Description
The purpose of this paper is to examine the stability or loyalty in the auditor-client
relationship. It explores the association between audit fees and auditor loyalty. Specifically, it
investigates whether clients paying less audit fees relative to other companies in their industries are
more likely to be loyal to their auditors.
Accounting Research Journal
Relative audit fees and client loyalty in the audit market
Magdy Farag Rafik Elias
Article information:
To cite this document:
Magdy Farag Rafik Elias, (2011),"Relative audit fees and client loyalty in the audit market", Accounting
Research J ournal, Vol. 24 Iss 1 pp. 79 - 93
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David S. J enkins, Thomas E. Vermeer, (2013),"Audit firm rotation and audit quality: evidence from
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ARJ -11-2012-0087
Kym Butcher, Graeme Harrison, J ill McKinnon, Philip Ross, (2011),"Auditor appointment in
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Relative audit fees and client
loyalty in the audit market
Magdy Farag
California State Polytechnic University, Pomona, California, USA, and
Ra?k Elias
California State University, Los Angeles, California, USA
Abstract
Purpose – The purpose of this paper is to examine the stability or loyalty in the auditor-client
relationship. It explores the association between audit fees and auditor loyalty. Speci?cally, it
investigates whether clients paying less audit fees relative to other companies in their industries are
more likely to be loyal to their auditors.
Design/methodology/approach – Logistic and ordinal regression analyses are used to compare
loyal clients to clients that switched audit ?rms after controlling for factors that are expected to be
associated with client loyalty.
Findings – Results show that relative audit fees have a signi?cant effect on the degree of loyalty of
clients to their audit ?rms. Additional analysis shows that the loyalty of clients that pay higher audit
fees relative to similar clients in their industry are highly affected by increases in audit fees. However,
the loyalty of clients who pay lower audit fees compared to similar clients in their industry is not
affected by further increases in relative audit fees.
Research limitations/implications – The study does not differentiate between auditor dismissal
and auditor resignation in the classi?cation of clients that switched auditors. It also does not classify
auditor switches into auditor-initiated switches and client-initiated switches.
Practical implications – It is cost effective for clients to stay with the same audit ?rm. Audit ?rms
should be careful when adjusting their audit fees from one period to another, as there is a higher
probability of losing a client, when increasing the audit fees, especially if this client is already paying
higher audit fees relative to similar clients.
Originality/value – The ?ndings of this study increase the understanding of how relative audit fees
affect client loyalty in the audit market.
Keywords United States of America, Relative audit fees, Client loyalty, Switchers, Audit market, Auditors
Paper type Research paper
1. Introduction
The audit market has seen remarkable changes during the last decade.
The Sarbanes-Oxley Act (SOX, US House of Representatives, 2002) and the preceding
accounting scandals signi?cantly changed the business relation between audit ?rms
and their clients. There were exceptional changes during the period from 2002 to 2004,
including the enactment of SOX in 2002, the implementation of SOX by the SEC in 2003,
and SOX Section 404’s requirements for internal control reporting and accelerated ?lers
by the end of 2004 (Huang et al., 2009). While SOX stops short of enforcing auditor
rotation, it does require that the lead and concurring partners to rotate every ?ve years.
An implicit assumption in the policy of mandatory partner rotation is that such rotation
enhances audit quality (Defond and Francis, 2005). Nevertheless, many audit ?rms kept
serving their clients consistently since the beginning of the new millennium. Similarly,
many clients continued to be loyal to their audit ?rms for several years[1].
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
Audit fees and
client loyalty
79
Accounting Research Journal
Vol. 24 No. 1, 2011
pp. 79-93
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309611111148788
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Several studies investigate switching activities in both dismissals and resignations
(Rama and Read, 2006; Ettredge et al., 2007; Grothe and Weirich, 2007). This study
examines the other side of dismissals and resignation, which is the stability or loyalty
in the auditor-client relationship. It explores the association between audit fees and
auditor loyalty. Speci?cally, it investigates whether clients paying less audit fees
relative to other companies in their industries, are more likely to be loyal to their
auditors. The study de?nes clients that stay with their current auditors as “loyal
clients” and those that change auditors as “switchers.” There are many reasons for the
client to switch auditors (Ettredge et al., 2007), and such switchers are not necessarily
“disloyal” clients. The study also investigates the association between the type of
auditor hired and the loyalty to the auditor.
To test the expectation that clients with lower audit fees relative to other companies
in their industries will be loyal to their audit ?rms, we use logistic and ordinal regression
analyses to compare loyal clients to clients that switched audit ?rms after controlling for
other factors that we expected to be associated with client loyalty. Results indicate that
companies experiencing lower audit fees relative to other companies are more loyal to
their audit ?rms. Additional analysis shows that the loyalty of clients that pay higher
audit fees relative to similar clients in their industry are highly affected by increases in
audit fees. However, the loyalty of clients that pay lower audit fees compared to similar
clients in their industry is not affected by increases in audit fees.
The remainder of this study includes ?ve sections. Section 2 reviews the related
literature and presents the study’s hypotheses. Section 3 presents the research design
including the data and the method used. Sections 4 and 5 discusses results of tests and
additional analysis, respectively. The last section concludes the study and presents its
limitations and future research suggestions.
2. Literature review and hypotheses
Audit risk and client retention
Prior research suggests that auditors take actions to control the audit risk[2] when
deciding to accept a new client or to retain a current client. Jones and Raghunandan
(1998) examined the proportions of certain risky clients audited by big audit ?rms and
other independent audit ?rms, and whether the relative proportions have changed
during a period of increasing litigation. They found that big audit ?rms were more
likely than small audit ?rms to have clients who were in ?nancial distress and clients
who were in high-tech industries. However, over a period of increasing litigation costs,
they observed a signi?cant reduction in the likelihood that big audit ?rms would audit
such clients. Johnstone and Bedard (2004) examined client acceptance and client
continuance decisions of a large audit ?rm as a way to provide empirical evidence on
the extent and nature of risk avoidance that the audit ?rm uses to purposefully manage
its client portfolio. Their results showed that this audit ?rm is shedding the riskier
clients in its portfolio, consistent with the risk avoidance theory of audit ?rm portfolio
management. Their results also showed that the ?rm’s newly accepted clients are less
risky than its continuing clients. They also found that audit risk factors are more
important in audit ?rm portfolio management decisions than are ?nancial risk factors.
Hackenbrack and Hogan (2005) investigated the supply-side explanations for
auditor/client pairings by examining the relationship between client retention and
engagement-level pricing. Their major assumption is that auditors base the client
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retention decision partly on engagement realization rates. They found that the difference
between realized realization rates and predicted realization rates is positively associated
with client retention over a ?ve-year window. The realization rate is the ratio of the audit
fee billed to the standard audit fee. These ?ndings suggest that pricing pressure is more
than an isolated occurrence, and the incumbent auditor’s inability to recover unexpected
high labor usage is related to the severing of the auditor-auditee relationship.
Audit fees for resigning and new clients
Owens-Jackson et al. (2008) claimed that in the recent period, the number of auditor
resignation and client dismissals has grown signi?cantly, and the trend increased
sharply in the period between 2001 and 2004. Carcello and Neal (2003) presented results
that suggested that higher audit fees were associated with higher likelihood of auditor
dismissal. Ettredge et al. (2007) also showed that auditor dismissals were associated
with higher relative fees in the post-SOX period. They presented results showing that
auditor dismissals are associated with both steeper fee increases and higher relative
fees in the post-SOX period, which were due to increases in effort and resource
constraints during this period. Based on these previous ?ndings, we expect clients that
realize they pay less audit fees to their auditors when compared to similar companies
are more likely stay loyal to their audit ?rms if their audit fees increase.
Using an international framework, Kallunki et al. (2007) investigated whether ?rms
paying relatively high audit fees are more likely to switch auditors. They also
investigated whether the legal liability environment affects the propensity to switch
auditors owing to audit fee under- and/or over-pricing. Since ?rms are expected to
purchase auditing services from the least-cost supplier, an auditor switch occurs if the
reduction in the audit fee due to the auditor switch exceeds the switching costs. Their
results showed that ?rms paying relatively higher audit fees are more likely to switch
auditors. They also documented that the level of audit fees and the ratio of audit fees to
sales are positively related to the stringency of the legal environment of the country.
Prior research has examined issues related to changes in audit fees for ?rst or initial
year audit. DeAngelo (1981), which is one of the early studies in this area, showed that
incumbent auditors earn quasi-rents from clients due to the start-up costs
accompanying new audits. Such quasi-rents lead to fee cuts (lowballing) in initial
year audit fees. However, clients will not switch auditors if switching costs are greater
than fee savings.
Initial year audits typically involve substantial set up cost and effort on the
auditor’s side that is considered sunk cost, which the auditor ?nds very dif?cult to
ignore. When this set up cost is not passed to the client in the form of additional fees,
the auditor views it as an investment that is expected to generate future returns. This
phenomenon generates the concerns about the impact of lowballing on auditor
independence, since an auditor who is concerned about the returns of his investment
would be more probably to go along with a client in order to keep their relationship
(Geiger and Raghunandan, 2002).
