The Competitive Advantage of Audit Support Systems: The Relationship between Extent of Structure and...

JOURNAL OF INFORMATION SYSTEMS American Accounting AssociationVol. 26, No. 1 DOI: 10.2308/isys-10256Spring 2012pp. 35–49

The Competitive Advantage of Audit SupportSystems: The Relationship between Extent of

Structure and Audit Pricing

Elizabeth Carson

The University of New South Wales

Carlin Dowling

University of Melbourne

ABSTRACT: This study investigates if different audit support system designs provide

firms with competitive advantages when competing in the market for audit services. We

examine the association between the extent of structure a system imposes on the audit

process and audit pricing. We find that audit firms with systems that impose more

structure on the audit process compete as lower-cost producers in industries more suited

to a structured audit approach. However, when these audit firms are also national

industry specialists, they retain a fee premium. This study provides evidence that audit

support system design is associated with economic consequences for audit firms and

clients. We include suggestions for future research.

Keywords: audit support systems; audit fees; audit structure; competitive advantage.

Data Availability: Data are available from sources described in the text with the exception

of the level of structure embedded within a firm’s audit support system,

which the audit firms provided on the condition of anonymity.

I. INTRODUCTION

Audit support systems integrate an audit firm’s electronic workpapers, decision aids, and

knowledge banks. These systems operationalize the firm’s audit methodology to facilitate

effective and efficient audits (Banker et al. 2002; Bedard et al. 2007; Dowling and Leech

2007; Fischer 1996; Janvrin et al. 2008; Manson et al. 2001).1 Based on interviews with partners

This paper has benefited from the comments of Jean C. Bedard, Mike Bradbury, Neil Fargher, Jere Francis, GraemeHarrison, Mozzafar Khan, Robert Knechel, Gary Monroe, Robyn Moroney, Richard Morris, Roger Simnett, GregTrompeter, Anne Wyatt, participants at the American Accounting Association Annual Meeting, New York, AFAANZAnnual Meeting, Christchurch, the 7th ANCAAR Audit Research Forum, and research seminars at Deakin University,Macquarie University, Monash University, The University of New South Wales, and University of Queensland. Wethank Stewart Leech for use of the audit support system classifications, and the audit firm partners and managers for theirtime providing information about their firms’ audit support system.

Published Online: May 2012

1 The terms ‘‘audit support system’’ and ‘‘system’’ are used interchangeably.

35

and managers at five international audit firms, Dowling and Leech (2007) document systematic

differences in the extent of structure these firms’ audit systems impose on the audit process. Firms

with more structured systems designed their systems to enforce the firm’s audit methodology and

promote consistency across engagements. These systems achieve enforcement through automated

decision support, automated tailoring, and automated integration across audit phases. Less

structured systems provide users with decision support in the form of checklists, and require users

to manually tailor and integrate data across the engagement file. These systems provide audit teams

with greater flexibility, but also increase the risk audit teams misallocate resources. The system

differences documented by Dowling and Leech (2007) provide unique insight into the strategic

choices audit firms have made regarding the role of their system in the audit production process.

Behavioral research has found that these differences are associated with higher levels of appropriate

audit support system use (Dowling 2009) and lower levels of acquired declarative knowledge

(Dowling et al. 2008).

This study extends Dowling and Leech (2007) by examining the economic consequences of

such system design choices. We do this by analyzing the association between extent of audit

support system structure and audit fees. Audit fee data capture market competitiveness and the costs

of producing audit services (Simunic 1980). We observe lower audit pricing for firms deploying

more structured systems in industries that lend themselves to the standardized audit approach

embedded in these systems. However, when these firms are also national specialists in an industry,

they retain an audit fee premium.

This study contributes to the literature investigating audit support systems in two ways. First,

this study complements extant audit support system research that has analyzed individual auditor

acceptance and use of these systems (e.g., Agoglia et al. 2009; Bedard et al. 2003, 2007; Brazel et

al. 2004; Dowling 2009; Dowling et al. 2008) by documenting a firm-level economic outcome

associated with different audit support system designs. Our results are consistent with the structure

of an audit support system facilitating multiple pricing strategies (Noble and Gruca 1999). In some

markets, audit firms leverage the standardization imposed by more structured systems to obtain

economies of scale and efficiency and adopt a low-price supplier strategy, whereas in other markets,

they adopt a price-leader strategy and retain a price premium.

