Electronic Journal of Education.
Social Economics and Technology Vol.
No.
2, .
, pp.
Article ID: 1020 ISSN 2723-6250 .
DOI: https://doi.
org/10.
33122/ejeset.
Research Article Determinants of Tax Efficiency in the Basic and Chemical Industry: The Moderating Role of Liquidity Kurnia Heriansyah*.
Lazarus Sinaga.
Cotoro Mukri Faculty of Economics and Business.
Universitas Pancasila.
Jakarta Selatan, 12630.
Indonesia *Corresponding Author: kurniaheriansyah@univpancasila.
ABSTRACT
This study explores tax efficiency practices in IndonesiaAos basic and chemical industry sectors during the economic uncertainty of the COVID-19 pandemic.
It examines the influence of profitability, capital structure, capital intensity, and audit committee on tax efficiency, with liquidity as a moderating variable.
Employing a quantitative causal associative approach and Structural Equation Modeling Partial Least Squares (SEM-PLS) using SmartPLS 3.
2, the research analyzes 9 IDX-listed companies from 2019 to 2023.
The results show that capital intensity and audit committee positively affect tax efficiency, while profitability and capital structure have no significant impact.
Liquidity does not directly influence tax efficiency but strengthens the relationship between capital structure and capital intensity with tax efficiency.
However, liquidity does not moderate the effects of profitability and audit committee.
The findings indicate that in capital-intensive industries, tax efficiency is more influenced by asset utilization and governance mechanisms rather than profitability or leverage.
The study emphasizes the importance of firm-specific characteristics in tax planning, especially during macroeconomic volatility.
It contributes to the discourse on ethical and sustainable tax management by offering insights for policymakers and corporate financial managers on the role of liquidity and internal governance in shaping tax behavior.
Keywords: Tax Efficiency.
Profitability.
Capital Structure.
Capital Intensity.
Audit Committee.
Liquidity.
Corporate Governance INTRODUCTION Tax efficiency is when businesses plan, carry out, and monitor their tax-related activities in a way that meets their tax responsibilities in the best way possible.
Pohan .
says that there are three main purposes of tax planning: efficiency, compliance, and financial strategy.
Tax efficiency, in particular, tries to lower the amount of taxes you have to pay by using the legal options that tax rules give you.
So, tax management isn't just about paying your taxes.
it's also about lowering your tax liabilities within the law and lowering your tax-related risks in the future (Suandy, 2.
This study's theoretical base comes from agency theory, which combines parts of economics, decision theory, sociology, and organizational theory.
Prasetya and Gayatri .
say that agency theory says that both principals .
and agents .
operate in their own best interests, which can lead to conflicts and knowledge gaps.
Companies under Indonesia's self-assessment tax system are given the freedom to figure out how much taxable income they have, which gives them chances to legally lower their tax bills.
Pohan .
also says that tax efficiency is when taxpayers try to lower their tax burden by arranging transactions in ways that legally avoid tax repercussions.
This frequently means taking advantage of gaps or flaws in current tax laws.
The Cash Effective Tax Rate (ETR), which is the amount of income tax paid divided by pre-tax income, is used in this study to quantify tax efficiency.
A company's tax efficiency approach depends on a number of things.
This study is based on the fact that corporate cash flows change and there are business risks that are unique to various industries, especially in the basic and chemical manufacturing sector on the Indonesia Stock Exchange (IDX) from 2019 to 2023.
This study's research gap comes from the fact that previous investigations have found different things.
For instance.
Arisandi .
Primasari .
, and Dewinta & Setiawan .
all found that tax efficiency and profitability .
easured by ROA) were linked in a good way.
On the other hand.
Fauzan .
and Irianto .
found no significant effect, and Zhu's .
study in Ghana found a negative relationship.
Rosalia .
also discovered that there was no strong link between ROA and tax efficiency.
Katz .
said that tax efficiency is affected not just by how profitable a business is now and in the future, but also by how well it uses its assets and manages its debt.
Fahmi .
says that capital structure shows how a company is financially structured, especially the ratio of long-term debt to shareholders' equity that it uses to run its business.
Anisah .
discovered that the structure of capital has a big impact on how well taxes work.
The DebtPage 1 of 12 Heriansyah et al Electronic Journal of Education.
Social Economic and Technology.
Vol.
No.
2, .
, pp.
Article ID: 1020 to-Equity Ratio (DER) is a standard way to look at capital structure.
It shows how much a company depends on equity to pay its bills (Riyadi & Melia, 2.
