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Eduvest � Journal of Universal Studies Volume 4 Number 12, December, 2024 p- ISSN 2775-3735- e-ISSN
2775-3727 |
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DETERMINANTS
AND CONSEQUENCES OF DYSFUNCTIONAL AUDITOR BEHAVIOR |
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Suwardi1*, Khomsiyah2 Falkutas Akuntansi, Universitas Trisakti,
Indonesia1 |
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ABSTRACT |
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Audit
quality became an important issue after a global corporate scandal that
revealed auditors' inability to detect financial statement fraud, leading to
corporate bankruptcies and scandals. Dysfunctional auditor behavior, such as
inindependence and incompetence in the implementation of duties, has the
potential to damage audit quality. This study aims to analyze the factors
that cause auditor behavior to deviate and their impact on audit quality in
Jakarta, Indonesia. The factors analyzed include time budget pressure, task
complexity, client importance, and organizational commitment. This survey
involved 103 respondents who were selected by purposive sampling. The results
showed that time budget pressure, client importance, and organizational
commitment contributed to auditor disfunctional behavior, while task
complexity had no significant effect. The auditor's disfunctional behavior
has a negative impact on the quality of audits, which in turn can damage
public trust in the audit profession. This study provides insights for
regulators and public accounting firms to identify factors that affect
auditor behavior and audit quality degradation, and develop approaches to
minimize dysfunctional auditor behavior without sacrificing audit value for
external users. |
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KEYWORDS |
Keywords are written in
English, 3�5 keywords or phrases |
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This work is licensed under a Creative Commons
Attribution-ShareAlike 4.0 International |
INTRODUCTION
Audit quality is a topic of common
concern for practitioners, investors, and regulators. In recent years, the
world has witnessed various scandals company, a series of corporate collapses
are mainly caused by the concealment of financial material information and
profit management behavior. Profit management and collusive fraud lead to
reduced quality of financial statements and low reliability in the usability of
information (Bing et al.,
2014; Nuristya & Ratmono, 2022). In OJK records, the audit report
that collapsed occurred in Indonesia in 2022 which befell PT Waskita Karya
(Persero) Tbk and PT Asuransi Adisarana Wanaartha or Wanaartha Life. Fraud that
occurred in their audited financial statements caused many stakeholders such as
investors, creditors, suppliers and customers to suffer losses (www.bisnis.com). As a
result, external auditors have been subjected to criticism over several
corporate scandals for misrepresenting facts. External auditors were ultimately
found guilty of their failure to work professionally and maintain the quality
of the audits provided.
Audit quality is the audit process
and the behavior of auditors in conducting the audit process. Auditors play a
crucial role in preparing useful and timely audit reports to reduce possible
audit risks and minimize company fraud (Khaneja et
al., 2017). Previous research has shown that
dysfunctional auditor behavior (DAB) has a significant negative impact on
audit quality. Dysfunctional auditor
behavior is characterized by any action taken during the audit program
that has the potential to degrade audit quality (Donnelly
& Mulcahy, 2008; Heo et al., 2021; Paino et al., 2019).
It is interesting to note, based
on the survey, that auditors knowingly and intentionally, conduct DAB.
Competition among public accounting firms (KAP) puts pressure on audit fees
because companies reduce budget proposals and maintain audit tasks and quality.
Less budget in audit hours, less likely to produce the same audit results,
putting time pressure on the audit team which can lead to a decrease in audit
quality. On the other hand, KAP must prepare an achievable budget to avoid DAB.� Among the most common audit problems,
"ticking" and "filling out forms" rather than performing
according to audit methodologies, either risk-based approaches or integrated
audit approaches. Auditors understand that
deadlines for some of the audited clients cannot be met, so they use
personal time to work outside of office hours (Nehme et al.,
2022).
Task complexity refers to the
auditor's perception of the auditor's ability, knowledge, and critical thinking
in analyzing audit tasks (Alqudah et
al., 2019). Due to the increasing complexity
of tasks and workloads, auditors tend to engage in dysfunctional behavior by
taking a simple audit approach to complete audit work on time. As such, auditors may not be able to provide high-quality work, which will degrade the
quality of the audit. The higher the level of complexity of the task, the more
work must be done by the auditor and the longer the time required. This will
have an impact on the dysfunctional
auditor behavior because the more complex the tasks performed by the
auditor cause the auditor's performance to decline. Task complexity can
increase auditors' stress levels, thereby reducing audit performance and audit
quality (Alqudah et
al., 2019).