Second, this study provides evidence that differences in audit support system design have

consequences for audit firms. This is important because many extant studies have an unstated

underlying assumption that differences in the design of these systems have minimal impact (e.g.,

Agoglia et al. 2009; Brazel et al. 2004). The measurable economic impact documented in this

study, combined with the findings of behavioral studies using these system descriptions (Dowling

2009; Dowling et al. 2008), highlights that it is important for future studies to consider differences

in audit support system designs.

The remainder of this paper is organized as follows. The research questions are developed in

Section II. Section III describes the research method. Section IV presents the findings and discusses

implications for future research. Section V concludes the paper.

II. BACKGROUND AND RESEARCH QUESTION DEVELOPMENT

Audit support systems are one of the most important and frequently used audit information

technology tools (Janvrin et al. 2008). All international audit firms have global technology groups

who are responsible for the design, development, deployment, and evolution of their firm’s system

(Dowling and Leech 2007). The proprietary audit support systems deployed at the large audit firms

are developed globally and deployed across all offices in a firm’s network. Dowling and Leech

(2007) document three major design differences between more and less structured systems: extent

36 Carson and Dowling

Journal of Information SystemsSpring 2012

of automated tailoring, extent of automated decision support, and the extent of automated

integration across audit phases.2 A brief summary of these differences is provided below.

Tailoring of Engagement File

All firms have standardized engagement files that audit teams tailor for each client. To facilitate

tailoring of the engagement file, audit support systems contain industry ‘‘packs.’’ These packs

contain industry-specific checklists and guidance. Audit support systems differ in how this

information is provided to auditors. Less structured systems contain static checklists that list risks

and audit procedures. When tailoring the engagement file, auditors select the relevant risks and

procedures and import the selected procedures into the engagement file by ‘‘dragging’’ and

‘‘dropping’’ or typing them in. Auditors can also enter additional identified risks or procedures into

the file. In contrast, more structured systems contain checklists which require auditors to answer

‘‘yes,’’ ‘‘no,’’ or ‘‘not applicable’’ to a series of questions. The system uses the auditor’s responses to

extract the appropriate risks and recommend audit procedures from an embedded knowledge bank.

The system populates these into the engagement file. Auditors can add additional risks and

procedures, but they cannot delete the populated risks and procedures. These systems are designed

to produce an efficient and effective audit plan based on the auditor’s responses to the checklist

questions.

Decision Support

Less structured systems provide decision support in the form of checklists or hyperlinks to a

textual summary of the firm’s audit methodology. The checklists and guidelines provide prompts

and explanations of the audit procedures. Examples include lists of expected controls and tests,

sample size selection tables, disclosure checklists, and reporting templates. More structured systems

include dynamic checklists (e.g., engagement acceptance, specialist involvement), which provide a

recommendation based upon an auditor’s responses. The checklists are dynamic in that a positive

(negative) response to one question opens (closes) further questions. Completion of these checklists

is mandatory, and the system retains the answers as part of the engagement file. Other examples of

automated decision support include sample selection calculators, which recommend the required

sample to be tested, materiality calculators, and sample evaluation calculators that extrapolate the

results to the population.

Integration across Audit Phases

Decisions in one phase of an audit have implications for subsequent phases. For example, risks

identified in the planning stage are relevant for control and substantive testing when executing the

audit. More structured systems facilitate audit effectiveness and efficiency by automatically

integrating data through the engagement file. For example, these systems are structured such that

risks identified in planning are automatically populated into the relevant audit execution

workpapers. This ensures relevant risks are addressed. In contrast, less structured systems require

auditors to refer to the risks stored in other parts of the engagement file or rekey the relevant risks

into the required workpaper. A partner interviewed by Dowling and Leech (2007) expressed

concern that less structured systems, which do not automatically integrate data through the audit

file, increase the risk of under- or over-testing of audit evidence.

2 Dowling and Leech (2007) use the term restrictiveness. We use the terms more or less structured to reflect theextent of restrictiveness.

Competitive Advantage of Audit Support Systems: Relationship between Structure and Audit Pricing 37


The differences in audit support system designs described above reflect philosophical

differences between audit firms regarding the extent of structure imposed on the audit process.

Partners from firms with less structured systems hold the view that more structured systems increase

the risk of mechanistic behavior and reduce the flexibility for auditors to address idiosyncratic client

requirements (Dowling and Leech 2007). However, providing flexibility increases the risk that the

system will be used inappropriately (Dowling 2009). Partners from firms with more structured

systems believe that the structure increases audit effectiveness by enforcing compliance with the

firm’s methodology (Dowling and Leech 2007). These philosophical differences have existed for

decades (see, for example, Cushing and Loebbecke 1986). Research comparing audit firms along

dimensions of structure has concluded that both have advantages and disadvantages, and neither is

optimal (for a review of this literature, see Bowrin [1998]).