A high DER means that a company is borrowing more money, which means it has to pay more in interest.
This lowers taxable income and makes taxes more efficient because interest is tax-deductible (Afrilia.
Studies by Arisandi .
Rachmat .
, and Pratiwi .
back this up by showing a favorable link between capital structure and tax efficiency.
But Dhaneswara .
Tan .
Irianto .
Anindyka et al.
, and Sari & Kinasih .
show that there is no strong link between the two.
Kasmir .
defines capital intensity as the ratio of fixed assets to total assets.
This shows how much of a company's resources are put into long-term physical assets.
Higher capital intensity usually means higher depreciation costs, which lower taxable revenue and make taxes more efficient.
Rahma et al.
Panjaitan et al.
Madjid & Akbar .
, and Humairoh & Triyanto .
all found that capital intensity had a beneficial influence on tax efficiency.
These studies show that companies with more fixed assets benefit more from depreciation deductions, which lowers their effective tax On the other hand, other researchers, such as Apridila et al.
Ekaputra et al.
, and Saputra et al.
, showed a negative association, which means that high capital intensity can make taxes less efficient.
This could be because certain tax rules let businesses write off fixed assets during their useful lives, and the timing and method of depreciation can change how much tax they owe.
If companies don't fully take advantage of depreciation benefits, they may not be as efficient.
Still, several researchers, such Marlinda et al.
Dewi & Oktaviani .
, and Irianto .
, said there was no substantial effect.
They say that depreciation no longer gives tax benefits for fixed assets that have outlived their useful lives.
In these situations, capital intensity doesn't help people escape paying taxes.
it just shows how much money is being put into operational infrastructure.
People generally agree that corporate governance processes are very important for running a business, especially when it comes to taxes.
Their job is to make sure that tax efficiency tactics are used in a way that is lawful and does not involve breaking the law, like not paying taxes.
Winata .
stresses that good governance can affect a company's tax planning plans, which in turn can affect how managers decide to comply with tax laws.
The values of excellent corporate governanceAi transparency, accountability, responsibility, fairness, independence, and equality are what makes businesses act in a moral and effective way.
When these rules are followed, they are likely to lead companies to pay their taxes in a legal and appropriate way.
In this situation, the audit committee is very important for making sure that the company follows the law and that its operations are in line with current tax laws and rules.
Ayu and Kartika .
say that having an audit committee can lower the risk of financial misreporting by keeping a closer eye on financial activities.
Studies that came before this one show that the quality of business audits is often linked to tax efficiency.
For example.
Tahilia .
observed that audit committees had a big effect on how well taxes work.
But this finding is different from what Puspita .
and Damayanti .
found, which said that there was no substantial link between audit committees and tax efficiency This study adds liquidity as a moderating variable to the independent variables that were talked about before.
company's liquidity is how well it can meet its short-term obligations.
Companies with little liquidity may have trouble paying their taxes and instead choose to keep cash flow going to keep their operations running.
On the other hand, companies with a lot of liquidity are probably in a stable financial situation and can better pay for things like taxes and other operating costs.
So, liquidity might affect a company's willingness to use tax efficiency measures, especially those that have to do with managing cash flows for operations, investments, and financing.
Because tax payments are part of operating costs, liquidity could make the link between tax efficiency and the company's financial features stronger or weaker.
There are many opinions on this problem based on real-world evidence.
Budianti and Curry .
and Abdullah .
both found that there is a positive link between liquidity and tax efficiency.
This means that enterprises that have a lot of cash on hand are better at managing their taxes.
Conversely, research by Artinasari and Mildawati .
Sarasati and Asyik .
, and Nur and Subardjo .
indicate a negative relationship, where firms with excess liquidity may lack motivation to optimize tax burdens.
Other research, such those by Fatimah et al.
and Gultom .
, on the other hand, don't discover any significant effects.
This suggests that liquidity may not have a direct effect on tax efficiency.
The goal of this study is to find out if profitability, capital structure, capital intensity, and the presence of an audit committee affect tax efficiency among companies in the basic and chemical industry sector that are listed on the IDX Main Board.
also looks into whether liquidity acts as a moderator, making these independent variables have a stronger or weaker effect on tax efficiency outcomes.
RESEARCH METHOD
This study uses a causal associative research strategy, which looks at the cause-and-effect interactions between two or more Sugiyono .
says that associative study looks at how variables are related to each other, while causal relationships are interactions where one variable has an effect on another.
