Abstract
Purpose
Capital structure is an important factor for the company because it will be directly related to the financial condition of the company. This study aims to determine the effect of asset structure, earning volatility, and financial flexibility on capital structure.
Design/methodology/approach
The population in this study was 52 companies in the consumer goods industry sector on the Indonesia stock exchange (IDX) and a sample of 39 companies obtained by purposive sampling method. The research method used in this study is multiple linear regression analysis using Eviews software.
Findings
The test results in the study show that asset structure and financial flexibility have a positive effect on capital structure, while earning volatility does not affect capital structure in companies in the consumer goods industry sector on the IDX.
Research limitations/implications
The results of this research can contribute to the addition of knowledge in the field of accounting, especially regarding the capital structure. Company management can use the results of this research as a reference and consideration to find out the factors that affect the capital structure so that company management can still maintain the company's survival and improve company performance.
Practical implications
The results of this study can contribute to the addition of knowledge in the field of accounting, especially regarding capital structure. Company management can use the results of this research as a reference and consideration to determine the factors that affect the capital structure so that company management can still maintain the survival of the company and improve company performance.
Social implications
This study only uses the variables of asset structure, financial flexibility and earning volatility as independent variables. Further research is recommended to consider the use of other variables that can affect capital structure and if using the same variable is expected to use research objects that have stable or increasing asset and income values, so that asset structure variables and profit volatility can show significant results and influences.
Originality/value
This study is one of the few studies that examines how the effect of asset structure, profit volatility and financial flexibility on capital structure in companies in the consumer goods industry sector on the IDX. Company management must pay attention to the composition of the capital structure as well as possible and make careful planning and the right decisions so as to produce a capital structure that can provide profits.
Keywords
Citation
Putri, R.L. and Willim, A.P. (2023), "Analysis of the effect of assets structure, earning volatility and financial flexibility on capital structure in consumer goods industry sector companies on the Indonesia stock exchange", LBS Journal of Management & Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/LBSJMR-11-2022-0069
Publisher
:Emerald Publishing Limited
Copyright © 2023, Rumanintya Lisaria Putri and Andre Prasetya Willim
License
Published in LBS Journal of Management & Research. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Current global economic conditions create an increasingly fierce competition between companies. This makes the company increasingly strive to maintain the survival of the company and improve its performance so that the company's goals can be achieved. The purpose of the company is to obtain the maximum profit and improve the welfare of shareholders so as to increase the value of the company. Companies need very strong supporting factors, especially in the field of funding. One of the problems in funding policy in the company is the problem of capital structure.
Capital structure is an important factor for the company because it will be directly related to the company's financial condition. An optimal capital structure will result in capital cost efficiency. Capital efficiency will make the stock price rise and make the company's value also rise. In determining the type of cost source chosen, managers must consider and use existing sources of funds as best as possible to determine investment activities and other operational activities that can later add or even maximize the value of the company. In general, the fixed assets of the company are purchased with debt. These fixed assets can be used as collateral to creditors to pay off their obligations. This indicates that companies with high levels of company fixed assets will be easier to get debt.
Whether or not the company's profit is stable is indicated by the degree of profit volatility. Companies with high levels of volatility tend to make managers difficult to make decisions and companies difficult to get external funding such as debt because in general creditors do not dare to make loans. With a high level of profit volatility, the company's debt level will be lower. Financial flexibility is a company's ability to obtain funding sources when times are tough. Companies with a high level of financial flexibility will be able to get through difficult times and take advantage of investment opportunities that arise suddenly because they have no difficulty finding funds to overcome losses and fund these investments. Companies that can utilize fixed assets efficiently to increase sales and earn profits will improve company performance, so that good performance will increase the level of confidence of creditors and investors.