Implications of Different System Designs for Market Competitiveness

Our objective is to investigate whether the system differences documented by Dowling and

Leech (2007) provide audit firms with competitive advantages when competing in the market for

audit services. Proprietary audit support systems codify the firm’s organizational knowledge. By

enforcing a firm’s audit methodology, more structured systems promote consistency across the

firm’s global network of offices, and facilitate efficient audits by recommending the appropriate

audit approach. However, it is unclear whether firms will price this efficiency into their fees or

retain the efficiency as economic rents. Important factors which may influence this are market

pressure to discount fees and the extent audit teams need to work around the system. Workarounds

are required when the system’s recommended audit plan is not sufficient to achieve an effective

audit. Examples of workarounds include deleting, adding, or changing system-recommended audit

programs (Dowling 2009), or working outside the audit system (Bedard et al. 2007). Because less

structured systems do not enforce an audit process, auditors have greater flexibility and are less

likely to need to work around the system.

We expect that need for workarounds will vary across industries. In less specialized industries,

fewer workarounds will be required because these industries are more suited to the application of a

standardized audit approach (Cairney and Young 2006). However, because these industries are less

specialized, audit firms are less likely to obtain a fee premium (Mayhew and Wilkins 2003), and

greater market competition will restrict audit firms from pricing inefficiency into their audit fees

(Knechel et al. 2009). We expect audit firms with more structured systems have a competitive

advantage to compete as lower-cost providers in these industries. However, in industries where

more workarounds are required or structured firms have an alternative market advantage, such as

being an industry specialist, these firms are less likely to discount fees.

In summary, differences in the design of audit support systems reflect different strategic

choices audit firms have made to produce their audit services. To investigate if these differences

provide competitive advantages when competing in the market for audit services, we propose the

following research questions:

RQ1: Is there an association between the extent of structure in a firm’s audit support system

and audit pricing?

RQ2: If RQ1 holds, does the association between extent of structure in a firm’s audit support

system and audit pricing vary across industries?

RQ3: If RQ1 holds, does the association between extent of structure in a firm’s audit support

system and audit pricing change if a firm is an industry specialist?



III. RESEARCH METHOD

Sample

We categorize the audit support systems of five international audit firms as documented by

Dowling and Leech (2007). This includes all of the Big 4 global audit firm networks and a large

non-Big 4 global audit firm network. Consistent with the information technology and knowledge-

sharing strategies of globally networked audit firms identified by Lenz and James (2007) and

Carson (2009), each of these firms has deployed their proprietary audit support system across all of

the offices of their global network. Table 1 summarizes the differences in the systems deployed at

these firms using the three design differences discussed in Section II: tailoring of the engagement

file, decision support, and integration. The data in Table 1 are based on the interview data reported

in Dowling and Leech (2007). As shown in Table 1, two firms are classified as having more

structured systems and three firms as having less structured systems.

Audit Fee Data

Audit fee and other financial statement data are sourced from Extel/Company Analysis for 2004,

the year Dowling and Leech (2007) documented the audit support systems for the five international

audit firms in our sample. An initial data screen of total assets reported for the 2004 financial year

yields 14,628 engagements from 60 countries.3 Of these engagements, 8,766 report audit fees with

TABLE 1

Comparison of Differences in Audit Support Systems

Firm Label in Dowling and Leech (2007) Firm B Firm E Firm A Firm C Firm D

Tailoring of engagement file Automated Automated Predominately

manual

Manual Manual

Extent of automated decision supporta High High Low Low Low

Extent of automated integration across

audit phasesbHigh High Medium None Low

System enforces compliance with

audit methodology

Yes Yes No No No

Classification of audit support system More

Structured

More

Structured

Less

Structured

Less

Structured

Less

Structured

a High ¼ system provides recommendations based on user input; Low ¼ embedded decision aids prompt users (e.g.,checklists).

b High ¼ output in one phase of system is automatically integrated as input into a following phase; Medium ¼combination of manual and automated integration; Low ¼ integration is mostly completed manually; None ¼ allintegration is completed manually.