Because public accounting firms are established to make a profit, the income received from auditing clients will be very
important financially to KAP. KAP has a high tendency to produce better and
quality opinions for critical clients and pay more for fear of losing clients.
In addition, clients are more results-oriented where they focus on the level of
satisfaction with the services provided by the auditors. Valentina (2024) argue that audit clients tend to
appoint responsive KAP and provide significant incentives to avoid negative
audit opinions issued by auditors. Auditors tend to compromise and are
reluctant to defy client explanations or perform excessive procedures that can
make clients unable to meet their deadlines. Auditors can also avoid audit
procedures that reveal findings that clients do not want to disclose. Because
client interests have the potential to influence auditor reporting behavior, it
is important to investigate the relationship between client interests and dysfunctional auditor behavior.
Rainer's research (2016) shows that in an organization
when employees feel or receive help, support, attention, or other kind
dispositions, they tend to reciprocate it showing positive and value-creating
work attitudes and behaviors (Cropanzano
& Mitchell, 2005). In addition, Raineri (2016) notes that in an organization,
support generally comes from three variants: (1) the organization itself,
through its general policies and human resources and management policies; (2)
direct supervisors (supervisors), through their management style; and (3)
co-workers through the support of behavior. Several studies have identified
organizational variables (commitment, intention to leave, and organizational
support) as factors that may explain auditors' dysfunctional behavior. Goenawan (2021) found in their research that
organizational involvement is an important thing related to auditor
dysfunctional behavior.
As a result of the many accounting
scandals and litigation faced by public accounting firms, this study will
assess whether the factors that cause the decline in audit quality are embedded
in the practice of public accounting firms. On the other hand, this study
will also provide empirical evidence of the influence of an auditor's
dysfunctional behavior that leads to a decrease in audit quality to overcome
quality concerns and ethical aspects in an audit process so as to be able to
mitigate audit failures and performance-related inefficiencies. To begin with,
the research method used in this study is quantitative in order to gain
in-depth knowledge.
Research Tze San Ong et al., (2022) focusing on the behavior of
external auditors in public accounting firms in Malaysia argues that time
budget pressures, complex tasks, and client interests affect dysfunctional auditor behavior, while
dysfunctional auditor behavior significantly
reduces audit quality in Malaysia. Research by Foka et al., (2023) argues that the level of
organizational commitment has a positive and significant influence on the auditor�s
dysfunctional behavior when measured by unprofessional behavior. These results
show that the lower the level of organizational commitment of employees, the
more auditors develop dysfunctional behaviors that reduce audit quality. These
findings can be used as a tool to help audit practitioners and partners to
explain the specific factors that cause DAB and help auditors avoid taking
similar actions.
�Research Tze San Ong et al., (2022) can be developed again because it
has a low response rate where the observations and respondents are mostly
senior auditors and junior auditors. Researchers cannot regulate the type of
respondents because most of them are audit trainees, thus limiting the use of
the survey approach. In addition, the study only looked at three independent
variables that affect audit quality, although other researchers have proposed
other determining factors that can affect audit quality. The research of Foka et al., (2023) considers a wider sample size and
consideration of other determinant variables such as locus of control,
corruption, and corporate culture.
Audit quality can be
caused by auditor dysfunctional behavior while auditor dysfunctional behavior
can be caused due to time budget pressure
and time deadline pressure, supervisor behavior, organizational commitment level, locus of
control. Based on the description above, the research was conducted to find out
the factors that affect the quality of audits in KAP.
The purpose of this study is to test and analyze the
determination of auditor behavior that deviates from audit quality. The
benefits of this research consist of three aspects: first, for the development
of knowledge, this research is expected to contribute ideas to other
researchers in developing auditing science and theory of reasoned action for
the advancement of education. Second, for public accounting firms, the results
of this study are expected to be useful as evaluation and input materials for
leaders, external auditors, and Quality Control at KAP to reduce auditors'
intentions to resign from their work. Third, for regulators and the government,
the results of this research are expected to be used as evaluation materials
and inputs in making regulations to improve the quality of audits.