2. Literature review and hypothesis development
Gordon Donaldson was the first expert to propose the Pecking Order Theory in 1961 and later in 1984 Myers and Majluf also developed it further. In Pecking order theory, it is stated that companies will determine the hierarchy of the most preferred funding sources (Husnan dan Pudjiastuti, 2012, p. 276). The pecking order theory states that: (1) companies like internal financing or funding from the company's operational results, (2) companies try to adjust dividend distribution by avoiding drastic changes in dividend payments, (3) dividend policies tend to be relatively rigid, accompanied by unpredictable fluctuations in profitability and investment opportunities, resulting in operating funds sometimes exceeding the need for funds to investment. If the operating proceeds are less than the investment requirement, the company will reduce its cash balance or sell its securities, (4) if Outside funding is needed, then the company will issue the safest securities first starting with the issuance of bonds, then followed by securities with option characteristics such as convertible bonds and finally if they are still insufficient, then a new share issuance is carried out (Husnan dan Pudjiastuti, 2012, p. 325). Based on the pecking order theory, it can be concluded that the company prefers and chooses to use its internal funding sources first, namely retained earnings and depreciation, if the internal funding sources have been maximized and are not sufficient, then the company will use external funding sources.
Pecking Order Theory states that companies in making funding decisions are carried out hierarchically from internal to external funding. The order of funding starts from funds sourced from retained earnings, then debt and finally comes to the issuance of new equity, meaning that funding starts from the source of funds at the lowest cost (Myers & Majluf, 1984). The theory of pecking order adheres to a system of funding decisions with a logical order of preference of investors towards the company's prospects and consistent with goals, so that managers are able to maximize welfare for shareholders. The pecking order theory assumes that firms tend to choose internal financing to fund their projects.
Pecking Order Theory arises because of the information asymmetry that occurs between companies and their financiers. Therefore, a corporate financing hierarchy emerges that starts with retained earnings because it has the lowest information asymmetry cost, followed by debt and finally equity derived from external sources with the highest information asymmetry cost. Information asymmetry is one of the driving factors for Pecking Order Theory in developing countries including Indonesia.
The composition of the capital structure must be considered by the company's management as well as possible and make careful planning and the right decisions so as to produce a capital structure that can provide profits. Companies that already have open status (go public) can use internal funding or external funding.
Capital structure is permanent expenditure that describes the comparison between long-term debt and own capital (Restiyowati & Widyawati, 2014). The issue of capital structure is a very important issue for many companies, because the capital structure in the company is a reflection of the company's financial condition. According to Riyanto (2011), capital structure is a comparison between debt and own capital whose sources come from internal and external, used to make decisions on company funding related to spending activities and investment financing. While Ghosh, Cai, and Li (2000) defines capital structure as a comparison between a company's debt (total debt) and total assets (total assets).
According to Harjito and Martono (2012, p. 256): “Capital structure is a comparison or balance of a company's long-term funding indicated by the comparison of long-term debt to own capital.” Weston and Brigham (2010, p. 150) state that capital structure is a combination of debt, common stock and preferred stock that the company wants in its corporate capital structure. According to Van Horne and Wachowicz (2013, p. 176) capital structure is a mix or proportion of permanent long-term financing of a company that can be represented by debt, preferred stock and common stock equity.
2.1 Effect of assets structure on capital structure
Asset structure is a comparison between fixed assets and total assets owned by the company. The position of the asset structure is very concrete in the process of funding the company. According to Alipour, Mohammadi, and Derakhshan (2015, p. 60): Fixed assets are generally obtained through external funding, namely debt, then used as a guarantee for creditors when the company is liquidated. According to Mujiatun, Rahmayati, and Ferina (2021) companies will have ease in debt procurement when they have a large fixed asset position, so companies tend to take advantage of these conditions by making debt as the first alternative to obtain external funding sources, this makes the company's capital structure increase.
The asset structure in a company is one of the factors that affect the capital structure because it plays a role in determining the amount of long-term debt obtained by the company. This can have an impact on determining the size of the capital structure owned by the company (Andika & Sedana, 2019). Companies that experience an increase in the number of assets tend to prefer to meet the needs of funds through debt. Asset structure refers to the proportion of an enterprise's investments in the form of fixed assets. These fixed assets can be used as collateral for companies when they owe money (Ernawati & Budiharjo, 2020).