This table is based upon data collected from semi-structured interviews reported in Dowling and Leech (2007). Thetranscribed interviews were used to code each firm’s cell entry. Interviewees provided independent validation of thecoding for their firm for all cells except classification of the system, as more or less structured. This classification wascompleted by Dowling and Leech (2007). We use Dowling and Leech’s (2007) dichotomous classification to classify theextent of structure embedded within each firm’s audit support system.

3 For a company to be included in the sample, the total assets, auditor, and industry fields are required. Wheremore than one auditor is noted as signing the audit opinion, this observation is discarded.



countries included approximately in proportion to the size of their capital markets.4 For an additional

426 engagements, audit fees are hand-collected from annual reports, leading to a total of 9,233

engagements. We exclude countries with fewer than ten observations and engagements for clients in

the financial sector.5 After restricting the sample to clients of the five audit firms for which we have

reliable and complete information on audit support system design (all of the Big 4 global audit firm

networks and a large international non-Big 4 audit firm network), the final sample is 5,828

engagements from 21 countries.

Audit Fee Model

We use an audit fee model to control for cross-sectional differences in factors that affect fees,

such as client size, audit complexity, and risk (Simunic 1980). A cross-sectional audit fee model

consistent with Francis and Craswell (1999) and Carson (2009) is used, with adjustments for data

availability across multiple national audit markets and for factors that control for cross-country

variation.6 The OLS regression model estimated is specified as follows:

lnðFeeÞ ¼ aþ b1lnðTAÞ þ b2CATAþ b3Quick þ b4Lossþ b5Leverageþ b6ROAþ b7Structureþ Country Indicator Variablesþ e: ð1Þ

Variable Direction Definition

ln(Fee) natural log of audit fee (thousands).

Control Variables:ln(TA) þ natural log of total assets (millions);

CATA þ current assets to total assets;

Quick � current assets (less inventories) to current liabilities;

Loss þ 1 if loss in current year, otherwise 0;

Leverage þ book value of debt divided by book value of assets;

ROA � net profit divided by total assets; and

Country ? indicator variable: country of observation.

Variable of Interest:Structure ? 1 if audit support system is classified as more structured,

otherwise 0 (as described in Table 1).

4 Notable exceptions are Japan, which is underrepresented in the sample, and Australia, which is overrepresentedin the sample. The countries included in the sample are representative of countries where audit fees aredisclosed, but are not necessarily representative of all national capital markets.

5 Financial sector firms are excluded because prior research has identified differences in the determinants of thelabor mix for servicing clients in this sector (Stein et al. 1994), as well as differences in drivers of audit fees(Fields et al. 2004).

6 We also run the model using alternative specifications of the fee model (see Hay et al. [2006] for a review) (nottabulated). Using the full sample, we replace CATA with (1) inventory/total assets (negative and significant), (2)receivables/total assets (positive and significant), or (3) (inventory and receivables)/total assets (positive andsignificant). In all cases, the results for audit support system structure are consistent with those reported later inthe paper. However, we are restricted in the alternative specifications we can run because reliable information onsubsidiaries, foreign operations, audit tenure, and audit opinions is not available on the Extel/Company Analysisdatabase. Furthermore, the legal and professional requirements for issuance of a qualified opinion vary betweencountries.



IV. RESULTS

Descriptive Statistics and the Audit Fee Model

Table 2 presents the descriptive statistics. Due to outliers, the following variables are

winsorized at the 5 percent level: ln(TA), Quick, Leverage, and ROA.7 All currencies are converted

to U.S. dollars using the conversion rate applicable at fiscal year-end. The average firm in the 2004

sample has audit fees of $1,226,600 USD and total assets of $2.674 billion USD. Current assets

comprise nearly 50 percent of total assets and, on average, firms are highly liquid with a mean

Quick ratio of 2. On average, firms are slightly unprofitable, and 29 percent of firms report losses. A

comparison between firms audited by auditors using more structured systems to those audited by

TABLE 2

Descriptive Statistics

n ¼ 5,828Structure ¼ 1

(n ¼ ;)Structure ¼ 0

(n ¼ ;)

t-test ofDifferences

Mean(S.D.) Min. Max.

Mean(S.D.)

Mean(S.D.)