In this case, independent variables including profitability, capital structure, capital intensity, and the audit committee are thought to have an effect on the dependent variable, tax efficiency, with liquidity acting as a moderating variable.
Adding liquidity as a moderating component is meant to give a more detailed picture of how the independent factors affect tax efficiency, filling in gaps in the current studies.
Page 2 of 12 Heriansyah et al Electronic Journal of Education.
Social Economic and Technology.
Vol.
No.
2, .
, pp.
Article ID: 1020 This method makes it possible to look at the factors that affect tax efficiency in a wider range of enterprises that work in the basic and chemical industry sector and are listed on the Indonesia Stock Exchange (IDX).
The study looks at firms in the basic and chemical industry that are on the main board from 2019 to 2023.
There were 17 companies in the research population, and 9 faceted sampling was used to choose 9 of them as the final sample.
The companies that were chosen had to have reported positive net income consistently throughout the study period, and outliers were excluded to make sure the data was accurate.
This study uses a quantitative method and mostly secondary data from the official IDX website.
We used Partial Least SquaresAeStructural Equation Modeling (PLS-SEM) to look at the dataset once we had collected and organized the data.
Ghozali .
says that SEM is a multivariate statistical method that combines factor analysis and regression analysis to look at how variables in a model are related to each other.
This includes looking at both the connections between indicators and their latent constructs and the relationships between constructs.
We used the Rule of Thumb to check the correctness of the measurement model .
uter mode.
by looking at a few important parameters.
If the factor loadings were higher than 0.
70 and the Average Variance Extracted (AVE) was higher than 0.
50, then convergent validity was established.
To check for discriminant validity, we made sure that the cross-loading values for the same variable were higher than 0.
We looked at the Variance Inflation Factor (VIF) numbers to see if there was multicollinearity between the indicators.
If the VIF result is between 5 and 10, it means that there is multicollinearity among the reflective We did reliability testing to see how consistent the measurement tools were inside.
If a construct's composite reliability score is higher than 0.
70, it means that the indicators consistently and accurately assess the latent variable they are supposed to.
The next step was to look at the structural model .
he inner mode.
to see how well it could predict the causal links between latent variables.
After the outer model passed the tests for validity and reliability, the next step was to use the coefficient of determination (RA) to see how well the inner model explained the data.
The RA number shows how much of the variation in the dependent variable can be explained by the independent variables.
If the RA value is over 0.
70, it is called strong.
if it is below 0.
67, it is considered substantial.
if it is below 0.
33, it is considered moderate.
and if it is below 19, it is considered weak.
We tested our hypothesis by looking at the path coefficients in the structural model.
Using tstatistics, we checked how important each path was.
A value of 1.
96 or above meant that the path was statistically significant at the 5% level.
Geisser and Stone came up with the bootstrapping approach, which was used to get standard errors and t-values.
You don't have to assume that the data is normally distributed using this resampling-based method, and it works even with small sample sizes.
RESULTS AND DISCUSSION
We used SmartPLS software version 3.
2 to do the analysis in this work.
It was done using Structural Equation Modeling (SEM).
The model includes independent factors including profitability, capital structure, capital intensity, and an audit Tax efficiency is the dependent variable.
Also, liquidity is added as a moderating variable to the model to see if there are any possible interaction effects.
The list of sample companies chosen for analysis is shown in Table 1 below.
These companies were chosen based on predetermined inclusion criteria and met the necessary requirements to be included in the final dataset.
Table 1.
Research Sample Code
Company Name
Listing Date
ANTM
Aneka Tambang Tbk.
27-Nov-97
BRPT
Barito Pacific Tbk.
01 Okt 1993
INCO
Vale Indonesia Tbk.
16 Mei 1990
INKP
Indah Kiat Pulp & Paper Tbk.
16-Jul-90
INTP
Indocement Tunggal Prakarsa Tb
05 Des 1989
ISSP
Steel Pipe Industry of Indones 22-Feb-13
LTLS
Lautan Luas Tbk.
21-Jul-97
PBID
Panca Budi Idaman Tbk.
13 Des 2017
UNIC
Unggul Indah Cahaya Tbk.
6-Nov-89 Source: Bursa Efek Indonesia Descriptive statistics give a basic picture of the research data by showing values like the mean, median, minimum, maximum, and standard deviation.
These measures show how the data is spread out and how it tends to be.
The Outer Model evaluation is all about looking at the study's measuring model.
Testing the data's validity and reliability is part of this procedure.
