1007 

  American Journal of Economic and 

Management Business 

e-ISSN: 2835-5199 

Vol. 4 No. 7 July 2025

 

Determinants of Audit Quality: Evidence from Consumer Sectors in 

Indonesia 
 

Septia Mitha Caesaria, Majidah  

School of Economics and Business, Telkom University, Indonesia  

Email: septiamithacaesaria@student.telkomuniversity.ac.id, majidah@telkomuniversity.ac.id 

 
Abstract 

Audit quality refers to the auditor's ability to detect and disclose irregularities, fraud, or violations in a company's 

financial statements through the resulting audit report. A high-quality audit is crucial for ensuring the reliability 

of financial information and strengthening investor and stakeholder confidence in the company's performance. 

This study examines the determinants of audit quality among companies listed in the cyclical and non-cyclical 

consumer sectors in Indonesia during the 2021–2023 period. The factors analyzed include corporate governance, 

audit fees, client importance, workload, and auditor industry specialization. The purposive sampling method was 

employed, resulting in a sample of 106 companies and 318 observational data points. The data were analyzed 

using panel data regression with a panel-corrected standard errors (PCSE) approach to address heteroscedasticity 

and autocorrelation issues. The results showed that client importance and auditor industry specialization are 

positive determinants of audit quality, while workload negatively affects audit quality. These findings have 

important implications for public accounting firms, regulators, and other stakeholders, encouraging enhanced 

audit reliability so that financial statements can be trusted and used as a basis for informed decision-making. 

Keywords: Audit Fee, Client Importance, Corporate Governance, Audit Quality, Auditor Industry Specialization, 

Workload 

 

 

INTRODUCTION 

An audit plays a crucial role in maintaining the credibility of the financial information 

presented by a company. A quality audit is characterized by the auditor's ability to detect and 

report material misstatements appropriately, whether caused by error or fraud (DeAngelo, 

1981; DeFond & Zhang, 2014). A quality audit is necessary to ensure transparency and 

accountability in financial reporting, as well as to build stakeholders' trust in the information 

provided (Quick et al. 2024; Nurbaiti & Sabilla, 2022). In that context, audit effectiveness 

depends not only on the technical procedures applied, but also on several external conditions 

and auditor characteristics that can affect the objectivity and rigour of the audit process 

(Knechel et al., 2013). Quality audits require external auditors to comply with Public 

Accountant Professional Standards (SPAP), complete audits on time, and follow procedures 

under accounting standards (Majidah et al., 2018). Various determinants of audit quality play 

a significant role, both directly and indirectly, as they can impact auditors' independence, 

objectivity, and professional accuracy in performing audit procedures. A deep understanding 

of these determinants is crucial for enhancing audit practices and ensuring that the financial 

statements produced are reliable for all stakeholders (Umar et al., 2019; Akuoko-Sarpong et 

al., 2024). 

Issues related to poor-quality audits remain a concern today. This is reflected in the results 

of the 2024 International Forum of Independent Audit Regulators (IFIAR) survey, which shows 

that audit quality globally has not been stable. There was a 4% decrease in findings in 2022 

compared to the previous year, but this trend was reversed, with a 6% increase in 2023. This 



American Journal of Economic and Management Business 

Vol. 4 No. 7 July 2025 

1008 

suggests that audit firms still face major challenges in maintaining audit quality, despite the 

introduction of the International Standard on Quality Management 1 (ISQM 1) (International 

Forum of Independent Audit Regulators, 2024). Internationally, the NMC Health audit case in 

the UK is a crucial example of the failure to maintain audit quality. Ernst & Young (EY) is 

being sued for £2 billion for allegedly failing to detect red flags in the 2012-2018 financial 

statements, including not checking the general ledger and failing to uncover US$4 billion in 

hidden debt (Kissin & Gray, 2025). Meanwhile, in Indonesia, similar issues are reflected in the 

case of PT Tiga Pilar Sejahtera Food Tbk (AISA), which allegedly manipulated its 2017 

financial statements, including inflating assets by up to Rp4 trillion, despite being audited by 

the Public Accounting Firm Amir Abdi Jusuf, Aryanto & Mawar affiliated with RSM 

International. An investigative audit by EY in 2019 revealed fraudulent practices by the old 

management, and the case led to a criminal conviction and examination of the KAP by the 

relevant authorities (Asmara, 2019; Nasional Kontan, 2021). 

In line with this phenomenon, academic studies also show inconsistencies in findings 

regarding the determinants of audit quality. Some studies indicate that corporate governance, 

audit fees, client importance, workload, and auditor industry specialization are key 

determinants of audit quality (Chtaoui et al., 2024; Dekeyser et al., 2024; Aly et al., 2023; 

Hossain et al. 2023; Heo et al., 2021). However, other studies have found inconsistent or 

contradictory determinants of audit quality (Sulaiman et al., 2022; Cahyanti et al., 2022). This 

difference in results may be influenced by different ways of measuring audit quality, such as 

audit opinion, the level of errors detected, or the level of financial statement manipulation. 

Additionally, differences in regulatory systems and sector-specific characteristics may also 

impact the results obtained. 

Research on various determinants of audit quality has been conducted using various 

approaches, one of which is through measurement of discretionary accruals, which reflect the 

extent to which auditors can detect earnings management that may be misleading (Bing et al., 

2014). Discretionary accruals can make financial statements appear better than the actual 

condition, misleading stakeholders' assessments of the company's performance and affecting 

their decision-making (Chi et al., 2009). In this case, the high value of discretionary accruals 

indicates low audit quality, as it suggests the auditor's inability to limit financial reporting 

aggressiveness (DeFond & Zhang, 2014). 

Although considerable research has been conducted on audit quality, most studies focus 

on developed countries with varying market structures and regulations. In Indonesia, research 

examining the combined determinants of corporate governance, audit fees, client importance, 

workload, and auditor industry specialization on audit quality remains relatively limited. 

