data, which is a combination of cross section data with time series data. This research uses quantitative methods and data analysis using panel data regression with a fixed effect model approach. The result is that asset structure, company size and profitability have a significant negative influence on the debt-to-asset ratio, but the growth shows no significant influence of about 69.27% between all independent variables with dependent variables and 30.73% contributed by other parties. Independent variables exclude asset structure, company size, growth and profitability.


INTRODUCTION
As time goes by, the number of human population in the world continues to grow from year to year (Vollset et al., 2020).Population growth will be highest in developing countries.Indonesia as a developing country has the 4th largest population in the world which every year experiences an increase in the number of Indonesian population has reached 259 million people, with a fairly high population growth like this, people's needs regarding products to meet primary needs, secondary needs, and tertiary needs are also increasing (Khalil et al., 2019;Rahayu & Day, 2015).This segment of the population is a very potential segment for manufacturing companies, to offer products that suit the needs of the population.In accordance with Porter's Five forces analysis, an increase in the number of Indonesian population followed by an increase in per capita income causes the bargaining power of buyers or bargaining power to increase (Pham & Petersen, 2021).This condition can cause company competition to increase.
Increasing industrial competition supports each company to increase the productivity of production, marketing and corporate strategy activities so that the sustainability of the company continues to be maintained.In addition, companies are also required to maximize the welfare of shareholders by making good decisions in terms of investment decisions, funding decisions and dividend decisions.
In the business world there are 2 types of funding that can be done by companies, namely internal funding and external funding.Internal funding is usually used to use the company's retained earnings obtained from the company's operations.While external funding is usually done in 2 ways, namely by issuing debt securities or by issuing shares (equity) (Bayless & Diltz, 1994;Fuller et al., 2023).A company's decision to use one of these two funding sources will directly affect the value of the firm.Each of these types of funding has disadvantages and advantages that can later also influence decision making on the source of funding that will be carried out by the company.
Therefore, the right composition is needed between the amount of debt and the number of shares to be issued by the company in order to get optimal value of the firm but still be able to meet the interests of shareholders and bondholders.To get funds from outside parties, of course, there are costs that must be incurred by the company in the form of dividend payments for shareholders and also interest payments and principal to bondholders.The costs incurred must also be considered by companies when making decisions in determining their capital structure.The company certainly wants the minimum cost in providing this source of funds.In this case, companies are required to know the factors that affect the funding structure as a basis for consideration to determine the most optimal funding level in order to be able to increase the value and competitiveness of the company (Bandyopadhyay & Barua, 2016).Good funding decisions of a company can be seen from the capital structure, namely financial decisions related to debt composition, company size, company growth and company profitability (Alves et al., 2015).
The author chose manufacturing companies as a matter of discussion in the research because manufacturing companies are a large business sector and have various subsectors that contribute greatly to GDP in Indonesia.These companies also play a major role in meeting the primary, secondary, and tertiary needs of the population in Indonesia, so that the performance of this industrial sector will have an impact on meeting the needs of the population in Indonesia.
From the phenomena expressed above, this topic is interesting to conduct research in Indonesia on factors affecting capital structure, especially go-public manufacturing companies on the Indonesia Stock Exchange for the period 2007-2016, from a population of 181 (one hundred eighty one) manufacturing companies on the Indonesia Stock Exchange during the period 2007-2016.
The company's decision in determining the funding source factor (capital structure) is strongly influenced by the characteristics of the place where the company is located and the uniqueness of each company.Every decision taken by the company will greatly affect the company's financial value which is reflected in the company's share price traded on the Indonesia Stock Exchange which can be very volatile (Dewandaru et al., 2014;Stereńczak & Kubiak, 2022).This is a market reaction to the announcement of changes in capital structure which indirectly indicates whether the company is moving slightly or further towards a more optimal capital structure or as targeted or not (Belkhir et al., 2016).
Previous research can also be used as comparison material in determining the optimal capital structure (Kim, 2021).According to previous research compiled by Nugroho et al (2006) where the capital structure is proxied with the Debt toEquity Ratio (DER), stated that the asset structure is not affected by DER, company growth has a positive effect on DER, and ROA negatively affects DER.According to research by Shah et al (2012) where capital structure is proxied with leverage, stated tangibility and profitability negatively affect leverage, while the size and growth of the company positively affect leverage.
Another study conducted by Ahmed & Hanif (2015) where capital structure is proxied with leverage, states tangibility has a positive effect on leverage, while company size, company growth, and profitability negatively affect leverage.In the research of Hijazi et al (2016) where capital structure is proxied with leverage, stated tangibility and company growth have a positive effect on leverage, while company size and profitability negatively affect leverage.Saputri et al (2014) where capital structure is proxied with leverage, states tangibility, and company size positively affect leverage, company growth has no effect on leverage, and profitability negatively affects leverage.
From some of these studies, it can be seen that there are several variables that affect capital structure still show different results, even contradicting the results of one study with another.This will be raised as a research gap in this study.Some of these variables include the company's asset structure, company size, company growth and company profitability.
From the annual report information obtained by the author on companies engaged in manufacturing listed on the Indonesia Stock Exchange for the period 2007-2016, there are 119 (one hundred and nineteen) companies that continue to survive during that period are recorded to have a strong capital structure in facing competition.Especially it can get through the American crisis period in 2008.Therefore, the author feels the need for further research related