RESEARCH
METHOD
The design of this study aims to design a valid, objective,
efficient, and effective research structure, using a survey strategy. Surveys
are a method to collect information from respondents regarding their knowledge,
attitudes, and behaviors, which allows researchers to collect quantitative and
qualitative data (Fink, 2003). The researcher used a questionnaire that was
managed and filled out by respondents through a computer, with primary data
obtained from the results of the questionnaire distribution. The researcher
also used the minimal interference method, which reduces interference with the
normal activities of the auditor.
The operational definition of variables in this study
includes several variables and indicators, such as time budget pressure and
task complexity. For example, for the variable of time budget pressure, the
indicator is the adequacy of time in auditing, which is measured by the ordinal
Likert scale (1 strongly disagrees to 4 strongly agrees) according to Tze San
Ong (2022). Task complexity variables are measured based on the auditor's
experience and the client's business diversification, on the same scale.
This study uses hypothesis testing techniques to analyze
the influence of independent variables such as time budget pressure, task
complexity, client importance, and organizational commitment to auditor
deviation behavior, with turnover intention as the dependent variable. The data
was collected through a questionnaire distributed at the Jakarta Public
Accounting Firm, then processed using Microsoft Excel and EViews 10 for
descriptive statistical analysis. The data is presented in the form of tables
to facilitate analysis. Hypothesis testing was carried out by F test (ANOVA) to
find out whether independent variables together have a significant effect on
dependent variables, with the aim of testing the proposed regression equation
model.
RESULT
AND DISCUSSION
A. Research Analysis
1.
Descriptive Statistical Analysis
Descriptive
statistics are used to describe pre-existing sample data without the intention
of making generalized conclusions or generalizations. The operation of the
descriptive statistics sub-menu on the EViews
20 includes almost all basic descriptive statistical elements, thus
presenting certain characteristics of a sample data. Thus, a brief overview of
the research data can be known.
Descriptive statistics for variables of time budget
pressure, task complexity, client importance, organizational
commitment, auditor behavior deviation and audit quality will be explained by minimum, maximum, mean
and standard deviation. The table below shows the descriptive statistics for each variable tested.
Table 1. Descriptive Statistics
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Minimum |
Maximum |
Mean |
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Time budget
pressure_1 |
1 |
4 |
2.59 |
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Task complexity |
1 |
4 |
3.22 |
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Cliens importance_1 |
2 |
4 |
3.24 |
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Cliens importance_1 |
1 |
4 |
2.66 |
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Dysfunctional
Audit behavior_1 |
1 |
4 |
1.69 |
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Audit Quality_1 |
1 |
4 |
3.12 |
Source: 2024 research
results, with Microsoft Excel for windows
From table 1, it can be concluded that the average
answers to the questionnaire questions vary in numbers 2 and 3 with the number
of respondents being 103 respondents, which indicates that the average answer
of the respondents answered in the direction of disagreeing and agreeing with
the scale from 1 to 4. The smallest mean number of each question is found in
the auditor behavior variable that deviates from the first question of 1.59 and
the largest mean number of each question is found in the audit quality variable
in the sixth question of 3.34. Furthermore, this mean
number, which is a transformation of this ordinal data, will be used in the
multiple linear regression test model.
2.
Classical Assumption Test
Classical assumption testing is a requirement that
must be met to use multiple regression analysis. The classical assumption tests
carried out in this study are multicoloniality tests, heterokedasticity tests
and normality tests, while other classical assumption tests, namely
autocorrelation tests, are not carried out. This is because the period in this study is cross sectional, which is a
momentary fact in the form of data that can only be used once in one
observation period, so there is no need to conduct an autocorrelation test
specifically for regression models whose period is time series (King, 2018).
a.
Multicoloniality Test
The
multicoloniality test aims to test whether the regression model finds a
correlation between independent variables. A good regression model should not
have correlations between independent variables. To detect the presence or
absence of multicoloniality, it can be seen from the VIF and Tolerance values. If the VIF value is
greater than 10 (≥ 10) and the Tolerance value is less than 0.10
(<0.1), it means that there is multicoloniality with the tested data (H. I.
Ghozali, 2018). The following is presented the output of the multicoloniality test to
determine the feasibility of the multiple linear regression model.
Table 2.