Emerging market conditions, such as those in Indonesia, have their own dynamics, including 

different levels of compliance, business pressures, and supervision compared to developed 

countries. Therefore, this study aims to examine the determinants of audit quality through five 

main aspects: corporate governance, audit fees, client importance, workload, and auditor 

industry specialization, in consumer cyclicals and consumer non-cyclicals sector companies 

listed on the Indonesia Stock Exchange for the period 2021-2023. By using discretionary 

accruals as a proxy for audit quality, this study is expected to contribute to the audit literature 

in emerging markets and provide practical insights for regulators and the auditor profession on 

improving audit quality in Indonesia.  
 

RESEARCH METHODS 

This research falls under the category of quantitative research, specifically descriptive, 

which aims to explain the relationship between variables. The method used is by analyzing 

secondary data. The approach used is deductive, where the research process begins by 



Septia Mitha Caesaria, Majidah 

1009 

observing phenomena related to the variables under study, and then formulates these 

observations into problems that are arranged within a framework based on previous studies and 

researchers' understanding. 

The object of this research is companies included in the consumer cyclicals and consumer 

non-cyclicals sectors listed on the Indonesia Stock Exchange during the 2021-2023 period. 

Data analysis was carried out using panel data regression. The sample is determined based on 

sampling criteria which include: (1) companies in the consumer cyclicals and consumer non-

cyclicals sectors that are listed on the Indonesia Stock Exchange in 2021-2023; (2) companies 

in the consumer cyclicals and consumer non-cyclicals sectors that consistently publish annual 

reports during 2021-2023; (3) companies in the consumer cyclicals and consumer non-cyclicals 

sectors that consistently state audit fees in annual reports during 2021-2023. Based on these 

criteria, the sample was obtained from as many as 106 companies, totalling 318 observations. 

Dependent Variable (Y) 

The dependent variable in this study is audit quality. This study measures audit quality 

using absolute value discretionary accruals, which are also used as indicators of earnings 

management (Choi et al., 2010; Mao et al., 2020; Hossain et al., 2023). Discretionary accrual 

is measured using the Modified Jones Model, which is an improved version of the original 

Jones Model developed by Dechow et al. (1995), which incorporates aspects of the previous 

period and includes all changes in receivables as part of earnings management. The absolute 

value of discretionary accruals to capture earnings management practices that increase or 

decrease earnings by managers (Bing et al., 2014). By this approach, the greater the absolute 

value of DA as indicated by the further the value is from zero, the greater the potential for 

earnings management, so that audit quality is assumed to be lower (DeFond & Zhang, 2014). 

The assumption is that managers use discretionary accruals to manipulate or degrade the quality 

of financial statements, and a high-quality audit should be able to identify such irregularities 

(DeFond & Zhang, 2014; Ayoola, 2022). Thus, a negative regression coefficient on DA is 

interpreted as a positive determinant of audit quality, and vice versa. The following is the 

formula for discretionary accrual: 

𝑇𝐴𝑡/𝐴𝑡−1 = 𝛼1(1/𝐴𝑡−1) + 𝛼2(∆𝑅𝐸𝑉𝑡 − ∆𝑅𝐸𝐶𝑡)/𝐴𝑡−1 + 𝛼3𝑃𝑃𝐸𝑡𝐴𝑡−1 

In which: 

𝑇𝐴𝑡 = Total accruals total accruals in year t, estimated as excess of net income in year t (𝑁𝐼𝑡) 

over cash flow from operations in year t (𝐶𝐹𝑂𝑡) 

𝐴𝑡−1 = total asset in year t-1 

∆𝑅𝐸𝑉𝑡 = change in revenues from year t – 1 to year t 

∆𝑅𝐸𝐶𝑡 = change in receivables from year t – 1 to year t 

𝑃𝑃𝐸𝑡 = property, plant, and equipment in year t 

The estimated value of total accruals, which represents non-discretionary accruals (𝑁𝐷𝐴𝑡), is 

obtained by inputting the estimated parameters 𝛼1, 𝛼2, 𝛼3 into equation (1). Furthermore, 

discretionary accruals (𝐷𝐴𝑡) are calculated in the following way: 

𝐷𝐴𝑡/ 𝐴𝑡−1 = 𝑇𝐴𝑡/𝐴𝑡−1 − 𝑁𝐷𝐴𝑡/𝐴𝑡−1 

 

Independent Variable (X) 

Corporate governance is assessed using a score-based system, which refers to the 

corporate governance indicators studied by Ahmar et al. (2024), a score of 1 indicates that the 

company meets the requirements of good corporate governance, while a score of 0 indicates 

that the company does not meet these requirements. Audit fees are measured using the natural 

logarithm of the audit fee paid by the client (auditee) to the public accounting firm. (Griffin et 

al., 2010). 

Client importance is measured by the total assets of the company, as referenced in 

research (Chen et al. 2010, Chang et al., 2019). A score of 1 is given if the natural logarithm of 



American Journal of Economic and Management Business 

Vol. 4 No. 7 July 2025 

1010 

the client's total assets is greater than the average natural logarithm of the client's total assets 

in a given year and a score of 0 is given if the client's total assets are smaller than that average. 

Workload is measured by dividing the number of audit clients handled by a KAP each 

year by the number of KAP partners (Pamungkas & Gantyowati, 2021). This measurement is 

used because capacity pressure on an office is estimated as an increase in audit workload 

attributable to clients (Suhardianto & Leung, 2020). 

Auditor industry specialization is measured using a market share audit measurement that 

refers to the research of Aurely et al. (2021) which uses a nominal scale, namely if the AIS 

value is below 10%, it is given a value of 0, if above 10%, it is given a value of 1. The use of 

market share audit proxies to calculate the auditor's industry specialization is because it is 

considered to reflect the level of industry priority compared to other auditors. The greater the 

market share an auditor owns, the higher the level of industry specialisation the auditor 

possesses (Dekeyser et al., 2024). 