Several pre-SOX research studies established that clients receive fee discounts when
switching auditors (Simon and Francis, 1988; Ettredge and Greenberg, 1990; Walker
and Casterella, 2000)[3]. However, with the resource constraints induced in the SOX
era, the traditional post-switch discount is reduced. Huang et al. (2009) examined
audit fees for clients changing auditors before and after SOX. They ?nd that there
Audit fees and
client loyalty
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is a signi?cant initial year audit fees discount in the pre-SOX period. However, audit
fees discounts for initial year audit engagements are much less likely in the post-SOX
period, at least for the Big 4 auditors; in fact, they observe a signi?cant initial year
audit fee premium for Big 4 clients in the post-SOX period.
Ebrahim (2010) examined the trend in the audit fee premium in the US audit market
for different groups of clients including non-accelerated ?lers and both small and large
accelerated ?lers to show which group was affected the most by SOX especially during
early years of its application. The study also examined the trend in auditor changes
around the SOX and the effect of these changes on audit fees. Results showed a
signi?cant shift in audit fee premium in the audit industry during initial years of SOX
compliance. This shift in audit fee premium was more obvious for small accelerated
?lers. However, the results showed some signs that audit fee premium started to wind
down during 2006. Moreover, this study showed an abnormally increasing trend to
switch from a big auditor to a non-big one and within the non-big segment during the
early years of SOX compliance. Clients who switched from big to non-big auditing
?rms have experienced less increase in their audit fees. Based on these ?ndings, our
study focuses on the impact of relative audit fees on client loyalty in 2006, a year
intended to be far from the effects of SOX and its preceding scandals.
Ettredge et al. (2007) using audit fees relative to similar clients showed that clients
with greater fee increases are more likely to consider alternative audit providers. They
highlighted that clients are expected to consider lower cost options following an
increase in the cost of audit services, both level and changes, as clients observing
similar companies paying lower fees are more likely to expect that lower fees are
attainable, and to ?nd an alternative audit provider charging such rates. Following
Ettredge et al. (2007) theory and results, we expect clients that observe similar
companies paying higher audit fees to be more likely loyal to their audit ?rms and
their degree of loyalty will vary based on the relative audit fees. Accordingly, we
present our hypothesis as follows:
H1a. The clients’ loyalty to their auditors is associated with relative audit fees,
other things being equal.
H1b. The degree of clients’ loyalty to their auditors is associated with relative audit
fees, other things being equal.
The current study complements this growing literature on audit fees and auditor choice
by investigating the stability or loyalty in the auditor-client relationship. We show how
relative audit fees affect the level of client loyalty to their auditors. Previous research
(Rama and Read, 2006; Ettredge et al., 2007) focuses on client retention and dismissal
from the audit ?rm standpoint. Client loyalty in the audit research has not been
investigated before. The motivation for applying the client loyalty concept is to
investigate auditor retention from the client standpoint. A client has the right to switch
auditors whenever there is a disagreement with the auditor or whenever there is
dissatisfaction with the service provided. Conversely, if a client does not switch auditors
for a speci?c period, it is assumed that this client is satis?ed with the audit ?rm and the
audit service for this period. Previous research (Rama and Read, 2006; Ettredge et al.,
2007) focuses on client retention and dismissal from the audit ?rm standpoint. To the
best of our knowledge, this study is the ?rst study to investigate client loyalty and howit
is affected by audit fees from a client viewpoint. Understanding loyalty from a client’s
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perspective is important for audit ?rms to capture the impact of the increase in audit fees
on their clients’ loyalty and the possibility of increasing audit fees to certain clients
without losing their loyalty.
3. Research design
Data
The study focuses on the impact of relative audit fees on client loyalty in a period
intended to be far fromthe effects of SOXand its precipitating scandals. Speci?cally, we
study audit fees paid for the ?scal year 2006, which is four years following the
signi?cant events related to SOX. The relative audit fees variable is measured using the
2006 audit fees data available in the Audit Analytics database. The study measures
client loyalty by identifying clients that did not switch auditors in the seven years period
from 2000 to 2006[4]. A total of 2,499 clients with full seven years data that were served
by only one auditor every year were identi?ed. Out of these 2,499 clients, 1,648 clients
(65.95 percent) never switched auditors (Loyal) and 851 clients (34.05 percent) switched
auditors at least once (Switchers); of which 306 clients (12.24 percent) were former
Arthur Andersen LLP clients that were audited in 2000 and 2001. Arthur Andersen LLP
clients are excluded fromthe sample because these clients were forced to switch auditors
after the demise of Arthur Andersen LLP, which deviates from the de?nition of client
loyalty. This reduced the sample to 2,193 clients, where 75 percent were loyal clients and
25 percent were clients that switched auditors. Necessary 2006 ?nancial data are
collected from COMPUSTAT. There were 539 ?rms with missing ?nancial data, which
reduced the ?nal data set to 1,654 clients, where 78 percent were loyal clients and
22 percent were clients that switched auditors.
Methodology
Our dependent variable addresses fees relative to similar clients. To do this, we use a
standard regression model that explains audit fees for each client. This model includes
all ?rms with audit fee data from Audit Analytics and essential ?nancial data from
COMPUSTAT (Ettredge et al., 2007). This variable is expected to be negatively
associated with loyalty measures, where clients observing higher fees compared to
similar companies are less likely to be loyal to their audit ?rms.
The estimates of abnormal fees are generated from the following simpli?ed
regression model (Simunic, 1980; Larcker and Richardson, 2004; Ettredge et al., 2007):
LogðFEEÞ ¼ b
0
þ b
1
Size þ b
2
Inventory þ b
3
Receivable þ b
4
Leverage
þb
5
ROA þb
6
Loss þ b
7
Big4 þb
8
Going þ b
9-15
Industry þ1 ð1Þ
where:
Log(FEE) ¼ natural logarithm of audit fees for ?scal year 2006 audit work
(measured in thousands).
Size ¼ natural logarithm of total assets for ?scal year 2006 (measured in
millions).
Inventory ¼ ratio of the dollar value of inventory to total assets for ?scal year
2006.
Audit fees and
client loyalty
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Receivable ¼ ratio of the dollar value of accounts receivable to total assets for
?scal year 2006.
Leverage ¼ ratio of total liabilities to total assets for ?scal year 2006.
ROA ¼ ratio of operating income after depreciation to average total assets
for ?scal year 2006.
Loss ¼ indicator variable that is equal to one if the client reports negative
income in the year of the audit and zero otherwise.
Big 4 ¼ indicator variable that is equal to one if the client’s auditor is one of
the Big 4 and zero otherwise.
Going ¼ indicator variable equal to one if the client receives a going concern
opinion in the year of the audit and zero otherwise.
Industry ¼ industry indicator variables based on SIC industry code[5].
The independent variables in Model (1) above are fairly standard and follow
well-established audit fee models. The difference between actual fees and fees
estimated by the previous model for each client is captured in the residual of the model.
The estimated residual ( ^ 1) is the proxy for abnormal fees (FEEABN).
Logistic and ordinal regression models
To examine the association between relative audit fees and loyalty, the study uses two
categorical models: a logistic regression model and an ordinal model. We de?ne loyalty
using two approaches, a binary approach, where clients are categorized into loyal
clients or clients that switched audit ?rms; and an ordinal approach, where clients are
categorized into four levels of loyalty that depend on how many times they switched
auditors during the seven years period. The advantage of using the categorical
approach is that it classi?es clients that switched audit ?rms into different levels of
clients rather than putting them all in one category. Following Ettredge et al. (2007),
we controlled our sample by identifying Big 4 and non-Big 4 audit ?rms’ clients.
We also control for effects of other factors that could affect clients’ loyalty: prior going
concern opinions, client size, prior losses, growth, and leverage. We ?nally include our
variable of interest (FEEABN), which is the relative audit fee measure. We applied the
following model:
Loyal ðDegree of LoyaltyÞ ¼ b
0
þb
1
FEEABN þb
2
Big4 þ b
3
Going þb
4
Size
þb
5
Loss þ b
6
Growth þ b
7
Leverage þ 1 ð2Þ
where:
Loyalty ¼ indicator variable that is equal to one if the client did not
change auditor in the period from 2000 to 2006 and zero
otherwise.
Degree of Loyalty ¼ ordinal variable that is equal to four if the client did not
change auditor, three if the client changed auditor once, two
if the client changed auditor twice, and one if the client
changed auditor three times in the period from 2000 to 2006.
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Big 4 ¼ indicator variable that is equal to one if the client’s auditor is
one of the Big 4 in 2006 and zero otherwise.
Going ¼ indicator variable equal to one if the client receives a going
concern opinion in the prior seven years and zero otherwise.
Size ¼ natural logarithm of client’s total assets in 2006.
Loss ¼ indicator variable equal to one if the client reported negative
income in the prior seven years and zero otherwise.
Growth ¼ client’s growth rate calculated as follows:
(sales
2006
2 sales
2000
)/sales
2000
.
Leverage ¼ client’s total liabilities divided by total assets in 2006.
4. Results
Descriptive statistics
Table I compares loyal clients (Loyal) and clients that switched audit ?rms (Switchers)
among Big 4 and non-Big 4 ?rms using our ?nal data set of 1,654 observations.
It shows that the percentage of loyal clients during the seven years period from 2000
to 2006 are higher for Big 4 audit ?rms. On the other hand, the percentage of clients
that switched audit ?rms is higher for non-Big 4 audit ?rms.