When models have reflective indicators.
Convergent Validity and Discriminant Validity are used to check their validity.
Composite Reliability and Cronbach's Alpha are used to check their reliability (Ghozali & Latan, 2.
The figure below shows the results of the Outer Model assessment that was done with SmartPLS.
Page 3 of 12 Heriansyah et al Electronic Journal of Education.
Social Economic and Technology.
Vol.
No.
2, .
, pp.
Article ID: 1020 Figure 1.
Outer model and loading factor This study uses a reflective measurement methodology to show how constructs and indicators are related.
In these kinds of models, the construct is thought to be the cause of the indicators that are seen, and the indicators are thought to be reflections of the latent variable (Ghozali & Latan, 2.
This means that the cause-and-effect relationship goes from the construct to the indicators, and the indicators all have the same variance that comes from the concept.
Figure 2 shows that all of the indicators have loading factor () values that are greater than or equal to 0.
5, and some even exceed 1.
This meets the standard for satisfactory convergent validity set by Bagozzi and Yi .
So, the indicators used in this study are thought to be accurate approximations of the characteristics they are meant to measure.
It is important to assess the validity of the measurement items to see if they can be used for more analysis.
This study uses both convergent validity and discriminant validity to check that the constructs are being measured correctly and separately.
Table 2.
Convergent Validity/Outer Loading Test Results Variable Cross Loadings Profitability (X.
1,000 Capital Structure (X.
1,000 Capital Intensity (X.
1,000 Audit Committee (X.
1,000 Tax Efficiency (Y) 1,000 Liquidity (M) 1,000 When each indication shows a strong link to its appropriate construct, convergent validity is reached.
Bagozzi and Yi .
say that this criteria is met when the loading factor () is greater than or equal to 0.
Table 5 shows that the Original Sample (O) values for all indicators across the investigated constructs are equal to or higher than this threshold.
This means that all indicators are convergently valid.
The outer loading value is 1.
000 for constructs that are measured by only one indicator.
This is seen as entirely valid.
The outer loading values for constructs with more than one indicator, as shown in Table 3 and Figure 2, are also higher than the minimum value of 0.
This includes the loading value of 1.
000 for the Tax Efficiency variable.
These results show that all of the indicators we looked at are good representations of their hidden variables.
We also figured out the Average Variance Extracted (AVE) to check convergent validity at the construct Bagozzi and Yi .
say that AVE values higher than 0.
50 mean that a construct explains more than half of the differences in its indicators.
Table 7 shows that all of the constructs in this investigation meet this requirement, which is more evidence that convergent validity exists.
Page 4 of 12 Heriansyah et al Electronic Journal of Education.
Social Economic and Technology.
Vol.
No.
2, .
, pp.
Article ID: 1020 Table 3.
Average Variance Extracted (AVE) Variable Average Variable Extracted Profitability (X.
1, 000 Capital Structure (X.
1, 000 Capital Intensity (X.
1, 000 Audit Committee (X.
1, 000 Tax Efficiency (Y) 1, 000 Liquidity (M) 1, 000 The Average Variance Extracted (AVE) value for the Tax Efficiency construct is 1.
000, which means that the construct meets the requirements for discriminant validity.
This is shown in the table above.
This means that the construct can explain all of the differences in its indicator.
, which means that its measurement is good enough.
After checking the validity, reliability testing was done to see how accurate, consistent, and precise the measurement tools were.
There were two main statistical indicators used in this process:
Cronbach's Alpha, which checks to see if the pieces in a construct are consistent with each other.
A value of more 70 is usually seen as satisfactory, which means that the items are connected enough Composite Reliability, which looks at how reliable a group of indicators is at measuring a hidden construct.
Values above 0.
70, like Cronbach's Alpha, show that the internal reliability is strong.
These reliability criteria make sure that the tools employed in this study are both statistically sound and reliable for more structural investigation.
Table 4.
CronbachAos Alpha test results Variable CronbachAos Alpha Profitability (X.
1, 000 Capital Structure (X.
1, 000 Capital Intensity (X.
1, 000 Audit Committee (X.
1, 000 Tax Efficiency (Y) 1, 000 Liquidity (M) 1, 000 Ghozali and Latan .
say that a construct is dependable if its Cronbach's Alpha score is at least 0.
Table 6 shows that the Tax Efficiency construct meets this condition, which means that the tool employed to measure this variable has good internal consistency.
In the same way.
Composite Reliability is another way to check how reliable measuring tools are.
