https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
DOI: https://doi.
org/10.
38035/dijefa.
https://creativecommons.
org/licenses/by/4.
The Effect of Liquidity.
Leverage.
Profitability and Inventory Intensity on Tax Aggressiveness Dian Nuningrum Adelia1.
Sari Andayani2* Universitas Pembangunan Nasional "Veteran" Jawa Timur.
Surabaya.
Indonesia, 21013010284@student.
Universitas Pembangunan Nasional "Veteran" Jawa Timur.
Surabaya.
Indonesia, ak@upnjatim.
Corresponding Author: sariandayani.
ak@upnjatim.
Abstract: Taxes are one of the main sources of state revenue that play a vital role in supporting national development as well as providing various public services and facilities to the public.
A corporation as a tax subject is obligated to pay taxes on its taxable income.
Since this duty is frequently viewed as a burden that lowers earnings, some businesses actively arrange their taxes to minimize the amount of taxes that must be paid.
This study aims to investigate the relationship between tax aggression and liquidity, leverage, profitability, and inventory Using secondary data from financial documents, this study employed a quantitative Purposive sampling techniques were used to choose 38 firms, or 114 data points, from the 76 companies that were listed on the Indonesia Stock Exchange between 2021 and 2023, which made up the research population.
Multiple linear regression is the analytical method that is employed.
Research results indicate that all four variables liquidity, leverage, profitability, and inventory intensity correlated negatively to tax aggressiveness.
It is recommended that future research involve larger numbers of samples and longer observation periods to obtain more representative and accurate results.
Keywords: Inventory Intensity.
Leverage.
Liquidity.
Profitability.
Tax Aggression.
INTRODUCTION
Tax is one of the main components in the state revenue structure because it has a significant role in supporting national development financing and the provision of public In Indonesia, the contribution of the taxation sector to total state revenue is very dominant, as reflected in the achievement of tax revenue in 2023 which reached IDR 2,155.
trillion from total state revenue of IDR 2,774.
3 trillion.
This shows the government's very large dependence on the taxation sector as a source of state financing (Magfira & Murtanto, 2.
The increase in tax revenue from year to year reflects the government's success in exploring tax potential optimally, accompanied by increasing tax compliance and awareness among taxpayers (Oktaviyoni, 2.
3163 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
Companies as entities included in the category of taxpayers who have the responsibility to pay taxes according to the amount of taxable income earned.
Taxes are often viewed as a cost component that can reduce profits, so many companies implement aggressive tax planning strategies to reduce the tax burden that must be paid (Laksmi & Narsa, 2.
This tax aggressiveness can be in the form of various legal and illegal strategies to reduce tax obligations (Sugeng et al.
, 2.
Since aggressive taxation practices can jeopardize a company's reputation among investors and the public, as was the case with transfer pricing in the food and beverage manufacturing industry, such as at PT.
Indofood Sukses Makmur, which affects declining stock prices and investor confidence, this phenomenon merits more research.
A number of the key elements influencing the degree of tax aggression in this industry is financial ratios.
Decisions on tax requirements may be influenced by the ratio's information about the company's performance and financial health (Destiani & Hendriyani, 2.
The four primary variables that will be examined in this study are inventory intensity, profitability, leverage, and liquidity.
The capacity of the business to promptly fulfill short-term obligations is referred to as liquidity (Hidayati et al.
, 2.
Leverage reflects how much the company uses debt to fund its operational activities as a whole (Herlinda & Rahmawati, 2.
Meanwhile, profitability is a ratio that shows the company's ability to generate overall profits from sales, assets, and investments (Andriani et al.
, 2.
This ratio also describes the extent to which the company is able to generate profits, which is closely related to the amount of tax to be paid.
On the other hand, inventory intensity shows the amount of the company's investment in the form of inventory (Regina, 2.
This ratio indicates the proportion of assets allocated to inventory, which can affect the structure of costs and taxable income, as well as impact the level of tax aggressiveness undertaken by the company.