Switchers are broken down into three categories, switched once, switched twice, and
switched three times. Results show that clients that switched once or twice during the
seven years period from 2000 to 2006 are higher for non-Big 4 audit ?rms. However,
clients that switched three times were limited to one client in each of the Big 4 and
non-Big 4 audit ?rms[6].
Table II presents descriptive statistics for variables used in Model (2) and the audit
fees data. It presents the means, median, and standard deviations of the sample. Table III
reports Pearson and Spearman (non-parametric) correlations. For variables used in
Model (2), there is a high correlation between the two dependent variables
(Loyal and Degree of Loyalty) used in Model (2). This high correlation is expected due
Big 4 Non-Big 4 Total
Loyal 1,139 (68.90) 155 (9.35) 1,294 (78.25)
Switched once 127 (7.70) 199 (12.0) 326 (19.70)
Switched twice 9 (0.55) 23 (1.4) 32 (1.95)
Switched three times 1 (0.05) 1 (0.05) 2 (0.10)
Switchers 137 (8.30) 223 (13.45) 360 (21.70)
Total 1,276 (77.20) 378 (22.80) 1,654 (100)
Notes: Loyal – clients that never switched auditors from 2000 to 2006; switched once – clients that
switched auditors once in the period from 2000 to 2006 excluding Arthur Andersen LLP former clients,
switched twice – clients that switched auditors twice in the period from 2000 to 2006 excluding
Arthur Andersen LLP former clients, and switched three times – clients that switched auditors three
times in the period from 2000 to 2006 excluding Arthur Andersen LLP former clients; switchers –
clients that switched auditors at least once in the period from 2000 to 2006 excluding Arthur Andersen
LLP former clients; numbers in parentheses represent the percentage of observations in a category to
the total sample
Table I.
Client loyalty during the
seven years period
(2000 to 2006)
Audit fees and
client loyalty
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to the fact that Degree of Loyalty is a detailed explanation of the other dependent
variable (Loyal).
Audit fees regression model
Results for the standard audit fees regression model (Model (1)) are presented in
Table IV. All coef?cients are generally signi?cantly with the expected signs. The
overall explanatory power of the model is consistent with prior studies (Simunic, 1980;
Huang et al., 2009), where the adjusted R
2
is equal to 82.51 percent.
Evaluation of Model’s (1) residuals (FEEABN) does not suggest any econometric
problems, where the residuals are normally distributed and there is no evidence of
heteroskedasticity.
Logistic and ordinal regression analysis for auditor loyalty
Table V presents results for the basic model (Model (2)) using abnormal fees
(FEEABN) as an independent variable. The ?rst two columns of Table V present the
results of Model (2) using the indicator variable (Loyal) as the dependent variable in
the model. The last two columns of Table V present the results of Model (2) using the
ordinal variable (Degree of Loyalty) as the model’s dependent variable. Clients are
categorized into four levels of loyalty that depend on how many times they switched
auditors during the seven years period. The advantage of using the categorical
Mean Median SD Minimum Maximum
Fees 2,107.975 630.000 5,527.068 2.500 87,700.000
Log(FEE) 13.223 13.353 1.698 7.824 18.289
Loyal 0.780 1.000 0.413 0.000 1.000
Degree of Loyalty 3.760 4.000 0.478 1.000 4.000
FEEABN 0.000 20.023 0.594 22.167 2.768
Big 4 0.770 1.000 0.420 0.000 1.000
Going 0.090 0.000 0.280 0.000 1.000
Assets 14,487.403 860.090 98,588.755 0.075 1,884,318.000
Size 6.710 6.758 2.307 0.072 14.450
Loss 0.520 0.000 0.500 0.000 1.000
Growth 4.720 0.585 93.508 20.967 36.287
Leverage 0.184 0.112 0.234 0.000 0.947
Notes: n ¼ 1,654, fees – audit fees for ?scal year 2006 audit work ($ thousands); log(FEE) – natural
logarithm of audit fees for ?scal year 2006 audit work; loyal – indicator variable that is equal to one if
the client did not change auditor in the period from 2000 to 2006 and zero otherwise; Degree of
Loyalty – ordinal variable that is equal to four if the client did not change auditor, three if the client
changed auditor once, two if the client changed auditor twice, and one if the client changed auditor
three times in the period from 2000 to 2006; FEEABN – audit fees relative to similar clients calculated
as the residual of the audit fees model; Big 4 – indicator variable that is equal to one if the client’s
auditor is one of the Big 4 and zero otherwise; going – indicator variable equal to one if the client
receives a going concern opinion in the prior seven years and zero otherwise; assets – client’s total
assets in 2006 ($ millions); size – natural logarithm of client’s total assets in 2006; loss – indicator
variable equal to one if the client reported negative income in the prior seven years and zero otherwise;
growth – client’s growth rate calculated as follows: (sales2006 2 sales2000)/sales2000; and leverage –
client’s total liabilities divided by total assets
Table II.
Descriptive statistics
ARJ
24,1
86
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p
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n
t
h
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s
e
s
Table III.
Correlation matrix
Audit fees and
client loyalty
87
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
approach is that it classi?es clients that switched auditors into different levels of
clients that could help to analyze clients’ loyalty at different categories.
Model (2) in Table V indicates that the variable, audit fees relative to similar clients
(FEEABN), is signi?cantly negative to the dependent variables (Loyalty).
The coef?cient for FEEABN is equal to 20.300 ( p-value , 0.05). Results also show
that the variable (FEEABN) is marginally signi?cant to the dependent variables
(Degree of Loyalty). The coef?cient for FEEABN is equal to 20.244 ( p-value , 0.10).
Therefore, clients that experienced higher audit fees relative to similar clients are found
to be less loyal to their audit ?rm, and their degree of loyalty decreases as their audit
fees relative to similar clients increases. These results supports the hypotheses of the
study and show that the relative audit fees variable (FEEABN) is associated with the
loyalty and the degree of loyalty of the clients to their audit ?rms.
Control variables consistently indicate that clients with higher loyalty to their audit
?rms are audited by one of the Big 4 audit ?rms and receive fewer going concern
opinions from their audit ?rms[7].
In summary, our results suggest that companies that were loyal to their audit ?rms
during the seven years period from 2000 to 2006, experienced lower relative audit fees
in 2006 compared to disloyal clients over the same period.
5. Additional analysis
For further evidence in regards to the association between loyalty and relative audit
fees, one additional analysis is conducted. The data set is split into two data sets: one
data set for clients with positive abnormal audit fees (n ¼ 821), and the other data set
for clients with negative abnormal audit fees (n ¼ 833). Kinney and Libby (2002) states
that abnormal fees capture the pro?tability of auditor-provided services. In particular,
Variable Expected sign Coef?cient p-value
Intercept ? 9.947 ,0.001
Size þ 0.546 ,0.001
Inventory þ 0.370 0.008
Receivable ^ 20.665 ,0.001
Leverage þ 20.007 0.844
ROA 2 20.166 ,0.001
Loss þ 0.296 ,0.001
Big 4 þ 0.510 ,0.001
Going þ 0.286 ,0.001
Notes: n ¼ 1,654, adjusted R
2
¼ 82.51 percent, this table presents the results from the regressions
with the natural logarithm of audit fees “log(FEE)” as the dependent variable; all p-values are two-
tailed; log(FEE) – natural logarithm of audit fees for ?scal year 2006 audit work; size – natural
logarithm of total assets in 2006; inventory – ratio of the dollar value of inventory to total assets;
receivable – ratio of the dollar value of accounts receivable to total assets; leverage – ratio of total
liabilities to total assets; ROA – ratio of operating income after depreciation to average total assets;
loss – indicator variable that is equal to one if the client reports negative income in the year of the
audit and zero otherwise; Big 4 – indicator variable that is equal to one if the client’s auditor is one of
the Big 4 and zero otherwise; and going – indicator variable equal to one if the client receives a going
concern opinion in the year of the audit and zero otherwise
Table IV.
Audit fees regression
results for 2006
ARJ
24,1
88
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O
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D
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S
I
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A
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2
1
:
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3
2
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a
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r
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2
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6
(
P
T
)
positive abnormal fees, namely actual fees in excess of normal fees, are likely to create
economic bonding of the auditor to the client, while negative abnormal fees are unlikely
to do so. Therefore, we expect that the loyalty incentive in relation to the audit fee level
is likely to differ systematically, depending on whether abnormal fees are positive or
negative.
Model (2) is applied on these two data sets. Table VI presents the results using
Loyalty and Degree of Loyalty as the dependent variables for positive and negative
abnormal audit fees. As shown in Table VI, clients with positive abnormal audit fees
have signi?cant negative association between the loyalty measure (Loyalty) and the
audit fees relative to similar clients (FEEABN). Similarly, there is a positive association
between the degree of loyalty measure (Degree of Loyalty) and the audit fees relative to
similar clients (FEEABN) for clients with positive abnormal audit fees[8].