 

Regression Model 

Data analysis in the study was conducted using panel data regression to determine the 

relationship between two variables. The formulation of this research problem involves the 

relationship between the independent variables namely, corporate governance (CG), audit fee 

(AF), client importance (CI), workload (WL), and auditor industry specialization (SA) and the 

dependent variable, audit quality (AQ). The panel data regression equation in this study is: 

𝐴𝑄 =  𝛼 + 𝛽1𝑖𝑡𝐶𝐺1𝑖𝑡 + 𝛽2𝑖𝑡𝐴𝐹2𝑖𝑡 + 𝛽3𝑖𝑡𝐶𝐼3𝑖𝑡 + 𝛽4𝑖𝑡𝑊𝐿4𝑖𝑡 + 𝛽5𝑖𝑡𝑆𝐴5𝑖𝑡 

I which: 

 AQ   : Audit Quality 

 α  : Constanta 

 𝛽1,𝛽2,𝛽3,𝛽4,𝛽5 : Regression Coefficient of Each Variable 

 𝐶𝐺  : Corporate Governance 

 𝐴𝐹  : Audit Fee 

 𝐶𝐼  : Client Importance 

 𝑊𝐿  : Workload 

 SA  : Auditor Industry Specialization 

 𝑡  : Period t 

 i  : Company i 

 𝜀  : Error term 

 

Data Analysis Strategy 

This study uses a significance level of 0.05. Descriptive statistical analysis was conducted 

first to provide an initial overview of the data used. The entire processing process was carried 

out using STATA software. The panel data regression model is applied because it can combine 

the characteristics of cross-section and time series data, which means data is collected from 

various observation units and observed over several periods. The use of panel data regression 

provides advantages in the form of greater degrees of freedom, to reduce the risk of omission 

of important variables (omitted variable bias) in the analysis model. The validity of the 

regression model used is ensured through the classical assumption test. 

 

 

 

 

 



Septia Mitha Caesaria, Majidah 

1011 

RESULT AND DISCUSSION 

Descriptive Statistic 

Table 1. Descriptive statistics 

Variable Obs Mean Std. dev. Min Max 

aq 318 .1719064 .6733011 .00077 8.41086 

cg 318 .8740566 .0782995 .6 1 

af 318 20.30484 1.338018 17.66 24.07 

ci 318 .5786164 .494559 0 1 

wl 318 59.2505 26.02668 15.75 146.13 

sa 318 .4245283 .4950502 0 1 

Source: Data processed by the author (2025) 

 

Table 1 presents descriptive statistics for all variables used in this study, based on 318 

observations from the 2021-2023 period. This study employs one dependent variable, namely 

audit quality, and five independent variables: corporate governance, audit fees, client 

importance, workload, and auditor industry specialization.  

Audit quality (AQ), as measured by discretionary accruals, has an average value of 0.171 

and a standard deviation of 0.673. These values indicate that the data is heterogeneous or 

widely dispersed in the consumer cyclicals and consumer non-cyclicals sectors listed on the 

Indonesia Stock Exchange during the 2021–2023 period. These findings suggest that the 

effectiveness of auditors in curbing earnings management varies across companies, indicating 

that audit quality may not yet be fully optimal in ensuring that financial statements are 

presented fairly by applicable accounting standards. 

Corporate Governance (CG) has an average value of 0.874 and a standard deviation of 

0.078. The relatively small standard deviation compared to the mean indicates that the data is 

homogeneous, clustered, and shows minimal variation. This suggests that most companies in 

this study have implemented corporate governance effectively and maintained consistency in 

its application. 

The audit fee (AF) has an average of 20.304 with a standard deviation of 1.338. This 

indicates that the data is relatively homogeneous and clustered, showing little variation. It 

reflects that most companies pay audit fees within a relatively high and consistent range. 

Meanwhile, client importance (CI) has an average value of 0.578 and a standard deviation 

of 0.494. This indicates that the data is relatively homogeneous, clustered, and does not vary 

significantly. It suggests that most clients in the sample are considered important by auditors. 

Larger companies tend to receive more thorough audits due to higher audit risks, prompting 

auditors to exercise greater diligence during the audit process. 

Workload (WL) has an average of 59.250 with a standard deviation of 26.026. This 

indicates that the data shows moderate variability, meaning that auditor workloads vary 

considerably across companies. In some cases, auditors may experience extremely high 

workloads, which could potentially compromise the quality of the audit procedures performed. 

Finally, auditor industry specialization (SA) has an average value of 0.424 and a standard 

deviation of 0.495. This indicates that the data is heterogeneous or widely dispersed. It suggests 

that many companies in the sample have not consistently employed auditors with industry-

specific expertise. 

 

Model Selection Tests 

The selection of the panel regression model is performed through the Chow Test, 

Lagrange Multiplier (LM) Test, and Hausman Test. Based on the test results, the fixed effect 

model was chosen because it met the significance criteria (<0.05) in all three tests. Before the 



American Journal of Economic and Management Business 

Vol. 4 No. 7 July 2025 

1012 

interpretation is performed, the model is first tested using the classical assumption test to ensure 

the validity of the regression model employed. 

 

Hausman Test (Random vs Fixed) 

The Hausman test is used to determine the most appropriate panel regression model 

between the fixed effects model and the random effects model. The null hypothesis (H0) states 

that the random effects model is more appropriate than the fixed effects model, while the 

alternative hypothesis (H₁) states otherwise. 

 

The Hausman test statistic is calculated with the formula: 

𝜒𝑜𝑏𝑠
2 = (�̂� − �̂�𝐺𝐿𝑆)

′
�̂�−1(�̂� − �̂�𝐺𝐿𝑆) 

 

The null hypothesis is rejected if the value of 𝜒𝑜𝑏𝑠
2  is greater than the critical value of 

𝜒𝛼;𝑝
2 or if the p-value ≤ α (0,05) which means that the fixed effect model is more suitable. 