A Composite Reliability score of 0.
70 or above means that the indicators consistently show what they are supposed to show (Ghozali & Latan, 2.
Table 5 below shows the results of the Composite Reliability test.
They show that the Tax Efficiency construct also meets this requirement, which adds to the model's measurement reliability.
Table 5.
Composite Reliability Test Results Variable Composite Reliability Profitability (X.
1, 000 Capital Structure (X.
1, 000 Capital Intensity (X.
1, 000 Audit Committee (X.
1, 000 Tax Efficiency (Y) 1, 000 Liquidity (M) 1, 000 As shown in Table 5, the Tax Efficiency construct meets the criteria for data reliability, confirming that the measurement indicators used are consistent and statistically dependable.
The next stage involves evaluating the structural model .
nner mode.
, which aims to predict and explain the causal relationships between latent variables.
This evaluation includes an assessment of the modelAos explanatory power and overall fit using two primary indicators:
Page 5 of 12 Heriansyah et al Electronic Journal of Education.
Social Economic and Technology.
Vol.
No.
2, .
, pp.
Article ID: 1020 Adjusted R-Square, which measures the proportion of variance in the dependent variable explained by the independent variables, adjusted for the number of predictors in the model.
Goodness of Fit (GoF), which evaluates how well the model as a whole fits the observed data.
The results of the R-Square and Adjusted R-Square analyses, calculated using the PLS Algorithm and Bootstrapping method, are presented in the Table 6.
Table 6.
R Square R Square R Square Adjusted Lik
0,616
0,578
Tax-Eff
0,389
0,311
Ghozali and Latan .
say that an R-Square (RA) or Adjusted R-Square value of 0.
75 means that the model is very good at making predictions, 0.
50 means that it is okay at making predictions, and 0.
25 means that it is not very good at making predictions.
This study uses Adjusted R-Square as the basis for interpretation because the model has more than two independent variables and uses a two-tailed hypothesis test.
Table 6, shows that the Adjusted R-Square value for the relationship between Profitability.
Capital Structure.
Capital Intensity, and Audit Committee on Tax Efficiency is 0.
which is close to the moderate level.
The R-Square value of 0.
311, on the other hand, means that the model is weak.
These results show that external factors, like the economic effects of the COVID-19 pandemic and the specific traits of companies in the basic and chemical industry, especially those on the main board of the Indonesia Stock Exchange, have a big effect on tax efficiency practices.
On the other hand, when Liquidity is used to moderate the association, the model has a better explanatory power, with an R-Square of 0.
616 and an Adjusted R-Square of 0.
578, which both show that the model is at a moderate level.
This means that a company's cash flow is quite important when it comes to making tax-related decisions.
corporations often use tax efficiency measures, but they do so with careful thought about their liquidity needs.
This is especially true for major, publicly traded corporations in the basic and chemical sectors.
Table 7.
Hypothesis Test Results Original Sample (O) Sample Mean (M) Standard Deviation T Statistics (STDEV) (|O/STDEV|) P Values Cap_In -> Lik
-0,476
-0,471
0,151
3,160
0,002
Cap_In -> Tax-Eff
0,378
0,372
0,144
2,624
0,009
K_Audit -> Lik
-0,103
-0,104
0,109
0,947
0,344
K_Audit -> Tax-Eff
0,381
0,367
0,163
2,338
0,020
Lik -> Tax-Eff
0,412
0,411
0,218
1,892
0,059
Profit -> Lik
-0,191
-0,187
0,141
1,358
0,175
Profit -> Tax-Eff
-0,243
-0,242
0,160
1,515
0,130
S_Modal -> Lik
-0,896
-0,894
0,131
6,835
0,000
S_Modal -> Tax-Eff
0,530
0,515
0,295
1,796
0,073
The results show that being profitable doesn't have a big impact on how well taxes are paid.
The route coefficient table shows that the p-value for the link between profitability (X.
and tax efficiency (Y) is 0.
> 0.
The original sample value is -0.
243, which means the link goes in the wrong direction.
This means that reporting a larger net income doesn't always mean that people will be better at paying their taxes.
In fact, companies in the basic and chemical industries may be less likely to do things that are good for their taxes if they have bigger net profits.
This finding is in line with what Fauzan .
Rosalia .
, and Irianto .
found in their own studies.
It is also in line with Zhu .
, who found that there was a negative link between profitability (ROA) and effective tax rate (ETR) in Ghana.