Incontrast, whenusingthe dataset of clients withnegative abnormal audit fees, results
were different. Neither loyaltynor the degree of loyalty is signi?cantly associated withthe
relative audit fees measure (FEEABN). These results indicate that the loyalty of clients
that pay higher audit fees relative to similar clients in their industry is highly affected by
further increases in relative audit fees. However, the loyalty of clients that pay lower audit
fees compared to similar clients in their industry is not affected by increases in relative
audit fees. The implication of this ?nding is believed to be of importance to audit ?rms
when adjusting their audit fees from one period to another. There is a higher probability
Loyal (n ¼ 1,654)
Degree of Loyalty
(n ¼ 1,654)
Dependent variable Coef. p-value Coef. p-value
Intercept 20.335 0.280
Threshold (Degree of Loyalty ¼ 1) 2 5.605 , 0.001
Threshold (Degree of Loyalty ¼ 2) 2 2.648 , 0.001
Threshold (Degree of Loyalty ¼ 3) 0.390 0.157
FEEABN 2 0.300 0.023 2 0.244 0.056
Big 4 2.442 , 0.001 2.393 , 0.001
Going 2 0.578 0.014 2 0.520 0.020
Size 0.036 0.362 0.050 0.199
Loss 20.248 0.131 20.242 0.133
Growth 0.008 0.434 0.006 0.505
Leverage 20.316 0.290 20.346 0.197
Model x
2
398.418 , 0.001 400.180 , 0.001
Pseudo R
2
32.8% 30.6%
Notes: All p-values are two-tailed; loyal – indicator variable that is equal to one if the client did not
change auditor in the period from 2000 to 2006 and zero otherwise; Degree of Loyalty – ordinal
variable that is equal to four if the client did not change auditor, three if the client changed auditor
once, two if the client changed auditor twice, and one if the client changed auditor three times in the
period from 2000 to 2006; FEEABN – audit fees relative to similar clients calculated as the residual of
the audit fees model; Big 4 – indicator variable that is equal to one if the client’s auditor is one of the
Big 4 and zero otherwise; going – indicator variable equal to one if the client receives a going concern
opinion in the prior seven years and zero otherwise; size – natural logarithm of client’s total assets in
2006; loss – indicator variable equal to one if the client reported negative income in the prior seven
years and zero otherwise; growth – client’s growth rate calculated as follows: (sales2006 2 sales2000)/
sales2000; and leverage – client’s total liabilities divided by total assets
Table V.
Logistic and ordinal
regression results for 2006
Audit fees and
client loyalty
89
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P
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N
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:
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;
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;
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;
g
r
o
w
t
h
–
c
l
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n
t
’
s
g
r
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w
t
h
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l
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t
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a
s
f
o
l
l
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w
s
:
(
s
a
l
e
s
2
0
0
6
2
s
a
l
e
s
2
0
0
0
)
/
s
a
l
e
s
2
0
0
0
;
a
n
d
l
e
v
e
r
a
g
e
–
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l
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e
n
t
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a
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s
s
e
t
s
Table VI.
Logistic and ordinal
regression results for
positive and negative
abnormal audit
fees in 2006
ARJ
24,1
90
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d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
of losing a client, when increasing the audit fees, if this client is already paying high audit
fees relative to similar clients. However, this probability is lower if this client is paying
lower amount of audit fees relative to other clients.
6. Conclusions and discussion
This study investigates the association between relative audit fees and loyalty in the
audit market. It complements the growing literature on audit fees and auditor choice by
investigating the stability or loyalty in the auditor-client relationship. We show how
relative audit fees affect the level of client loyalty to their auditors. Previous research
focuses on client retention and dismissal from the audit ?rm standpoint. To the best of
our knowledge, this study is the ?rst study to investigate client loyalty and how it is
affected by audit fees from a client viewpoint. A client has the right to switch auditors
whenever there is a disagreement with the auditor or whenever there is dissatisfaction
with the service provided. Conversely, if a client does not switch auditors for a speci?c
period, it is assumed that this client is satis?ed with the audit ?rm and the audit service
for this period.
Clients paying less audit fees relative to other companies in their industries are more
likely to be loyal to their auditors. Controlling for client and auditor characteristics that
were expected to affect client loyalty to their auditor, the study found that clients
experiencing smaller changes in their audit fees are more loyal to their audit ?rms.
Additionally, relative audit fees have a signi?cant effect on the degree of loyalty of
clients to their audit ?rms. Clients that are loyal to their auditors are found to receive
less going concern opinions and are audited by one of the Big 4 audit ?rms.
Additional analysis found that the loyalty of clients that pay higher audit fees relative
to similar clients in their industry are highly affected by further increases in relative
audit fees. However, the loyalty of clients that pay lower audit fees compared to similar
clients in their industry is not affected by increases in relative audit fees. These results
have implications for managers, audit committees, and board of directors by showing
that the longer their company can stay with the same audit ?rm, the lower the amount of
audit fees paid to that audit ?rm compared to switching to different audit ?rms.
The results should be also of interest to audit ?rms. The fact that it is more cost effective
for clients to stay with the same audit ?rm may imply less income to audit ?rms.
However, they will have more predictable clients. Moreover, audit ?rms should be
careful when adjusting their audit fees from one period to another, as there is a higher
probability of losing a client, when increasing the audit fees, especially if this client is
already paying higher audit fees relative to similar clients. However, this probability is
lower if this client is paying lower amount of audit fees relative to other clients.
This study has several limitations. One important limitation is that it failed to
differentiate between auditor dismissal and auditor resignation in the classi?cation of
clients that switched auditors. We also did not classify auditor switches into
auditor-initiated switches and client-initiated switches as applied by Lee et al. (2004).
Future research can investigate if relative audit fees can be affected by the classi?cation
of loyalty and the switching initiation. Furthermore, the period used to measure the
clients’ loyalty variable covers the period from 2000 to 2006 which involves pre- and
post-SOX periods. The study did not incorporate the potential impact of SOX as it just
measures the impact on relative audit fees in 2006. Further research can also study the
impact of SOX on clients’ loyalty by comparing relative audit fees pre- and post-SOX.
Audit fees and
client loyalty
91
D
o
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n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
1
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Notes
1. In this study, the term “client” refers to US companies audited by an external audit ?rm.
2. Audit risk is de?ned as the risk that the auditor may unknowingly fail to appropriately
modify his/her opinion in ?nancial statements that are materially misstated (AICPA, 1983,
AU 312.02).
3. Publicly available audit fees data is available beginning 2000. Therefore, research regarding
pre-SOX audit fees is to some extent limited.
4. Under the Financial Reporting Release No. 56 (SEC, 2000), SEC registrants were required to
publicly disclose audit fee data in proxy statements ?led with the SEC on or after February 5,
2001, which covers the proxy statements of the preceding ?scal year of 2000.
5. The industry groups identi?ed in the study are agriculture and ?shing, mining and
construction, manufacturing, transportation and communications, wholesale and retail
trade, ?nance and insurance, services and public administration, and international affairs.
6. Clients that switched three times are small in number. This size limitation could lead to a
problem in identifying whether the results are due to the small sample size or there is
actually no difference between the categories of loyalty.
7. While we do not show the values, none of the variance in?ation factors exceeds 2.0,
suggesting that multicollinearity is not a problem in the model.
8. The sub-sample of positive abnormal fees does not have any observations with client ?rms
switching auditors three times during the period from 2000 to 2006.
References
AICPA (1983), “Audit risk and materiality in conducting an audit”, Statement on Auditing
Standards No. 47, American Institute of Certi?ed Public Accountants, New York, NY.
Carcello, J. and Neal, T. (2003), “Audit committee characteristics and auditor dismissals following
‘new’ going-concern reports”, The Accounting Review, Vol. 78 No. 1, pp. 95-117.
DeAngelo, L.E. (1981), “Auditor size and audit quality”, Journal of Accounting and Economics,
Vol. 3 No. 3, pp. 183-99.
Defond, M.L. and Francis, J.R. (2005), “Audit research after Sarbanes-Oxley”, Auditing: A Journal
of Practice & Theory, Vol. 24, pp. 5-30.
Ebrahim, A. (2010), “Audit fee premium and auditor change: the effect of Sarbanes-Oxley Act”,
Managerial Auditing Journal, Vol. 25 No. 2, pp. 102-21.
Ettredge, M. and Greenberg, R. (1990), “Determinants of fee cutting on initial audit
engagements”, Journal of Accounting Research, Vol. 28 No. 1, pp. 198-210.
Ettredge, M., Li, C. and Scholz, S. (2007), “Audit fees and auditor dismissals in the
Sarbanes-Oxley era”, Accounting Horizons, Vol. 21 No. 4, pp. 371-86.
Geiger, M. and Raghunandan, K. (2002), “Auditor tenure and audit reporting”, Auditing:
A Journal of Practice & Theory, Vol. 21 No. 1, pp. 67-78.
Grothe, M. and Weirich, T.R. (2007), “Analyzing auditor changes: lack of disclosure hinders
accountability to investors”, The CPA Journal, Vol. 77 No. 12, pp. 14-23.
Hackenbrack, K.E. and Hogan, C.E. (2005), “Client retention and engagement-level pricing”,
Auditing: A Journal of Practice & Theory, Vol. 24 No. 1, pp. 7-20.
Huang, H.W., Raghunandan, K. and Rama, D. (2009), “Audit fees for initial audit engagement
before and after SOX”, Auditing: A Journal of Practice & Theory, Vol. 28 No. 1, pp. 171-90.