Audit Fees

(USD thousands)

1,226.60 0.017 81,200 1,102.61 1,265.04 1.568

(3,360.930) (3,107.72) (3,435.052)

Total Assets

(USD millions)

2,674.450 0.007 750,330 3,018.34 2,567.86 0.938

(15,578.994) (17,449.601) (14,952.294)

CATA 0.486 0 1 0.497 0.483 1.834*

(0.247) (0.251) (0.246)

Quicka 2.087 0 11.574 2.047 2.099 0.712

(2.364) (2.330) (2.375)

Leveragea 0.201 0 0.632 0.199 0.202 0.508

(0.183) (0.186) (0.183)

ROAa �0.0003 �0.506 0.170 �0.012 �0.0003 2.397**

(0.154) (0.162) (0.151)

Categorical VariablesLoss 29% 0 1 28% 30% 1.591

Structure ; 0 1 NA NA NA

*, ** Indicate p , 0.10 and p , 0.05, respectively (two-tailed).a These variables are winsorized at the 5 percent level. Descriptives are reported post-winsorization.; Detailed information about structure is not reported to maintain the confidentiality of the audit firms’ identities.

Variable Definitions:CATA ¼ current assets as a percentage of total assets;Quick ¼ current assets (less inventories) to current liabilities;Leverage ¼ book value of debt divided by book value of assets;ROA ¼ net profit divided by total assets;Loss ¼ indicator variable equals 1 if loss in current year, otherwise 0; andStructure ¼ 1 if audit support system is classified as more structured, otherwise 0.

7 This procedure reduced skewness in these variables from unacceptable levels (e.g., above 28) to acceptablelevels (, 1, with the exception of transformed Quick, which is , 3), consistent with the distribution of thetransformed variables being closer to normality. The level of kurtosis is also reduced to acceptable levels (,þ /�1) with the exception of transformed Quick (, 7).



auditors using less structured systems demonstrates that the two groups of clients are similar, with

the exception of CATA and profitability, where we observe small differences. From simple

correlation analyses of continuous variables (not reported), the natural log of audit fees and the

natural log of Total Assets are highly correlated (0.829), consistent with prior research using the

audit fee model. All other correlations are below 0.300.

RQ1 Findings

To investigate whether there is evidence of differential pricing associated with the extent of

structure in a firm’s audit support system, we ran the audit fee model discussed in the previous

section. Table 3 presents the key findings, with Column 1 being a base audit fee model and Column

2 including the variable of interest, being Structure.

The models shown in Table 3 demonstrate consistently high explanatory power. The control

variables are highly significant and in the predicted direction (with the minor exception of

Leverage). The base model in Column 1, which includes indicator variables for each of the

TABLE 3

Global Audit Fee Models

Model1

n ¼ 5,8282

n ¼ 5,828

Intercept 1.598 1.619

(31.547)*** (31.639)***

ln(TA) (þ) 0.624 0.623

(95.802)*** (95.492)***

CATA (þ) 0.810 0.809

(18.243)*** (18.241)***

Quick (�) �0.061 �0.061

(13.323)*** (13.418)***

Loss (þ) 0.163 0.162

(5.128)*** (5.104)***

Leverage (þ) �0.052 �0.050

(0.869) (0.847)

ROA (�) �0.599 �0.603

(6.271)*** (6.310)***

Variable of Interest:

Structure (?) �0.074

(3.256)***

Adj. R2 0.829 0.830

*** Indicates p , 0.01 (two-tailed test).Country Indicator variables are included, but not reported.

Variable Definitions:ln(TA) ¼ natural log of total assets;CATA¼ current assets as a percentage of total assets;Quick ¼ current assets (less inventories) to current liabilities;Loss ¼ indicator variable equals 1 if loss in current year, otherwise 0;Leverage ¼ book value of debt divided by book value of assets;ROA ¼ net profit divided by total assets; andStructure ¼ 1 if audit support system is classified as more structured, otherwise 0.



countries in the sample, demonstrates that the global audit fee model fits this sample well. Column 2

demonstrates that the inclusion of an indicator variable for a more structured audit support system is

negative and highly significant (t ¼ 3.256, p , 0.01).8,9,10 This provides evidence that audit firms

with more structured audit support systems, on average, price their audit engagements lower. To

demonstrate the economic significance of the price differential, we determine the magnitude of the

intercept shift across two subsets of observations as a percentage change. Using this method on the

model reported in Table 3, Column 2, we find that firms deploying structured audit support systems

discount their audit fees by 7 percent relative to firms deploying a less structured audit support

system. This equates to an average discount of approximately $85,000 USD. We conclude that the

extent of structure in an audit support system has economic impacts for audit firms and audit clients.