 

Table 2. Hausman Test 
Coefficients (b) 

fixed 

(B) random (b-B) 

Difference 

sqrt(diag(V_b-V_B)) 

Std. err. 

cg .8394415 .3253241 .5141174 .5275063 

af -.0019947 -.017711 .0157164 .0518862 

ci .0637633 .0086784 .0550849 .0317284 

wl .0000386 .0017872 -.0017487 .000341 

sa .059998 .0023899 .0576081 .0478533 

b = Consistent under H0 and Ha; obtained from xtreg. 

B = Inconsistent under Ha, efficient under H0; obtained from xtreg. 

Test of H0: Difference in coefficients not systematic 

chi2(5)  = (b−B)′[(V_b−V_B)^(−1)](=b−B)  

= 33.65 

Prob > chi2 = 0.0000 

Source: Data processed by the author (2025) 

 

Based on the test results (Table 2), the p-value is 0.0000 (<0.05), which means that the 

null hypothesis (H0) is rejected. This indicates that the fixed effects model provides a better fit 

than the random effects model, and thus it is chosen for further analysis. 

 

Classical Assumption Tests 

Normality Test (Skewness-Kurtosis Test) 

The normality test is conducted to evaluate whether the distribution of residuals in the 

regression model meets the assumption of normality. This test uses the Skewness-Kurtosis 

approach with the null hypothesis (H0) that the residuals are normally distributed and the 

alternative hypothesis (H₁) that the residuals are not normally distributed. The test decision is 

based on the probability value (p-value), where H0 is rejected if the p-value ≤ 0.05. 

 

Table 3. Normality Test Skewness and kurtosis tests for normality 
    Joint test 

Variable Obs Pr(skewness) Pr(kurtosis) Adj chi2(2) Prob>chi2 

resid 318 0.0000 0.0000 345.28 0.0000 

Source: Data processed by the author (2025) 

 



Septia Mitha Caesaria, Majidah 

1013 

The results show (Table 3) reject H0 and conclude that the data is not normally 

distributed because the p-value is 0.000 <0.05. However, in line with the Central Limit 

Theorem and large number theory, if n > 30, the data can be assumed to be approximately 

normally distributed (Islam, 2018). 

Homoscedasticity Test (Breusch-Pagan Test) 

The homoscedasticity test is conducted to assess whether the residual variance in the 

regression model is constant (homoscedastic). This test uses the Breusch-Pagan method, in 

which squared errors are regressed against the independent variables to detect any systematic 

pattern in the residual variance. The null hypothesis (H0) states that the data is homoscedastic, 

while the alternative hypothesis (H₁) states that heteroscedasticity exists. Decision-making 

criteria are determined based on the probability value (p-value), where H₀ is rejected if the p-

value ≤ 0.05. 

Table 4. Homokedasticity Test 

Breusch-Pagan/Cook-Weisberg test for heteroskedasticity  

Assumption: Normal error terms  

Variable: Fitted values of aq  

H0: Constant variance  

chi2(1) = 1238.04  

Prob > chi2 = 0.0000 

Source: Data processed by the author (2025) 

 

Based on the test results (Table 4), the p-value of 0.0000 <0.05 is obtained, so H0 is 

rejected. It can be concluded that the model is not free of heteroscedasticity. 

 

Autocorrelation Test (Wooldridge Test) 

The autocorrelation test is conducted to determine whether a serial relationship exists 

among errors across different observations in the panel regression model. This test uses the 

Lagrange Multiplier (LM) test with the null hypothesis (H0) that there is no autocorrelation (ρ 

= 0 or E(εᵢ, εⱼ) = 0). In contrast, the alternative hypothesis (H₁) states that there is autocorrelation 

(ρ ≠ 0 or E(εᵢ, εⱼ) ≠ 0). The test decision is based on the probability value (p-value), with the 

criterion that H0 is rejected if the p-value <0.05. 

 

Table 5 Autocorrelation Test 

Wooldridge test for autocorrelation in panel data  

H0: no first-order autocorrelation  

F( 1, 105) = 14.813  

Prob > F = 0.0002 

Source: Data processed by the author (2025) 

 

Based on the test results (Table 5), the p-value is 0.0002 <0.05, so H0 is rejected. It can 

be concluded that the model is not free of autocorrelation. 

 

Multicollinearity Test 

The multicollinearity test is conducted to evaluate whether there is a high correlation 

between independent variables in the regression model, which may affect the validity of the 

coefficient estimates. The null hypothesis (H0) states that there is no multicollinearity (ρᵢⱼ = 0 

for i ≠ j), while the alternative hypothesis (H₁) states that there is multicollinearity (ρᵢⱼ ≠ 0 for i 



American Journal of Economic and Management Business 

Vol. 4 No. 7 July 2025 

1014 

≠ j). The evaluation is done using the Variance Inflation Factor (VIF) value, with the criterion 

that H0 is rejected if VIF > 10. 

 

Table 6 Multicollinearity Test 
Variable VIF 1/VIF 

af 1.79 0.558396 

sa 1.69 0.590816 

wl 1.07 0.931986 

ci 1.06 0.947362 

cg 1.01 0.986907 

Mean VIF 1.33 
 

Source: Data processed by the author (2025) 

 

Based on the test results (Table 6), all VIF values are <10. Therefore, H0 is not rejected 

and it can be concluded that the model is free from multicollinearity assumptions. 

It can be concluded that there are only violations of heteroscedasticity and autocorrelation 

assumptions. Therefore, the selected FEM model was transformed with Panel-Corrected 

Standard Error (PCSE) (Hoechle, 2007). 

 

Panel Regression Analysis 

Panel data regression with panel-corrected standard error (PCSE) is used in this study 

to overcome heteroscedasticity and autocorrelation problems while analyzing the data. Before 

performing panel data regression, several classical assumption tests, including normality, 

multicollinearity, heteroscedasticity, and autocorrelation, were conducted. The test results 

indicate that heteroscedasticity and autocorrelation issues persist in the data. Therefore, the 

PCSE method is a suitable approach because it can correct the standard error in panel data 

regression models.  