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Johnstone, K.M. and Bedard, J.C. (2004), “Audit ?rm portfolio management decisions”, Journal of
Accounting Research, Vol. 42 No. 4, pp. 659-90.
Jones, F.L. and Raghunandan, K. (1998), “Client risk and recent changes in the market for audit
services”, Journal of Accounting Policy, Vol. 17 No. 2, pp. 169-81.
Kallunki, J., Sahlstro¨m, P. and Zerni, M. (2007), “Propensity to switch auditors and strictness of
legal liability environment: the role of audit mispricing”, International Journal of Auditing,
Vol. 11 No. 3, pp. 165-85.
Kinney, W.R. Jr and Libby, R. (2002), “Discussion of the relation between auditors’ fees for
nonaudit services and earnings management”, The Accounting Review, Vol. 77, pp. 107-14.
Larcker, D.F. and Richardson, S.A. (2004), “Fees paid to audit ?rms, accrual choices and
corporate governance”, Journal of Accounting Research, Vol. 42 No. 3, pp. 625-58.
Lee, H.Y., Mande, V. and Ortman, R. (2004), “The effect of audit committee and board of director
independence on auditor resignation”, Auditing: A Journal of Practice & Theory, Vol. 23
No. 2, pp. 131-46.
Owens-Jackson, L.A., Robinson, D.R. and Shelton, S.W. (2008), “Auditor resignation and
dismissals: their effect on the profession”, The CPA Journal, Vol. 78 No. 1, pp. 28-31.
Rama, D.V. and Read, W.J. (2006), “Resignations by the Big 4 and the market for audit services”,
Accounting Horizons, Vol. 20 No. 2, pp. 97-109.
Simon, D.T. and Francis, J.R. (1988), “The effects of auditor change on audit fees: tests of price
cutting and price recovery”, The Accounting Review, Vol. 63 No. 2, pp. 255-69.
Simunic, D. (1980), “The pricing of audit services: theory and evidence”, Journal of Accounting
Research, Vol. 18 No. 1, pp. 161-90.
US House of Representatives (2002), “The Sarbanes-Oxley Act of 2002”, Public Law 107-204
[H. R. 3763], Government Printing Of?ce, Washington, DC.
Walker, P.L. and Casterella, J.R. (2000), “The role of auditee pro?tability in pricing new audit
engagements”, Auditing: A Journal of Practice & Theory, Vol. 19 No. 1, pp. 157-67.
Corresponding author
Magdy Farag can be contacted at: [email protected]
Audit fees and
client loyalty
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This article has been cited by:
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doc_881396450.pdf
The purpose of this paper is to examine the stability or loyalty in the auditor-client
relationship. It explores the association between audit fees and auditor loyalty. Specifically, it
investigates whether clients paying less audit fees relative to other companies in their industries are
more likely to be loyal to their auditors.
Accounting Research Journal
Relative audit fees and client loyalty in the audit market
Magdy Farag Rafik Elias
Article information:
To cite this document:
Magdy Farag Rafik Elias, (2011),"Relative audit fees and client loyalty in the audit market", Accounting
Research J ournal, Vol. 24 Iss 1 pp. 79 - 93
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David S. J enkins, Thomas E. Vermeer, (2013),"Audit firm rotation and audit quality: evidence from
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ARJ -11-2012-0087
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Relative audit fees and client
loyalty in the audit market
Magdy Farag
California State Polytechnic University, Pomona, California, USA, and
Ra?k Elias
California State University, Los Angeles, California, USA
Abstract
Purpose – The purpose of this paper is to examine the stability or loyalty in the auditor-client
relationship. It explores the association between audit fees and auditor loyalty. Speci?cally, it
investigates whether clients paying less audit fees relative to other companies in their industries are
more likely to be loyal to their auditors.
Design/methodology/approach – Logistic and ordinal regression analyses are used to compare
loyal clients to clients that switched audit ?rms after controlling for factors that are expected to be
associated with client loyalty.
Findings – Results show that relative audit fees have a signi?cant effect on the degree of loyalty of
clients to their audit ?rms. Additional analysis shows that the loyalty of clients that pay higher audit
fees relative to similar clients in their industry are highly affected by increases in audit fees. However,
the loyalty of clients who pay lower audit fees compared to similar clients in their industry is not
affected by further increases in relative audit fees.
Research limitations/implications – The study does not differentiate between auditor dismissal
and auditor resignation in the classi?cation of clients that switched auditors. It also does not classify
auditor switches into auditor-initiated switches and client-initiated switches.
Practical implications – It is cost effective for clients to stay with the same audit ?rm. Audit ?rms
should be careful when adjusting their audit fees from one period to another, as there is a higher
probability of losing a client, when increasing the audit fees, especially if this client is already paying
higher audit fees relative to similar clients.
Originality/value – The ?ndings of this study increase the understanding of how relative audit fees
affect client loyalty in the audit market.
Keywords United States of America, Relative audit fees, Client loyalty, Switchers, Audit market, Auditors
Paper type Research paper
1. Introduction
The audit market has seen remarkable changes during the last decade.
The Sarbanes-Oxley Act (SOX, US House of Representatives, 2002) and the preceding
accounting scandals signi?cantly changed the business relation between audit ?rms
and their clients. There were exceptional changes during the period from 2002 to 2004,
including the enactment of SOX in 2002, the implementation of SOX by the SEC in 2003,
and SOX Section 404’s requirements for internal control reporting and accelerated ?lers
by the end of 2004 (Huang et al., 2009). While SOX stops short of enforcing auditor
rotation, it does require that the lead and concurring partners to rotate every ?ve years.
An implicit assumption in the policy of mandatory partner rotation is that such rotation
enhances audit quality (Defond and Francis, 2005). Nevertheless, many audit ?rms kept
serving their clients consistently since the beginning of the new millennium. Similarly,
many clients continued to be loyal to their audit ?rms for several years[1].
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1030-9616.htm
Audit fees and
client loyalty
79
Accounting Research Journal
Vol. 24 No. 1, 2011
pp. 79-93
qEmerald Group Publishing Limited
1030-9616
DOI 10.1108/10309611111148788
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Several studies investigate switching activities in both dismissals and resignations
(Rama and Read, 2006; Ettredge et al., 2007; Grothe and Weirich, 2007). This study
examines the other side of dismissals and resignation, which is the stability or loyalty
in the auditor-client relationship. It explores the association between audit fees and
auditor loyalty. Speci?cally, it investigates whether clients paying less audit fees
relative to other companies in their industries, are more likely to be loyal to their
auditors. The study de?nes clients that stay with their current auditors as “loyal
clients” and those that change auditors as “switchers.” There are many reasons for the
client to switch auditors (Ettredge et al., 2007), and such switchers are not necessarily
“disloyal” clients. The study also investigates the association between the type of
auditor hired and the loyalty to the auditor.
To test the expectation that clients with lower audit fees relative to other companies
in their industries will be loyal to their audit ?rms, we use logistic and ordinal regression
analyses to compare loyal clients to clients that switched audit ?rms after controlling for
other factors that we expected to be associated with client loyalty. Results indicate that
companies experiencing lower audit fees relative to other companies are more loyal to
their audit ?rms. Additional analysis shows that the loyalty of clients that pay higher
audit fees relative to similar clients in their industry are highly affected by increases in
audit fees. However, the loyalty of clients that pay lower audit fees compared to similar
clients in their industry is not affected by increases in audit fees.
The remainder of this study includes ?ve sections. Section 2 reviews the related
literature and presents the study’s hypotheses. Section 3 presents the research design
including the data and the method used. Sections 4 and 5 discusses results of tests and
additional analysis, respectively. The last section concludes the study and presents its
limitations and future research suggestions.
2. Literature review and hypotheses
Audit risk and client retention
Prior research suggests that auditors take actions to control the audit risk[2] when
deciding to accept a new client or to retain a current client. Jones and Raghunandan
(1998) examined the proportions of certain risky clients audited by big audit ?rms and
other independent audit ?rms, and whether the relative proportions have changed
during a period of increasing litigation. They found that big audit ?rms were more
likely than small audit ?rms to have clients who were in ?nancial distress and clients
who were in high-tech industries. However, over a period of increasing litigation costs,
they observed a signi?cant reduction in the likelihood that big audit ?rms would audit
such clients. Johnstone and Bedard (2004) examined client acceptance and client
continuance decisions of a large audit ?rm as a way to provide empirical evidence on
the extent and nature of risk avoidance that the audit ?rm uses to purposefully manage
its client portfolio. Their results showed that this audit ?rm is shedding the riskier
clients in its portfolio, consistent with the risk avoidance theory of audit ?rm portfolio
management. Their results also showed that the ?rm’s newly accepted clients are less
risky than its continuing clients. They also found that audit risk factors are more
important in audit ?rm portfolio management decisions than are ?nancial risk factors.
Hackenbrack and Hogan (2005) investigated the supply-side explanations for
auditor/client pairings by examining the relationship between client retention and
engagement-level pricing. Their major assumption is that auditors base the client
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retention decision partly on engagement realization rates. They found that the difference
between realized realization rates and predicted realization rates is positively associated
with client retention over a ?ve-year window. The realization rate is the ratio of the audit
fee billed to the standard audit fee. These ?ndings suggest that pricing pressure is more
than an isolated occurrence, and the incumbent auditor’s inability to recover unexpected
high labor usage is related to the severing of the auditor-auditee relationship.