Multicoloniality Test Results for Multiple Linear Regression Models
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Variable |
Bright |
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Time budget pressure |
1,021619 |
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Task complexity |
1,041936 |
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Cliens importance |
1,025193 |
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Organizational commitment |
1,023895 |
Source: �EViews Software Processing Results
Based
on Table 2, the results of the multicollinearity test show that the VIF for each independent variable is < 10 and the tolerance
value for each independent variable > 0.1, so it can be concluded
that the multiple linear regression
model is free from the multicoloniality problem.
Table 3. Multicollinearity Test with VIF
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Variable |
BRIGHT |
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And |
1,00000 |
Source: �EViews Software Processing Results
Based
on Table 3, the results of the multicollinearity test show that the VIF for each independent variable is < 10 and the tolerance value for
each independent variable > 0.1, so it can be concluded that the simple linear regression model is
free from the multicoloniality problem.
b.
Heterokedasticity Test
The
Heterokedasticity test aims to test whether there is an unevenness in variance in the regression model from
the residual of one observation to another. Heterokedasticity
occurs when the variance from the
residual of one observation to another is different. A good regression model is
one in which heterokedasticity does not occur (I. Ghozali
& Ratmono, 2018). In this study, the test used to
determine the occurrence of heteroscedasticity is the Arch Test. The basis for
decision-making is that if the p-value ≥ 0.05, then there is no
heteroscedalysis problem, but if the p-value ≤ 0.05, it means that there
is a heteroscedasticity problem.
Table 4. Heteroscedasticity Test with Arch Test
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Heteroskedasticity Test: ARCH |
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F-statistic |
0.667326 |
Prob.
F (1,100) |
0.4159 |
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Obs*R-squared |
0.676161 |
Prob. Chi-Square (1) |
0.4109 |
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Source: �EViews Software Processing Results
Based
on the results of the Arch test in Table 4, the Prob value is known. Chi-Square
0.4109 > 0.05 which means that there is no heteroscedasticity in the
multiple regression meodel.
Table 5. Heteroscedasticity Test with Arch Test
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F-statistic |
2.938497 |
Prob. F (1,100) |
0.0896 |
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Obs*R-squared |
2.911706 |
Prob. Chi-Square (1) |
0.0879 |
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Source: �EViews Software Processing Results
Based
on the results of the Arch test in Table 5, it is known that the Prob value.
Chi-Square is 0.0879 > 0.05, which means that there is no heteroscedasticity
in the regression meodel.
c. Normality Test
The
normality test aims to test whether in the regression model, the perturbing or
residual variables have a normal distribution. As is well known, the t and F
tests assume that the residual values follow a normal distribution. If this
assumption is violated, the statistical test becomes invalid for a small
sample. (I. Ghozali,
2014). The Jarque-Bera test is a
statistical test to find out if the data is normally distributed. To test
normally distributed data or not can be done in two ways, namely if the
probability value ≥ 0.05 (greater than 5%), then the data can be said to
be normally distributed and if the probability ≤ 0.05 (less than 5%),
then the data can be said to be not normally distributed.
Figure 1. Normality Test with Jarque-Bera Test
Source: �EViews Software Results
Based on Figure 1, it is known that the probability value of
the J-B statistic is 0.300026. Because the probability value of p is greater
than the significance level, which is 0.05. This means that the assumption of
normality is met.
Figure 2. Normality Test with Jarque-Bera Test
Source: �EViews Software Results
Based on Figure 2, it is known that the probability
value of the J-B statistic is 0.542846, which is greater than the significance
level, which is 0.05. This means that the assumption of normality is met.
3. Hypothesis
Testing
a.
Multiple and Simple Linear Regression Model Tests
After conducting a classical assumption test, it
was found that in the normality test of normally distributed residues, there
was no multicoloniality and heterokedasticity. This means that the multiple
regression model is good and suitable for research. The collected data was then
analyzed by multiple linear regression method using the EViews for Windows
10 program. The following is presented the output of the multiple
regression analysis test with independent variables, namely time budget
pressure factors, task complexity, client importance factors, organizational
commitment and auditor behavior factors that deviate as dependent variables.
Table 6. Multiple Linear Regression Test Results
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Dependent
Variable: LNY |
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Method:
Least Squares |
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Date:
11/22/24�� Time: 07:33 |
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Sample:
1 103 |
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Included
observations: 103 |
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Variable |
Coefficient |
Std.