RQ2 Findings

To investigate whether the results reported for RQ1 vary across industries, we conduct two

tests. First, we conduct an analysis in which we run our model in Column 2 of Table 3 for each

GICS industry sector.11 Table 4 presents the results of this analysis. The coefficient for Structure isnegative in seven of the nine industry groups, but is only significant and negative in three industry

sectors: Cyclical Consumer Goods (t ¼ 2.179, p , 0.05), Non-Cyclical Consumer Goods (t ¼2.566, p , 0.05), and Information Technology (t ¼ 2.088, p , 0.05).12

The results in Table 4 provide evidence that the association between audit support system

structure and audit fees varies across industry sectors. Because these industry sectors are grouped at

a very high level, it is difficult to interpret what is driving this result. As previously discussed,

certain industries are more likely to have more engagements where the standardized audit program

embedded in more structured audit support systems can be deployed more efficiently. One industry

that fits this expectation is the manufacturing industry, which is a less specialized industry (Knechel

et al. 2009) and, therefore, is less likely to require workarounds. The price sensitivity of the

manufacturing industry reduces the opportunity for audit firms to impound inefficiency into their

fees (Knechel et al. 2009).

We classify the industries in the nine industry sectors using Standard and Poor’s GICS

description. If the description includes ‘‘manufacturing,’’ we classify the industry as comprising of

manufacturing clients. Of the 31 industry subsectors in Table 4, 18 are classified as manufacturing

8 We also run the analysis on a country-by-country basis for the three countries with the greatest number ofobservations represented in the sample: Australia, the United Kingdom, and the United States (not tabulated).The audit fee model in each of these countries explains the data well, with adjusted R2 ranging from 60 to 80percent. The coefficient for Structure is negative and significant in both the Australian (b¼�0.129, t¼ 2.115, p, 0.05) and the United States samples (b¼�0.174, t¼4.498, p , 0.01), but is not significant in the U.K. sample(b ¼ 0.011, t ¼ 0.201, p . 0.10). This confirms our results are not driven by any one national market.

9 We rerun the analyses eliminating the large non-Big 4 global audit firm network from our sample. Eliminatingthe clients of this non-Big N firm reduces the sample to 5,656 firms. Using a model consistent with Model 2 inTable 3, the coefficient on Structure is negative and significant (b¼�0.058, t¼ 2.438, p , 0.05) (not tabulated).This confirms that the inclusion of a large non-Big N firm in our sample is not driving the results.

10 To ensure that larger firms obtaining fee discounts are not driving the results, we exclude Business Week Global1000 firms (leaving a sample of n¼ 5,372). The coefficient on structure remains negative and significantly (p ,0.01) associated with audit fees in the full model (not tabulated). The results are consistent with those reported inTable 3, suggesting that the results are robust to the exclusion of the largest firms.

11 GICS industry sectors are the categorization scheme used by the London Stock Exchange and the AustralianStock Exchange. These industry sectors are outlined in Table 4.

12 To ensure that larger firms obtaining fee discounts are not driving the results, we exclude BusinessWeek Global1000 firms. Cyclical Consumer Goods, Non-Cyclical Consumer Goods, and Information Technology continue tobe negatively and significantly (p , 0.05) associated with audit fees. We then exclude the top quartile of firms byassets overall and in each industry sector. The results are consistent with those reported previously, suggestingthat the results are robust to the exclusion of the largest firms in our sample and by industry sector.



(n ¼ 3,497).13 We run a model consistent with Column 2 in Table 3, also including an indicator

variable for whether a firm is classified as in a manufacturing industry (Manufacturing), and the

interaction between this indicator variable Manufacturing and Structure. The coefficient on

Structure is�0.015 (t¼ 0.441, p . 0.10), the coefficient on Manufacturing is 0.155 (t¼ 6.725; p ,

0.01), and the coefficient on our variable of interest, the interaction of Structure and Manufacturing,

is negative and significant (b ¼�0.093, t ¼ 2.076, p , 0.05) (not tabulated). The results provide

TABLE 4

Analysis of Structure Variable in Global Audit Fee Models by Industry Sector

Industry SectorIndustry (n) n

Coefficientfor Structure t-value

Resources Sector

Mining (279); Oil and Gas (232) 511 �0.099 1.127

Basic Industries Sector

Chemicals (126); Construction & Building Materials (314);

Forestry & Paper (40); Steel & Other Metals (68)

548 �0.030 0.396

General Industrials Sector

Aerospace & Defense (25); Diversified Industrials (111);

Electrical & Electronic Equipment (595); Engineering &

Machinery (398)