Audit fees for resigning and new clients
Owens-Jackson et al. (2008) claimed that in the recent period, the number of auditor
resignation and client dismissals has grown signi?cantly, and the trend increased
sharply in the period between 2001 and 2004. Carcello and Neal (2003) presented results
that suggested that higher audit fees were associated with higher likelihood of auditor
dismissal. Ettredge et al. (2007) also showed that auditor dismissals were associated
with higher relative fees in the post-SOX period. They presented results showing that
auditor dismissals are associated with both steeper fee increases and higher relative
fees in the post-SOX period, which were due to increases in effort and resource
constraints during this period. Based on these previous ?ndings, we expect clients that
realize they pay less audit fees to their auditors when compared to similar companies
are more likely stay loyal to their audit ?rms if their audit fees increase.
Using an international framework, Kallunki et al. (2007) investigated whether ?rms
paying relatively high audit fees are more likely to switch auditors. They also
investigated whether the legal liability environment affects the propensity to switch
auditors owing to audit fee under- and/or over-pricing. Since ?rms are expected to
purchase auditing services from the least-cost supplier, an auditor switch occurs if the
reduction in the audit fee due to the auditor switch exceeds the switching costs. Their
results showed that ?rms paying relatively higher audit fees are more likely to switch
auditors. They also documented that the level of audit fees and the ratio of audit fees to
sales are positively related to the stringency of the legal environment of the country.
Prior research has examined issues related to changes in audit fees for ?rst or initial
year audit. DeAngelo (1981), which is one of the early studies in this area, showed that
incumbent auditors earn quasi-rents from clients due to the start-up costs
accompanying new audits. Such quasi-rents lead to fee cuts (lowballing) in initial
year audit fees. However, clients will not switch auditors if switching costs are greater
than fee savings.
Initial year audits typically involve substantial set up cost and effort on the
auditor’s side that is considered sunk cost, which the auditor ?nds very dif?cult to
ignore. When this set up cost is not passed to the client in the form of additional fees,
the auditor views it as an investment that is expected to generate future returns. This
phenomenon generates the concerns about the impact of lowballing on auditor
independence, since an auditor who is concerned about the returns of his investment
would be more probably to go along with a client in order to keep their relationship
(Geiger and Raghunandan, 2002).
Several pre-SOX research studies established that clients receive fee discounts when
switching auditors (Simon and Francis, 1988; Ettredge and Greenberg, 1990; Walker
and Casterella, 2000)[3]. However, with the resource constraints induced in the SOX
era, the traditional post-switch discount is reduced. Huang et al. (2009) examined
audit fees for clients changing auditors before and after SOX. They ?nd that there
Audit fees and
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81
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is a signi?cant initial year audit fees discount in the pre-SOX period. However, audit
fees discounts for initial year audit engagements are much less likely in the post-SOX
period, at least for the Big 4 auditors; in fact, they observe a signi?cant initial year
audit fee premium for Big 4 clients in the post-SOX period.
Ebrahim (2010) examined the trend in the audit fee premium in the US audit market
for different groups of clients including non-accelerated ?lers and both small and large
accelerated ?lers to show which group was affected the most by SOX especially during
early years of its application. The study also examined the trend in auditor changes
around the SOX and the effect of these changes on audit fees. Results showed a
signi?cant shift in audit fee premium in the audit industry during initial years of SOX
compliance. This shift in audit fee premium was more obvious for small accelerated
?lers. However, the results showed some signs that audit fee premium started to wind
down during 2006. Moreover, this study showed an abnormally increasing trend to
switch from a big auditor to a non-big one and within the non-big segment during the
early years of SOX compliance. Clients who switched from big to non-big auditing
?rms have experienced less increase in their audit fees. Based on these ?ndings, our
study focuses on the impact of relative audit fees on client loyalty in 2006, a year
intended to be far from the effects of SOX and its preceding scandals.
Ettredge et al. (2007) using audit fees relative to similar clients showed that clients
with greater fee increases are more likely to consider alternative audit providers. They
highlighted that clients are expected to consider lower cost options following an
increase in the cost of audit services, both level and changes, as clients observing
similar companies paying lower fees are more likely to expect that lower fees are
attainable, and to ?nd an alternative audit provider charging such rates. Following
Ettredge et al. (2007) theory and results, we expect clients that observe similar
companies paying higher audit fees to be more likely loyal to their audit ?rms and
their degree of loyalty will vary based on the relative audit fees. Accordingly, we
present our hypothesis as follows:
H1a. The clients’ loyalty to their auditors is associated with relative audit fees,
other things being equal.
H1b. The degree of clients’ loyalty to their auditors is associated with relative audit
fees, other things being equal.
The current study complements this growing literature on audit fees and auditor choice
by investigating the stability or loyalty in the auditor-client relationship. We show how
relative audit fees affect the level of client loyalty to their auditors. Previous research
(Rama and Read, 2006; Ettredge et al., 2007) focuses on client retention and dismissal
from the audit ?rm standpoint. Client loyalty in the audit research has not been
investigated before. The motivation for applying the client loyalty concept is to
investigate auditor retention from the client standpoint. A client has the right to switch
auditors whenever there is a disagreement with the auditor or whenever there is
dissatisfaction with the service provided. Conversely, if a client does not switch auditors
for a speci?c period, it is assumed that this client is satis?ed with the audit ?rm and the
audit service for this period. Previous research (Rama and Read, 2006; Ettredge et al.,
2007) focuses on client retention and dismissal from the audit ?rm standpoint. To the
best of our knowledge, this study is the ?rst study to investigate client loyalty and howit
is affected by audit fees from a client viewpoint. Understanding loyalty from a client’s
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perspective is important for audit ?rms to capture the impact of the increase in audit fees
on their clients’ loyalty and the possibility of increasing audit fees to certain clients
without losing their loyalty.
3. Research design
Data
The study focuses on the impact of relative audit fees on client loyalty in a period
intended to be far fromthe effects of SOXand its precipitating scandals. Speci?cally, we
study audit fees paid for the ?scal year 2006, which is four years following the
signi?cant events related to SOX. The relative audit fees variable is measured using the
2006 audit fees data available in the Audit Analytics database. The study measures
client loyalty by identifying clients that did not switch auditors in the seven years period
from 2000 to 2006[4]. A total of 2,499 clients with full seven years data that were served
by only one auditor every year were identi?ed. Out of these 2,499 clients, 1,648 clients
(65.95 percent) never switched auditors (Loyal) and 851 clients (34.05 percent) switched
auditors at least once (Switchers); of which 306 clients (12.24 percent) were former
Arthur Andersen LLP clients that were audited in 2000 and 2001. Arthur Andersen LLP
clients are excluded fromthe sample because these clients were forced to switch auditors
after the demise of Arthur Andersen LLP, which deviates from the de?nition of client
loyalty. This reduced the sample to 2,193 clients, where 75 percent were loyal clients and
25 percent were clients that switched auditors. Necessary 2006 ?nancial data are
collected from COMPUSTAT. There were 539 ?rms with missing ?nancial data, which
reduced the ?nal data set to 1,654 clients, where 78 percent were loyal clients and
22 percent were clients that switched auditors.
Methodology
Our dependent variable addresses fees relative to similar clients. To do this, we use a
standard regression model that explains audit fees for each client. This model includes
all ?rms with audit fee data from Audit Analytics and essential ?nancial data from
COMPUSTAT (Ettredge et al., 2007). This variable is expected to be negatively
associated with loyalty measures, where clients observing higher fees compared to
similar companies are less likely to be loyal to their audit ?rms.
The estimates of abnormal fees are generated from the following simpli?ed
regression model (Simunic, 1980; Larcker and Richardson, 2004; Ettredge et al., 2007):
LogðFEEÞ ¼ b
0
þ b
1
Size þ b
2
Inventory þ b
3
Receivable þ b
4
Leverage
þb
5
ROA þb
6
Loss þ b
7
Big4 þb
8
Going þ b
9-15
Industry þ1 ð1Þ
where:
Log(FEE) ¼ natural logarithm of audit fees for ?scal year 2006 audit work
(measured in thousands).
Size ¼ natural logarithm of total assets for ?scal year 2006 (measured in
millions).
Inventory ¼ ratio of the dollar value of inventory to total assets for ?scal year
2006.
Audit fees and
client loyalty
83
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Receivable ¼ ratio of the dollar value of accounts receivable to total assets for
?scal year 2006.
Leverage ¼ ratio of total liabilities to total assets for ?scal year 2006.
ROA ¼ ratio of operating income after depreciation to average total assets
for ?scal year 2006.
Loss ¼ indicator variable that is equal to one if the client reports negative
income in the year of the audit and zero otherwise.
Big 4 ¼ indicator variable that is equal to one if the client’s auditor is one of
the Big 4 and zero otherwise.
Going ¼ indicator variable equal to one if the client receives a going concern
opinion in the year of the audit and zero otherwise.
Industry ¼ industry indicator variables based on SIC industry code[5].