 

Table 7. Regression Using Panel-Corrected Standard Error (PCSE) 
Linear regression, correlated panels corrected standard errors (PCSes) 

Group variable: Code  Number of obs 318 

Time variable: Year  Number of groups 106 

Panels: correlated (balanced)  Obs per group: 
 

Autocorrelation: no autocorrelation  min = 3  
avg = 3 

max = 3 

Estimated covariances 5671  R-squared = 0.1270 

Estimated autocorrelations 0  Wald chi2(5) = 57.52 

Estimated coefficients 6  Prob > chi2 = 0.0000 

 Panel-corrected     

aq Coefficient Std. err. z P>|z| [95% conf. interval] 

cg .0664393  .1625305  0.41  0.683  -.2521147   .3849933  

af  .0499845   0258176   1.94   .053   -.000617   .100586  

ci  -.100505   0334567   -3.00   .003   -.166079   -.0349311  

wl  .0090442   0021146   4.28   .000   .0048997   .0131887  

sa  -.1261295   0411136   -3.07   .002   -.2067106   -.0455484  

_cons  -1.325269   .691787   -1.92  0.055   -2.681147   .0306089  

Source: Data processed by the author (2025) 

 

The panel data regression equation estimated in this study is as follows: 

𝐴𝑄 = −1,3253 + 0,0664𝐶𝐺𝑖𝑡 + 0,0499𝐴𝐹𝑖𝑡 − 0,1005𝐶𝐼𝑖𝑡 + 0,0090𝑊𝐿𝑖𝑡 − 0,1261𝑆𝐴𝑖𝑡 

Description: 

AQ  : Audit Quality 



Septia Mitha Caesaria, Majidah 

1015 

𝐶𝐺 : Corporate Governance 

𝐴𝐹 : Audit Fee 

𝐶𝐼 : Client Importance 

𝑊𝐿 : Workload 

SA : Auditor Industry Specialization 

𝑡 : Period t 

i : Company i 

 

Hypothesis Testing 

Overall Significance Test (F-test) 

The overall model significance test is conducted to determine whether the independent 

variables in the regression model are jointly significant in predicting the dependent variable. In 

regression with Panel-Corrected Standard Errors (PCSE), this test is assessed through the Chi-

square p-value statistic. Furthermore, according to Saraswati et al. (2024), good corporate 

governance is characterised by ethical behaviour, accountability, transparency, and 

sustainability, which collectively form trust and enhance the company's reputation in the eyes 

of investors. The hypothesis tested is as follows: 

𝐻0:  β1= β2= β3 = β4= β5 =0 (there is no simultaneous determination of audit quality) 

𝐻1 :  at least one  𝛽𝑗 ≠ 0 (there is a simultaneous determination of audit quality) 

The model shows acceptable determination if the probability value (p-value) of the Chi-square 

statistic is less than the specified significance level (α = 0.05). 

 

Table 8 Overall Significance Test 

Wald chi2(5) = 57.52 

Prob > chi2 = 0.0000 

Source: Data processed by the author (2025) 

 

Based on the test results, the Chi-square statistical value with a p-value of 0.000 was 

obtained. This value is smaller than the 5% significance level, so H0 is rejected. Thus, it can be 

concluded that there is a simultaneous determinant of audit quality. 

 

Partial Significance Test (t-Test) 

The t-test was conducted to assess the contribution of each determinant in partially 

explaining audit quality (AQ). 

The hypotheses tested are: 

𝐻0 : βj = 0 (the j-variable is not a determinant of audit quality) 

𝐻1 :  βj ≠ 0 (variable j is a determinant of audit quality) 

The decision is based on the t-statistic value and the resulting probability (p-value). H₀ 

can be rejected if the t-statistic value is greater than the t-table value at the 5% significance 

level (>1.64), or if the p-value is less than 0.05 (p-value ≤ 0.05). Thus, a variable is declared as 

a determinant of audit quality if it meets these test criteria. 

 

Table 9 Partial Significance Test 
 Panel-corrected     

aq Coefficient Std. err. z P>|z| [95% conf. interval] 

cg .0664393  .1625305  0.41  0.683  -.2521147   .3849933  

af .0499845   0258176  1.94   .053   -.000617   .100586  

ci -.100505   0334567  -3.00   .003   -.166079   -.0349311  

wl .0090442   0021146  4.28   .000   .0048997   .0131887  

sa -.1261295   0411136  -3.07   .002   -.2067106   -.0455484  



American Journal of Economic and Management Business 

Vol. 4 No. 7 July 2025 

1016 

_cons -1.325269  .691787  -1.92  0.055   -2.681147   .0306089  

Source: Data processed by the author (2025) 

 

 

 

Discussion of Findings 

This study examines the determinants of corporate governance, audit fees, client 

importance, workload, and auditor industry specialisation on audit quality in companies listed 

on the Indonesia Stock Exchange in the Consumer Cyclicals and Consumer Non-Cyclicals 

sectors from 2021 to 2023. Audit quality is measured using discretionary accruals by adopting 

the Modified Jones Model developed by Dechow et al. (1995). The value of discretionary 

accruals is inversely related to audit quality, where the higher the value of discretionary 

accruals indicates lower audit quality. Therefore, a negative regression coefficient on DA 

indicates a positive relationship with audit quality, and vice versa. 

Based on the analysis results, it is found that corporate governance (CG) exhibits a 

regression coefficient of 0.066 with a p-value of 0.683 (0.683 > α = 0.05), indicating that CG 

is not a determinant of audit quality. This finding does not support the research hypothesis 

which suspects a positive relationship between corporate governance and audit quality. 

Although CG principles, such as transparency, accountability, responsibility, independence, 

and fairness, have been included in the company's annual report as a form of compliance with 

regulations, their implementation has not been substantial, which has an impact on the low 

effectiveness of supervision and internal control (Hartono et al., 2023). Thus, the expected audit 

quality cannot be achieved optimally. For example, in this study, the accountability aspect 

shows the weak implementation of corporate governance. This is reflected in the low disclosure 

of the reward and punishment system, where only 15% of companies include this information 

in their annual reports. This suggests that the reward and punishment mechanism, which should 

be a crucial part of the management supervision system, has not been implemented effectively. 