The connection between principals .
and agents .
in an organization is explained by agency theory, which also emphasizes the possibility of conflicts of interest when managers make choices that are not entirely in line with the objectives of the owners.
managing profitability and inventory intensity, managers as agents may choose aggressive strategies related to taxes and steps that can increase personal profits, even though this has the potential to create legal risks and damage the company's reputation.
Therefore, this theory recognizes the possibility of a conflict of interest between principals and agents, which can lead to agent actions that are detrimental to the principal (Chenkiani & Prasetyo, 2.
The current research uses the multiple-linear regression technique to examine how liquidity, leverage, profitability, and inventory intensity affect a company's level of tax It is anticipated that this study would yield more thorough insights into corporate tax behavior patterns and offer helpful suggestions for regulators and business management in creating efficient tax plans that comply with relevant laws.
METHOD
Type of Research and Research Object The present research uses an approach that is quantitative.
Quantitative analysis is a research approach that analyzes events and the connections between different variables by using numerical data.
Corporations in the food and beverage production industry that were listed on the Indonesia Stock Exchange (IDX) between 2021 and 2023 are the subjects of the Operational Definition and Measurement of Liquidity Variables An indicator of a company's capacity to make its debt payments on schedule is the liquidity ratio.
The study's computation of this ratio makes reference to the methodology employed in Istiqomah & Trisnaningsih's earlier investigation from 2022:
3164 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
ycNycuycycayco yaycycycyceycuyc yaycycyceycyc
CR =
ycNycuycycayco yaycycycyceycuyc yaycnycaycaycnycoycnycycnyceyc Leverage The leverage ratio serves as an indicator to assess the level of debt utilization by a company in financing its assets.
The calculation of this ratio in the study refers to the approach used in the previous study by Dewi & Yasa, .
DAR =
ycNycuycycayco yaycnycaycaycnycoycnycycnyceyc ycNycuycycayco yaycycyceyc Profitabilitas One metric for assessing a company's capacity to turn a profit is the profitability ratio.
The method employed in the earlier study by Dewi & Yasa .
is cited in the study's computation of this ratio.
ycNycuycycayco ycEycycuyceycnyc ROA = ycNycuycycayco yaycycyceycyc Inventory Intensity The inventory intensity ratio is used to measure how large the proportion of inventory is in the company's total asset structure.
The calculation of this ratio in the study refers to the approach used in the previous study by Sugeng et al.
, .
RIP = ycNycuycycayco yaycuycyceycuycycuycyc ycNycuycycayco yaycycyceyc Tax Aggressiveness Tax aggressiveness refers to actions taken by companies to reduce taxable income through tax planning (Karlina, 2.
ETR = ycNycuycycayco ycNycaycu yaycuycyyceycuycyceyc ycNycuycycayco ycEycycuyceycnyc yaAyceyceycuycyce ycNycaycu Population and sampling Sugiyono .
defines a population as a group of items or people with certain attributes that have been identified by researchers and serve as the foundation for inferences in a study.
All manufacturing businesses involved in the food and beverage industry that were listed on the Indonesia Stock Exchange between 2021 and 2023 comprise the population considered in this study.
Multiple linear regression and descriptive statistical analysis were used to process the data.
Purposive sampling was used as the sample selection technique.
The following procedures were used to complete the sample elimination stage:
Table 1.
Sample Criteria No.
Sample Criteria In 2021Ae2023, firms that produce food and beverages went public on the Indonesia Stock Exchange.
Companies that produce food and beverages and released yearly financial reports in 2021Ae2023.
Businesses that produce food and beverages that reported 2021Ae2023 profits.
It is not in accordance with in accordance .
3165 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
Number of Research Data .
x 3 Year.
RESULTS AND DISCUSSION Descriptive Statistical Analysis Table 2.
Descriptive Analysis Descriptive Statistics DAR
ROA
RIP
Minimum
0,01
0,02
0,00
0,03
Maximum
5,10
0,86
0,23
0,41
Mean
2,1097
0,3757
0,0920
0,1757
Std.