If the company goes bankrupt in fulfilling its obligations, the fixed assets owned can be used as collateral or sold to be converted into cash. This means that the larger the asset structure, the company's capital structure derived from debt will increase. This is supported by previous research conducted by Touil and Mamoghli (2019), Aulia, Al Ashry, and Fitra (2019), and Mujiatun et al. (2021) yang menyatakan bahwa assets structure berpengaruh positif terhadap capital structure.
Assets Structure berpengaruh positif terhadap Capital Structure
2.2 The effect of earning volatility on capital structure
Earnings volatility refers to how stable or unstable a company's earnings are. Investing in companies whose profits fluctuate greatly is a risky decision for investors. Fluctuating profits will make it difficult for management to borrow funds for long-term investments. Cash flows estimated to meet obligations may not materialize. This can be a serious problem resulting in asset confiscation by lenders and in more extreme cases could result in bankruptcy (Yeo, 2016).
Earning volatility reflects the rate of return of a total investment before deducting taxes and interest on loans. Earning volatility is useful for measuring the operational effectiveness of company management and is one of the considerations for investors to provide credit to companies, because companies that have relatively stable revenues will have a greater opportunity to obtain loans or foreign capital than companies that have unstable income. Companies that have stable income are able to meet obligations, due to the use of foreign capital. Conversely, companies with unstable earnings may bear the risk of not being able to pay interest expenses or installments.
Earning volatility is one of the proxies for measuring company risk. According to According to Brigham and Houston (2019, p. 479): One of the important determinants of capital structure is business risk which is the risk inherent in a company's operations if the company does not use debt in their funding. Brigham and Ehrhardt (2017, p. 645):
The company will have little risk if the demand for products is stable, the prices of inputs and products are relatively constant, can adjust the price of products easily in case of an increase in costs, and if the percentage of high costs can change and will decrease if sales decline. In other words, the lower the company's business risk, the debt ratio will increase.
Companies with high risk generally use more internal funding, so the higher the company's risk level, the capital structure will decrease; this is supported by previous research conducted by Alipour et al. (2015) which states that earning volatility negatively affects capital structure.
Earning volatility negatively affects capital structure
2.3 The effect of financial flexibility on capital structure
Signaling theory states that any event in the form of an announcement of a corporate action, or publication of a company's financial performance, whether intentional or unintentional, will have information content as a signal conveyed to the market (Brigham & Houston, 2006). One way to reduce asymmetric information is to signal to outsiders by providing financial information that is trustworthy and has integrity, so that it will reduce uncertainty about the company's prospects in the future. The publication of the company's financial performance can provide signals to parties outside the company about the actual situation that occurs in the company. Companies will manage their financial strength through financial slack (a situation where the company has excess funds) such as cash, real assets and securities in the market (Cherkasova & Kuzmin, 2018). Thus, a company that has higher financial flexibility will reflect that the company has a low level of debt.
Flexibility is the level of capacity and speed of the company to be able to mobilize its financial resources or take preventive, reactive and exploitative actions in order to maximize company value (Murti, Achsani, & Andati, 2016). According to Brigham and Houston (2019, pp. 502-504) financial flexibility is one of the factors that affect capital structure. The goal of maintaining financial flexibility from an operational standpoint is to maintain adequate reserve borrowing capacity. According to Bancel and Mittoo (2011), financial flexibility is one part of a company's business strategy and is very important for capital structure decisions. Companies will generally try to find decisions to avoid risk. In terms of funding, the company will assess and take policies to minimize the possible cost of capital that must be incurred in the sense of returning to investors.
Financial flexibility becomes an important quality for business entities or organizations, as it helps them survive in difficult situations and take the right decisions to achieve long-term goals. Based on the approach of signal theory and pecking order theory, companies with a higher level of financial flexibility will use less debt and take advantage of investment opportunities so that the capital structure will decrease. This is supported by previous research conducted by Alipour et al. (2015) which states that financial flexibility negatively affects capital structure.