The statistics used span the 2019Ae2023 timeframe, during which enterprises in this industry were still recovering from the economic repercussions of the COVID-19 epidemic.
The analysis also shows that the structure of capital doesn't have a big effect on how well taxes The p-value for the link between capital structure (X.
and tax efficiency (Y) is 0.
> 0.
, even though the coefficient is positive at 0.
Even while using more debt can lower taxable income by allowing companies to deduct interest, the data show that debt levels do not directly affect how efficiently companies in this sector pay their taxes.
Page 6 of 12 Heriansyah et al Electronic Journal of Education.
Social Economic and Technology.
Vol.
No.
2, .
, pp.
Article ID: 1020 This results is in line with what Dhaneswara .
Tan .
Irianto .
Anindyka et al.
, and Sari & Kinasih .
On the other hand, the data show that capital intensity has a big effect on tax efficiency.
The p-value 009 .
< 0.
and the positive coefficient of 0.
378 show that there is a strong and positive link between capital intensity (X.
and tax efficiency (Y).
Companies having a lot of fixed assets usually get more depreciation expenses, which lower their taxable revenue.
In the basic and chemical industries, fixed assets are very important for production capacity.
Depreciation deductions are a good way to plan for taxes.
Rahma et al.
Panjaitan et al.
Madjid & Akbar .
, and Humairoh & Triyanto .
all found similar results.
The analysis also indicates a strong positive link between the audit committee (X.
and tax efficiency (Y), with a p-value of 0.
< 0.
and a coefficient of 0.
This means that having an active audit committee as part of company governance helps attempts to make taxes more efficient.
This result backs up what Tahilia .
found, but it goes against what Puspita .
and Damayanti .
found, which was no effect.
There is no significant link between liquidity (M) and tax efficiency, with a p-value of 0.
> 0.
and a positive coefficient of Higher liquidity means a healthy cash flow that can help pay taxes on time, but the results show that liquidity alone doesn't have a big effect on decisions about tax efficiency.
This results is in line with what Fatimah et al.
Gultom .
, and Alam & Fidiana .
The relationship between liquidity and profitability does not have a big effect on how profitability affects tax efficiency, as evidenced by a pvalue of 0.
> 0.
and a negative coefficient of -0.
This shows that profitability does not affect tax efficiency techniques, even when liquidity is taken into account.
The negative direction shows that tax efficiency measures are based on more than just cash flow or profit.
On the other hand, liquidity has a big effect on how capital structure affects tax efficiency, with a p-value of 0.
< 0.
and a large negative coefficient of -0.
This means that corporations with a lot of debt and good cash flow may be more careful about how they manage their taxes, since too much debt can raise interest costs and lower taxable income.
So, liquidity helps control how much debt is used for tax planning.
In the same way, liquidity has a big effect on the relationship between capital intensity and tax efficiency, with a p-value of 0.
< 0.
and a coefficient of -0.
This conclusion shows that companies that invest more in fixed assets and have a steady cash flow are more likely to plan their taxes strategically.
Controlled cash flow and asset intensity together make it more likely that you'll be able to take tax deductions based on depreciation.
Finally, the relationship between liquidity and the audit committee does not have a big impact on tax efficiency, as shown by a p-value of 0.
> 0.
and a coefficient of -0.
This conclusion implies that the company's financial health, as assessed by liquidity, does not affect how well audit committees can help with tax planning.
CONCLUSION
This study's data analysis shows that capital intensity and the audit committee have a big effect on tax efficiency, but profitability and capital structure do not.
Also, when looked at on its own, liquidity doesn't have a big effect on tax efficiency.
But liquidity seems to change the link between capital structure and capital intensity when it comes to tax efficiency.
the other hand, it does not change the link between tax efficiency and either profitability or the audit committee.
This study only looks at companies in the basic and chemical industry sector, namely those that were on the Main Board of the Indonesia Stock Exchange between 2019 and 2023.
The COVID-19 epidemic may have had an effect on how well businesses did financially and how they paid their taxes, which could make it harder to apply the results to other areas or times.
The results add to what we know about how to manage business taxes, stressing that good tax planning is important, especially when the global economy is still uncertain.
When companies make plans to save money on taxes, they should think about how long those plans will last and how moral they are.
Tax planning shouldn't just be about lowering tax bills.
it should also follow the rules of good company governance and appropriate financial management.
It would be helpful for future studies to add more variables to the model, like government fiscal policy, macroeconomic conditions, and global political This could help us understand how tax efficiency works in different industries and economic environments.
REFERENCES