Deviation 1,26088 0,20161 0,06129 0,09364 ETR Valid N .
0,16 0,26 0,2169 0,02282 Source: Process SPSS data, 2025 114 reliable data points from the company's financial accounts are shown in Table 2.
According to the findings of the descriptive statistical analysis, every variable has a minimum, maximum, average, and standard deviation according to its values.
Classical Assumption Test Data Normality Test Table 3.
Results of the Kolmogorov-Smirnov (K-S) Test One-Sample Kolmogorov-Smirnov Test Unstandardized Residual Normal Parametersa,b Mean Std.
Deviation Most Extreme Differences Absolute Positive Negative Test Statistic Asymp.
Sig.
-taile.
Test distribution is Normal.
Calculated from data.
Lilliefors Significance Correction.
0,0000000
0,02246939
0,080
0,057
-0,080
0,080
,073c Source: Process SPSS data, 2025 Table 3, data normality test is 0.
080 and significance is 0.
From asymp.
Sig shows that the data in the study is normally distributed.
3166 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
Multicollinearity Test Table 4.
Multicollinearity Test Results Coefficientsa Collinearity Statistics Model 1 (Constan.
Tolerance VIF
0,616
1,625
DAR
0,596
ROA
0,825
RIP
0,805
Dependent Variable: ETR
1,678
1,212
1,243
Source: Process SPSS data, 2025 Table 4 shows that the tolerance value meets the multicollinearity test criteria set at 0.
On the other hand, the VIF values are all below the threshold of 10.
Therefore, this study did not find any indication of multicollinearity among the independent variables.
Heteroscedasticity Test Model 1 (Constan.
DAR
ROA
RIP
Table 5.
Heteroscedasticity Test Result Coefficientsa Unstandardized Standardized Coefficients Coefficients Std.
Error
Beta
0,015
0,006
0,001
0,001
0,090
0,010
0,008
0,146
-0,044
0,024
-0,191
0,003
0,016
0,019
2,368
0,760
1,210
-1,866
0,182
Sig.
0,020
0,449
0,229
0,065
0,856
Dependent Variable: ABS_RES Source: Process SPSS data, 2025 Table 5 shows no relationship between the independent variables, as seen from the significance value which is greater than 0.
05, so there is no heteroscedasticity.
Autocorrelation Test Table 6.
Autocorrelation Test Results Ae Run Test Runs Test Test Valuea Unstandardized Residual 0,00078 Cases < Test Value Cases >= Test Value Total Cases Number of Runs -1,693 Asymp.
Sig.
-taile.
0,090 3167 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
Median Source: Process SPSS data, 2025 The Asymp.
Sig.
-taile.
value of 0.
090 is larger than 0.
05, according to the data displayed in Table 6.
This demonstrates that the residuals have no autocorrelation and are distributed randomly.
Multiple Regression Analysis Test Table 7.
Multiple Regression Analysis Coefficientsa Unstandardized Coefficients Model 1 (Constan.
Std.
Error
0,220
0,010
Standardized Coefficients Beta
21,254
Sig.
0,000
0,001
0,002
0,034
0,283
0,778
DAR
0,015
0,014
0,133
1,087
0,279
ROA
-0,033
0,039
-0,088
-0,852
0,396
RIP
-0,039
0,026
-0,161
-1,529
0,129
Dependent Variable: ETR Source: Process SPSS data, 2025 The regression equation obtained is as follows:
ETR = 0,220 0,001CR 0,015DAR Oe 0,033ROA Oe 0,039RIP e Multiple linear regression results: .
A positive constant of 0.
220 indicates that if all independent variables (DER.
INT, and CR) are zero, the dependent variable ETR will be at a value of 0.
Liquidity (CR) is 0.
001, with a positive relationship.
This means that every 1% increase in Liquidity will be followed by a decrease in Tax Aggressiveness (ETR) of 0.
Leverage (DAR) is 0.
015, indicating a positive relationship.