The absence of this mechanism has the potential to weaken the internal control function and 

create room for earnings management practices without clear consequences. 

Moreover, audit fee (AF) has a coefficient of 0.050 with a p-value of 0.053 (0.053 > α = 

0.05) which indicates that AF is not a determinant of audit quality. This finding does not support 

the second hypothesis which states that audit fees are a positive determinant of audit quality. 

However, this result reflects a favourable condition, as it can be seen as an indication that 

auditors maintain professional independence by providing audit results that accurately reflect 

the company's actual condition, without being influenced by the amount of fees paid. The 

amount of compensation received does not necessarily affect the quality of the resulting audit, 

as auditors are still required to comply with auditing standards and the code of ethics, and apply 

the principles of independence, objectivity, and professional scepticism at all stages of the 

audit. In practice, audit fees tend to reflect the complexity of the business and the audit 

procedures performed, rather than guaranteeing better audit quality (Aly et al., 2023; Feng et 

al., 2023). Therefore, the resulting audit opinion should not be influenced by the amount of 

compensation received. 

Furthermore, client importance (CI) has a coefficient of -0.100 with a p-value of 0.003 

(0.003 < α = 0.05) which indicates that CI is a positive determinant of audit quality. These 

results support the hypothesis that the more important the client's position is to the auditor, the 

higher the audit quality provided. In this context, companies with large assets are generally 

regarded as major or strategic clients, so auditors tend to increase rigour and care in the audit 

process to maintain their reputation and long-term relationships. Audit complexity in large-

scale companies requires auditors to involve a larger team, longer processing times, and more 

in-depth procedures, which ultimately makes the client a key consideration for the auditor 



Septia Mitha Caesaria, Majidah 

1017 

(Hossain et al., 2023). This finding suggests that reputational pressure and the potential risk of 

litigation from important clients can motivate auditors to enhance the quality of their work. 

This finding is supported by previous research by Pamungkas & Gantyowati (2021) and 

Hossain et al. (2023). 

In addition, workload (WL) shows a coefficient of 0.009 with a p-value of 0.000 (p < 

0.05), indicating that WL is a negative determinant of audit quality. This finding supports the 

proposed hypothesis, namely that high workload tends to reduce audit quality. Auditors who 

face time pressure and limited resources due to high workloads are at risk of compromising 

thoroughness in the audit process. This condition can lead to dysfunctional auditor behaviour, 

which reduces the auditor's ability to detect material misstatements in the client's financial 

statements. An excessive workload can impair the auditor's level of concentration and diminish 

their ability to identify errors or irregularities in the financial statements (Cheng et al., 2021). 

Therefore, workload is a negative determinant of audit quality because an increase in workload 

can reduce auditor focus, resulting in lower audit quality. This finding aligns with the research 

of Hwang & Hong (2022) and Heo et al. (2021), which demonstrate that workload can cause 

fatigue in auditors and lead to a decrease in audit quality. 

Additionally, auditor industry specialization (SA) shows a coefficient of -0.126 with a p-

value of 0.002 (0.002 < α = 0.05) which indicates that SA is a positive determinant of audit 

quality. This finding supports the proposed hypothesis that auditors with industry specialisation 

will better understand the operational characteristics, risks, and regulations specific to their 

industry (Rijal et al., 2023). A deep understanding of industry dynamics enables auditors to 

more quickly identify deviant patterns and accurately assess the fairness of financial statement 

presentation, which can be challenging for auditors without specialised expertise. This 

minimises the possibility of misdetecting material misstatements. Additionally, strong sector 

knowledge enhances the auditor's ability to design more relevant and effective audit 

procedures. This indicates that the presence of specialist auditors can enhance audit quality by 

increasing accuracy and thoroughness in the examination process. This finding aligns with 

previous research by Rijal et al. (2023) and Dekeyser et al. (2024), which also indicate that 

auditor industry specialisation is a positive determinant of audit quality. 

 

CONCLUSION 

 

This study confirms that client importance and auditor industry specialization positively 

influence audit quality, while excessive workload negatively impacts it, as auditors tend to 

exercise greater caution with large, complex clients due to the significant risks of audit failure, 

consistent with agency theory’s emphasis on rigorous oversight to mitigate managerial 

opportunism. Specialized auditors leverage sector-specific expertise to enhance performance, 

whereas heavy workloads hinder focus and can lead to quality degradation. Contrary to 

expectations, corporate governance and audit fees were not significant determinants, indicating 

a possible gap between formal governance mechanisms and their practical enforcement, as well 

as auditor independence being maintained regardless of compensation levels. These findings 

align with both agency theory, highlighting audits’ role in resolving principal-agent conflicts 

by ensuring financial statement credibility, and stakeholder theory, emphasizing how high-

quality audits bolster corporate accountability and public trust particularly vital within 

Indonesia’s complex regulatory environment. Despite advancing understanding of audit quality 

determinants in Indonesia’s Consumer Cyclicals and Non-Cyclicals sectors, the study’s 

limitations include its reliance on discretionary accruals as the sole proxy for audit quality, a 

narrow sectoral focus, and the absence of mediating or moderating variables. Future research 

should consider alternative audit quality measures, extend analyses to other industries, and 



American Journal of Economic and Management Business 

Vol. 4 No. 7 July 2025 

1018 

explore additional factors such as auditor ethics and internal control effectiveness to offer a 

more comprehensive assessment of audit quality drivers. 