Financial flexibility negatively affects capital structure
3. Research methods
The population taken in this study is 52 companies in the consumer goods industry sector listed on the Indonesia stock exchange (IDX). The sampling technique in this study used purposive sampling. The criteria for determining the sample in this study are companies in the consumer goods industry sector that conducted IPOs before 2015 and companies that met the criteria were as many as 39 companies. The data collection technique used is a documentary study; the data used is financial statements obtained through www.idx.co.id site. Data analysis techniques carried out in this study are descriptive statistical analysis, classical assumption tests, influence analysis using multiple linear regression analysis, coefficients of determination, correlation coefficients and hypothesis tests, namely F test and t test. Data analysis in this study uses the Eviews application. The conceptual framework in this study is as illustrated below (see Figure 1):
The following is the formula used to calculate the research data:
- 1.
According to Kasmir (2010, p. 123): The formula for Debt Assets Ratio is:
- 2.
According to Alipour et al. (2015, p. 65): The formula for calculating Asset Structure is:
- 3.
According to Alipour et al. (2015, p. 65): The formula for calculating Earning Volatility is:
- 4.
According to Alipour et al. (2015, p. 65): The formula for calculating Financial Flexibility is:
4. Results and discussion
4.1 Descriptive statistical analysis
The following is presented Table 1 which shows the results of descriptive statistical analysis:
In Table 1 it is known that the amount of data used is 195 data. The capital structure shows a minimum value of 0.070700, a maximum value of 1.248600, an average value of 0.435419 and a standard deviation value of 0.208609. The assets structure shows a minimum value of 0.0380700, a maximum value of 1.248600, an average value of 0.414473 and a standard deviation value of 0.150413. Earning volatility indicates a minimum value of 0.003900, a maximum value of 0.395200, an average value of 0.036200 and a standard deviation value of 0.061349. Financial flexibility shows a minimum value of 0.4614, The maximum value is 0.799900, the average value is 0.360626 and the standard deviation value is 0.240475.
4.2 Classical assumption test
The following is presented Table 2 which is the result of the multicollinearity test and Table 3 which shows the results of the heteroscedasticity test:
Based on the table above, we can understand that the value of the partial correlation between independent variables does not have a coefficient of more than 0.8. This shows that in this regression model there are no symptoms of multicollinearity.
Based on the results above that each independent variable shows a prob value of >0.05 so that it can be concluded that this regression has passed the classical assumption test of heteroschasidasticity.
4.3 Normality test
In panel data regression, FEM and Customer Experience Management (CEM) models use ordinary least square (OLS) approaches. So normality testing is not mandatory on the OLS approach, while mandatory for the GLS approach.
4.3.1 Multiple linear analysis
The following is presented Table 4 showing the results of multiple linear regression analysis.
Here is the result of the multiple linear analysis equation:
4.3.2 Test of coefficient of determination and correlation
The following is presented Table 5 which is the test result of the coefficient of determination and correlation coefficient:
Based on Table 5, the value of the coefficient of determination in this study is 0.497564, meaning that the percentage of contribution of the influence of the independent variable to the dependent variable is 49.7564% while the remaining 50.2436% is influenced by other variables that are not included in this study.
4.3.3 Test F
The following is presented Table 6 which is the result of test F:
Based on the table above, it can be concluded or explained that the value of F-statistic is 1.580815 and also has a prob value in (prob (f-statistic)) of 0.024898 < of a significant value of 0.05. Thus, it can be concluded that the research model above is feasible to continue testing, meaning that if all independent variables change simultaneously, it will have a significant effect on the dependent variable Y.
4.3.4 Uji t
The following is presented Table 7 which is the result of t test:
5. Discussion of hypotheses
5.1 The effect of asset structure on capital structure
H1: Asset structure has a positive effect on capital structure.