Every 1% increase in Leverage will be followed by an increase in Tax Aggressiveness (ETR) of 1.
Profitability is -0.
033, indicating a negative relationship.
This means that every 1% increase in Profitability will be followed by a decrease in Tax Aggressiveness (ETR) 3%, continuously.
Inventory Intensity (RIP) is -0.
039, also indicating a negative Every 1% increase in Inventory Intensity will be followed by a 3.
9% decrease in Tax Aggressiveness (ETR), on a continuous basis.
Determination Coefficient Test Table 8.
Determination Coefficient AuModel Summaryb Model ,175a R Square 0,031 Adjusted R Square -0,005 Std.
Error of the Estimate 0,02288 Predictors: (Constan.
RIP.
CR.
ROA.
DAR Dependent Variable: ETRAy Source: Process SPSS data, 2025 The determination coefficient test (R.
findings are displayed in Table 8, and the R Square value of 0.
031 suggests that the independent variable can only account for 3.
1% of the 3168 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
variance in the dependent variable, which is tax aggressiveness (ETR).
Stated otherwise, this regression model accounts for around 3.
1% of the effect on tax aggression, with other factors outside the variables utilized in this study influencing the remaining 96.
9% .
% -3.
1%).
Simultaneous Test (F Tes.
Table 9.
Simultaneous Test (F Tes.
ANOVAa
Mean
Model
Sum of Squares Square Regression 0,002 0,000 Residual 0,057 0,001 Total
0,059
Dependent Variable: ETR
0,861
Sig.
,490b Predictors: (Constan.
RIP.
CR.
ROA.
DAR Source: Process SPSS data, 2025 Table 9 presents the results of the F test, which indicate a F value of 0.
861 at a significance level of 0.
As this significance value exceeds 0.
05, the null hypothesis (H.
is deemed to be valid.
Thus, the independent factors .
rofitability, leverage, liquidity, and inventory intensit.
do not all concurrently affect the dependent variable .
ax aggressivenes.
Individual Parameter Significance Test .
-Tes.
Table 10.
Individual Parameter Significance Test .
-Tes.
Coefficientsa Unstandardized Coefficients Model 1 (Constan.
Std.
Error
0,220
0,010
Standardized Coefficients Beta
0,034
21,254
Sig.
0,000
0,283
0,778
0,001
0,002
DAR
0,015
0,014
0,133
1,087
0,279
ROA
-0,033
0,039
-0,088
-0,852
0,396
RIP
-0,039
0,026
-0,161
-1,529
0,129
Dependent Variable: ETR Source: Process SPSS data, 2025 The aforementioned table displays the results of the hypothesis testing for each independent variable in relation to the dependent variable, enabling the following deductions:
The initial assumption is accepted even if the results are not significant since the significance value is greater than 0.
778>0.
for the liquidity variable (CR), which has a t-value of 283 and a significance value of 0.
The other assumption is confirmed by the leverage variable's (DAR) t-value of 1.
087 and significance value of 0.
279, which is also not significant .
279>0.
However, the third hypothesis is rejected since the profitability variable (ROA) has a significance value of 0.
396 with a t-value of -0.
852, which is still over the significance level of 0.
396> 0.
The fourth hypothesis is supported when the Inventory Intensity (INT) variable displays a significance value of 0.
129 and a t-value of -1.
529, both of which are not significant .
129 > 0.
3169 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
The Effect of Liquidity on Tax Aggressiveness According to the first theory, tax aggression is unaffected by liquidity.
With a t-test significance value of 0.
778 (>0.
and a t-count of 0.
283, the hypothesis test findings indicate that the Liquidity (CR) variable has no discernible impact on Tax Aggressiveness.
Due to the generally uniform liquidity circumstances of the sample's features in the food and beverage industry, this conclusion suggests that strong business liquidity does not promote tax dodging Theoretically, although high liquidity provides more resources for complex tax strategies, companies with a stable financial structure tend to avoid risks related to tax aggressiveness in order to maintain relationships with creditors and maintain performance Agency theory explains that companies with debt focus more on cash flow stability than tax optimization, so high liquidity actually reduces incentives for aggressive actions.