The independent variables in Model (1) above are fairly standard and follow
well-established audit fee models. The difference between actual fees and fees
estimated by the previous model for each client is captured in the residual of the model.
The estimated residual ( ^ 1) is the proxy for abnormal fees (FEEABN).
Logistic and ordinal regression models
To examine the association between relative audit fees and loyalty, the study uses two
categorical models: a logistic regression model and an ordinal model. We de?ne loyalty
using two approaches, a binary approach, where clients are categorized into loyal
clients or clients that switched audit ?rms; and an ordinal approach, where clients are
categorized into four levels of loyalty that depend on how many times they switched
auditors during the seven years period. The advantage of using the categorical
approach is that it classi?es clients that switched audit ?rms into different levels of
clients rather than putting them all in one category. Following Ettredge et al. (2007),
we controlled our sample by identifying Big 4 and non-Big 4 audit ?rms’ clients.
We also control for effects of other factors that could affect clients’ loyalty: prior going
concern opinions, client size, prior losses, growth, and leverage. We ?nally include our
variable of interest (FEEABN), which is the relative audit fee measure. We applied the
following model:
Loyal ðDegree of LoyaltyÞ ¼ b
0
þb
1
FEEABN þb
2
Big4 þ b
3
Going þb
4
Size
þb
5
Loss þ b
6
Growth þ b
7
Leverage þ 1 ð2Þ
where:
Loyalty ¼ indicator variable that is equal to one if the client did not
change auditor in the period from 2000 to 2006 and zero
otherwise.
Degree of Loyalty ¼ ordinal variable that is equal to four if the client did not
change auditor, three if the client changed auditor once, two
if the client changed auditor twice, and one if the client
changed auditor three times in the period from 2000 to 2006.
ARJ
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Big 4 ¼ indicator variable that is equal to one if the client’s auditor is
one of the Big 4 in 2006 and zero otherwise.
Going ¼ indicator variable equal to one if the client receives a going
concern opinion in the prior seven years and zero otherwise.
Size ¼ natural logarithm of client’s total assets in 2006.
Loss ¼ indicator variable equal to one if the client reported negative
income in the prior seven years and zero otherwise.
Growth ¼ client’s growth rate calculated as follows:
(sales
2006
2 sales
2000
)/sales
2000
.
Leverage ¼ client’s total liabilities divided by total assets in 2006.
4. Results
Descriptive statistics
Table I compares loyal clients (Loyal) and clients that switched audit ?rms (Switchers)
among Big 4 and non-Big 4 ?rms using our ?nal data set of 1,654 observations.
It shows that the percentage of loyal clients during the seven years period from 2000
to 2006 are higher for Big 4 audit ?rms. On the other hand, the percentage of clients
that switched audit ?rms is higher for non-Big 4 audit ?rms.
Switchers are broken down into three categories, switched once, switched twice, and
switched three times. Results show that clients that switched once or twice during the
seven years period from 2000 to 2006 are higher for non-Big 4 audit ?rms. However,
clients that switched three times were limited to one client in each of the Big 4 and
non-Big 4 audit ?rms[6].
Table II presents descriptive statistics for variables used in Model (2) and the audit
fees data. It presents the means, median, and standard deviations of the sample. Table III
reports Pearson and Spearman (non-parametric) correlations. For variables used in
Model (2), there is a high correlation between the two dependent variables
(Loyal and Degree of Loyalty) used in Model (2). This high correlation is expected due
Big 4 Non-Big 4 Total
Loyal 1,139 (68.90) 155 (9.35) 1,294 (78.25)
Switched once 127 (7.70) 199 (12.0) 326 (19.70)
Switched twice 9 (0.55) 23 (1.4) 32 (1.95)
Switched three times 1 (0.05) 1 (0.05) 2 (0.10)
Switchers 137 (8.30) 223 (13.45) 360 (21.70)
Total 1,276 (77.20) 378 (22.80) 1,654 (100)
Notes: Loyal – clients that never switched auditors from 2000 to 2006; switched once – clients that
switched auditors once in the period from 2000 to 2006 excluding Arthur Andersen LLP former clients,
switched twice – clients that switched auditors twice in the period from 2000 to 2006 excluding
Arthur Andersen LLP former clients, and switched three times – clients that switched auditors three
times in the period from 2000 to 2006 excluding Arthur Andersen LLP former clients; switchers –
clients that switched auditors at least once in the period from 2000 to 2006 excluding Arthur Andersen
LLP former clients; numbers in parentheses represent the percentage of observations in a category to
the total sample
Table I.
Client loyalty during the
seven years period
(2000 to 2006)
Audit fees and
client loyalty
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to the fact that Degree of Loyalty is a detailed explanation of the other dependent
variable (Loyal).
Audit fees regression model
Results for the standard audit fees regression model (Model (1)) are presented in
Table IV. All coef?cients are generally signi?cantly with the expected signs. The
overall explanatory power of the model is consistent with prior studies (Simunic, 1980;
Huang et al., 2009), where the adjusted R
2
is equal to 82.51 percent.
Evaluation of Model’s (1) residuals (FEEABN) does not suggest any econometric
problems, where the residuals are normally distributed and there is no evidence of
heteroskedasticity.
Logistic and ordinal regression analysis for auditor loyalty
Table V presents results for the basic model (Model (2)) using abnormal fees
(FEEABN) as an independent variable. The ?rst two columns of Table V present the
results of Model (2) using the indicator variable (Loyal) as the dependent variable in
the model. The last two columns of Table V present the results of Model (2) using the
ordinal variable (Degree of Loyalty) as the model’s dependent variable. Clients are
categorized into four levels of loyalty that depend on how many times they switched
auditors during the seven years period. The advantage of using the categorical
Mean Median SD Minimum Maximum
Fees 2,107.975 630.000 5,527.068 2.500 87,700.000
Log(FEE) 13.223 13.353 1.698 7.824 18.289
Loyal 0.780 1.000 0.413 0.000 1.000
Degree of Loyalty 3.760 4.000 0.478 1.000 4.000
FEEABN 0.000 20.023 0.594 22.167 2.768
Big 4 0.770 1.000 0.420 0.000 1.000
Going 0.090 0.000 0.280 0.000 1.000
Assets 14,487.403 860.090 98,588.755 0.075 1,884,318.000
Size 6.710 6.758 2.307 0.072 14.450
Loss 0.520 0.000 0.500 0.000 1.000
Growth 4.720 0.585 93.508 20.967 36.287
Leverage 0.184 0.112 0.234 0.000 0.947
Notes: n ¼ 1,654, fees – audit fees for ?scal year 2006 audit work ($ thousands); log(FEE) – natural
logarithm of audit fees for ?scal year 2006 audit work; loyal – indicator variable that is equal to one if
the client did not change auditor in the period from 2000 to 2006 and zero otherwise; Degree of
Loyalty – ordinal variable that is equal to four if the client did not change auditor, three if the client
changed auditor once, two if the client changed auditor twice, and one if the client changed auditor
three times in the period from 2000 to 2006; FEEABN – audit fees relative to similar clients calculated
as the residual of the audit fees model; Big 4 – indicator variable that is equal to one if the client’s
auditor is one of the Big 4 and zero otherwise; going – indicator variable equal to one if the client
receives a going concern opinion in the prior seven years and zero otherwise; assets – client’s total
assets in 2006 ($ millions); size – natural logarithm of client’s total assets in 2006; loss – indicator
variable equal to one if the client reported negative income in the prior seven years and zero otherwise;
growth – client’s growth rate calculated as follows: (sales2006 2 sales2000)/sales2000; and leverage –
client’s total liabilities divided by total assets
Table II.
Descriptive statistics
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Table III.
Correlation matrix
Audit fees and
client loyalty
87
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
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E
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A
t
2
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1
3
2
4
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a
n
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a
r
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2
0
1
6
(
P
T
)
approach is that it classi?es clients that switched auditors into different levels of
clients that could help to analyze clients’ loyalty at different categories.
Model (2) in Table V indicates that the variable, audit fees relative to similar clients
(FEEABN), is signi?cantly negative to the dependent variables (Loyalty).
The coef?cient for FEEABN is equal to 20.300 ( p-value , 0.05). Results also show
that the variable (FEEABN) is marginally signi?cant to the dependent variables
(Degree of Loyalty). The coef?cient for FEEABN is equal to 20.244 ( p-value , 0.10).
Therefore, clients that experienced higher audit fees relative to similar clients are found
to be less loyal to their audit ?rm, and their degree of loyalty decreases as their audit
fees relative to similar clients increases. These results supports the hypotheses of the
study and show that the relative audit fees variable (FEEABN) is associated with the
loyalty and the degree of loyalty of the clients to their audit ?rms.
Control variables consistently indicate that clients with higher loyalty to their audit
?rms are audited by one of the Big 4 audit ?rms and receive fewer going concern
opinions from their audit ?rms[7].
In summary, our results suggest that companies that were loyal to their audit ?rms
during the seven years period from 2000 to 2006, experienced lower relative audit fees
in 2006 compared to disloyal clients over the same period.