1,129 �0.055 1.157

Cyclical Consumer Goods Sector

Automobiles & Parts (102); Household Goods & Textiles

(243)

345 �0.163 2.179**

Non-Cyclical Consumer Goods Sector

Beverages (42); Food Producers & Processors (205); Health

(219); Packaging (72); Personal Care & Household Products

(20); Pharmaceuticals & Biotechnology (257); Tobacco (14)

829 �0.152 2.566**

Cyclical Services Sector

General Retailers (237); Distributors (173); Leisure,

Entertainment & Hotels (232); Media & Photography (242);

Support Services (380); Transport (235)

1,499 �0.073 1.624

Non-Cyclical Services Sector

Food & Drug Retailers (36); Telecommunication Services

(158)

194 0.037 0.281

Utilities Sector

Electricity (112); Gas Distribution (47); Water (20) 179 0.048 0.394

Information Technology Sector

Information Technology Hardware (79); Software & Computer

Services (515)

594 �0.139 2.088**

** Indicates p , 0.05 (two-tailed test).Range of adjusted R2 is 81.6 percent to 87.6 percent.Structure ¼ 1 if audit support system is classified as more structured, otherwise 0.

13 The industries classified as manufacturing are: Oil and Gas, Chemicals, Construction and Building Materials,Forestry and Paper, Steel and Other Metals, Aerospace and Defense, Electrical and Electronic Equipment,Engineering and Machinery, Automobiles and Parts, Household Goods and Textiles, Beverages, Food Producersand Processors, Health, Packaging, Personal Care and Household Products, Pharmaceuticals and Biotechnology,Tobacco, Information Technology Hardware, Software and Computer Services.



evidence that the association between audit fees and audit support system structure varies across

industries. This finding is consistent with audit firms with more structured systems competing as

lower-cost providers in markets where a generic audit approach can be efficiently deployed.

RQ3 Findings

To investigate whether being an industry specialist changes the association between audit fee

pricing and audit support system structure, we include an additional variable for National IndustryMarket Share (calculated using share of total assets audited by each audit firm across the full

sample of 14,628 engagements identified in initial data screens). Column 2 of Table 5 reports that

Structure remains negative and significant, and National Industry Market Share is significant and

positive, which is consistent with national industry specialists receiving an audit fee premium.

Importantly, we also find that the interaction of National Industry Market Share and Structure is

positive and significant.14 This suggests that when firms deploying a more structured system are

also national specialists, they earn a higher audit fee than firms with a less structured system and

firms with more structured systems that are not industry specialists. The result is consistent with

audit firms with more structured systems retaining any efficiency gains as economic rents when

they are a market leader.

Implications for Future Research

The results above provide evidence that the extent of structure an audit support system imposes

on the audit process has economic implications for audit firms. To the extent that audit fees reflect

the costs of audit production (Godfrey and Hamilton 2005), the results suggest that differences in

audit support system design impact the audit production process. Prior studies investigating audit

production focus on the quantity and mix of labor hours (e.g., Dopuch et al. 2003; Hackenbrack and

Knechel 1997; Knechel et al. 2009; O’Keefe et al. 1994; Stein et al. 1994). With the exception of

Banker et al. (2002), who investigated the impact of electronic versus handwritten workpapers on

audit efficiency, very little is known about how audit support systems, and specifically, whether

differences in their design, affect the audit production process. One aspect that should be of interest

is whether different audit support system designs influence a firm’s internal labor flexibility (Caroli

2007; Conner and Prahalad 1996). Internal labor flexibility is the ‘‘flexibility manifested by the pool

of human resources in the organization at a certain point in time’’ that enables efficient cross-

functional or vertical reassignment of employees (Beltran-Martin et al. 2009, 1579). If audit support

systems facilitate internal labor flexibility, do firms predominately utilize functional or vertical

redeployment? Does this differ by audit support system design? What features of an audit support

system facilitate this? Investigating whether different audit support system designs influence

internal labor flexibility would provide important insight into understanding how different designs

influence the development of auditor knowledge. Prior research has found that auditors who use a

more structured system have lower levels of declarative knowledge (Dowling et al. 2008).

However, it is unclear whether this is because of the automation in structured systems reducing

opportunities for knowledge acquisition (as implied by the Theory of Technology Dominance

[Arnold and Sutton 1998]), or whether it is due to firms with more structured systems leveraging

this structure to redeploy auditors in a manner that reduces opportunities to acquire knowledge

through task or industry experience.