Error |
t-Statistic |
Prob. |
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Time budget pressure |
32.35128 |
9.789482 |
3.304697 |
0.0013 |
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Task complexity |
10.94772 |
9.447183 |
1.158835 |
0.2493 |
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Cliens importance |
23.33816 |
11.23906 |
2.076522 |
0.0405 |
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Organizational commitment |
28.09262 |
10.21818 |
2.749278 |
0.0071 |
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C |
-267.9032 |
58.03834 |
-4.615970 |
0.0000 |
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R-squared |
0.201613 |
Mean dependent
var |
0.230521 |
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Adjusted
R-squared |
0.169026 |
S.D. dependent
var |
63.51100 |
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S.E. of
regression |
57.89522 |
Akaike info
criterion |
11.00247 |
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Sum squared
resid |
328482.0 |
Black criterion |
11.13037 |
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Log likelihood |
-561.6273 |
Hannan-Quinn criter. |
11.05428 |
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F-statistic |
6.186878 |
Durbin-Watson
stat |
2.105895 |
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Prob(F-statistic) |
0.000176 |
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From table 6 it can be seen that the multiple
linear regression model can be arranged as follows: Y = -267.9032 + 32.35128X1
+ 10.94772X2 + 23.33816X3 + 28.09262X4 + e.
From
the multiple linear regression equation, it can be seen that the value of the
constant is -267.9032 units, which means that if the variables X1 to X4 are
considered constant, then Y is -267.9032 units. If X1 increases by 1 unit,
while another X is considered constant, then Y will increase by 32.35128 units. If X2 increases by 1 unit, while
another X is considered constant, then Y will experience an increase of
10.94772 units. If X3 increases by 1 unit, while another X is considered
constant, then Y will experience an increase of 23.33816 units. If X4 increases
by 1 unit, while other X is considered constant, then Y will experience an
increase of 28.09262 units.
After conducting multiple linear regression
analysis with independent variables, namely time budget pressure factors, task
complexity, client importance factors, organizational commitment, and auditor
behavior factors that deviate as dependent variables, then the next step is to
conduct a simple linear regression analysis, with auditor behavior that
deviates as independent variables and audit qualityas a dependent
variable. The following is presented the output of a simple regression analysis
test:
Table 7. Results of Regression Dysfunctional Auditor
Behavior
on Audit Quality
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Dependent
Variable: LNZ |
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Method:
Least Squares |
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Date:
11/22/24�� Time: 07:36 |
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Sample:
1 103 |
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Included
observations: 103 |
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Variable |
Coefficient |
Std.
Error |
t-Statistic |
Prob. |
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Dysfunctional
audit behavior |
0.304738 |
0.090861 |
3.353890 |
0.0011 |
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C |
0.434547 |
5.742628 |
0.075670 |
0.9398 |
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R-squared |
0.100211 |
Mean dependent
var |
0.504796 |
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Adjusted
R-squared |
0.091303 |
S.D. dependent
var |
61.13878 |
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S.E. of
regression |
58.28093 |
Akaike info
criterion |
10.98765 |
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Sum squared
resid |
343063.3 |
Black criterion |
11.03881 |
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Log likelihood |
-563.8641 |
Hannan-Quinn criter. |
11.00837 |
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F-statistic |
11.24858 |
Durbin-Watson
stat |
1.871490 |
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Prob(F-statistic) |
0.001123 |
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From table 7 it can be seen that a simple linear
regression model can be arranged as follows: Y = 0.434547 + 0.304738Y + e from the simple linear regression
equation, it can be seen that the value of the constant is 0.434547 which means
that if X1 is considered constant, then Y is 0.434547 units. If X1 increases by
1 unit then Y will increase by 0.304738 units.
b.
Determination Coefficient Test (R2)
The R2 test was used to determine the percentage of
contribution of the influence of the independent variable (X) in a silmutant
manner to the dependent variable (Y). The value of the coefficient of determination is between zero and one. A
small R2 value means that the ability of independent variables to explain
variables is very limited. A value close to one means that the independent
variable provides almost all the information needed to predict the variation of
the dependent variable (H. I.
Ghozali, 2018).