The first hypothesis test (H1) was conducted to determine the effect of asset structure on capital structure. Based on the results of the tests conducted, it is known that there is an influence of asset structure on capital structure in companies in the consumer goods industry sector on the IDX. The test results through the t test show that the asset structure variable has a Prob. value of 0.0107 smaller than 0.05 and with a positive directional regression coefficient of 0.169937. Based on this value, it can be stated that the asset structure has a positive effect on the capital structure, then the first hypothesis in this study is accepted. Research results support previous research conducted by Touil and Mamoghli (2019), Aulia et al. (2019) and Mujiatun et al. (2021) which stated that asset structure has a positive effect on capital structure. This means that the addition of fixed assets in the consumer goods industry sector companies is used as collateral to obtain debt by the company.
Companies when they have a good asset structure will use debt as a source of company funding. The use of corporate debt will increase when the condition of the company's fixed assets increases (Aulia et al., 2019). Companies will more easily obtain funds or debt loans from external parties by pledging fixed assets owned by the company in debt payments. The company will also obtain a loan in overcoming the company's financial difficulties. An understanding of the effect of asset structure on capital structure can help companies in making more informed decisions related to financing and financial management. Effective capital structure management will help the company achieve an optimal balance between risk and profit and improve financial performance and business growth in a sustainable manner.
5.2 The effect of earnings volatility on capital structure
H2: Earning volatility negatively affects capital structure.
The second hypothesis test (H2) was conducted to determine the effect of earning volatility on capital structure. Based on the results of the tests conducted, it is known that there is no influence of earning volatility on the capital structure of companies in the consumer goods industry sector on the IDX. The test results through the t test show that the variable earning volatility has a Prob. value of 0.84246 greater than 0.05 and with a positive directional regression coefficient of 0.074237. Based on the results of data processing, it can be concluded that earning volatility has no effect on capital structure, so the second hypothesis in this study is rejected. The results of this study do not support previous research conducted by Alipour et al. (2015) which stated that earning volatility negatively affects capital structure. This means that fluctuations in the profits of companies in the consumer goods industry sector are not the main determining factor for management in determining the capital structure.
The results of the study stated that fluctuations in the company's profits did not have a significant influence on the company's decision in choosing funding sources. Companies do not tend to change their capital structure based on fluctuations in profits. According to Setiawati (2018), profits obtained by companies in the consumer goods industry sector are erratic and based on industry average analysis, 46% of company funding is financed by debt.
5.3 The effect of financial flexibility on capital structure
H3: Financial flexibility negatively affects capital structure.
The third hypothesis test (H3) was conducted to determine the effect of financial flexibility on capital structure. Based on the results of the tests that have been conducted, it is known that there is an influence of financial flexibility on the capital structure of companies in the consumer goods industry sector on the IDX. The test results through the t test show that the financial flexibility variable has a Prob value of 0.00326 which is smaller than 0.05 and with a positive directional regression coefficient of 0.259536. Based on the results of data processing, it can be concluded that financial flexibility has a positive effect on capital structure, then the third hypothesis in this study is rejected. The results of this study are not in line with research conducted by Alipour et al. (2015) which states that financial flexibility negatively affects capital structure. Companies with a good level of financial flexibility have the ability to manage company finances more adaptively and responsively to changes in market or financial conditions. By having high flexibility, companies can more easily overcome unexpected financial challenges, take advantage of investment opportunities and optimize the use of available funds.
The financial flexibility of the company indicates great access to financing. Companies will choose cheaper financing with this large access to financing. Companies that have high financial flexibility have more options in financing investment projects. Companies have the freedom to choose between using share capital or debt according to their needs and risks. In situations where borrowing funds seems more profitable, companies can take out loans more easily because creditors believe in the company's ability to manage debt effectively. According to Rahayu (2020), debt financing is cheaper financing than equity. Interest costs arising from debt can provide benefits in the form of tax deductions. Therefore, with high financial flexibility, companies will tend to increase debt financing. This means that companies in the consumer goods industry sector with a high degree of financial flexibility, indicating that companies to earn high profits tend to use debt in their funding.