5. Additional analysis
For further evidence in regards to the association between loyalty and relative audit
fees, one additional analysis is conducted. The data set is split into two data sets: one
data set for clients with positive abnormal audit fees (n ¼ 821), and the other data set
for clients with negative abnormal audit fees (n ¼ 833). Kinney and Libby (2002) states
that abnormal fees capture the pro?tability of auditor-provided services. In particular,
Variable Expected sign Coef?cient p-value
Intercept ? 9.947 ,0.001
Size þ 0.546 ,0.001
Inventory þ 0.370 0.008
Receivable ^ 20.665 ,0.001
Leverage þ 20.007 0.844
ROA 2 20.166 ,0.001
Loss þ 0.296 ,0.001
Big 4 þ 0.510 ,0.001
Going þ 0.286 ,0.001
Notes: n ¼ 1,654, adjusted R
2
¼ 82.51 percent, this table presents the results from the regressions
with the natural logarithm of audit fees “log(FEE)” as the dependent variable; all p-values are two-
tailed; log(FEE) – natural logarithm of audit fees for ?scal year 2006 audit work; size – natural
logarithm of total assets in 2006; inventory – ratio of the dollar value of inventory to total assets;
receivable – ratio of the dollar value of accounts receivable to total assets; leverage – ratio of total
liabilities to total assets; ROA – ratio of operating income after depreciation to average total assets;
loss – indicator variable that is equal to one if the client reports negative income in the year of the
audit and zero otherwise; Big 4 – indicator variable that is equal to one if the client’s auditor is one of
the Big 4 and zero otherwise; and going – indicator variable equal to one if the client receives a going
concern opinion in the year of the audit and zero otherwise
Table IV.
Audit fees regression
results for 2006
ARJ
24,1
88
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(
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)
positive abnormal fees, namely actual fees in excess of normal fees, are likely to create
economic bonding of the auditor to the client, while negative abnormal fees are unlikely
to do so. Therefore, we expect that the loyalty incentive in relation to the audit fee level
is likely to differ systematically, depending on whether abnormal fees are positive or
negative.
Model (2) is applied on these two data sets. Table VI presents the results using
Loyalty and Degree of Loyalty as the dependent variables for positive and negative
abnormal audit fees. As shown in Table VI, clients with positive abnormal audit fees
have signi?cant negative association between the loyalty measure (Loyalty) and the
audit fees relative to similar clients (FEEABN). Similarly, there is a positive association
between the degree of loyalty measure (Degree of Loyalty) and the audit fees relative to
similar clients (FEEABN) for clients with positive abnormal audit fees[8].
Incontrast, whenusingthe dataset of clients withnegative abnormal audit fees, results
were different. Neither loyaltynor the degree of loyalty is signi?cantly associated withthe
relative audit fees measure (FEEABN). These results indicate that the loyalty of clients
that pay higher audit fees relative to similar clients in their industry is highly affected by
further increases in relative audit fees. However, the loyalty of clients that pay lower audit
fees compared to similar clients in their industry is not affected by increases in relative
audit fees. The implication of this ?nding is believed to be of importance to audit ?rms
when adjusting their audit fees from one period to another. There is a higher probability
Loyal (n ¼ 1,654)
Degree of Loyalty
(n ¼ 1,654)
Dependent variable Coef. p-value Coef. p-value
Intercept 20.335 0.280
Threshold (Degree of Loyalty ¼ 1) 2 5.605 , 0.001
Threshold (Degree of Loyalty ¼ 2) 2 2.648 , 0.001
Threshold (Degree of Loyalty ¼ 3) 0.390 0.157
FEEABN 2 0.300 0.023 2 0.244 0.056
Big 4 2.442 , 0.001 2.393 , 0.001
Going 2 0.578 0.014 2 0.520 0.020
Size 0.036 0.362 0.050 0.199
Loss 20.248 0.131 20.242 0.133
Growth 0.008 0.434 0.006 0.505
Leverage 20.316 0.290 20.346 0.197
Model x
2
398.418 , 0.001 400.180 , 0.001
Pseudo R
2
32.8% 30.6%
Notes: All p-values are two-tailed; loyal – indicator variable that is equal to one if the client did not
change auditor in the period from 2000 to 2006 and zero otherwise; Degree of Loyalty – ordinal
variable that is equal to four if the client did not change auditor, three if the client changed auditor
once, two if the client changed auditor twice, and one if the client changed auditor three times in the
period from 2000 to 2006; FEEABN – audit fees relative to similar clients calculated as the residual of
the audit fees model; Big 4 – indicator variable that is equal to one if the client’s auditor is one of the
Big 4 and zero otherwise; going – indicator variable equal to one if the client receives a going concern
opinion in the prior seven years and zero otherwise; size – natural logarithm of client’s total assets in
2006; loss – indicator variable equal to one if the client reported negative income in the prior seven
years and zero otherwise; growth – client’s growth rate calculated as follows: (sales2006 2 sales2000)/
sales2000; and leverage – client’s total liabilities divided by total assets
Table V.
Logistic and ordinal
regression results for 2006
Audit fees and
client loyalty
89
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Table VI.
Logistic and ordinal
regression results for
positive and negative
abnormal audit
fees in 2006
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of losing a client, when increasing the audit fees, if this client is already paying high audit
fees relative to similar clients. However, this probability is lower if this client is paying
lower amount of audit fees relative to other clients.
6. Conclusions and discussion
This study investigates the association between relative audit fees and loyalty in the
audit market. It complements the growing literature on audit fees and auditor choice by
investigating the stability or loyalty in the auditor-client relationship. We show how
relative audit fees affect the level of client loyalty to their auditors. Previous research
focuses on client retention and dismissal from the audit ?rm standpoint. To the best of
our knowledge, this study is the ?rst study to investigate client loyalty and how it is
affected by audit fees from a client viewpoint. A client has the right to switch auditors
whenever there is a disagreement with the auditor or whenever there is dissatisfaction
with the service provided. Conversely, if a client does not switch auditors for a speci?c
period, it is assumed that this client is satis?ed with the audit ?rm and the audit service
for this period.
Clients paying less audit fees relative to other companies in their industries are more
likely to be loyal to their auditors. Controlling for client and auditor characteristics that
were expected to affect client loyalty to their auditor, the study found that clients
experiencing smaller changes in their audit fees are more loyal to their audit ?rms.
Additionally, relative audit fees have a signi?cant effect on the degree of loyalty of
clients to their audit ?rms. Clients that are loyal to their auditors are found to receive
less going concern opinions and are audited by one of the Big 4 audit ?rms.
Additional analysis found that the loyalty of clients that pay higher audit fees relative
to similar clients in their industry are highly affected by further increases in relative
audit fees. However, the loyalty of clients that pay lower audit fees compared to similar
clients in their industry is not affected by increases in relative audit fees. These results
have implications for managers, audit committees, and board of directors by showing
that the longer their company can stay with the same audit ?rm, the lower the amount of
audit fees paid to that audit ?rm compared to switching to different audit ?rms.
The results should be also of interest to audit ?rms. The fact that it is more cost effective
for clients to stay with the same audit ?rm may imply less income to audit ?rms.
However, they will have more predictable clients. Moreover, audit ?rms should be
careful when adjusting their audit fees from one period to another, as there is a higher
probability of losing a client, when increasing the audit fees, especially if this client is
already paying higher audit fees relative to similar clients. However, this probability is
lower if this client is paying lower amount of audit fees relative to other clients.
This study has several limitations. One important limitation is that it failed to
differentiate between auditor dismissal and auditor resignation in the classi?cation of
clients that switched auditors. We also did not classify auditor switches into
auditor-initiated switches and client-initiated switches as applied by Lee et al. (2004).
Future research can investigate if relative audit fees can be affected by the classi?cation
of loyalty and the switching initiation. Furthermore, the period used to measure the
clients’ loyalty variable covers the period from 2000 to 2006 which involves pre- and
post-SOX periods. The study did not incorporate the potential impact of SOX as it just
measures the impact on relative audit fees in 2006. Further research can also study the
impact of SOX on clients’ loyalty by comparing relative audit fees pre- and post-SOX.
Audit fees and
client loyalty
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Notes
1. In this study, the term “client” refers to US companies audited by an external audit ?rm.
2. Audit risk is de?ned as the risk that the auditor may unknowingly fail to appropriately
modify his/her opinion in ?nancial statements that are materially misstated (AICPA, 1983,
AU 312.02).
3. Publicly available audit fees data is available beginning 2000. Therefore, research regarding
pre-SOX audit fees is to some extent limited.
4. Under the Financial Reporting Release No. 56 (SEC, 2000), SEC registrants were required to
publicly disclose audit fee data in proxy statements ?led with the SEC on or after February 5,
2001, which covers the proxy statements of the preceding ?scal year of 2000.
5. The industry groups identi?ed in the study are agriculture and ?shing, mining and
construction, manufacturing, transportation and communications, wholesale and retail
trade, ?nance and insurance, services and public administration, and international affairs.
6. Clients that switched three times are small in number. This size limitation could lead to a
problem in identifying whether the results are due to the small sample size or there is
actually no difference between the categories of loyalty.
7. While we do not show the values, none of the variance in?ation factors exceeds 2.0,
suggesting that multicollinearity is not a problem in the model.
8. The sub-sample of positive abnormal fees does not have any observations with client ?rms
switching auditors three times during the period from 2000 to 2006.
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Corresponding author
Magdy Farag can be contacted at: [email protected]
Audit fees and
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93
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