Data Analysis Methods
This study uses quantitative methods with linear regression analysis tools panel data, because the data used is panel data, which is combined data between cross section data and time series data.Panel data regression is a regression analysis based on panel data to observe the relationship between one dependent variable and one or more independent variables or explanatory variables.In addition, panel data regression is able to provide more information and is more efficient compared to using multiple linear regression.Therefore, the data analysis technique in this study uses a panel data regression model.Based on the calculation results in table 1, it appears that the average asset structure data during the observation period from 2007 to 2016 was the smallest value of 0.0100, the largest value was 0.8533 from the total average of 0.3542 with a standard deviation of 0.2002.The company's asset structure variable identifies a good return because the standard deviation that reflects deviations from the company's asset structure variable data is low, because the standard of development is smaller than its average value.So there are not many outliers and the data tends to be homogeneous with low fluctuations.

RESULT AND DISCUSSION Research Results
Based on the calculation results in table 1, it appears that the average company size data during the observation period from 2007 to 2016 was the smallest value of 3.9493, the largest value was 7.9891 from the total average of 6.0343 with a standard deviation of 0.6993.The company size variable identifies good results because the standard deviation reflecting deviations from the data of the firm size variable is low because the standard deviation is smaller than its average value.So that the emergence of outliers is small and the data tends to be homogeneous with low fluctuations.
Based on the calculation results in table 1, it appears that the average company growth data during the observation period from 2007 to 2016 was the smallest value of -0.3611, the largest value was 1.8553 from the total average of 0.1280 with a standard deviation of 0.2588.The company's growth variable identifies poor data results because the standard deviation that reflects deviations from the company's growth variable data is quite high because the standard deviation is greater than its average value.So that many outliers appear and the data tends to be heterogeneous with high fluctuations.
Based on the calculation results in table 1, it appears that the average company profitability data during the observation period from 2007 to 2016 has the smallest value of -0.6618, the largest value is 0.7530 of the total average of 0.0558 with a standard deviation of 0, 1390.The company profitability variable identifies poor data results because the standard deviation which reflects deviations from the company profitability variable data is quite high because the standard deviation is greater than the average value.So many outliers appear and the data tends to be heterogeneous with high fluctuations.
Based on the calculation results in table 1, it appears that the average capital structure data proxied by the company's debt to asset ratio during the observation period from 2007 to 2016 was the smallest value of 0.0744, the largest value was 0.7350 from the total average of 0.5905 with a standard deviation of 3.4707.The company's growth variable identifies good data results because the standard deviation that reflects deviations from the company's capital structure variable data is quite low because the standard deviation is smaller than its average value.So that there are many outliers and the data tends to be heterogeneous with low fluctuations (Pili et al., 2017).Based on the output above, that probability value is 0.000.Since the probability alue (0.000) < 0.05, it can be concluded that the residual is not normally distributed.Based on the results of the output above, it can be seen that there is no multicollinearity because all variables have a colleration value of < 0.8.Based on the output table above, it appears that the chi-square probability value for the estimated results of the glacier test is 0.000.Since the value of prob.chi-square (0.000) < 0.05, it can be concluded that heteroscedasticity occurs.And it can also be seen that the prob.chisquarevalue for the estimated results of the Chow test is 0.0000.Because the value of prob.Chi-square < 0.05, prob value.Chi-square (0.0000) is smaller than 0.005, so it can be concluded that the model used is a fixed effect model.Based on the output table above, the HAUSMAN value (calculation value of the hausman formula) of the hausman test result is 16.5860.The chi-square value of the table with degrees of freedom (d.f) 4 is obtained at 9.4877.Since the value of HAUSMAN ≥ χ k-1 2, the value of HAUSMAN (16.5860) is greater than the value of χ k-1 2 (9.4877).And it can also be seen that the value of prob.The chi-square for the estimated Hausman test is 0.0023.Because the value of prob.Chi-square < 0.05, so it can be concluded that a suitable approach uses a fixed effect model.The value of the regression coefficient in the independent variables illustrates that if it is estimated that the independent variable increases by one unit and the value of the other independent variable is estimated to be constant or equal to zero, then the value of the dependent variable is estimated to rise or can decrease according to the sign of the coefficient of the independent variable.