6. Conclusion
Based on the results of the tests that have been carried out, it can be concluded that financial flexibility variables and asset structure have a positive effect on capital structure, while earnings volatility does not affect capital structure. By understanding the effect of financial flexibility, asset structure and earnings volatility on capital structure, companies can make smarter and more effective financing decisions. Setting the right capital structure can assist the company in achieving business goals, reducing financial risks and increasing the overall value of the company. The author's suggestion is that if future research considers the use of the same variables, it is expected to use research objects that have stable or increasing asset and income values, so that the variables of asset structure and earning volatility can show significant results and influences.
7. Limitations and advice
This study only uses the variables of asset structure, financial flexibility and earning volatility as independent variables. The findings of this study are that there are other factors beyond these variables that also affect capital structure. This is evidenced by the value of the coefficient of determination in this study of 0.497564, meaning that the percentage of contribution of the influence of the independent variable to the dependent variable is 49.7564%, while the remaining 50.2436% is influenced by other variables that are not included in this study.
Further research is recommended to consider the use of other variables that can affect capital structure and if using the same variable is expected to use research objects that have stable or increasing asset and income values, so that asset structure variables and profit volatility can show significant results and influences.
Figures
Statistic descriptive
| Y_CS | X1_AS | X2_EV | X3_FF | |
|---|---|---|---|---|
| Mean | 0.435419 | 0.413473 | 0.036200 | 0.360626 |
| Median | 0.385700 | 0.413200 | 0.013800 | 0.328000 |
| Maximum | 1.248600 | 1.248600 | 0.395200 | 0.799900 |
| Minimum | 0.070700 | 0.038700 | 0.003900 | −0.461400 |
| Std. Dev | 0.208609 | 0.150413 | 0.061349 | 0.240475 |
| Skewness | 0.550605 | 0.923429 | 3.045362 | −0.193061 |
| Kurtosis | 2.938223 | 7.389738 | 12.90325 | 3.036210 |
| Skewness | 0.550605 | 0.923429 | 3.045362 | −0.193061 |
| Kurtosis | 2.938223 | 7.389738 | 12.90325 | 3.036210 |
| Jarque-Bera | 9.883894 | 184.2805 | 1098.267 | 1.222008 |
| Probability | 0.007141 | 0.000000 | 0.000000 | 0.542806 |
| Sum | 84.90670 | 80.62730 | 7.059000 | 70.32200 |
| Sum Sq. Dev | 8.442454 | 4.389057 | 0.730153 | 11.21868 |
| Observations | 195 | 195 | 195 | 195 |
Source(s): Table by authors
Multicollinearity test results
| X1_AS | X2_EV | X3_FF | |
|---|---|---|---|
| X1_AS | 1.000000 | 0.013232 | 0.036285 |
| X2_EV | 0.013232 | 1.000000 | 0.116704 |
| X3_FF | 0.036285 | 0.116704 | 1.000000 |
Source(s): Table by authors
Heteroscedasticity test results
| Variable | Coefficient | Std. Error | t-Statistic | Prob |
|---|---|---|---|---|
| X1_AS | 0.092991 | 0.059278 | 1.568735 | 0.1188 |
| X2_EV | −0.195142 | 0.210284 | −0.927990 | 0.3549 |
| X3_FF | −0.053246 | 0.038335 | −1.388968 | 0.1669 |
| C | 0.157788 | 0.028355 | 5.564726 | 0.0000 |
| Effects specification | |||
|---|---|---|---|
| Cross-section fixed (dummy variables) | |||
| R-squared | 0.230416 | Mean dependent var | 0.169971 |