 

 

 

REFERENCES 

Ahmar, N., Rahmah, L. Al, & Darminto, D. P. (2024). Green Banking Disclosure From The 

Perspective of Corporate Governance, Financial Slack and Human Resource Slack in 

Indonesia. Banks and Bank Systems, 19(2), 101–114. 

https://doi.org/10.21511/bbs.19(2).2024.08 

Akuoko-Sarpong, R., Tawiah Gyasi, S., & Affram, H. (2024). Audit Quality and its 

Determinants: Study the Factors that Influence Audit Quality, Including Auditor 

Independence, Firm Size, and Regulatory Environment. International Journal of 

Innovative Science and Research Technology (IJISRT), 1584–1598. 

https://doi.org/10.38124/ijisrt/ijisrt24aug1058 

Aly, S., Diab, A., & Abdelazim, S. I. (2023). Audit Quality, Firm Value and Audit Fees: Does 

Audit Tenure Matter? Egyptian Evidence. Journal of Financial Reporting and 

Accounting. https://doi.org/10.1108/JFRA-04-2023-0203 

Asmara, C. G. (2019). RSM Sudah Dipanggil Kemenkeu Terkait AISA, Bakal Kena Sanksi? 

CNBC Indonesia. https://www.cnbcindonesia.com/market/20190329105210-17-

63632/rsm-sudah-dipanggil-kemenkeu-terkait-aisa-bakal-kena-sanksi 

Aurely, C., Destiana, R., & Saadah, K. (2021). Pengaruh Audit Tenure, Kualitas Laba Dan 

Auditor Spesialisasi Industri Terhadap Audit Delay. Indonesian Accounting Literacy 

Journal, 01(03), 734–750. https://doi.org/https://doi.org/10.35313/ialj.v1i3.3525 

Ayoola, T. J. (2022). Audit fees, Audit Seasonality and Audit Quality in Nigeria: A Mediation 

Analysis. Journal of Financial Reporting and Accounting. https://doi.org/10.1108/JFRA-

01-2022-0010 

Barghathi, Y., Collison, D., & Crawford, L. (2018). Earnings Management and Audit Quality: 

Stakeholders’ Perceptions. Journal of Management and Governance, 22(3), 629–659. 

https://doi.org/10.1007/s10997-017-9396-2 

Bing, J. (Jimmy), Huang, C. X., Li, A. (Linda), & Zhu, X. (Lydia). (2014). Audit Quality 

Research Report. In Australian National Centre for Audit and Assurance Research. 

Cahyanti, C. N., Hastuti, A. W., & Werdiningsih, S. (2022). The Effect of Financial Distress, 

Company Size, Asset Growth, Auditor Switching, Audit Tenure and Audit Fee on Audit 

Quality (Study on Manufacturing Companies Listed on the Indonesia Stock Exchange 

2015-2019). International Journal of Scientific and Academic Research, 02(01), 01–10. 

https://doi.org/10.54756/ijsar.2022.v.2.101 

Chang, H., Fang, J., & Mo, P. L. L. (2023). Auditor Career Concerns, Audit Fees and Audit 

Quality. Asia-Pacific Journal of Accounting and Economics, 30(1), 30–55. 

https://doi.org/10.1080/16081625.2021.1947860 

Chang, Y. S., Chiang, C. Y., Liu, L. L. (Sunny), & Xie, X. (Lucy). (2019). Audit Partner 

Independence and Business Affiliation: Evidence From Taiwan. Advances in Accounting, 

46. https://doi.org/10.1016/j.adiac.2019.100428 

Chen, S., Li, Z., & Chi, W. (2018). Client Importance and Audit Quality: Evidence from China. 

Asia-Pacific Journal of Accounting and Economics, 25(5), 624–638. 

https://doi.org/10.1080/16081625.2016.1268061 

Chen, S., Sun, S. Y. J., & Wu, D. (2010). Client Importance, Institutional Improvements, and 

Audit Quality in China: an Office and Individual Auditor Level Analysis. Accounting 

Review, 85(1), 127–158. https://doi.org/10.2308/accr.2010.85.1.127 



Septia Mitha Caesaria, Majidah 

1019 

Cheng, Y., Haynes, C. M., & Yu, M. D. (2021). The Effect of Engagement Partner Workload 

on Audit Quality. Managerial Auditing Journal, 36(8), 1068–1091. 

https://doi.org/10.1108/MAJ-04-2020-2635 

Choi, J. H., Kim, J. B., & Zang, Y. (2010). Do Abnormally High Audit Fees Impair Audit 

Quality? Auditing, 29(2), 115–140. https://doi.org/10.2308/aud.2010.29.2.115 

Choudhary, P. (2018). Who is Responsible For Ensuring a High-Quality Audit That Achieves 

Accurate Financial Reporting? Maandblad Voor Accountancy En Bedrijfseconomie, 

92(7/8), 201–203. https://doi.org/10.5117/mab.92.29989 

Chtaoui, A., Hajji, Z., Benhrimida, M., Farrat, O., & Nabaoui, A. (2024). The Influence of 

Governance Mechanisms on The Quality of External Audits, Case of Moroccan 

Companies Listed on The Stock Exchange. Revista de Gestao Social e Ambiental , 18(6). 

https://doi.org/10.24857/rgsa.v18n6-115 

DeFond, M., & Zhang, J. (2014). A Review of Archival Auditing Research. Journal of 

Accounting and Economics, 58(2–3), 275–326. 

https://doi.org/10.1016/j.jacceco.2014.09.002 

Dekeyser, S., He, X., Xiao, T., & Zuo, L. (2024). Auditor Industry Range and Audit Quality. 