14 This analysis is re-performed for each industry grouping (not tabulated). For Cyclical Consumer Goods, Non-Cyclical Consumer Goods, and Information Technology, Structure is found to be consistently negative andsignificant. In four industry groups (Resources, General Industrials, Non-Cyclical Consumer Goods, andInformation Technology), evidence of premium audit fees to national industry specialists are observed.



We document a significant association between audit fees and audit support system structure.

Audit fees are an outcome negotiated by the client and the audit firm. An interesting extension of

this study, and an unexplored area, is whether audit clients’ perceptions differ in regard to audit

support system design. Manson et al. (2001) report that auditors perceive that clients do not have

knowledge of the audit firm’s systems. However, the promotion of their audit support system in the

marketing material of the large audit firms suggests that audit firms view their system as an

important factor in differentiating their services. Research could investigate clients’ perceptions

about the quality of the audit received by auditors using different audit support system designs.

Finally, this study has provided evidence that there are economic consequences associated with

different audit support system designs. This study uses a coarse classification of audit support

TABLE 5

Analysis of Audit Fee Models and Audit Firm Characteristics

Model1

n ¼ 5,8282

n ¼ 5,828

Intercept 1.590 1.617

(30.843)*** (30.694)***

ln(TA) (þ) 0.617 0.616

(92.437)*** (92.392)***

CATA (þ) 0.808 0.809

(18.242)*** (18.278)***

Quick (�) �0.062 �0.062

(13.532)*** (13.560)***

Loss (þ) 0.165 0.164

(5.177)*** (5.175)***

Leverage (þ) �0.052 �0.054

(0.880) (0.918)

ROA (�) �0.588 �0.589

(6.167)*** (6.178)***

Variables of Interest:

Structure (?) �0.065 �0.128

(2.854)*** (3.761)***

National Industry Market Share (?) 0.190 0.131

(4.362)*** (2.640)***

Structure 3 National Industry Market Share (?) 0.235

(2.494)**

Adj. R2 0.830 0.830

**, *** Indicate p , 0.05 and p , 0.01, respectively (two-tailed test).Country Indicator variables are included, but not reported.Variable Descriptions:ln(TA) ¼ natural log of total assets;CATA¼ current assets as a percentage of total assets;Quick ¼ current assets (less inventories) to current liabilities;Loss ¼ indicator variable equals 1 if loss in current year, otherwise 0;Leverage ¼ book value of debt divided by book value of assets;ROA ¼ net profit divided by total assets;Structure ¼ 1 if audit support system is classified as more structured, otherwise 0; andNational Industry Market Share¼ share of total assets audited by auditor in national industries as defined using GICScodes.



system structure. Future research could investigate how different features of audit support systems

impact auditor performance, audit quality, and audit efficiency.

V. CONCLUSION

We provide evidence consistent with audit firms utilizing their audit support systems to obtain

a competitive advantage through applying a multiple pricing strategy. In industries more suited to

the application of the standardized audit approach embedded in more structured systems, firms with

these systems compete as lower-cost producers. However, when these firms are also national

industry specialists, they retain a fee premium. Our results suggest that the relationship between

audit support system design and audit fees is complex and should be considered in the context of

the competitive nature of the audit market.

Our results should be interpreted in light of the following limitations. Our cross-sectional

design restricts us from making conclusions of the causation of the association we document

between extent of structure and audit pricing. Future research is needed to understand the

antecedents of this association. Consistent with prior research, we maintain that the level of audit

assurance is constant across client engagements. All of the firms in our study are international firms

that have invested significantly in developing their brand name and offer high-quality audits. When

we limit our analysis to Big 4 firms, our results hold. Additionally, our sample is restricted to five

large audit firms for which we have identified stable audit support systems design over the period

analyzed, and our findings are not able to be generalized beyond this group of audit firms.

We use a dichotomous measure based upon the field work of Dowling and Leech (2007) to

capture our variable of interest, extent of audit support system structure. A limitation of our study is

that this is a coarse measure. Even so, the dichotomy we investigate is a summary measure of

high-level system differences that capture important philosophical differences across audit firms.

We provide evidence that these high-level differences have important economic consequences for

audit firms and their clients. Future research is needed to investigate how the design features and

decision tools embedded in audit support systems impact the audit process and audit quality. In

particular, research exploring the determinants and consequences of auditor workaround behaviors

would provide important knowledge that could inform improvements in the design and utilization

of audit support systems.

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