These outcomes align with research conducted by Poerwanti et al.
and Herlinda & Rahmawati .
, which did not identify a significant correlation between tax aggressiveness and liquidity.
The Effect of Leverage on Tax Aggressiveness According to the second hypothesis, tax aggressiveness is unaffected by leverage.
Given the hypothesis test results, which show that the Debt to Asset Ratio (DAR)'s Leverage variable has no discernible impact on Tax Aggressiveness .
-test significance value of 0.
279 (> 0.
and a t-count of 1.
, the hypothesis is accepted and it can be said that leverage has no bearing on a company's decision to engage in tax aggressive behavior.
Although in theory high leverage is considered to encourage companies to be more aggressive in tax strategies by utilizing debt interest expenses, the results of this study indicate that this does not apply to the sample studied, possibly because most companies obtain funding from related parties so that interest expenses do not effectively reduce taxable profit.
In addition, companies with high leverage tend to prioritize financial stability and fulfillment of obligations to creditors, as well as consider reputational risk, regulatory compliance, and tax authority supervision, so that tax aggressiveness decisions are not only determined by the level of leverage.
This finding is in line with research by Dewi & Yasa .
and Simamora & Rahayu .
which also stated that leverage does not have a significant effect on tax aggressiveness.
The Effect of Profitability on Tax Aggressiveness The third theory states that profitability has an impact on tax aggressiveness.
The findings of the hypothesis test for the profitability variable proxied by ROA show that profitability has no discernible impact on tax aggression, with a t-test significance more than 0.
05 is 0.
396 and t-count of -0.
Accordingly, the third hypothesis is rejected, and it can be held that even when a company has a high profitability value, profitability has no bearing on its decision to engage in tax aggressiveness.
In theory, profitability can indeed encourage companies to implement more aggressive tax avoidance strategies, but the results of this study prove that companies with good financial performance and high profits are not always encouraged to carry out tax avoidance, possibly because companies prioritize reputation, regulatory compliance, and long-term business sustainability, as well as considering tax management strategies, tax authority supervision, and reputation risk.
This finding is in line with the research of Andriani et al.
also stated that profitability does not significantly affect tax aggressiveness, so that a company's decision to take tax aggressive actions is not only determined by the level of profitability, but is also influenced by reputation and compliance considerations.
The Effect of Inventory Intensity on Tax Aggressiveness According to the fourth theory, tax aggressiveness is unaffected by inventory intensity.
A t-test significance value of 0.
129 (>0.
and a t-count of -1.
529 indicate that the Inventory 3170 | P a g e https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
Intensity (RIP) variable has no significant impact on Tax Aggressiveness.
Accordingly, the hypothesis is accepted and it is determined that a company's decision to engage in tax aggressive behavior is unaffected by its inventory intensity.
According to Zimmerman's political cost theory, which holds that businesses with strong performance are more cautious about upholding their reputation by paying taxes, this finding suggests that investing in inventory is not a good way to engage in aggressive tax planning because businesses with large inventory tend to avoid the risk of regulatory oversight and tax penalties.
In addition, high inventory storage and maintenance costs can actually reduce net income, so they do not directly affect tax policy.
These results are in line with research by Wulansari et al.
and Istiqomah & Trisnaningsih .
which state that inventory intensity is not significant for tax
CONCLUSION
After collecting data and testing the problem using multiple regression models, the conclusion obtained is that: liquidity, leverage, profitability, and inventory intensity do not affect tax aggressiveness.
This study has several limitations, including the sampling technique that only selects companies that do not experience losses in a particular year and incomplete financial report data, so that companies that do not meet these criteria cannot be sampled and cause the number of populations used to be more limited.
In addition, the use of independent variables in this study only includes liquidity, leverage, profitability, and inventory intensity, so that the contribution of their influence on tax aggressiveness is only 3,1%, while the rest comes from other variables not included in this study.
REFERENCES