The
results of the analysis of the determination coefficient of independent variables,
time budget pressure, task complexity, client importance, and organizational
commitment, on the variables of auditor behavior that deviate as dependent
variables can be seen in table 8. Based on table 8, the figure 0.2016 was
obtained or 20.16%. This shows the percentage of contribution of the influence
of time budget pressure variables, task complexity, client importance,
organizational commitment, to the variable of auditor behavior that deviates
from 20.16%. While the remaining 79.84% can be explained by other variables
that are not included in this research model.
The
results of the analysis of the determination coefficient of the independent
variable of auditor behavior that deviates from the audit quality variable as a dependent variable can be seen in
table 8. The table shows the percentage of auditor behavior that deviates from
the audit quality variable, which is 10.02%. While the remaining 89.98% can be
explained by other variables that are not included in this research model.
c.
�ANOVA Coefficient Testing (Test F)
According
to Priyatno (2010), the F test was carried out to
find out whether the independent variables together have a significant
influence on the independent variables. It can also be interpreted that the F
test will test the results of the equation model in the regression model. If Prob. (F-statistics) greater than 0.05, then
there is no effect of the independent variables together on the non-independent
variable or H0 is accepted. If Prob.
(F-statistics) less than 0.05 from then there is an effect of the
independent variables together on the variables that are not free or Ha are
accepted. The results of the F test table with the variables of time budget
pressure, task complexity, client importance, organizational commitment as
independent variables to the dependent variables, namely auditor behavior that
deviates.
From
table 7, it is known that the calculated Sig value is smaller than the
determined significance value of 0.05, so Ha cannot be rejected. This means
that there is an influence of independent variables, namely time budget
pressure, task complexity, client importance, organizational commitment to
auditor behavior that deviates from the dependent variables together.
From
table 7 of the F test with the auditor behavior variable that deviates as an
independent variable to the dependent variable, namely audit quality, it is known that the calculated Sig value is
smaller than the determined significance value of 0.05, so Ha cannot be
rejected. This means that there is an influence of auditor behavior that
deviates from the audit quality.
d.
�Partial Regression Coefficient
Testing (t-Test)
According
to Priyanto (2010:68) this test is used to find out
in the regression model the independent variable (Time budget pressure, Task
complexity) partially affects the dependent variable (Y). The condition of the
t-test is with a significance of 5% which means a confidence level of 95%. The
basis for making the decision is to use a significance probability number, that
is, if the calculation < sig is 0.05, then Ha is accepted. If the
calculation > sig is 0.05, then Ha is rejected. The following is presented a
t-test table with variables such as time budget pressure, task complexity,
client importance, and organizational commitment as independent variables to
the dependent variable, namely dysfunctional auditor behavior.
Table 8. Test Results t
|
Dependent
Variable: LNY |
|
|
||
|
Method:
Least Squares |
|
|
||
|
Date:
11/22/24�� Time: 07:33 |
|
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|
Sample:
1 103 |
|
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|
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Included
observations: 103 |
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|
Variable |
Coefficient |
Std.
Error |
t-Statistic |
Prob. |
|
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|
|
|
|
|
|
|
Time budget pressure |
32.35128 |
9.789482 |
3.304697 |
0.0013 |
|
Task complexity |
10.94772 |
9.447183 |
1.158835 |
0.2493 |
|
Cliens importance |
23.33816 |
11.23906 |
2.076522 |
0.0405 |
|
Organizational commitment |
28.09262 |
10.21818 |
2.749278 |
0.0071 |
|
C |
-267.9032 |
58.03834 |
-4.615970 |
0.0000 |
Based on the results of hypothesis
testing, it was found that time budget pressure had a significant effect on the
auditor's behavior that deviated with a significance value of 0.0013
(<0.05), so that Ha was accepted. The complexity of the task had no
significant influence on the dysfunctional auditor
behavior due to the significance value
of 0.2493 (>0.05), so Ha was rejected. The importance of the client showed a
significant influence on the auditor's behavior which deviated with a
significance value of 0.0405 (<0.05), so Ha was accepted. Organizational
commitment also had a significant influence on auditor behavior that deviated
with a significance value of 0.071 (<0.05), so Ha was accepted. Furthermore,
the dysfunctional auditor behavior
had a significant effect on the audit quality with a significance value of
0.0071 (<0.05), so that Ha was accepted. Overall, these results show that
time budget pressures, client importance, and organizational commitment
contribute to auditor misconduct, which ultimately affects audit quality.