| Adjusted R-squared | 0.024188 | S.D. dependent var | 0.112456 |
| S.E. of regression | 0.111087 | Akaike info criterion | −1.368793 |
| Sum squared resid | 1.888080 | Schwarz criterion | −0.663839 |
| Log likelihood | 175.4573 | Hannan-Quinn criter | −1.083366 |
| F-statistic | 1.117288 | Durbin-Watson stat | 1.905721 |
| Prob(F-statistic) | 0.309524 | ||
Source(s): Table by authors
Multiple linear regression analysis
| Model | Unstandardized coefficients | Standardized coefficients | |
|---|---|---|---|
| B | Std. Error | Beta | |
| (Constant) | 0.350 | 0.058 | |
| ASST | 0.097 | 0.083 | 0.094 |
| VOLAT | 1.019 | 0.776 | 0.093 |
| FLEX | −0.257 | 0.047 | −0.412 |
| AUR | 0.066 | 0.027 | 0.188 |
Source(s): Output Eviews (2022); Table by authors
Test the coefficient of determination and the correlation coefficient
| R-squared | 0.497564 | Mean dependent var | 0.435419 |
| Adjusted R-squared | 0.409329 | S.D. dependent var | 0.208609 |
| S.E. of regression | 0.196876 | Akaike info criterion | −0.224281 |
| Sum squared resid | 5.930285 | Schwarz criterion | 0.480673 |
| Log likelihood | 63.86740 | Hannan-Quinn criter | 0.061146 |
| F-statistic | 1.580815 | Durbin-Watson stat | 2.307924 |
| Prob(F-statistic) | 0.024898 |
Source(s): Table by authors
Uji F
| Variable | Coefficient | Std. Error | t-Statistic | Prob |
|---|---|---|---|---|
| C | 0.596591 | 0.050252 | 11.87190 | 0.0000 |
| X1_AS | −0.169937 | 0.105055 | −1.617597 | 0.1078 |
| X2_EV | 0.074237 | 0.372678 | 0.199199 | 0.8424 |
| X3_FF | −0.259536 | 0.067940 | −3.820094 | 0.0002 |
| Effects specification | |||
|---|---|---|---|
| Cross-section fixed (dummy variables) | |||
| R-squared | 0.297564 | Mean dependent var | 0.435419 |
| Adjusted R-squared | 0.109329 | S.D. dependent var | 0.208609 |
| S.E. of regression | 0.196876 | Akaike info criterion | −0.224281 |
| Sum squared resid | 5.930285 | Schwarz criterion | 0.480673 |
| Log likelihood | 63.86740 | Hannan-Quinn criter | 0.061146 |
| F-statistic | 1.580815 | Durbin-Watson stat | 2.307924 |
| Prob(F-statistic) | 0.024898 | ||
Source(s): Table by authors
Uji t
| Variable | Coefficient | Std. Error | t-Statistic | Prob |
|---|---|---|---|---|
| C | 0.596591 | 0.050252 | 11.87190 | 0.00000 |
| X1_AS | 0.169937 | 0.105055 | −1.617597 | 0.01278 |
| X2_EV | 0.074237 | 0.372678 | 0.199199 | 0.84246 |
| X3_FF | 0.259536 | 0.067940 | −3.820094 | 0.00326 |
Source(s): Table by authors
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Further reading
Brigham, E. F., & Gapenski, L. C. (1995). Intermediate financial management. Fifth Brigham (Eugene Edition). Florida: The Dryden Press.
Ghozali, H., & Imam (2016). Aplikasi analisis multivariete dengan program IBM SPSS versi 23 (Vol. 8). Jakarta: Badan Penerbit Universitas Diponegoro. edisi.
Sheikh, N. A., & Wang, D. Z. (2011). Determinants of capital structure an empiciral study of firms in manufacturing industry of Pakistan. Managerial Finance, 37(2), 117–133, hal.
Siyoto, S. M., & Ali, S. (2015). Dasar metode penelitian. Kediri. Literasi Media Publishing.
Sugiyono (2013). Metode penelitian pendidikan pendekatan kuantitatif, kualitatif, dan R&D. Bandung: Penerbit Alfabeta.
Wahidahwati (2002). Pengaruh kepemilikan manajerial dan kepemilikan institusional pada kebijakan hutang perusahaan: Sebuah perspektif theory agency. JRAI – Jurnal Riset Akuntansi Indonesia, 5, 1–16.