Journal of Accounting and Economics, 77(2–3). 

https://doi.org/10.1016/j.jacceco.2023.101669 

Feng, Z., Li, L. Z., Wong, H. Y., & Wong, J. (2023). Regulatory Intervention and Audit Quality: 

New Evidence from Audit Firm Suspension. Meditari Accountancy Research, 31(5), 

1165–1189. https://doi.org/10.1108/MEDAR-07-2021-1372 

Freihat, F. A. Y., Kadir, M. R. A., Kamarudin, K. A., & Sapingi, R. (2024). The Effect of 

Corporate Governance Mechanisms on Audit Quality towards Earnings Quality of 

Jordanian Non-Financial Firms. Pakistan Journal of Life and Social Sciences, 22(2), 

12832–12857. https://doi.org/10.57239/PJLSS-2024-22.2.00918 

Griffin, P. A., Lont, D. H., & Sun, Y. (2010). Agency Problems and Audit Fees: Further Tests 

of The Free Cash Flow Hypothesis. Accounting and Finance, 50(2), 321–350. 

https://doi.org/10.1111/j.1467-629X.2009.00327.x 

Hartono, S., Al Musadieq, M., Rahardjo, K., & Afrianty, T. W. (2023). The Critical Factors 

Affecting the Implementation of Corporate Governance in Indonesia: A Structural 

Equation Modeling Analysis. Economies, 11(5). 

https://doi.org/10.3390/economies11050139 

Heo, J. S., Kwon, S. Y., & Tan, H. T. (2021). Auditors’ Responses to Workload Imbalance and 

the Impact on Audit Quality. Contemporary Accounting Research, 38(1), 338–375. 

https://doi.org/10.1111/1911-3846.12612 

Hossain, S., Coulton, J., & Wang, J. J. (2023). Client Importance and Audit Quality at the 

Individual Audit Partner, Office, and Firm Levels. Abacus, 59(2), 650–696. 

https://doi.org/10.1111/abac.12289 

Hwang, S., & Hong, P. K. (2022). Auditors’ Workload and Audit Quality Under Audit Hour 

Budget Pressure: Evidence From The Korean Audit Market. International Journal of 

Auditing, 26(3), 371–387. https://doi.org/10.1111/ijau.12286 

International Forum of Independent Audit Regulators. (2024). Survey of Inspection Findings 

2023. www.ifiar.org 

Islam, M. R. (2018). Sample Size and Its Role in Central Limit Theorem (CLT). International 

Journal of Physics & Mathematics, 1, 37–47. https://doi.org/10.31295/ijpm.v1n1.42 

Kissin, E., & Gray, A. (2025, May 20). EY Accused of ‘Serious’ Failings in Audits of Collapsed 

NMC Health. Financial Times. https://www.ft.com/content/f2420f88-9b3b-40bf-b130-

40d9ed596acc 



American Journal of Economic and Management Business 

Vol. 4 No. 7 July 2025 

1020 

Knechel, W. R., Krishnan, G. V., Pevzner, M., Bhaskar, L. S., & Velury, U. (2013). Audit 

Quality Indicators: Insights from the Academic Literature. Auditing: A Journal of 

Practice & Theory, 32. https://doi.org/10.2139/ssrn.2040754 

López, D. M., & Peters, G. F. (2011). Auditor Workload Compression and Busy Season Auditor 

Switching. Accounting Horizons American Accounting Association, 25(2). 

https://doi.org/10.2308/a 

Majidah, M., Isynuwardhana, D., & Anna, Y. D. (2018). Audit Quality: Time Budget Pressure, 

Dysfunctional Auditor Behaviour and The Understanding of Information Technology as 

Moderator. Smart Collaboration for Business in Technology and Information Industries, 

2016: SCBTII Proceeding, 295–301. 

Mansur, H., Aziz, A. R. A., Meero, A., & Shatnawi, A. (2022). The Perceptions of External 

Auditors on The Relationship Between Audit Fees and Audit Quality. Cogent Business 

and Management, 9(1). https://doi.org/10.1080/23311975.2022.2113203 

Mao, J., Ettredge, M., & Stone, M. (2020). Group Audits: Are Audit Quality and Price 

Associated With The Lead Auditor’s Decision to Accept Responsibility? Journal of 

Accounting and Public Policy, 39(2). https://doi.org/10.1016/j.jaccpubpol.2020.106718 

Mnif, Y., & Cherif, I. (2022). Audit Partner Workload, Gender and Audit Quality. Journal of 

Applied Accounting Research, 23(5), 1047–1070. https://doi.org/10.1108/JAAR-08-

2021-0219 

Nasional Kontan. (2021, August 5). Manipulasi Laporan Keuangan, Dua Eks Bos Tiga Pilar 

(AISA) Divonis 4 Tahun Penjara. Nasional Kontan. 

Nurbaiti, A., & Sabilla, R. (2022). Audit Quality: Client Importance, Audit Capacity Stress, 

and Audit Fee. Proceedings of the International Conference on Industrial Engineering 

and Operations Management, 2217–2225. https://doi.org/10.46254/EU05.20220439 

Pamungkas, W. A. P., & Gantyowati, E. (2021). The Affecting Factors of Audit Quality with 

Earnings Surprise Benchmark Approach as a Measure of Audit Quality. European 

Journal of Business and Management Research, 6(4), 230–236. 

https://doi.org/10.24018/ejbmr.2021.6.4.983 

Quick, R., Sánchez Toledano, D., & Sánchez Toledano, J. (2024). Measures For Enhancing 

Auditor Independence: Perceptions of Spanish Non-Professional Investors and Auditors. 

European Research on Management and Business Economics, 30(2). 

https://doi.org/10.1016/j.iedeen.2024.100250 

Rijal, S., Bakri, A. A., & Arjang. (2023). Effect of Auditor Specialization, Auditor 

Characteristics, Board Independence on Audit Quality Through Intellectual Capital: 

Study on Service Companies. The ES Accounting And Finance, 1(02), 95–103. 

https://doi.org/10.58812/esaf.v1i02.66 

Saraswati, R. S., Yadiati, W., Suharman, H., & Soemantri, R. (2024). Corporate Governance, 

Intellectual Capital, and Organizational Culture: Their Influence on Lean Manufacturing 

and Firm Value. Cogent Business and Management, 11(1). 

https://doi.org/10.1080/23311975.2024.2382335 

Shubita, M. F., Alrawashedh, N. H., & Alqam, M. A. (2024). Relationship Between Corporate 

Governance and Audit Quality in The Industry Sector: Moderating Role of Firm 

Performance. Problems and Perspectives in Management, 22(3), 643–652. 

https://doi.org/10.21511/ppm.22(3).2024.49 

 

Copyright holders: 

Septia Mitha Caesaria, Majidah (2025) 

First publication right: 

AJEMB – American Journal of Economic and Management Business