However, the complexity of the task has no significant influence on dysfunctional auditor behavior.
Discussion
This study aims to determine the
influence of time budget pressure, task complexity, client importance,
organizational commitment to dysfunctional
auditor behavior and the influence of dysfunctional auditor behavior on audit quality. The test results show that the dysfunctional auditor behavior can be
explained by time budget pressure, task complexity, client importance,
organizational commitment, by 20.06%, while audit quality can be explained by dysfunctional auditor behavior by 10.02%.
From the Anova test, it was found
that the variables of time budget pressure, task
complexity, client
importance, and organizational commitment to auditor behavior variables
diverged together. The Anova test also showed the influence of dysfunctional auditor behavior on audit quality variables.
The four independent
variables, there are
three variables that have a significant influence on the dysfunctional auditor behavior,
namely time budget pressure, client importance and organizational commitment.
One variable that does not have a significant influence on auditor behavior is
the complexity of the task. Meanwhile, audit
quality was found to be significantly affected by auditor behavior.
The dysfunctional auditor behavior significantly affects the audit quality in support of the
research results conducted by Tze San Ong et
al., (2022). When auditors reject problematic
samples or receive weak client explanations, they tend to rely on the
information provided by the audit client without further clarification. Audit
clients may hide important information or manipulate financial statements for
their benefit. In addition, if the auditor fails to investigate the suitability
of accounting treatment, it will affect the reliability and accuracy of financial
information and influence stakeholder decisions. In summary, dysfunctional
auditor behavior has reduced the auditor's ability to detect possible fraud or
deliberate misrepresentation committed by clients. There is a high possibility
that it can lead to the failure of the company when irregularities have been
identified after a few years (San Ong et
al., 2022).
Time budget pressure influences
auditor behavior that deviates in favor of the research results of Tze San Ong et al. Research., (2022). The reason for time budget
pressure affects the dysfunctional
auditor behavior because it indicates that the auditor feels pressured
because he cannot complete the audit task as expected due to the constraints of
strict deadlines in gathering sufficient evidence. Auditors experience time
budget pressures and feel that planning is not achievable most of the time.
Auditors need to spend extra time completing audit tasks, which seems to use
dysfunctional behavior in overcoming such pressures. Auditors tend to omit
certain parts of audit procedures or take shortcuts by taking previous audit
paperwork to understand and assess internal control systems, which indicates a
tendency to sacrifice audit quality. Therefore, these findings are consistent
with previous research by Tze San Ong et
al., (2022), follows the assumption of
reasoned actions that show that the greater the time budget pressure on the
auditor, the more likely it is to engage in dysfunctional audit behavior� due to time budget constraints during the
audit period. According to the theory of reasoned action, a person's beliefs
that consider profit or loss as well as the consequences that occur make him
behave. If an auditor has a very tight audit time, it tends to consider the
effects of its delay which usually affects the auditor's performance so that it
prefers to carry out dysfunctional
auditor behavior so that the work is completed faster by sacrificing or
reducing audit procedures.
Task complexity was found to have
no significant influence on auditor behavior and supported the research of
Desmond et al (2013) but contradicted the research contrary to the research
conducted by Tze San Ong et al, (2022). Task complexity can put strong
pressure on auditors so that auditors will conduct behavioral deviations such
as URT so that during performance
appraisals they get a good assessment because they can complete complex tasks
within a predetermined time. However, the complexity of the existing tasks
usually receives close supervision from the auditor team and becomes an in-depth
discussion, making it difficult to make deviations because it is a significant
risk. In the Audit Standard, significant risks must get the attention of
superiors, including from partners. dysfunctional
auditor behavior of PMSO and ARAP will also be carried out in order
to make it easier to complete these complex tasks easily, but with supervision
and supervision from superiors, dysfunctional behavior can be prevented. From normative
beliefs and importance norms, a person who is supervised and
supervised will perform complex work carefully and carefully so that it will
reduce dysfunctional behavior because the motivation to do the job correctly
will arise to prove that he is capable of doing the job and get praise.