Regression Equation
From the regression equation above, a constant value of 1.593367 is obtained.That is, if the capital structure variable (Y) is not influenced by the four independent variables of asset structure (X1), company size (X2), company growth (X3) and company profitability (X4) (zero value), then the average size of capital structure (Y) will be 1.593367.
The sign of the regression coefficient of the independent variable indicates the direction of the relationship of the variable concerned with the capital structure (Y).The regression coefficient for the independent variable X1 is negative, indicating that there is an undirected relationship between the company's asset structure (X1) negatively affecting the capital structure (Y).The regression coefficient of variable X1 of -0.285783 means that every increase in the company's asset structure (X1) by one unit will cause a decrease in capital structure (Y) by 0.285783.
The regression coefficient for the independent variable X2 is negative, indicating an undirected relationship between the size of the company (X2) and the capital structure (Y).That is, the size of the company (X2) negatively affects the capital structure (Y).The regression coefficient of the variable X2 of -0.1433305 means that for every increase in company size (X2) by one unit will cause a decrease in capital structure (Y) by 0.1433305.
The regression coefficient for the independent variable X3 is negative, indicating an undirected relationship between company growth (X3) and capital structure (Y).That is, the growth of the company (X3) negatively affects the capital structure (Y).The regression coefficient of the variable X3 of -0.030992 means that every increase in company growth (X3) by one unit will cause a decrease in capital structure (Y) by 0.030992.
The regression coefficient for the independent variable X4 is negative, indicating a disproportionate relationship between the company's profitability (X4) and capital structure (Y).That is, the profitability of the company (X4) negatively affects the capital structure (Y).The regression coefficient of the variable X4 of -0.587207 means that for every increase in the company's profitability (X4) by one unit will cause a decrease in capital structure (Y) by 0.587207.

Statistical test (Regression Coefficient Meaningfulness Test)
1. Simultaneous hypothesis testing (F test)  FCALCULATE value (22.9668) is greater than the FTABEL value (1.2393).And it can also be seen that the value of prob.FCALCULATE of 0.000000.Because the value of prob.FCALCULATE (0.000000) < 0.05, THEN H0 is rejected.Thus, it can be concluded that simultaneously there is a significant influence between the company's asset structure (X1), company size (X2), company growth (X3) and company profitability (X4) on capital structure (Y).Based on the table above, the following results are obtained:

Partial Hypothesis Testing (t Test)
1) For the variable structure of company assets (X1).The regression coefficient of the variable X1 is (-0.285783)= (β1 ≤ 0) then, H0 is accepted, meaning that there is no positive influence, that is, negative.Furthermore, the calculated t value is obtained, which is (-3.247311).Since t counts (-3.247311) < -t table (-1.962), the significance is significant.Therefore, it can be concluded that the company's asset structure (X1) partially has a negative and significant influence on the company's capital structure (Y).2) For the company size variable (X2).The regression coefficient of the variable X2 is (-0.1433305)= (β2 ≤ 0) then, H0 is accepted, meaning that there is no positive influence, that is, negative.Furthermore, the calculated t value is obtained, which is (-4.198432).Since t counts (-4.198432) < -t table (-1.962), the significance is significant.Therefore, it can be concluded that the size of the company (X2) partially has a negative and significant influence on the capital structure of the company (Y).3) For the company's growth variable (X3).The regression coefficient of the variable X3 is (-0.030992)= (β3 ≤ 0) then, H0 is accepted, meaning that there is no positive influence, that is, negative.Furthermore, the calculated t value is obtained, which is (-1.298716).Since t counts (-1.298716) > -t table (-1.962), the significance is insignificant.Therefore, it can be concluded that the growth of the company (X3) partially has a negative but not significant influence on the company's capital structure (Y).4) For the variable profitability of the enterprise (X4).The regression coefficient of the variable X4 is (-0.587207)= (β4 < 0) then, H0 is rejected, Ha is accepted, meaning that there is a negative influence.Furthermore, the calculated t value is obtained, which is (-8.784013).Since t counts (-8.784013) < -t table (-1.962), the significance is significant.Therefore, it can be concluded that the profitability of the company (X4) partially has a negative and significant influence on the capital structure of the company (Y).Based on the output of Eviews 8 above, an Adjusted R-squared value of 0.6927 was obtained.This shows that the contribution of asset structure (X1), company size (X2), company growth (X3) and company profitability (X4) to capital structure (Y) is 69.27% while the remaining 30.73% is the contribution of other variables besides the independent variable studied.Each of the independent variables can be interpreted as having an effect on the capital structure as follows: a.For the variable structure of company assets (X1) obtained The regression coefficient of variable X1 of -0.285783 means that for every increase in the company's asset structure (X1) by one unit will cause a decrease in capital structure (Y) by 0.285783.It indicates a negative direction.From Table also obtained a calculated t value of -3.247311 < -t table (-1.962), then the level of significance is significant.This shows that the company's asset structure (X1) partially has a negative and significant influence on the company's capital structure (Y).With a high asset structure, it is easier to get a loan, because the high asset structure can be used as collateral to borrow.However, with a high composition of fixed assets, business risks are also attached, namely uncertainty in the return of principal and interest on loans, and there is a possibility of default.

Discussion
Because to convert fixed assets into current assets takes a long time, thus reducing the company's ability to meet obligations on debt and interest.Meanwhile, to meet debt and interest obligations requires liquidity, so with the increasing asset structure, companies choose not to use debt in making funding decisions.This is consistent with pecking order theory and supports research conducted by (Shah et al., 2012).b.For the company size variable (X2), the regression coefficient of the variable X2 of -0.1433305 means that every increase in company size (X2) by one unit will cause a negative direction.From table 4.14 also obtained the calculated t value of (-4.198432) < -t table (-1.962), then the level of significance is significant.This shows that the size of the company (X2) partially has a significant negative influence on the capital structure (Y).This happens because of the increasing sales value of manufacturing industry products caused by the increasing demand for goods, so that the company's income derived from sales value will also be greater, and internal funds are getting bigger.With the increasing internal funds, the company's management will determine the order of funding decisions first from internal funds derived from income first, then debt, and external capital as the last choice.In this case, internal funds derived from sales are used as the first order of funding decisions, so that it will minimize the use of debt.This is consistent with pecking order theory and supports research conducted by Ozkan (2011), Suranta andMediastuty (2003), Ahmed & Hanif (2015), Hijazi et al (2016).c.For the company growth variable (X3), the regression coefficient of the variable X3 of -0.030992 means that for every increase in company growth (X3) by one unit will cause a decrease in capital structure (Y) by 0.030992.It indicates a negative direction.From table 4.14 also obtained the calculated t value of (-1.298716) > -t table (-1.962), then the level of significance is not significant.This shows that the growth of the company (X3) partially has a negative but not significant influence on the company's capital structure (Y).For manufacturing companies, the increase in assets, especially those that are fixed assets, is a spur so that the company is able to produce.With the expectation, the increase in fixed assets will have a positive impact on sales growth.On the other hand, the increase in assets will require the company to provide adequate funds, so that the additional income derived from the increase in the amount of assets does have a negative influence on the capital structure, but the effect of the movement is slight (not significant).This is consistent with trade off theory and supports research conducted by Ozkan (2011), Bhaduri (2010), Demir et al., (2019).d.For the variable company profitability (X4), the regression coefficient of the variable X4 of -0.587207 means that every increase in Company Profitability (X4) by one unit will cause a decrease in capital structure (Y) by 0.587207.It indicates a negative direction.
From Table also obtained a calculated t value of -8.784013 < -t table (-1.962), then the level of significance is significant.This shows that the profitability of the company (X4) partially has a negative and significant influence on the company's capital structure (Y).The presence of negative influences indicates that profitability is a characteristic that the company has internal funds to fund the company's activities.The higher profitability makes the company able to meet funding needs using its internal funds so that the use of debt becomes relatively small.This is consistent with pecking order theory and supports research conducted by Titman and Wessel (2008), Ozkan (2011), Hidayati (2011), Mutamimah (2013), Nugroho (2006), Shah et al (2012), Ahmed & Hanif (2015), Hijazi et al (2016), Saputri et al (2014).From the results of simultaneous hypothesis testing (Test F) in Manufacturing Companies for the period 2007 to 2016, it can be interpreted the effect on capital structure seen from FCALCULATE (22.9668) > FTABLE (1.2393).And it can also be seen that the value of prob.F count (0.000000) < 0.05, means that simultaneously the company's asset structure, company size, company growth, and company profitability have a significant influence on the company's capital structure.
Based on the results of testing the coefficient of determination with the adjust Rsquared approach , an Adjusted R-squared value of 0.6927 or 69.27% was obtained.This shows that the contribution of asset structure (X1), Company Size (X2), Company Growth (X3), and Company Profitability (X4) to Capital Structure (Y) is 69.27% while the remaining 30.73% is the contribution of other variables besides the independent variables studied.