The importance of the client
influences the dysfunctional auditor
behavior. This is contrary to the research conducted by Tze San Ong et
al, (2022), but supports the research
conducted by Brown (2012). The importance of the client
affects the auditor's dysfunctional behavior because the interests of the
client affect the auditor's reporting behavior. This implies that the auditor
is prejudiced against the client's interests and that the client's interests
influence the auditor's judgment and decisions because the auditor is afraid of
losing an important client who contributes to the KAP. This can indicate
that the auditor is not willing to compromise because of their relationship
with the client. Compromise so as not to lose clients will cause auditors to
reduce the amount of work required by audit standards and eliminate procedures
that should be carried out. Attitude towards the behavior describes those
beliefs about the consequences of behavior or normative beliefs. Attitude
factors towards the impact of losing the importance of clients to KAP such as
individual performance, bonus and salary increases will make auditors have
intentions and carry out dysfunctional
auditor behavior intentions.
Organizational commitment
influences the dysfunctional auditor
behavior. This supports research conducted by Tze San Ong et al, (2022). Organizational commitment
affects the auditor's dysfunctional behavior because the lower the level of
organizational commitment, the more the auditor develops dysfunctional behavior
that reduces the quality of the audit. Auditors with low levels of commitment
are more likely to engage in dysfunctional behavior than those with higher
levels of affective commitment. When an audit firm through its various actions
is willing to appreciate the contribution of auditors and concern for their well-being in the workplace, it creates an organizational commitment in the
auditors that creates a sense of obligation in them to work well. From high
organizational commitment, it will encourage important norms and culture in the
Company so that if the organizational's commitment is
high and good, the behavior of auditors or employees will also be good.
Based on the results of the
research and discussion above, the advice given to Public Accounting Firms in
order to improve the quality of audits is to reduce the behavior of auditors
who are dysfunctional. One way to do this is to provide more training and
supervision to auditors so that dysfunctional
auditor behavior can be reduced. This training can include training on
audit standards and audit ethics consistently so that auditors can realize that
if they violate standards and ethics, there are acceptable sanctions or
criminal penalties. One of them, according to Law No. 5 of 2011, is
administrative sanctions such as written warnings, fines, written warnings and
criminal penalties of up to 5 years. Supervision and supervision must also be
carried out consistently to small clients as well as large clients. Usually,
KAP only focuses on increasing revenue without caring about existing resources
so that supervision and supervision are lacking. Partners and audit managers
have many clients to handle so that supervision becomes less which causes dysfunctional auditor behavior.
Senior and junior auditors who are poorly supervised will be confused in doing
their work which is required to be completed immediately which causes auditors
to commit deviations in behavior. The Company Culture must also be highly
supportive in upholding applicable ethics, standards and regulations so that
the subjective norms in the KAP are not an excuse for dysfunctional auditor behavior.
In addition, it does not only use
performance appraisal as the only indicator in determining the success of an
auditor in carrying out his duties. Public Accounting Firms must also create a
conducive working atmosphere and not only focus on profits, there must be
targets that can be achieved and are not impossible. As for auditors, it is
recommended to maintain their professionalism at all times and consult at all
times with their seniors if there is a problem in the implementation of their
duties so that problems in the work will be reduced and can be solved without
committing dysfunctional auditor
behavior that ultimately reduces the quality of the audit.
CONCLUSION
This study aims to examine the influence of time budget
pressure, task complexity, client importance, and organizational commitment on
auditor behavior and the influence of auditor behavior on audit quality. The
test results show that the dysfunctional auditor behavior can be explained by 20.16% by
these factors, while the turnover intention is explained by 10.02% by the dysfunctional
auditor behavior. The ANOVA test showed that there was a significant
influence together between independent variables (time budget pressure, task
complexity, client importance, and organizational commitment) on auditor
deviation behavior, as well as the influence of auditor behavior deviation on
audit quality. Of the four independent variables, three of them�time budget
pressure, client importance, and organizational commitment�were shown to have a
significant influence on dysfunctional auditor behavior, while task complexity
had no significant effect. The dysfunctional auditor behavior significantly
affects the quality of audits, which supports the findings of previous research
by Tze San Ong et al. (2022). Dysfunctional
auditor behavior is generally carried out to improve performance
appraisal, but this can reduce audit quality by ignoring the necessary
procedures, which in turn reduces the auditor's ability to detect errors or
fraud, so that the published financial statements do not meet the applicable
audit standards or regulations.
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