CONCLUSION
Based on the statistical output from Eviews 8, the research concludes the following: Company asset structure has a significant negative influence on the corporate capital structure, in line with the pecking order theory and previous research; Company size also has a significant negative impact on the corporate capital structure, in accordance with the pecking order theory and previous research findings; Company growth has a negative influence but is not significant to the corporate capital structure, aligning with the trade-off theory and earlier studies; Company profitability has a significant negative impact on the corporate capital structure, consistent with the pecking order theory and prior research; Simultaneously, the company's asset structure, size, growth, and profitability all significantly affect the corporate capital structure, contributing 69.27%, while the remaining 30.73% is attributed to external factors beyond the independent variables under investigation.Therefore, it is advisable for companies to determine an optimal capital structure, adhering to the principle that the optimal capital structure balances debt and equity to maximize company value and optimize the cost of the capital structure, as explained by Riyanto (2005).

Table 1
Descriptive Statistics Table of Manufacturing Companies from 2007 to 2016 The Effect of Asset Structure, Size, Growth and Profitability of The Company on The Capital Structure of Manufacturing Companies 992Return: Study of Management, Economic And Business, Vol 2 (10), October 2023

Table 3
Heteroskedasticity Test The Effect of Asset Structure, Size, Growth and Profitability of The Company on The Capital Structure of Manufacturing Companies 993Return: Study of Management, Economic And Business, Vol 2 (10), October 20234.Autocorrelation Test

Table 4
Autocorrelation Test Based on the table above, the Durbin-Watson value is 1.929985.Since the DW value is between dU (1.715) < DW (1.929985) < 4 -dU (2.285), it can be concluded that there

is no autocorrelation. Panel Data Regression Estimation Model Selection
1. Chow Test

Table 5
Chow Test Based on the output table above, the CHOW (statistical F) value of the chow test results is 19.8964.The F value of the table with degrees of success (d.f) 118 and 1067 was obtained at 1.2393.Because the CHOW value >

F table, the CHOW value (
19.8964) is greater than the F value of the table 91.2393).

Table 6
Hausman Test Source: Secondary Data, ICMD 2007-2016 Processed The Effect of Asset Structure, Size, Growth and Profitability of The Company on The Capital Structure of Manufacturing Companies 994Return: Study of Management, Economic And Business, Vol 2 (10), October 2023

Table 7
Multiple Regression Analysis Test

Table 8
Simultaneous hypothesis test (F test)From the output table above, the F-Statistic value (FCALCULATE, F calculation value using the formula) is 22.9668.FTABLE values with degrees of freedom (d.f) 118 and 1067 were obtained at 1.2393.Because the FCALCULATE value > FTABLE, the

Table 9
Partial Hypothesis Test (t Test) The Effect of Asset Structure, Size, Growth and Profitability of The Company on The Capital Structure of Manufacturing Companies 996Return: Study of Management, Economic And Business, Vol 2 (10), October 2023

Table 10
Coefficient of Determination Analysis Test